# Patent

## Title
System and method for identifying objects of interest in image data

## Abstract

A system and method for identifying objects of interest in image data is provided. The present invention utilizes principles of dynamic discontinuity in which objects in images, when subjected to special transformations, will exhibit radically different responses based on the chemical properties of the imaged objects. Using the system and methods of the present invention, certain objects that appear indistinguishable from other objects to the eye or computer recognition systems, or are otherwise statistically identical, generate radically different and statistically significant differences that can be easily measured.

## Background
REFERENCE TO RELATED APPLICATIONS
This application claims priority to U.S. Provisional Patent Application No. 60/574,220, filed May 26, 2004, U.S. Provisional Patent Application No. 60/574,221, filed May 26, 2004, U.S. Provisional Patent Application No. 60/578,872 filed Jun. 14, 2004 and U.S. Provisional Application No. 60/661,477, filed Mar. 15, 2005, which are incorporated herein by reference in their entirety.


BACKGROUND OF THE INVENTION
1. Field of the Invention
This invention relates to image analysis and, more specifically, to a system and method for identifying objects of interest in image data.
2. Background of the Related Art
Computer-aided image recognition systems rely solely on the pixel content contained in a two-dimensional image. The image analysis relies entirely on pixel luminance or color, and/or spatial relationship of pixels to one another. In addition, image recognition systems utilize statistical analysis methodologies that must assume that the forms of the underlying density (distribution) functions distinguishing the image objects are known (i.e., parametric densities). Classical parametric densities are usually unimodal with a single local maximum distribution of optic characteristics, such as density or color.
However, most real-world image analysis problems involve multi-modal densities, often with distributed low-dimensional densities making identification with existing pattern recognition approaches difficult, if not impossible. The following are some of the specific issues limiting existing image analysis methodologies:
(1) input data (image objects) need to be parametric;
(2) did not adjust for scale, rotation, perspective, size, etc.;
(3) classes of objects need to be statistically distinct in the image;
(4) black and white and grayscale processing is insufficient to identify complex images; and
(5) color processing can be very computationally intensive.
SUMMARY OF THE INVENTION
An object of the invention is to solve at least the above problems and/or disadvantages and to provide at least the advantages described hereinafter.
Therefore, an object of the present invention is to provide an expert system capable of detecting objects of interest in image data with a high degree of statistical confidence and accuracy.
Another object of the present invention is to provide a system and method that does not rely on a prior knowledge of an objects shape, volume, texture or density to be able to locate and identify a specific object or object type in an image.
Another object of the present invention is to provide a system and method of identifying objects of interest in image data that is effective at analyzing images in both two- and three-dimensional representational space using either pixels or voxels.
Another object of the present invention is to provide a system and method of distinguishing a class of known objects from objects of similar color and texture in image data, whether or not they have been previously observed by the system.
Another object of the present invention is to provide a system and method of identifying objects of interest in image data that works with very difficult to distinguish/classify image object types, such as: (i) random data; (ii) non-parametric data; and (iii) different object types in original images.
Another object of the present invention is to provide a system and method of identifying objects of interest in image data that can cause either convergence or divergence of image object characteristics.
Another object of the present invention is to provide a system and method of identifying objects of interest in image data that can preserve object self-similarity during transformations.
Another object of the present invention is to provide a system and method of identifying objects of interest in image data that is deterministic and stable in its behavior.
To achieve the at least above objects, in whole or in part, there is provided a method of identifying a threat object of interest in X-ray image data, comprising receiving the X-ray image data, and applying at least one bifurcation transform to the X-ray image data to effect divergence of the threat object of interest from other objects.
To achieve the at least above objects, in whole or in part, there is also provided an apparatus configured to identify a threat object of interest in X-ray image data, comprising an input device configured to receive the X-ray image data, and an image transformation recognition system configured to apply at least one bifurcation transform to the X-ray image data to effect divergence of the threat object of interest from other objects.
To achieve the at least above objects, in whole or in part, there is also provided a method of creating a bifurcation transform for a class of threat objects, comprising selecting a point operation, performing said point operation on a subset of images, wherein said subset of images includes at least one image containing an object in said class of threat objects, and repeating said selecting and said performing steps until said point operation bifurcates said object.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objects and advantages of the invention may be realized and attained as particularly pointed out in the appended claims.



BRIEF DESCRIPTION OF THE DRAWINGS
The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Patent Office upon request and payment of the necessary fee.
The invention will be described in detail with reference to the following drawings, in which like reference numerals refer to like elements, wherein:
FIG. 1 is a bifurcation diagram;
FIG. 2 is a block diagram of a system for identifying an object of interest in image data, in accordance with one embodiment of the present invention;
FIG. 3 is a flowchart of a method for identifying an object of interest in image data, in accordance with one embodiment of the present invention;
FIGS. 4A-4E are histograms of various point operations, in accordance with the present invention;
FIGS. 5A-5C are histograms of nonlinear point operations, in accordance with the present invention;
FIG. 6A is an input x-ray image of a suitcase, in accordance with the present invention;
FIG. 6B is the x-ray image of FIG. 6a after application of the image transformation recognition process of the present invention;
FIG. 7 is a flowchart of a method for identifying an object of interest in image data, in accordance with another embodiment of the present invention;
FIGS. 8A-8M are x-ray images of a suitcase at different stages in the image transformation recognition process of the present invention;
FIG. 8N is an example of a bifurcation transform applied to an x-ray image during the image transformation recognition process of the present invention;
FIG. 9A is an original input medical image of normal and cancerous cells; and
FIG. 9B is the image of FIG. 9A after application of the image transformation recognition process of the present invention.



DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
Definition of Terms
The following definitions hold throughout the contents of this application. If additional or alternative definitions of the same or similar words are provided herein, those definitions should be included herein as well.
Statistically identical: Two sets of data are referred to as “statistically identical” if under one or more types of statistics or observation there is essentially no discernable difference between them.
Point operation: Point operation is a mapping of a plurality of data from one space to another space which, for example, can be a point-to-point mapping from one coordinate system to a different coordinate system. Such data can be represented, for example, by coordinates such as (x, y) and mapped to different coordinates (α,β) values of pixels in an image.
Z effective (Zeff): Is the effective atomic number for a mixture/compound of elements. It is an atomic number of a hypothetical uniform material of a single element with an attenuation coefficient equal to the coefficient of the mixture/compound. Z effective can be a fractional number and depends not only on the content of the mixture/compound, but also on the energy spectrum of the x-rays.
Transform: A transform is an operation that changes or transforms a first set of data points into a second set of data points.
Bifurcate: To bifurcate is to cause a data set to change in a manner such that information that was otherwise not readily or easily obtainable becomes available or accessible.
Bifurcation transform: A bifurcation transform is a transform which when operating on data such as a segment or subset of an image, causes information relating to the content of the data that otherwise would not have been readily or easily apparent to become available or more easily apparent or accessible.
For example, when applying a bifurcation transform to an image or a segment of the image, information regarding the contents of the image which would not have been easily recognized prior to application of the bifurcation transform becomes more apparent or known. For example, two objects in the same image that are statistically indistinguishable become statistically distinguishable after the bifurcation transform is applied.
Hyperspectral data: Hyperspectral data is data that is obtained from a plurality of sensors at a plurality of wavelengths or energies. A single pixel or hyperspectral datum can have hundreds or more values, one for each energy or wavelength. Hyperspectral data can include one pixel, a plurality of pixels, or a segment of an image of pixels, etc., with said content. As contained herein, it should be noted that hyperspectral data can be treated in a manner analogous to the manner in which data resulting from a bifurcation transform is treated throughout this application for systems and methods for threat or object recognition, identification, image normalization and all other processes and systems discussed herein.
For example, a bifurcation transform can be applied to hyperspectral data in order to extract information from the hyperspectral data that would not otherwise have been apparent. Bifurcation transforms can be applied to a plurality of pixels at a single wavelength of hyperspectral data or multiple wavelengths of one or more pixels of hyperspectral data in order to observe information that would otherwise not have been apparent.
Nodal point: A nodal point is a point on a transform at which pixels close to the nodal point can observe a significantly distinguishable change in value upon application of the transform.
Object: An object can be a person, place or thing.
Object of interest: An object of interest is a class or type of object such as explosives, guns, tumors, metals, knives, etc. An object of interest can also be a region with a particular type of rocks, vegetation, etc.
Threat: A threat is a type of object of interest which typically but not necessarily could be dangerous.
Image receiver: An image receiver can include a process, a processor, software, firmware and/or hardware that receives image data.
Image mapping unit: An image mapping unit can be a processor, a process, software, firmware and/or hardware that maps image data to predetermined coordinate systems or spaces.
Comparing unit: A comparing unit can be hardware, firmware, software, a process and/or processor that can compare data to determine whether there is a difference in the data.
Color space: A color space is a space in which data can be arranged or mapped. One example is a space associated with red, green, blue (RGB). However, it can be associated with any number and types of colors or color representations in any number of dimensions.
Predetermined color space: A predetermined color space is a space that is designed to represent data in a manner that is useful and that could, for example, causes information that may not have otherwise been apparent to present itself or become obtainable or more apparent.
RGB DNA: RGB DNA refers to a representation in a predetermined color space of most or all possible values of colors which can be produced from a given image source. Here, the values of colors again are not limited to visual colors but are representations of values, energies, etc., that can be produced by the image system.
Signature: A signature can be a representation of an object of interest or a feature of interest in a predetermined space and a predetermined color space. This applies to both hyperspectral data and/or image data.
Template: A template is part or all of an RGB DNA and corresponds to an image source or that corresponds to a feature or object of interest for part or all of a mapping to a predetermined color space.
Algorithms: From time to time, transforms and/or bifurcation transforms are referred to herein as algorithms.
Algorithms and systems discussed throughout this application can be implemented using software, hardware, and firmware.
The analysis capabilities of the present invention can apply to a multiplicity of input devices created from different electromagnetic and sound emanating sources such as ultraviolet, visual light, infra-red, gamma particles, alpha particles, etc.
Image Transformation Recognition System and Method
General Overview
The present invention identifies objects of interest in image data utilizing a process herein termed “Image Transformation Recognition” (ITR). The ITR process can cause different yet statistically identical objects in a single image to diverge in their measurable properties. This phenomenon can be compared to the dynamic discontinuities observed in other fields of science, such as fluid mechanics. An aspect of the present invention is the discovery that objects in images, when subjected to special transformations, will exhibit radically different responses based on the physical properties of the imaged objects. Using the system and methods of the present invention, certain objects that appear indistinguishable from other objects to the eye or computer recognition systems, or are otherwise statistically identical, generate radically different and statistically significant differences that can be measured.
An aspect of the present invention is the discovery that objects in images can be driven to a point of non-linearity by certain transform parameters. As these transform parameters are increased, the behavior of the system progresses from one of simple stability, through a series of structural changes, to a state of a unique and radical change based on the interaction of the real object to the imaging modality characteristics. This point of rapid departure from stability is called the “point of bifurcation.” Bifurcation theory, simply stated, means “applying a little bit to achieve a big difference.”
FIG. 1 is an example of a bifurcation diagram. A single object type in an image is represented as a simple point on the left of the diagram. There are several branches in the diagram as the line progresses from the original image representation on the left, indicating node points where bifurcation occurs. Finally, on the far right, the image moves into what Catastrophe theory describes as chaos. Between the original image and chaos lies the basis for discrimination in the system and methods of the present invention.
Catastrophe theory, of which bifurcation theory is a subset, indicates that a dynamical system is or can become chaotic if it: (1) has a dense collection of points; (2) is sensitive to the initial condition of the system (so that initially nearby points can evolve quickly into very different states); and (3) is topologically transitive (neighborhoods of points eventually get flung out to “big” sets).
Images meet all three of the above criteria. An aspect of the present invention is that one can apply this “principle of bifurcation” to the apparent (non-dynamic) stability of fixed points or pixels in an image and, by altering one or more parameter values, give rise to a set of new, distinct and clearly divergent image objects. Because each original object captured in an image responds uniquely at its point of bifurcation, the methods of the present invention can be used in an image recognition system to distinguish and measure objects. It is particularly useful in separating and identifying objects that have almost identical color, density and volume.
The ITR system and method provides at least the following advantages over prior image extraction methodologies:
(1) It is an expert system capable of detecting objects with a high degree of statistical confidence and accuracy/precision;
(2) It does not rely on a prior knowledge of an objects shape, volume, texture or density to be able to locate and identify a specific object or object type in the image;
(3) It is effective at analyzing images in multi-dimensional representational space using either pixels or voxels;
(4) It is most powerful where a class of known objects is to be distinguished from objects of similar color and texture, whether or not they have not been previously observed or trained by the ITR system;
(5) It works with very difficult to distinguish/classify image object types, such as: (i) random data; (ii) non-parametric data; and (iii) different object types in original images (threats and non-threats for example or different types of threats) have indistinguishable differences between their features when analyzed statistically (they continually are determined to be in the same class when applying pattern recognition/statistical analytic methods);
(6) It performs equally well with both parametric and nonparametric statistical data sampling techniques;
(7) It can more effectively apply statistical analysis tools to distinguish data;
(8) It can cause either convergence or divergence of image object features;
(9) It can preserve object geometrical integrity during transformations; and
(10) It is deterministic and stable in its behavior.
In one exemplary embodiment of the present invention, special transformations are applied to images in an iterative “filter chain” sequence. This process is herein referred to as a Simple Signature Iterative Clustering (SSIC) process.
The nature of the sequence of transforms causes objects in the image to exhibit radically different responses based on the physical properties inherent in the original objects. Using the SSIC process, certain objects that appear indistinguishable to the eye or computer recognition systems from other objects, generate radically different and statistically significant differences that can be easily measured.
As transform parameters are increased, the behavior of the objects progresses from one of simple stability, through a sequence of structural changes, to a state of a unique and radical change (point of non-linearity) based on the interaction of the real object to the imaging modality characteristics.
The ITR process works with an apparently stable set of fixed points or pixels in an image and, by altering one or more parameter values, giving rise to a set of new, distinct, and clearly divergent image objects. The ITR process is most effective when applied to images that exhibit the following three characteristics:

    
    
        (1) The image has a dense collection of points;
        (2) The image is sensitive to the initial condition of the system (so that initially nearby points can evolve quickly into very different states); and
        (3) The image is topologically transitive (neighborhoods of points eventually get flung out to “big” sets in color space).
    
    


Because of the nature of the transformations known and utilized in the science of digital image processing to date, the ITR system and method of the present invention has neither been known nor utilized. Commonly used and understood transforms work within the domain where images maintain equilibrium. These transforms do not work where image/object discontinuities occur.
As will be discussed in more detail below, the ITR method starts by first segmenting the image into objects of interest, then applying different filter sequences to the same original pixels in the identified objects of interest using the process. In this way, the process is not limited to a linear sequence of filter processing.
Because of the unique nature of the segmentation process using this iterative approach, objects within objects can be examined. As an example, an explosive inside of a metal container can be located by first locating all containers, remapping the original pixel data with known coordinates in the image and then examining the remapped original pixels in the identified object(s) in the image for threats with additional filter sequences.
With the ITR process, transforms can be tuned to optimize clustering of images. In addition, the process works for both image segmentation and feature generation through an iterative process of applying image transforms. It is defined mathematically as a reaching a “Point of Attraction”.
Exemplary Embodiments
FIG. 2 is a block diagram of a system 100 for identifying an object of interest in image data, in accordance with one embodiment of the present invention. The system 100 comprises an input channel 110 for inputting image data 120 from an image source (not shown) and an ITR system 130. The ITR system generates transformed image data, in which the object of interest is distinguishable from other objects in the image data.
The operation of the ITR system 130 of FIG. 2 will now be explained in connection with FIG. 3, which is a flowchart of a method for identifying an object of interest in image data, in accordance with one embodiment of the present invention. The method starts at step 100, where image data 120 is received via the input channel 110. The object of interest can be any type of object. For example, the object of interest can be a medical object of interest, in which case the image data can be computer tomography (CT) image data, x-ray image data, or any other type of medical image data. As another example, the object of interest can be a threat object, such as weapons, explosives, biological agents, etc., that may be hidden in luggage. In the case, the image data is typically x-ray image data from luggage screening machines.
At step 210, at least one bifurcation transform is applied to the image data 120 by the ITR system 130, and transformed image data 140 is generated. The at least one bifurcation transform is adapted to cause the object of interest to diverge from other objects in the image. The at least one bifurcation transform will be discussed in more detail below.
Next, at step 220, the object of interest is identified in the transformed image data 140 based on the object's response to the at least one bifurcation transform.
The at least one bifurcation transform is preferably a point operation. A point operation converts a single input image into a single output image. Each output pixel's value depends only on the gray level of its corresponding pixel in the input image. Input pixel coordinates correlate to output pixel coordinates such that Xi, Yi→Xo, Yo. A point operation does not change the spatial relationships within an image. This is quite different from local operations where the value of neighboring pixels determines the value of the output pixel.
Point operations can correlate both gray levels and individual color channels in images. One example of a point operation is shown in the histogram of FIG. 4A. In FIG. 4, 8 bit (256 shades of gray) input levels are shown on the horizontal axis and output levels are shown on the vertical axis. If one were to apply the point operation of FIG. 4 to an input image, there would be a 1 to 1 correlation between the input and the output (transformed) image. Thus, input and output images would be the same.
Point operations are predictable in how they modify the gray-level histograms of an image. Point operations are typically used to optimize images by adjusting the contrast or brightness of an image. This process is known as contrast enhancing or gray-scale transformations. They are typically used as a copying technique, except that the gray levels are modified according to the specified gray-scale transformation function. Point operations are also typically used for photometric calibration, contrast enhancement, monitor display calibration, thresholding and clipping to limit the number of levels of gray in an image. The point operation is specified by the transformation function and can be defined as:

B(x,y)=ƒ[A(x,y)],

where A is an input image and B is an output image.

The at least one bifurcation transform used in the ITR system 130 can be either linear or non-linear point operations, or both. Linear point operations contain straight lines in their histogram representation, while non-linear (logarithmic, exponential, and hyperbolic/parabolic) point operations have curved lines. Non-linear point operations are used for changing the brightness/contrast of a particular part of an image relative to the rest of the image. This can allow the midpoints of an image to be brightened or darkened while maintaining blacks and white in the picture.
FIG. 4B is a histogram a linear point operation, and FIGS. 4C-4E are histograms of some non-linear point operations. An aspect of the present invention is the discovery that point operations can be used as bifurcation transforms for bringing images to a point of non-linearity. This typically requires a radical change in the output slope of the resultant histogram, such as that provided in the point operation illustrated by the histogram of FIG. 5A.
The present invention utilizes radical grayscale, color channel or a combination of luminance and color channel bifurcation transforms (point operations) to achieve image object bifurcation for purposes of image analysis and pattern recognition of objects. The placement of the nodal points in the bifurcation transform is one key parameter. An example of nodal point placements are shown in the bifurcation transform example illustrated by the histogram of FIG. 5B.
The nodal points in the bifurcation transforms (point operations) used in the present invention are placed so as to frequently create radical differences in color or luminance between image objects that otherwise statistically identical.
This is illustrated in the sample bifurcation transform of FIG. 5C. Using this bifurcation transform, two objects that are very close in color/luminance in an original image would be on opposite sides of a grayscale representation in the output (transformed) image. FIG. 6A shows an input image, and FIG. 6B shows the changes made to the input image (the transformed image obtained) as a result of applying the bifurcation transform of FIG. 5C. The input image is an x-ray image of a suitcase taken by a luggage scanner. In this example, the objects of interest are shoes 300 and a bar 310 on the left side of the suitcase.
Note that the orange background has gone a very different color from the shoes 300 and the bar 310 on the left side of the suitcase. The bifurcation transform of FIG. 5C uniquely delineates the objects of interest, while eliminating the background clutter in the image.
As can be seen by the input and transformed images shown in FIGS. 6A and 6B, respectively, the orange background in the image makes a radical departure from the orange objects of interest (300 and 310) and other objects that are almost identical to the objects of interest. The use of different nodal points in the bifurcation transform will cause the objects of interest to exhibit a different color from other objects.
Data points connecting the nodes can be calculated using several established methods. A common method of mathematically calculating the data points between nodes is through the use of cubic splines.
Additional imaging processes are preferably applied in the process of object recognition to accomplish specific tasks. Median and dilate algorithms cause neighboring pixels to behave in similar ways during the bifurcation transformation, and may be applied to assure the objects' integrity during the transformation process.
FIG. 7 is a flowchart of a method for identifying an object of interest in image data, in accordance with another embodiment of the present invention. The method steps in the flowchart of FIG. 7 will be explained with reference to the images shown in FIGS. 8A-8M, which are x-ray images of a suitcase at different stages in the ITR process. These images are just one example of the types of images that can be analyzed with the present invention. Other types of images, e.g., medical images from X-ray machines or CT scanners, or quantized photographic images can also be analyzed with the system and methods of the present invention.
The method starts at step 400, where the original image, such as the suitcase image shown in FIG. 8A, is received. The sample image shown in FIG. 8A contains clothing, shoes, cans of spray, a hair dryer, a jar of peanuts, peanut butter, a bottle of water, fruit and the object of interest.
At step 410, the image is segmented by applying a color determining transform that affect specifically those objects that match a certain color/density/effective atomic number characteristics. Objects of interest are isolated and identified by their responses to the sequence of filters. The image segmentation step is preferably a series of sub-steps. FIGS. 8B-8H show the image after each segmentation sub-step. The resulting areas of green in FIG. 8G are analyzed to see if they meet a minimum size requirement. This removes the small green pixels. The remaining objects of interest are then re-mapped to a new white background, resulting in the image of FIG. 8H. Most of the background, organic substances, and metal objects are eliminated in this step, leaving the water bottle 500, fruit 510, peanut butter 520 and object of interest 530.
At step 420, features are extracted by subjecting the original pixels of the areas of interest identified in step 410 to at least one feature extraction process. It is at this step that at least one bifurcation transform is applied to the original pixels of the areas of interest identified in step 410.
In the image examples shown in FIGS. 8I-8M, two feature extraction processes are applied. The first process in this example uses the following formulation (in the order listed):
(1) Replace colors
(2) Maximum filter 3×3
(3) Median filter 3×3
(4) Levels and Gamma Luminance=66 black level and 255 white level and Green levels=189 black, 255 white and gamma=9.9
(5) Apply bifurcation transform
(6) Maximum filter 3×3
(7) Replace black with white
(8) Median filter 3×3
The image shown in FIG. 8I results after process step (4) above, the image shown in FIG. 8J results after process step (5) above, and the image shown in FIG. 8K results after process step (7) above. Note that most of the fruit 510 and the water bottle 500 pixels on the lower left-hand side of the image in FIG. 8K have either disappeared or gone to a white color. This is in contrast to the preservation of large portions of the peanut butter jar 520 pixels and object of interest 530 pixels, which are now remapped to a new image in preparation for the second feature extraction process.
The second feature extraction process applied as part of step 420 distinguishes the two remaining objects 520 and 530. Replace color algorithms (same as process step (2) above) are applied, then two levels (two of process step (4) above) and then the bifurcation transform shown in FIG. 8N is applied. FIG. 8L shows the image after replace color algorithms and two levels are applied. FIG. 8M shows the image after the bifurcation transform of FIG. 8N is applied.
At step 430, the objects are classified based on their response at the feature extraction step (step 420). The object of interest 530 is measured in this process for its orange content. The peanut butter jar 520 shows green as its primary vector, and is therefore rejected. At step 440, the remaining object 530 is identified as an object of interest.
Determination of distinguishing vectors generated at step 420 is determined by statistical analysis of both objects of interest and other possible objects. This can be accomplished by applying existing statistical analysis. One example of a decision tree based on one process, along with a sample logic rule set for the decision tree (which is integrated into software) is shown below in Appendix A. In one approach, only leafs that show 100%/0% differences between objects with no overlap are used.
This is then entered into code and accessed from an object oriented scripting language called TAL. TAL (Threat Access Language) is linked to functions and logic in the PinPoint software code. Its design allows for rapid extension of the principals to new objects of interest. A sample of TAL is shown below.












call show_msg(“C4 Process 3a”)


endif


call set_gray_threshold(255)


call set_area_threshold(400)


call color_replace_and(image_wrk,dont_care,dont_care,greater_than,0,0,45,255,255,255)


call color_replace_and(image_wrk,less_than,dont_care,less_than,128,0,15,255,255,255)


call apply_curve(image_wrk,purple_path)


call color_replace_and(image_wrk,equals,equals,equals,65,65,65,255,255,255)


call color_replace_and(image_wrk,equals,equals,equals,0,255,0,255,255,255)


call color_replace_and(image_wrk,greater_than,equals,equals,150,0,255,0,255,0)


call color_replace_and(image_wrk,equals,equals,equals,0,0,255,255,255,255)


call color_replace_and(image_wrk,dont_care,less_than,less_than,0,255,255,255,255,255)


call color_replace_and(image_wrk,dont_care,equals,dont_care,0,0,0,255,255,255)


#if (show_EOP = 1)


# call display_and_wait(image_wrk)


#endif


call pix_map = get_first_aoi(image_wrk,ALLCHAN,1,0)


if (pix_map = 0)









jump @done_with_file







endif


call destroy_pixmap(AOI_wrk)


call AOI_wrk = copy_pixmap


call color_replace (image_tmp,greater_than,greater_than,greater_than,−1,−1,−1,255,255,255)


aoinum = 1


@C4loop3









call show_AOI_bounding_box( )







# if(show_AOI = 1)


# call display_and_wait(AOI_wrk)


# endif









call AOI_masked = get_pixmap_from_bbox(scan_org,0)



call image_tmp2 = composite_aoi(image_tmp,AOI_masked,255,255,255)



call destroy_pixmap(image_tmp)



call image_tmp = copy_pixmap(image_tmp2)



call destroy_pixmap(image_tmp2)



call destroy_pixmap(AOI_masked)



call pix_map = get_next_aoi( )



if (pix_map = 0)









call destroy_aoi_list( )



jump @C4Process3b









endif



call destroy_pixmap(AOI_wrk)



call AOI_wrk = copy_pixmap



aoinum = aoinum + 1







jump @C4loop3









A second pass is now made with all images. The rules defined above can now eliminate objects identified in process 1. A second process that follows the logic rules will now create objects of new colors for the remaining objects of interest. The vectors (metrics) of the transformed objects of interest are examined. Multiple qualitative approaches may be used in the evaluation of the objects, such as prototype performance and figure of merit. Metrics in the spatial domain, such as image amplitude (luminance, tristimulus value, spectral value) utilizing different degrees of freedom, the quantitative shape descriptions of a first-order histogram, such as Standard Deviation, Mean, Median, Skewness, Kurtosis, Energy and Entropy, % Color for red, green, and blue ratios between colors (total number of yellow pixels in the object/the total number of red pixels in the object), object symmetry, are some, but not all, of the possible measurements that can be used. Additional metrics can be created by applying spectrally-based processes such as Fourier and Wavelet transforms to the previously modified objects of interest or by analyzing eigenvalue plots produced from a Principal Components Analysis.
A color replacement technique is used to further emphasize tendencies of color changes. For example, objects that contain a value on the red channel>100, can be remapped to a level of 255 red so all bright red colors are made pure red. This is used to help identify metal objects that have varying densities. The ratio of these two colors is now a fixed ratio between the two. Color 1/Color 2=Invariant Ratio (IR).
This IR can now help indicate the presence of a certain metal objects regardless of its orientation in the image. It can also be correlated to geometric measurements using tools that determine boundaries and shapes. An example would be the correlation of IR with boundaries and centroid location. Other process may additionally be used as well.
The system and methods of the present invention are based on a methodology that is not restricted to a specific image type or imaging modality. It is capable of identifying and distinguishing a broad range of object types across a broad range of imaging applications. It works equally as well in applications such as CT scans, MRI, PET scans, mammography, cancer cell detection, geographic information systems, and remote sensing. It can identify and distinguish metal objects as well.
In medicine, the present invention is capable of, for example, distinguishing cancer cell growth in blood samples and is being tested with both mammograms and x-rays of lungs. For example, FIG. 9A shows an original input image with normal and cancerous cells. FIG. 9B shows the image after the ITR process of the present invention has been applied, with only cancer cells showing up in green.
The statistical processing provided by the present invention can be extended to integrate data from a patient's familial history, blood tests, x-rays, CT, PET (Positron Emission Tomography), and MRI scans into a single integrated analysis for radiologists, oncologists and the patient's personal physician. It can also assist drug companies in reducing costs by minimizing testing time for new drug certification.
The ITR system 130 can be implemented with a general purpose computer. However, it can also be implemented with a special purpose computer, programmed microprocessor or microcontroller and peripheral integrated circuit elements, ASICs or other integrated circuits, hardwired electronic or logic circuits such as discrete element circuits, programmable logic devices such as FPGA, PLD, PLA or PAL or the like. In general, any device on which a finite state machine capable of executing code for implementing the process steps of FIGS. 3 and 7 can be used to implement the ITR system 130.
Input channel 110 may be, include or interface to any one or more of, for instance, the Internet, an intranet, a PAN (Personal Area Network), a LAN (Local Area Network), a WAN (Wide Area Network) or a MAN (Metropolitan Area Network), a storage area network (SAN), a frame relay connection, an Advanced Intelligent Network (AIN) connection, a synchronous optical network (SONET) connection, a digital T1, T3, E1 or E3 line, Digital Data Service (DDS) connection, DSL (Digital Subscriber Line) connection, an Ethernet connection, an ISDN (Integrated Services Digital Network) line, a dial-up port such as a V.90, V.34bis analog modem connection, a cable modem, and ATM (Asynchronous Transfer Mode) connection, or an FDDI (Fiber Distributed Data Interface) or CDDI (Copper Distributed Data Interface) connection. Input channel 110 may furthermore be, include or interface to any one or more of a WAP (Wireless Application Protocol) link, a GPRS (General Packet Radio Service) link, a GSM (Global System for Mobile Communication) link, CDMA (Code Division Multiple Access) or TDMA (Time Division Multiple Access) link such as a cellular phone channel, a GPS (Global Positioning System) link, CDPD (Cellular Digital Packet Data), a RIM (Research in Motion, Limited) duplex paging type device, a Bluetooth radio link, or an IEEE 802.11-based radio frequency link. Input channel 110 may yet further be, include or interface to any one or more of an RS-232 serial connection, an IEEE-1394 (Firewire) connection, a Fiber Channel connection, an IrDA (infrared) port, a SCSI (Small Computer Systems Interface) connection, a USB (Universal Serial Bus) connection or other wired or wireless, digital or analog interface or connection.
The foregoing embodiments and advantages are merely exemplary, and are not to be construed as limiting the present invention. The present teaching can be readily applied to other types of apparatuses. The description of the present invention is intended to be illustrative, and not to limit the scope of the claims. Many alternatives, modifications, and variations will be apparent to those skilled in the art. Various changes may be made without departing from the spirit and scope of the present invention, as defined in the following claims.










APPENDIX A












Columns:
Role:
Type:
Levels:







THREAT
dependent
categorical
NOT C4






C4



Red %
independent
continuous



Green %
independent
continuous



Blue %
independent
continuous



Black %
independent
continuous



Yellow %
independent
continuous



Magenta %
independent
continuous



Cyan %
independent
continuous



RedMean
independent
continuous



RedMed
independent
continuous



RedStd
independent
continuous



GreenMean
independent
continuous



GreenMed
independent
continuous



GreenStd
independent
continuous



BlueMean
independent
continuous



BlueMed
independent
continuous



BlueStd
independent
continuous



RGBMean
independent
continuous



RGBMed
independent
continuous



RGBStd
independent
continuous




















Predicting THREAT: (1 Trees)

















Tree #1



Tree Structure:



[1]



[2] - ( Red % < 45.59520750 ) −−> NOT C4 (3480)



[4] - ( GreenStd < 51.03263250 ) −−> NOT C4 (3289)



[8] - ( RedMean >= 48.48295400 ) −−> NOT C4 (1933)



[16] - ( RGBStd < 52.36232400 ) −−> NOT C4 (1931)



[32] - ( RGBMean < 81.01520150 ) −−> NOT C4 (1633)



[64] - ( Black % < 15.09932550 ) −−> NOT C4 (540)



[65] - ( Black % >= 15.09932550 ) −−> NOT C4 (1093)



[130] - ( Black % >= 15.12923250 ) −−> NOT C4 (1091)



[260] - ( GreenMean < 75.62565250 ) −−> NOT C4 (1086)



[520] - ( Red % >= 31.87044900 ) −−> NOT C4 (126)



[521] - ( Red % < 31.87044900 ) −−> NOT C4 (960)



[1042] - ( RedStd < 50.30793950 ) −−> NOT C4 (926)



[2084] - ( RedStd >= 49.63398000 ) −−> NOT C4 (83)



[2085] - ( RedStd < 49.63398000 ) −−> NOT C4 (843)



[4170] - ( RedStd < 43.81623850 ) −−> NOT C4 (66)



[4171] - ( RedStd >= 43.81623850 ) −−> NOT C4 (777)



[8342] - ( Black % < 15.87751300 ) −−> NOT C4 (59)



[8343] - ( Black % >= 15.87751300 ) −−> NOT C4 (718)



[16686] - ( Yellow % < 0.03592250 ) −−> NOT C4 (628)



[33372] - ( RedMean >= 51.05065700 ) −−> NOT C4 (511)



[66744] - ( GreenMean < 70.19668950 ) −−> NOT C4 (498)



[133488] - ( Red % < 20.61720300 ) −−> NOT C4 (92)



[133489] - ( Red % >= 20.61720300 ) −−> NOT C4 (406)



[266978] ( Red % >= 20.67461300 ) −−> NOT C4 (404)



[533956] - ( RedMean < 52.41380900 ) −−> NOT C4 (82)



[533957] - ( RedMean >= 52.41380900 ) −−> NOT C4 (322)



[1067914] - ( RedMean >= 52.49773800 ) −−> NOT C4 (316)



[1067915] - ( RedMean < 52.49773800 ) −−> NOT C4 (6)



[266979] - ( Red % < 20.67461300 ) −−> C4 (2)



[66745] - ( GreenMean >= 70.19668950 ) −−> NOT C4 (13)



[33373] - ( RedMean < 51.05065700 ) −−> NOT C4 (117)



[66746] - ( GreenMean < 51.01018550 ) −−> NOT C4 (112)



[66747] - ( GreenMean >= 51.01018550) −−> C4 (5)



[16687] - ( Yellow % >= 0.03592250 ) −−> NOT C4 (90)



[33374] - ( RGBStd < 47.80018250 ) −−> NOT C4 (51)



[33375] - ( RGBStd >= 47.80018250 ) −−> NOT C4 (39)



[1043] - ( RedStd >= 50.30793950 ) −−> NOT C4 (34)



[261] - ( GreenMean >= 75.62565250 ) −−> C4 (5)



[131] - ( Black % < 15.12923250 ) −−> C4 (2)



[33] - ( RGBMean >= 81.01520150 ) −−> NOT C4 (298)



[66] - ( BlueStd < 47.80709650 ) −−> NOT C4 (249)



[132] - ( BlueStd < 41.91957850 ) −−> NOT C4 (64)



[133] - ( BlueStd >= 41.91957850 ) −−> NOT C4 (185)



[266] - ( RGBMean < 94.15494150 ) −−> NOT C4 (175)



[267] - ( RGBMean >= 94.15494150 ) −−> NOT C4 (10)



[67] - ( BlueStd >= 47.80709650 ) −−> NOT C4 (49)



[134] - ( BlueMean >= 82.78315000 ) −−> NOT C4 (46)



[135] - ( BlueMean < 82.78315000 ) −−> C4 (3)



[17] - ( RGBStd >= 52.36232400 ) −−> C4 (2)



[9] - ( RedMean < 48.48295400 ) −−> NOT C4 (1356)



[18] - ( Yellow % < 0.03146250 ) −−> NOT C4 (1305)



[36] - ( Red % > 44.50125100 ) −−> NOT C4 (97)



[37] - ( Red % < 44.50125100 ) −−> NOT C4 (1208)



[74] - ( BlueStd < 45.50279600 ) −−> NOT C4 (364)



[148] - ( RedMean >= 37.87216600 ) −−> NOT C4 (264)



[149] - ( RedMean < 37.87216600 ) −−> NOT C4 (100)



[75] - ( BlueStd >= 45.50279600 ) −−> NOT C4 (844)



[150] - ( RGBStd >= 44.46212600 ) −−> NOT C4 (838)



[300] - ( RedMean < 48.34769250 ) −−> NOT C4 (831)



[600] - ( BlueStd >= 52.70366700 ) −−> NOT C4 (412)



[1200] - ( BlueStd < 55.50064650 ) −−> NOT C4 (195)



[1201] - ( BlueStd >= 55.50064650 ) −−> NOT C4 (217)



[2402] - ( Black % < 37.13927650 ) −−> NOT C4 (197)



[4804] - ( BlueStd >= 55.88707900 ) −−> NOT C4 (193)



[9608] - ( BlueMean < 56.23658350 ) −−> NOT C4 (46)



[9609] - ( BlueMean >= 56.23658350 ) −−> NOT C4 (147)



[19218] - ( GreenMean >= 42.14803700 ) −−> NOT C4 (108)



[19219] - ( GreenMean < 42.14803700 ) −−> NOT C4 (39)



[4805] - ( BlueStd < 55.88707900 ) −−> NOT C4 (4)



[2403] - ( Black % >= 37.13927650 ) −−> NOT C4 (20)



[601] - ( BlueStd < 52.70366700 ) −−> NOT C4 (419)



[1202] - ( BlueStd < 52.68909100 ) −−> NOT C4 (417)



[2404] - ( Red % < 43.02504550 ) −−> NOT C4 (361)



[4808] - ( Red % >= 42.25179100 ) −−> NOT C4 (30)



[4809] - ( Red % < 42.25179100 ) −−> NOT C4 (331)



[9618] - ( Red % < 42.24141500 ) −−> NOT C4 (329)



[19236] - ( RedStd >= 47.49008550 ) −−> NOT C4 (100)



[19237] - ( RedStd < 47.49008550 ) −−> NOT C4 (229)



[38474] - ( GreenMean < 42.52050950 ) −−> NOT C4 (79)



[38475] - ( GreenMean >= 42.52050950 ) −−> NOT C4 (150)



[76950] - ( Red % < 40.27006350 ) −−> NOT C4 (144)



[153900] - ( Red % >= 38.90005650 ) −−> NOT C4 (15)



[153901] - ( Red % < 38.90005650 ) −−> NOT C4 (129)



[307802] - ( Red % < 38.82381650 ) −−> NOT C4 (127)



[615604] - ( Black % < 33.49176400 ) −−> NOT C4 (54)



[615605] - ( Black % >= 33.49176400 ) −−> NOT C4 (73)



[1231210] - ( BlueMean < 51.00749950 ) −−> NOT C4 (71)



[2462420] - ( Red % < 34.90112650 ) −−> NOT C4 (9)



[2462421] - ( Red % >= 34.90112650 ) −−> NOT C4 (62)



[4924842] - ( Red % >= 34.97943300 ) −−> NOT C4 (60)



[9849684] - ( Black % >= 33.68111600 ) −−> NOT C4 (58)



[9849685] - ( Black % < 33.68111600 ) −−> C4 (2)



[4924843] - ( Red % < 34.97943300 ) −−> C4 (2)



[1231211] - ( BlueMean >= 51.00749950 ) −−> C4 (2)



[307803] - ( Red % >= 38.82381650 ) −−> C4 (2)



[76951] - ( Red % >= 40.27006350 ) −−> C4 (6)



[9619] - ( Red % >= 42.24141500 ) −−> C4 (2)



[2405] - ( Red % >= 43.02504550 ) −−> NOT C4 (56)



[4810] - ( Red % > 43.17405900 ) −−> NOT C4 (52)



[4811] - ( Red % < 43.17405900) −−> C4 (4)



[1203] - ( BlueStd >= 52.68909100 ) −−> C4 (2)



[301] - ( RedMean >= 48.34769250 ) −−> C4 (7)



[151] - ( RGBStd < 44.46212600 ) −−> C4 (6)



[19] - (Yellow % >= 0.03146250 ) −−> NOT C4 (51)



[38] - ( Black % < 44.57350350 ) −−> NOT C4 (47)



[76] - ( Red % < 31.90947800) −−> NOT C4 (7)



[77] - ( Red % >= 31.90947800 ) −−> NOT C4 (40)



[154] - ( Yellow % < 0.54979900 ) −−> NOT C4 (36)



[308] - ( BlueStd < 45.17869600 ) −−> NOT C4 (7)



[309] - ( BlueStd >= 45.17869600 ) −−> NOT C4 (29)



[618] - ( RGBStd >= 47.75511750 ) −−> NOT C4 (21)



[619] - ( RGBStd < 47.75511750 ) −−> C4 (8)



[155] - (Yellow % >= 0.54979900 ) −−> C4 (4)



[39] - ( Black % >= 44.57350350 ) −−> C4 (4)



[5] - ( GreenStd >= 51.03263250 ) −−> NOT C4 (191)



[10] - ( Yellow % < 0.02421300 ) −−> NOT C4 (144)



[20] - ( BlueMean >= 47.87181850 ) −−> NOT C4 (141)



[40] - ( RedStd >= 51.71918100 ) −−> NOT C4 (45)



[41] - ( RedStd < 51.71918100 ) −−> NOT C4 (96)



[82] - ( BlueStd < 53.69071950 ) −−> NOT C4 (57)



[83] - ( BlueStd >= 53.69071950 ) −−> NOT C4 (39)



[166] - ( RGBStd >= 51.64918700 ) −−> NOT C4 (33)



[167] - ( RGBStd < 51.64918700 ) −−> C4 (6)



[21] - ( BlueMean < 47.87181850 ) −−> C4 (3)



[11] - ( Yellow % >= 0.02421300 ) −−>C4 (47)



[22] - ( RedMean >= 60.68355200 ) −−> NOT C4 (16)



[23] - ( RedMean < 60.68355200 ) −−> C4 (31)



[3] - ( Red % >= 45.59520750 ) −−> NOT C4 (3330)



[6] - ( Yellow % < 0.01466550 ) −−> NOT C4 (3081)



[12] - ( Black % < 64.98848350 ) −−> NOT C4 (2036)



[24] - ( GreenStd < 47.90018300 ) −−> NOT C4 (1937)



[48] - ( Red % < 58.16014100 ) −−> NOT C4 (1130)



[96] - ( RGBMean < 28.78376100 ) −−> NOT C4 (123)



[97] - ( RGBMean >= 28.78376100 ) −−> NOT C4 (1007)



[194] - ( BlueMean >= 28.93594150 ) −−> NOT C4 (1005)



[388] - ( Black % >= 43.40800500 ) −−> NOT C4 (911)



[776] - ( Red % >= 45.73560700 ) −−> NOT C4 (902)



[1552] - ( Black % < 52.87984300 ) −−> NOT C4 (706)



[3104] - ( Black % >= 51.76409350 ) −−> NOT C4 (68)



[3105] - ( Black % < 51.76409350 ) −−> NOT C4 (638)



[6210] - ( BlueMean >= 43.23308750 ) −−> NOT C4 (81)



[6211] - ( BlueMean < 43.23308750 ) −−> NOT C4 (557)



[12422] - ( BlueMean < 43.14976500 ) −−> NOT C4 (555)



[24844] - ( BlueMean < 38.42803750 ) −−> NOT C4 (380)



[49688] - ( RedMean >= 30.67255650 ) −−> NOT C4 (334)



[99376] - ( RGBStd < 47.19750600 ) −−> NOT C4 (328)



[198752] - ( BlueStd >= 44.62378500 ) −−> NOT C4 (157)



[198753] - ( BlueStd < 44.62378500 ) −−> NOT C4 (171)



[397506] - ( BlueMean < 36.43159850 ) −−> NOT C4 (167)



[397507] - ( BlueMean >= 36.43159850 ) −−> C4 (4)



[99377] - ( RGBStd >= 47.19750600 ) −−> NOT C4 (6)



[49689] - ( RedMean < 30.67255650) −−> NOT C4 (46)



[24845] - ( BlueMean >= 38.42803750 ) −−> NOT C4 (175)



[49690] - ( RedStd >= 45.06529400) −−> NOT C4 (87)



[49691] - ( RedStd < 45.06529400 ) −−> NOT C4 (88)



[99382] - ( RedMean < 31.73246550 ) −−> NOT C4 (39)



[99383] - ( RedMean >= 31.73246550 ) −−> NOT C4 (49)



[198766] - ( BlueMean >= 41.89118200 ) −−> NOT C4 (7)



[198767] - ( BlueMean < 41.89118200 ) −−> NOT C4 (42)



[397534] - ( RedStd < 44.94667250 ) −−> NOT C4 (39)



[795068] - ( BlueMean < 41.45530350 ) −−> NOT C4 (36)



[1590136] - ( BlueMean >= 41.19118150 ) −−> NOT C4 (7)



[1590137] - ( BlueMean < 41.19118150 ) −−> NOT C4 (29)



[3180274] - ( Red % >= 47.11168700 ) −−> NOT C4 (26)



[3180275] - ( Red % < 47.11168700 ) −−> C4 (3)



[795069] - ( BlueMean >= 41.45530350 ) −−> C4 (3)



[397535] - ( RedStd >= 44.94667250 ) −−> C4 (3)



[12423] - ( BlueMean >= 43.14976500 ) −−> C4 (2)



[1553] - ( Black % >= 52.87984300 ) −−> NOT C4 (196)



[3106] - ( GreenMean < 33.90735800 ) −−> NOT C4 (185)



[6212] - ( Black % >= 52.91334750 ) −−> NOT C4 (183)



[12424] - ( RedStd >= 42.39215050 ) −−> NOT C4 (111)



[12425] - ( RedStd < 42.39215050 ) −−> NOT C4 (72)



[24850] - ( GreenStd < 42.26553150 ) −−> NOT C4 (64)



[24851] - ( GreenStd >= 42.26553150 ) −−> NOT C4 (8)



[6213] - ( Black % < 52.91334750 ) −−> C4 (2)



[3107] - ( GreenMean >= 33.90735800 ) −−> C4 (11)



[777] - ( Red % < 45.73560700 ) −−> NOT C4 (9)



[389] - ( Black % < 43.40800500 ) −−> NOT C4 (94)



[778] - ( GreenStd >= 44.41347850 ) −−> NOT C4 (54)



[779] - ( GreenStd < 44.41347850 ) −−> NOT C4 (40)



[1558] - ( RGBStd < 46.21739000 ) −−> NOT C4 (28)



[1559] - ( RGBStd >= 46.21739000 ) −−> C4 (12)



[195] - ( BlueMean < 28.93594150 ) −−> C4 (2)



[49] - ( Red % >= 58.16014100 ) −−> NOT C4 (807)



[98] - ( GreenStd < 42.89170450 ) −−> NOT C4 (707)



[196] - ( Black % < 53.84187500 ) −−> NOT C4 (27)



[197] - ( Black % >= 53.84187500) > NOT C4 (680)



[394] - ( Black % >=  64.68735500 ) −−> NOT C4 (27)



[395] - ( Black % < 64.68735500 ) −−> NOT C4 (653)



[790] - ( RGBMean < 28.10519400) −−> NOT C4 (595)



[1580] - ( GreenStd < 42.78296850 ) −−> NOT C4 (593)



[3160] - ( BlueMean >= 28.28960050 ) −−> NOT C4 (150)



[3161] - ( BlueMean < 28.28960050 ) −−> NOT C4 (443)



[6322] - ( RGBStd >= 35.22638500 ) −−> NOT C4 (434)



[12644] - ( BlueStd < 37.90874250 ) −−> NOT C4 (62)



[12645] - ( BlueStd >= 37.90874250 ) −−> NOT C4 (372)



[25290] - ( RGBStd >= 38.07899050 ) −−> NOT C4 (333)



[50580] - ( RGBMean < 23.07209300 ) −−> NOT C4 (21)



[50581] - ( RGBMean >= 23.07209300 ) −−> NOT C4 (312)



[101162] - ( RedMean >= 22.47274750 ) −−> NOT C4 (290)



[202324] - ( RedMean < 26.44887950 ) −−> NOT C4 (259)



[404648] - ( BlueMean >= 24.16304400 ) −−> NOT C4 (231)



[809296] - ( RGBMean < 24.47212150 ) −−> NOT C4 (42)



[809297] - ( RGBMean >= 24.47212150 ) −−> NOT C4 (189)



[1618594] - ( RedMean >= 24.22689100 ) −−> NOT C4 (168)



[3237188] - ( RGBMean < 25.65376000 ) −−> NOT C4 (103)



[3237189] - ( RGBMean >= 25.65376000 ) −−> NOT C4 (65)



[1618595] - ( RedMean < 24.22689100 ) −−> NOT C4 (21)



[404649] - ( BlueMean < 24.16304400 ) −−> NOT C4 (28)



[202325] - ( RedMean >= 26.44887950 ) −−> NOT C4 (31)



[101163] - ( RedMean < 22.47274750 ) −−> NOT C4 (22)



[25291] - ( RGBStd < 38.07899050 ) −−> NOT C4 (39)



[50582] - ( RedStd < 38.04919800 ) −−> NOT C4 (36)



[50583] - ( RedStd >= 38.04919800 ) −−> C4 (3)



[6323] - ( RGBStd < 35.22638500 ) −−> NOT C4 (9)



[1581] - ( GreenStd >= 42.78296850 ) −−> C4 (2)



[791] - ( RGBMean >= 28.10519400) −−> NOT C4 (58)



[1582] - ( GreenStd >= 42.13297450 ) −−> NOT C4 (19)



[1583] - ( GreenStd < 42.13297450 ) −−> NOT C4 (39)



[3166] - ( Black % < 58.09482200 ) −−> NOT C4 (30)



[3167] - ( Black % >= 58.09482200 ) −−> C4 (9)



[99] - ( GreenStd >= 42.89170450 ) −−> NOT C4 (100)



[198] - ( BlueStd >= 51.43109700 ) −−> NOT C4 (13)



[199] - ( BlueStd < 51.43109700 ) −−> NOT C4 (87)



[398] - ( RGBStd >= 43.94751350 ) −−> NOT C4 (71)



[796] - ( RedMean < 30.70032950 ) −−> NOT C4 (66)



[1592] - ( RedMean >= 29.93333650 ) −−> NOT C4 (7)



[1593] - ( RedMean < 29.93333650 ) −−> NOT C4 (59)



[3186] - ( Red % < 60.60153550 ) −−> NOT C4 (29)



[3187] - ( Red % >= 60.60153550 ) −−> NOT C4 (30)



[6374] - ( Red % >= 62.39068600 ) −−> NOT C4 (17)



[6375] - ( Red % < 62.39068600 ) −−> C4 (13)



[797] - ( RedMean >= 30.70032950 ) −−> C4 (5)



[399] - ( RGBStd < 43.94751350 ) −−> C4 (16)



[25] - ( GreenStd >= 47.90018300 ) −−> NOT C4 (99)



[50] - (RedStd >= 47.16206350 ) −−> NOT C4 (96)



[100] - ( BlueMean < 53.94523250 ) −−> NOT C4 (93)



[200] - ( Black % < 41.41240100 ) −−> NOT C4 (5)



[201] - ( Black % >= 41.41240100 ) −−> NOT C4 (88)



[402] - ( GreenStd >= 47.94884100 ) −−> NOT C4 (86)



[804] - ( RGBStd < 53.28121950 ) −−> NOT C4 (84)



[1608] - ( Red % < 46.07311100 ) −−> NOT C4 (6)



[1609] - ( Red % >= 46.07311100 ) −−> NOT C4 (78)



[3218] - ( Black % >= 43.52703650 ) −−> NOT C4 (73)



[6436] - ( BlueMed >= 13.00000000 ) −−> NOT C4 (36)



[6437] - ( BlueMed < 13.00000000 ) −−> NOT C4 (37)



[12874] - ( RedMean < 36.80363800 ) −−> NOT C4 (19)



[12875] - ( RedMean >= 36.80363800 ) −−> C4 (18)



[3219] - ( Black % < 43.52703650 ) −−> C4 (5)



[805] - ( RGBStd >= 53.28121950 ) −−> C4 (2)



[403] - ( GreenStd < 47.94884100 ) −−> C4 (2)



[101] - ( BlueMean >= 53.94523250 ) −−> C4 (3)



[51] - ( RedStd < 47.16206350 ) −−> C4 (3)



[13] - ( Black % >= 64.98848350 ) −−> NOT C4 (1045)



[26] - ( GreenStd < 41.43569950 ) −−> NOT C4 (1024)



[52] - ( GreenStd < 37.92868250 ) −−> NOT C4 (892)



[104] - ( RGBStd >= 38.03495050 ) −−> NOT C4 (54)



[105] - ( RGBStd < 38.03495050 ) −−> NOT C4 (838)



[210] - ( Red % >= 89.23843750 ) −−> NOT C4 (16)



[211] - ( Red % < 89.23843750 ) −−> NOT C4 (822)



[422] - ( Black % < 74.10862350 ) −−> NOT C4 (395)



[844] - ( Black % >= 73.70447550 ) −−> NOT C4 (23)



[845] - ( Black % < 73.70447550 ) −−> NOT C4 (372)



[1690] - ( Red % >= 65.05397050 ) −−> NOT C4 (370)



[3380] - ( Red % < 66.00984600 ) −−> NOT C4 (13)



[3381] - ( Red % >= 66.00984600 ) −−> NOT C4 (357)



[6762] - ( Black % >= 66.09868600 ) −−> NOT C4 (350)



[13524] - ( RedMean >= 17.57113300 ) −−> NOT C4 (213)



[27048] - ( Red % >= 72.55765550 ) −−> NOT C4 (15)



[27049] - ( Red % < 72.55765550 ) −−> NOT C4 (198)



[54098] - ( BlueStd < 39.93703450 ) −−> NOT C4 (185)



[108196] - ( Red % < 72.34632500 ) −−> NOT C4 (181)



[216392] - ( Red % >= 71.43363600 ) −−> NOT C4 (31)



[216393] - ( Red % < 71.43363600 ) −−> NOT C4 (150)



[432786] - ( BlueMean >= 21.16735350 ) −−> NOT C4 (27)



[432787] - ( BlueMean < 21.16735350 ) −−> NOT C4 (123)



[865574] - ( BlueMean < 20.85527150 ) −−> NOT C4 (115)



[1731148] - ( BlueMean >= 20.25724800 ) −−> NOT C4 (20)



[1731149] - ( BlueMean < 20.25724800 ) −−> NOT C4 (95)



[3462298] - ( RedMean < 20.05789100 ) −−> NOT C4 (89)



[6924596] - ( BlueStd < 34.00269100 ) −−> NOT C4 (11)



[6924597] - ( BlueStd >= 34.00269100 ) −−> NOT C4 (78)



[13849194] - ( RGBMean >= 19.82349700 ) −−> NOT C4 (9)



[13849195] - ( RGBMean < 19.82349700 ) −−> NOT C4 (69)



[27698390] - ( RGBStd < 36.33854900 ) −−> NOT C4 (59)



[55396780] - ( RGBStd >= 35.63215850 ) −−> NOT C4 (18)



[55396781] - ( RGBStd < 35.63215850 ) −−> NOT C4 (41)



[110793562] - ( Red % < 71.37550350 ) −−> NOT C4 (39)



[110793563] - ( Red % >= 71.37550350 ) −−> C4 (2)



[27698391] - ( RGBStd >= 36.33854900 ) −−> NOT C4 (10)



[3462299] - ( RedMean >= 20.05789100 ) −−> C4 (6)



[865575] - ( BlueMean >= 20.85527150 ) −−> C4 (8)



[108197] - ( Red % >= 72.34632500 ) −−> C4 (4)



[54099] - ( BlueStd >= 39.93703450 ) −−> NOT C4 (13)



[13525] - ( Red Mean < 17.57113300 ) −−> NOT C4 (137)



[27050] - ( RedMean < 17.10107150 ) −−> NOT C4 (87)



[54100] - ( BlueStd >= 42.46681600 ) −−> NOT C4 (14)



[54101] - ( BlueStd < 42.46681600 ) −−> NOT C4 (73)



[108202] - ( RGBStd < 34.84770050 ) −−> NOT C4 (57)



[108203] - ( RGBStd >= 34.84770050 ) −−> NOT C4 (16)



[27051] - ( RedMean >= 17.10107150 ) −−> NOT C4 (50)



[54102] - ( RedStd >= 33.69779800 ) −−> NOT C4 (38)



[108204] - ( Red % < 73.59689350 ) −−> NOT C4 (27)



[108205] - ( Red % >= 73.59689350 ) −−> C4 (11)



[54103] - ( RedStd < 33.69779800 ) −−> C4 (12)



[6763] - ( Black % < 66.09868600 ) −−> C4 (7)



[1691] - ( Red % < 65.05397050 ) −−> C4 (2)



[423] - ( Black % >= 74.10862350 ) −−> NOT C4 (427)



[846] - ( RGBMean < 16.67037250 ) −−> NOT C4 (397)



[1692] - ( GreenStd < 35.39174850 ) −−> NOT C4 (393)



[3384] - ( RedStd >= 34.45686900 ) −−> NOT C4 (14)



[3385] - ( RedStd < 34.45686900 ) −−> NOT C4 (379)



[6770] - ( GreenStd < 34.60126100 ) −−> NOT C4 (377)



[13540] - ( Red % < 77.70377350 ) −−> NOT C4 (91)



[27080] - ( RGBMean < 16.00073950 ) −−> NOT C4 (76)



[27081] - ( RGBMean >= 16.00073950 ) −−> NOT C4 (15)



[13541] - ( Red % >= 77.70377350 ) −−> NOT C4 (286)



[27082] - ( RGBStd < 31.63435750 ) −−> NOT C4 (202)



[54164] - ( BlueStd >= 32.18647800 ) −−> NOT C4 (41)



[54165] - ( BlueStd < 32.18647800 ) −−> NOT C4 (161)



[108330] - ( RGBStd < 30.98291200) −−> NOT C4 (154)



[216660] - ( Black % >= 77.73876550 ) −−> NOT C4 (152)



[433320] - ( BlueStd >= 25.27611950 ) −−> NOT C4 (142)



[866640] - ( BlueMean < 10.15660150 ) −−> NOT C4 (38)



[866641] - ( BlueMean >= 10.15660150 ) −−> NOT C4 (104)



[1733282] - ( Black % < 82.35655600 ) −−> NOT C4 (71)



[3466564] - ( RedStd >= 30.41704200 ) −−> NOT C4 (8)



[3466565] - ( RedStd < 30.41704200 ) −−> NOT C4 (63)



[6933130] - ( Red % >= 78.59946850 ) −−> NOT C4 (61)



[13866260] - ( RGBStd < 30.32782150 ) −−> NOT C4 (58)



[13866261] - ( RGBStd >= 30.32782150 ) −−> C4 (3)



[6933131] - ( Red % < 78.59946850 ) −−> C4 (2)



[1733283] - ( Black % >= 82.35655600 ) −−> C4 (33)



[3466566] - ( GreenStd < 29.93382900 ) −−> NOT C4 (30)



[6933132] - ( RedStd >= 28.70312000 ) −−> NOT C4 (9)



[6933133] - ( RedStd < 28.70312000 ) −−> C4 (21)



[3466567] - ( GreenStd >= 29.93382900 ) −−> C4 (3)



[433321] - ( BlueStd < 25.27611950 ) −−> C4 (10)



[216661] - ( Black % < 77.73876550 ) −−> C4 (2)



[108331] - ( RGBStd >= 30.98291200 ) −−> C4 (7)



[27083] - ( RGBStd >= 31.63435750 ) −−> NOT C4 (84)



[54166] - ( RGBStd >= 31.90780100 ) −−> NOT C4 (73)



[108332] - ( GreenMean >= 12.01756750 ) −−> NOT C4 (67)



[216664] - ( RGBMean < 13.54910200 ) −−> NOT C4 (13)



[216665] - ( RGBMean >= 13.54910200 ) −−> NOT C4 (54)



[433330] ( Black % < 78.90671950 ) −−> NOT C4 (42)



[433331] - ( Black % >= 78.90671950 ) −−> C4 (12)



[108333] - ( GreenMean < 12.01756750 ) −−> C4 (6)



[54167] - ( RGBStd < 31.90780100 ) −−> C4 (11)



[6771] - ( GreenStd >= 34.60126100 ) −−> C4 (2)



[1693] - ( GreenStd >= 35.39174850 ) −−> C4 (4)



[847] - ( RGBMean >= 16.67037250 ) −−> C4 (30)



[53] - ( GreenStd >= 37.92868250 ) −−> NOT C4 (132)



[106] - ( Black % < 70.04750450 ) −−> NOT C4 (91)



[212] - ( Black % >= 69.01367950 ) −−> NOT C4 (13)



[213] - ( Black % < 69.01367950 ) −−> NOT C4 (78)



[426] - ( RedMean < 20.53832700 ) −−> NOT C4 (7)



[427] - ( RedMean >= 20.53832700 ) −−> NOT C4 (71)



[854] - ( Black % < 68.36993400 ) −−> NOT C4 (66)



[1708] - ( BlueMean < 25.21327900 ) −−> NOT C4 (52)



[3416] - ( RedMean < 23.73270800 ) −−> NOT C4 (50)



[6832] - ( Black % < 66.05030800 ) −−> NOT C4 (18)



[6833] - ( Black % >= 66.05030800 ) −−> NOT C4 (32)



[3417] - ( RedMean >= 23.73270800 ) −−> C4 (2)



[1709] - ( BlueMean >= 25.21327900 ) −−> C4 (14)



[855] - ( Black % >= 68.36993400 ) −−> C4 (5)



[107] - ( Black % >= 70.04750450 ) −−> C4 (41)



[214] - ( GreenStd < 39.85793300 ) −−> C4 (33)



[428] - ( BlueStd >= 40.15871400 ) −−> NOT C4 (17)



[429] - ( BlueStd < 40.15871400 ) −−> C4 (16)



[215] - ( GreenStd >= 39.85793300 ) −−> C4 (8)



[27] - ( GreenStd >= 41.43569950 ) −−> C4 (21)



[7] - ( Yellow % >= 0.01466550 ) −−> C4 (249)



[14] - ( Red % < 48.52728250 ) −−> NOT C4 (26)



[15] - ( Red % >= 48.52728250 ) −−> C4 (223)



[30] - (Yellow % < 0.05884450 ) −−> C4 (91)



[60] - ( BlueStd >= 45.94578350 ) −−> C4 (50)



[120] - ( RedStd < 46.83117300 ) −−> C4 (38)



[240] - ( Red % >= 53.21278750 ) −−> NOT C4 (27)



[480] - ( Red % < 54.46098150 ) −−> NOT C4 (4)



[481] - ( Red % >= 54.46098150 ) −−> C4 (23)



[962] - ( Red % >= 56.73616400 ) −−> NOT C4 (18)



[963] - ( Red % < 56.73616400 ) −−> C4 (5)



[241] - ( Red % < 53.21278750 ) −−> C4 (11)



[121] - ( RedStd >= 46.83117300 ) −−> C4 (12)



[61] - ( BlueStd < 45.94578350 ) −−> C4 (41)



[31] - (Yellow % >= 0.05884450 ) −−> C4 (132)





















Left Split

Number




Node
Parent
Position
Definition
Score
Records
Entrophy
Probabilities





1
root
root
( Red % <
NOT
6810
5,769.42588812
0.84948605





45.59520750 )
C4


0.15051395


2
1
left
( GreenStd <
NOT
3480
1,859.05633837
0.92471264





51.03263250 )
C4


0.07528736


3
1
right
( Yellow % <
NOT
3330
3,584.55795628
0.77087087





0.01466550 )
C4


0.22912913


4
2
left
( RedMean >=
NOT
3289
1,593.85548926
0.93432654





48.48295400 )
C4


0.06567346


5
2
right
( Yellow % <
NOT
191
210.88065573
0.75916230





0.02421300 )
C4


0.24083770


6
3
left
( Black % <
NOT
3081
2,912.05292403
0.81921454





64.98848350 )
C4


0.18078546


7
3
right
( Red % <
C4
249
229.14335095
0.17269076





48.52728250 )



0.82730924


8
4
left
( RGBStd <
NOT
1933
727.80641599
0.95344025





52.36232400 )
C4


0.04655975


9
4
right
( Yellow % <
NOT
1356
838.66671504
0.90707965





0.03146250 )
C4


0.09292035


10
5
left
( BlueMean >=
NOT
144
123.11922742
0.84722222





47.87181850 )
C4


0.15277778


11
5
right
( RedMean >=
C4
47
65.13455677
0.48936170





60.68355200 )



0.51063830


12
6
left
( GreenStd <
NOT
2036
1,604.70064657
0.86591356





47.90018300 )
C4


0.13408644


13
6
right
( GreenStd <
NOT
1045
1,222.67448565
0.72822967





41.43566950 )
C4


0.27177033


14
7
left
(leaf)
NOT
26
33.54172389
0.65384615






C4


0.34615385


15
7
right
( Yellow % <
C4
223
160.59532097
0.11659193





0.05884450)



0.88340807


16
8
left
( RGBMean <
NOT
1931
715.49563342
0.95442776





81.01520150 )
C4


0.04557224


17
8
right
(leaf)
C4
2
0.00000000
0.00000000









1.00000000


18
9
left
( Red % >=
NOT
1305
735.37704086
0.91877395





44.50125100 )
C4


0.08122605


19
9
right
( Black % <
NOT
51
68.30971692
0.60784314





44.57350350 )
C4


0.39215686


20
10
left
( RedStd >=
NOT
141
111.48047296
0.86524823





51.71918100 )
C4


0.13475177


21
10
right
(leaf)
C4
3
0.00000000
0.00000000









1.00000000


22
11
left
(leaf)
NOT
16
0.00000000
1.00000000






C4


0.00000000


23
11
right
(leaf)
C4
31
33.11788073
0.22580645









0.77419355


24
12
left
( Red % <
NOT
1937
1,431.63672010
0.87867837





58.16014100 )
C4


0.12132163


25
12
right
( RedStd >=
NOT
99
131.85057076
0.61616162





47.16206350 )
C4


0.38383838


26
13
left
( GreenStd <
NOT
1024
1,171.01875201
0.74121094





37.92868250 )
C4


0.25878906


27
13
right
(leaf)
C4
21
13.20867245
0.09523810









0.90476190


30
15
left
( BlueStd >=
C4
91
90.51596849
0.19780200





45.94578350 )



0.80219780


31
15
right
(leaf)
C4
132
60.35881463
0.06060606









0.93939394


32
16
left
( Black % <
NOT
1633
533.67259132
0.96142070





15.09932550 )
C4


0.03857930


33
16
right
( BlueStd <
NOT
298
171.75232654
0.91610738





47.80709650 )
C4


0.08389262


36
18
left
(leaf)
NOT
97
0.00000000
1.00000000






C4


0.00000000


37
18
right
( BlueStd <
NOT
1208
718.26985716
0.91225166





45.50279600 )
C4


0.08774834


38
19
left
( Red % <
NOT
47
60.28382877
0.65957447





31.90947800 )
C4


0.34042553


39
19
right
(leaf)
C4
4
0.00000000
0.00000000









1.00000000


40
20
left
(leaf)
NOT
45
9.59093629
0.97777778






C4


0.02222222


41
20
right
( BlueStd <
NOT
96
92.65489251
0.81250000





53.69071950 )
C4


0.18750000


48
24
left
( RGBMean <
NOT
1130
716.95071744
0.90353982





28.78376100 )
C4


0.09646018


49
24
right
( GreenStd <
NOT
807
699.18949906
0.84386617





42.89170450 )
C4


0.15613383


50
25
left
( BLueMean <
NOT
96
125.95387703
0.63541667





53.94523250 )
C4


0.36458333


51
25
right
(leaf)
C4
3
0.00000000
0.00000000









1.00000000


52
26
left
( RGBStd >=
NOT
892
973.61796432
0.76457399





38.03495050 )
C4


0.23542601


53
26
right
( Black % <
NOT
132
179.30702222
0.58333333





70.04750450 )
C4


0.41666667


60
30
left
( RedStd <
C4
50
62.68694576
0.32000000





46.83117300 )



0.68000000


61
30
right
(leaf)
C4
41
15.98251235
0.04878049









0.95121951


64
32
left
(leaf)
NOT
540
99.59334192
0.98148148






C4


0.01851852


65
32
right
( Black % >=
NOT
1093
424.18472668
0.95150961





15.12923250 )
C4


0.04849039


66
33
left
( Blue Std <
NOT
249
102.424717
0.94779116





41.91957850
C4


0.05220884


67
33
right
( BlueMean >=
NOT
49
54.55270408
0.75510204





82.78315000 )
C4


0.24489796


74
37
left
( RedMean >=
NOT
364
143.33900002
0.95054945





37.87216600 )
C4


0.04945055


75
37
right
( RGBStd >=
NOT
844
564.39157075
0.89573460





44.46212600 )
C4


0.10426540


76
38
left
(leaf)
NOT
7
0.00000000
1.00000000






C4


0.00000000


77
38
right
( Yellow % <
NOT
40
53.84093336
0.60000000





0.54979900 )
C4


0.40000000


82
41
left
(leaf)
NOT
57
33.88411868
0.91228070






C4


0.08771930


83
41
right
( RGBStd >=
NOT
39
49.64810513
0.66666667





51.64918700 )
C4


0.33333333


96
48
left
(leaf)
NOT
123
20.44345066
0.98373984






C4


0.01626016


97
48
right
( BlueMean >=
NOT
1007
681.97207338
0.89374379





28.93594150 )
C4


0.10625621


98
49
left
( Black % <
NOT
707
542.88067232
0.87128713





53.84187500 )
C4


0.12871287


99
49
right
( BlueStd >=
NOT
100
129.48932781
0.65000000





51.43109700 )
C4


0.35000000


100
50
left
( Black % <
NOT
93
119.72979652
0.65591398





41.41240100 )
C4


0.34408602


101
50
right
(leaf)
C4
3
0.00000000
0.00000000









1.00000000


104
52
left
(leaf)
NOT
54
17.10834157
0.96296296






C4


0.03703704


105
52
right
( Red % >=
NOT
838
939.16352283
0.75178998





89.23843750 )
C4


0.24821002


106
53
left
( Black % >=
NOT
91
110.66420475
0.70329670





69.01367950 )
C4


0.29670330


107
53
right
( GreeStd <
C4
41
51.22077361
0.31707317





39.85793300 )



0.68292683


120
60
left
( Red % >=
C4
38
51.72784108
0.42105263





53.21278750 )



0.57894737


121
60
right
(leaf)
C4
12
0.00000000
0.00000000









1.00000000


130
65
left
( GreenMean <
NOT
1091
412.00639091
0.95325390





75.62565250 )
C4


0.04674610


131
65
right
(leaf)
C4
2
0.00000000
0.00000000









1.00000000


132
66
left
(leaf)
NOT
64
0.00000000
1.00000000






C4


0.00000000


133
66
right
( RGBMean <
NOT
185
94.10487196
0.92972973





94.15494150 )
C4


0.07027027


134
67
left
(leaf)
NOT
46
45.47704055
0.80434783






C4


0.19565217


135
67
right
(leaf)
C4
3
0.00000000
0.00000000









1.00000000


148
74
left
(leaf)
NOT
264
71.69921022
0.96969697






C4


0.03030303


149
74
right
(leaf)
NOT
100
65.01659468
0.90000000






C4


0.10000000


150
75
left
( RedMean <
NOT
838
545.71742325
0.89976134





48.34769250 )
C4


0.10023866


151
75
right
(leaf)
C4
6
7.63817002
0.33333333









0.66666667


154
77
left
( BlueStd <
NOT
36
45.82902012
0.66666667





45.17869600 )
C4


0.33333333


155
77
right
(leaf)
C4
4
0.00000000
0.00000000









1.00000000


166
83
left
(leaf)
NOT
33
36.55464315
0.75757576






C4


0.24242424


167
83
right
(leaf)
C4
6
5.40673451
0.16666667









0.83333333


194
97
left
( Black % >=
NOT
1005
672.97082167
0.89552239





43.40800500 )
C4


0.10447761


195
97
right
(Leaf)
C4
2
0.00000000
0.00000000









1.00000000


196
98
left
(leaf)
NOT
27
0.00000000
1.00000000






C4


0.00000000


197
98
right
( Black % >=
NOT
680
535.28373098
0.86617647





64.68735500 )
C4


0.13382353


198
99
left
(leaf)
NOT
13
0.00000000
1.00000000






C4


0.00000000


199
99
right
( RGBStd >=
NOT
87
117.26430161
0.59770115





43.94751350 )
C4


0.40229885


200
100
left
(leaf)
NOT
5
0.00000000
1.00000000






C4


0.00000000


201
100
right
( GreenStd >=
NOT
88
115.36479221
0.63636364





47.94884100 )
C4


0.36363636


210
105
left
(leaf)
NOT
16
0.00000000
1.00000000






C4


0.00000000


211
105
right
( Black % <
NOT
822
929.93159655
0.74695864





74.10862350 )
C4


0.25304136


212
106
left
(leaf)
NOT
13
0.00000000
1.00000000






C4


0.00000000


213
106
right
( RedMean <
NOT
78
100.62517166
0.65384615





20.53832700 )
C4


0.34615385


214
107
left
( BlueStd >=
C4
33
44.25152482
0.39393939





40.15871400 )



0.60606061


215
107
right
(leaf)
C4
8
0.00000000
0.00000000









1.00000000


240
120
left
( Red % <
NOT
27
37.09592514
0.55555556





54.46098150 )
C4


0.44444444


241
120
right
(leaf)
C4
11
6.70199414
0.09090909









0.90909091


260
130
left
( Red % >=
NOT
1086
393.27581373
0.95580110





31.87044900 )
C4


0.04419890


261
130
right
(leaf)
C4
5
6.73011667
0.40000000









0.60000000


266
133
left
(leaf)
NOT
175
70.94510275
0.94857143






C4


0.05142857


267
133
right
(leaf)
NOT
10
13.46023334
0.60000000






C4


0.40000000


300
150
left
( BlueStd >=
NOT
831
526.53516855
0.90373045





52.70366700 )
C4


0.09626955


301
150
right
(leaf)
C4
7
9.56071347
0.42857143









0.57142857


308
154
left
(leaf)
NOT
7
0.00000000
1.00000000






C4


0.00000000


309
154
right
( RGBStd >=
NOT
29
39.33614485
0.58620690





47.75511750 )
C4


0.41379310


388
194
left
( Red % >=
NOT
911
569.56683640
0.90559824





45.73560700 )
C4


0.09440176


389
194
right
( GreenStd >=
NOT
94
94.62752083
0.79787234





44.41347850 )
C4


0.20212766


394
197
left
(leaf)
NOT
27
0.00000000
1.00000000






C4


0.00000000


395
197
right
( RGBMean <
NOT
653
527.35522137
0.86064319





28.10519400 )
C4


0.13935681


398
199
left
( RedMean <
NOT
71
90.84008412
0.66197183





30.70032950 )
C4


0.33802817


399
199
right
(leaf)
C4
16
19.87476399
0.31250000









0.68750000


402
201
left
( RGBStd <
NOT
86
111.23650269
0.65116279





53.28121950 )
C4


0.34883721


403
201
right
(leaf)
C4
2
0.00000000
0.00000000









1.00000000


422
211
left
( Black % >=
NOT
395
400.80019606
0.79493671





73.70447550 )
C4


0.20506329


423
211
right
( RGBMean <
NOT
427
519.80054254
0.70257611





16.67037250 )
C4


0.29742389


426
213
left
(leaf)
NOT
7
0.00000000
1.00000000






C4


0.00000000


427
213
right
(Black % <
NOT
71
94.31666399
0.61971831





68.36993400 )
C4


0.38028169


428
214
left
(leaf)
NOT
17
22.07444407
0.64705882






C4


0.35294118


429
214
right
(leaf)
C4
16
12.056664516
0.12500000









0.87500000


480
240
left
(leaf)
NOT
4
0.00000000
1.00000000






C4


0.00000000


481
240
right
( Red % >=
C4
23
31.84127834
0.47826087





56.73616400 )



0.52173913


520
260
left
(leaf)
NOT
126
11.66460623
0.99206349






C4


0.00793651


521
260
right
( RedStd <
NOT
960
375.23831364
0.95104167





50.30793950 )
C4


0.04895833


600
300
left
( BlueStd <
NOT
412
204.62630691
0.93203883





55.50064650 )
C4


0.06796117


601
300
right
( BlueStd <
NOT
419
314.27088769
0.87589499





52.68909100 )
C4


0.12410501


618
309
left
(leaf)
NOT
21
23.05272415
0.76190476






C4


0.23809524


619
309
right
(leaf)
C4
8
6.02832258
0.12500000









0.87500000


776
388
left
( Black % <
NOT
902
549.56351962
0.90909091





52.87984300 )
C4


0.09090909


777
388
right
(leaf)
NOT
9
12.36530838
0.55555556






C4


0.44444444


778
389
left
(leaf)
NOT
54
28.51762160
0.92592593






C4


0.07407407


779
389
right
( RGBStd <
NOT
40
52.92505905
0.62500000





46.21739000 )
C4


0.37500000


790
395
left
( GreenStd <
NOT
595
454.64037415
0.87226891





42.78296850 )
C4


0.12773109


791
395
right
( GreenStd >=
NOT
58
66.30667324
0.74137931





42.13297450 )
C4


0.25862069


796
398
left
( RedMean >=
NOT
66
80.97012653
0.69696970





29.93333650 )
C4


0.30303030


797
398
right
(leaf)
C4
5
5.00402424
0.20000000









0.80000000


804
402
left
( Red % <
NOT
84
106.93438027
0.66666667





46.07311100 )
C4


0.33333333


805
402
right
(leaf)
C4
2
0.00000000
0.00000000









1.00000000


844
422
left
(leaf)
NOT
23
0.00000000
1.00000000






C4


0.00000000


845
422
right
( Red % >=
NOT
372
389.88212303
0.78225806





65.05397050 )
C4


0.21774194


846
423
left
( GreenStd <
NOT
397
462.72910096
0.73047859





35.39174850 )
C4


0.26952141


847
423
right
(leaf)
C4
30
38.19085010
0.33333333









0.66666667


854
427
left
( BlueMean <
NOT
66
84.01987021
0.66666667





25.21327900 )
C4


0.33333333


855
427
right
(leaf)
C4
5
0.00000000
0.00000000









1.00000000


962
481
left
(leaf)
NOT
18
24.05694520
0.61111111






C4


0.38888889


963
481
right
(leaf)
C4
5
0.00000000
0.00000000









1.00000000


1042
521
left
( RedStd >=
NOT
926
335.77604143
0.95572354





49.63398000 )
C4


0.04427646


1043
521
right
(leaf)
NOT
34
31.68794947
0.82352941






C4


0.17647059


1200
600
left
(leaf)
NOT
195
53.58834252
0.96923077






C4


0.03076923


1201
600
right
( Black % <
NOT
217
142.39975565
0.89861751





37.13927650 )
C4


0.10138249


1202
601
left
( Red % <
NOT
417
305.85601197
0.88009592





43.02504550 )
C4


0.11990408


1203
601
right
(leaf)
C4
2
0.00000000
0.00000000









1.00000000


1552
776
left
( Black % >=
NOT
706
381.38832956
0.92351275





51.76409350 )
C4


0.07648725


1553
776
right
( GreenMean <
NOT
196
160.76559677
0.85714286





33.90735800 )
C4


0.14285714


1558
779
left
(leaf)
NOT
28
31.49076810
0.75000000






C4


0.25000000


1559
779
right
(leaf)
C4
12
15.27634004
0.33333333









0.66666667


1580
790
left
( BlueMean >=
NOT
593
446.36269269
0.87521079





28.28960050 )
C4


0.12478921


1581
790
right
(leaf)
C4
2
0.00000000
0.00000000









1.00000000


1582
791
left
(leaf)
NOT
19
0.00000000
1.00000000






C4


0.00000000


1583
791
right
( Black % <
NOT
39
51.96971851
0.61538462





58.09482200 )
C4


0.38461538


1592
796
left
(leaf)
NOT
7
0.00000000
1.00000000






C4


0.00000000


1593
796
right
( Red % <
NOT
59
75.56231904
0.66101695





60.60153550 )
C4


0.33898305


1608
804
left
(leaf)
NOT
6
0.00000000
1.00000000






C4


0.00000000


1609
804
right
( Black % >=
NOT
78
101.84082385
0.64102564





43.52703650 )
C4


0.35897436


1690
845
left
( Red % <
NOT
370
383.74532199
0.78648649





66.00984600 )
C4


0.21351351


1691
845
right
(leaf)
C4
2
0.00000000
0.00000000









1.00000000


1692
846
left
( RedStd >=
NOT
393
452.12924803
0.73791349





34.45686900 )
C4


0.26208651


1693
846
right
(leaf)
C4
4
0.00000000
0.00000000









1.00000000


1708
854
left
( RedMean <
NOT
52
58.48285504
0.75000000





23.73270800 )
C4


0.25000000


1709
854
right
(leaf)
C4
14
18.24918371
0.35714286









0.64285714


2084
1042
left
(leaf)
NOT
83
0.00000000
1.00000000






C4


0.00000000


2085
1042
right
( RedStd <
NOT
843
327.89117470
0.95136418





43.81623850 )
C4


0.04863582


2402
1201
left
( BlueStd >=
NOT
197
106.08232899
0.92385787





55.88707900 )
C4


0.07614213


2403
1201
right
(leaf)
NOT
20
25.89786556
0.65000000






C4


0.35000000


2404
1202
left
( Red % >=
NOT
361
234.27015383
0.90027701





42.25179100 )
C4


0.09972299


2405
1202
right
( Red % >=
NOT
56
62.98153620
0.75000000





43.17405900 )
C4


0.25000000


3104
1552
left
(leaf)
NOT
68
0.00000000
1.00000000






C4


0.00000000


3105
1552
right
( BlueMean >=
NOT
638
369.98502458
0.91536050





43.23308750 )
C4


0.08463950


3106
1553
left
( Black % >=
NOT
185
130.90552457
0.88648649





52.91334750 )
C4


0.11351351


3107
1553
right
(leaf)
C4
11
14.42059903
0.36363636









0.63636364


3160
1580
left
(leaf)
NOT
150
73.47900804
0.93333333






C4


0.06666667


3161
1580
right
( RGBStd >=
NOT
443
365.91333814
0.85553047





35.22638500 )
C4


0.14446953


3166
1583
left
(leaf)
NOT
30
34.79491029
0.73333333






C4


0.26666667


3167
1583
right
(leaf)
C4
9
9.53471158
0.22222222









0.77777778


3186
1593
left
(leaf)
NOT
29
29.56931058
0.79310345






C4


0.20689655


3187
1593
right
( Red % >=
NOT
30
41.45539856
0.53333333





62.39068600 )
C4


0.46666667


3218
1609
left
( BlueMed >=
NOT
73
92.46210534
0.67123288





13.00000000 )
C4


0.32876712


3219
1609
right
(leaf)
C4
5
5.00402424
0.20000000









0.80000000


3380
1690
left
(leaf)
NOT
13
0.00000000
1.00000000






C4


0.00000000


3381
1690
right
( Black % >=
NOT
357
377.37324829
0.77871148





66.09868600 )
C4


0.22128852


3384
1692
left
(leaf)
NOT
14
0.00000000
1.00000000






C4


0.00000000


3385
1692
right
( GreenStd <
NOT
379
443.43699400
0.72823219





34.60126100 )
C4


0.27176781


3416
1708
left
( Black % <
NOT
50
52.69079614
0.78000000





66.05030800 )
C4


0.22000000


3417
1708
right
(leaf)
C4
2
0.00000000
0.00000000









1.00000000


4170
2085
left
(leaf)
NOT
66
0.00000000
1.00000000






C4


0.00000000


4171
2085
right
( Black % <
NOT
777
321.03066023
0.94723295





15.87751300 )
C4


0.05276705


4804
2402
left
( BlueMean <
NOT
193
95.24526330
0.93264249





56.23658350 )
C4


0.06735751


4805
2402
right
(leaf)
NOT
4
5.54517744
0.50000000






C4


0.50000000


4808
2404
left
(leaf)
NOT
30
0.00000000
1.00000000






C4


0.00000000


4809
2404
right
( Red % <
NOT
331
227.67354069
0.89123867





42.24141500 )
C4


0.10876133


4810
2405
left
(leaf)
NOT
52
50.91339694
0.80769231






C4


0.19230769


4811
2405
right
(leaf)
C4
4
0.00000000
0.00000000









1.00000000


6210
3105
left
(leaf)
NOT
81
18.75541364
0.97530864






C4


0.02469136


6211
3105
right
( BlueMean <
NOT
557
345.60431698
0.90664273





43.14976500 )
C4


0.09335727


6212
3106
left
( RedStd >=
NOT
183
122.02706241
0.89617486





42.39215050 )
C4


0.10382514


6213
3106
right
(leaf)
C4
2
0.00000000
0.00000000









1.00000000


6322
3161
left
( BlueStd <
NOT
434
348.73797167
0.86175115





37.90874250 )
C4


0.13824885


6323
3161
right
(leaf)
NOT
9
12.36530838
0.55555556






C4


0.44444444


6374
3187
left
(leaf)
NOT
17
20.59711498
0.70588235






C4


0.29411765


6375
3187
right
(leaf)
C4
13
16.04828601
0.30769231









0.69230769


6436
3218
left
(leaf)
NOT
36
38.13884633
0.77777778






C4


0.22222222


6437
3218
right
( RedMean <
NOT
37
50.61514404
0.56756757





36.80363800 )
C4


0.43243243


6762
3381
left
( RedMean >=
NOT
350
358.44043130
0.79142857





17.57113300 )
C4


0.20857143


6763
3381
right
(leaf)
C4
7
5.74162846
0.14285714









0.85714286


6770
3385
left
( Red % <
NOT
377
438.19724906
0.73209549





77.70377350 )
C4


0.26790451


6771
3385
right
(leaf)
C4
2
0.00000000
0.00000000









1.00000000


6832
3416
left
(leaf)
NOT
18
7.72412959
0.94444444






C4


0.05555556


6833
3416
right
(leaf)
NOT
32
39.74952797
0.68750000






C4


0.31250000


8342
4171
left
(leaf)
NOT
59
0.00000000
1.00000000






C4


0.00000000


8343
4171
right
( Yellow % <
NOT
718
314.37048816
0.94289694





0.03592250 )
C4


0.05710306


9608
4804
left
(leaf)
NOT
46
0.00000000
1.00000000






C4


0.00000000


9609
4804
right
( GreenMean
NOT
147
87.87743083
0.91156463





>=
C4


0.08843537





42.14803700 )


9618
4809
left
( RedStd >=
NOT
329
218.69802409
0.89665653





47.49008550 )
C4


0.10334347


9619
4809
right
(leaf)
C4
2
0.00000000
0.00000000









1.00000000


12422
6211
left
( BlueMean <
NOT
555
336.04829215
0.90990991





38.42803750 )
C4


0.09009009


12423
6211
right
(leaf)
C4
2
0.00000000
0.00000000









1.00000000


12424
6212
left
(leaf)
NOT
111
52.23965558
0.93693694






C4


0.06306306


12425
6212
right
( GreenStd <
NOT
72
64.88081408
0.83333333





42.26553150 )
C4


0.16666667


12644
6322
left
(leaf)
NOT
62
17.67072756
0.96774194






C4


0.03225806


12645
6322
right
( RGBStd >=
NOT
372
322.02684321
0.84408602





38.07899050 )
C4


0.15591398


12874
6437
left
(leaf)
NOT
19
19.55682029
0.78947368






C4


0.21052632


12875
6437
right
(leaf)
C4
18
22.91451006
0.33333333









0.66666667


13524
6762
left
( Red % >=
NOT
213
193.54009369
0.83098592





72.55765550 )
C4


0.16901408


13525
6762
right
( RedMean <
NOT
137
159.83281094
0.72992701





17.10107150 )
C4


0.27007299


13540
6770
left
( RGBMean <
NOT
91
84.63037404
0.82417582





16.00073950 )
C4


0.17582418


13541
6770
right
( RGBStd <
NOT
286
348.04802915
0.70279720





31.63435750 )
C4


0.29720280


16686
8343
left
( RedMean >=
NOT
628
241.02406446
0.95222930





51.05065700 )
C4


0.04777070


16687
8343
right
( RGBStd <
NOT
90
66.83928397
0.87777778





47.80018250 )
C4


0.12222222


19218
9609
left
(leaf)
NOT
108
46.34477752
0.94444444






C4


0.05555556


19219
9609
right
(leaf)
NOT
39
36.70796853
0.82051282






C4


0.17948718


19236
9618
left
(leaf)
NOT
100
33.58882955
0.96000000






C4


0.04000000


19237
9618
right
( GreenMean <
NOT
229
177.83751449
0.86899563





42.52050950 )
C4


0.13100437


24844
12422
left
( RedMean >=
NOT
380
189.64691759
0.93157895





30.67255650 )
C4


0.06842105


24845
12422
right
( ReeStd >=
NOT
175
139.90999630
0.86285714





45.06529400 )
C4


0.13714286


24850
12425
left
(leaf)
NOT
64
48.22658064
0.87500000






C4


0.12500000


24851
12425
right
(leaf)
NOT
8
11.09035489
0.50000000






C4


0.50000000


25290
12645
left
( RGBMean <
NOT
333
263.75803769
0.86486486





23.07209300 )
C4


0.13513514


25291
12645
right
( RedStd <
NOT
39
49.64810513
0.66666667





38.04919800 )
C4


0.33333333


27048
13524
left
(leaf)
NOT
15
0.00000000
1.00000000






C4


0.00000000


27049
13524
right
( BlueStd <
NOT
198
187.75916797
0.81818182





39.93703450 )
C4


0.18181818


27050
13525
left
( BlueStd >=
NOT
87
76.76732901
0.83908046





42.46681600 )
C4


0.16091954


27051
13525
right
( RedStd >=
NOT
50
68.99437585
0.54000000





33.69779800 )
C4


0.46000000


27080
13540
left
(leaf)
NOT
76
59.18533992
0.86842105






C4


0.13157895


27081
13540
right
(leaf)
NOT
15
20.19035001
0.60000000






C4


0.40000000


27082
13541
left
( BlueStd >=
NOT
202
226.07817087
0.75247525





32.18647800 )
C4


0.24752475


27083
13541
right
( RGBStd >=
NOT
84
114.10446869
0.58333333





31.90780100 )
C4


0.41666667


33372
16686
left
( GreenMean <
NOT
511
149.13537651
0.96673190





70.19668950 )
C4


0.03326810


33373
16686
right
( GreenMean <
NOT
117
81.62671043
0.88888889





51.01018550 )
C4


0.11111111


33374
16687
left
(leaf)
NOT
51
9.843691400
0.98039216






C4


0.01960784


33375
16687
right
(leaf)
NOT
39
44.40294840
0.74358974






C4


0.25641026


38474
19237
left
(leaf)
NOT
79
31.65918877
0.94936709






C4


0.05063291


38475
19237
right
( Red % <
NOT
150
138.33973998
0.82666667





40.27006350 )
C4


0.17333333


49688
24844
left
( RGBStd <
NOT
334
134.36920497
0.94910180





47.19750600 )
C4


0.05089820


49689
24844
right
(leaf)
NOT
46
45.47704055
0.80434783






C4


0.19565217


49690
24845
left
(leaf)
NOT
87
38.27167697
0.94252874






C4


0.05747126


49691
24845
right
( RedMean <
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91.81590051
0.78409091





31.73246550 )
C4


0.21590909


50580
25290
left
(leaf)
NOT
21
0.00000000
1.00000000






C4


0.00000000


50581
25290
right
( RedMean >=
NOT
312
257.44358153
0.85576923





22.47274750 )
C4


0.14423077


50582
25291
left
(leaf)
NOT
36
42.54064173
0.72222222






C4


0.27777778


50583
25291
right
(leaf)
C4
3
0.00000000
0.00000000









1.00000000


54098
27049
left
( Red % <
NOT
185
163.99802613
0.83783784





72.34632500 )
C4


0.16216216


54099
27049
right
(leaf)
NOT
13
17.94482758
0.53846154






C4


0.46153846


54100
27050
left
(leaf)
NOT
14
0.00000000
1.00000000






C4


0.00000000


54101
27050
right
( RGBStd <
NOT
73
71.36405480
0.80821918





34.84770050 )
C4


0.19178082


54102
27051
left
( Red % <
NOT
38
47.39776857
0.68421053





73.59689350 )
C4


0.31578947


54103
27051
right
(leaf)
C4
12
6.88406359
0.08333333









0.91666667


54164
27082
left
(leaf)
NOT
41
21.46468760
0.92682927






C4


0.07317073


54165
27082
right
( RGBStd <
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161
194.44508473
0.70807453





30.98291200 )
C4


0.29192547


54166
27083
left
( GreenMean
NOT
73
93.82799012
0.65753425





>=
C4


0.34246575





12.01756750 )


54167
27083
right
(leaf)
C4
11
6.70199414
0.09090909









0.90909091


66744
33372
left
( Red % <
NOT
498
127.60588179
0.97188755





20.61720300 )
C4


0.02811245


66745
33372
right
(leaf)
NOT
13
14.04530770
0.76923077






C4


0.23076923


66746
33373
left
(leaf)
NOT
112
62.63953320
0.91964286






C4


0.08035714


66747
33373
right
(leaf)
C4
5
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0.200000000









0.80000000


76950
38475
left
( Red % >=
NOT
144
123.11922742
0.84722222





38.90005650 )
C4


76951
38475
right
(leaf)
C4
6
7.63817002
0.33333333









0.66666667


99376
49688
left
( BlueStd >=
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121.85222379
0.95426829





44.62378500 )
C4


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99377
49688
right
(leaf)
NOT
6
7.63817002
0.66666667






C4


0.33333333


99382
49691
left
(leaf)
NOT
39
15.77729414
0.94871795






C4


0.05128205


99383
49691
right
( BlueMean >=
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49
63.26203774
0.65306122





41.89118200 )
C4


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101162
50581
left
( Red Mean <
NOT
290
221.42792850
0.87241379





26.44887950 )
C4


0.12758621


101163
50581
right
(leaf)
NOT
22
28.84119805
0.63636364






C4


0.36363636


108196
54098
left
( Red % >=
NOT
181
152.49933301
0.85082873





71.43363600 )
C4


0.14917127


108197
54098
right
(leaf)
C4
4
4.49868116
0.25000000









0.75000000


108202
54101
left
(leaf)
NOT
57
42.46280190
0.87719298






C4


0.12280702


108203
54101
right
(leaf)
NOT
16
21.93005463
0.56250000






C4


0.43750000


108204
54102
left
(leaf)
NOT
27
18.83693318
0.88888889






C4


0.11111111


108205
54102
right
(leaf)
C4
11
10.43106489
0.18181818









0.81818182


108330
54165
left
( Black % >=
NOT
154
178.47884504
0.73376623





77.73876550 )
C4


0.26623377


108331
54165
right
(leaf)
C4
7
5.74162846
0.14285714









0.85714286


108332
54166
left
( RGBMean <
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81.68564550
0.70149254





13.54910200 )
C4


0.29850746


108333
54166
right
(leaf)
C4
6
5.40673451
0.16666667









0.83333333


133488
66744
left
(leaf)
NOT
92
0.00000000
1.00000000






C4


0.00000000


133489
66744
right
( Red % >=
NOT
406
121.79587797
0.96551724





20.67461300 )
C4


0.03448276


153900
76950
left
(Leaf)
NOT
15
0.00000000
1.00000000






C4


0.00000000


153901
76950
right
( Red % <
NOT
129
117.84036180
0.82945736





38.82381650 )
C4


0.17054264


198752
99376
left
(leaf)
NOT
157
21.42680791
0.98726115






C4


0.01273885


198753
99376
right
( BlueMean <
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91.98022259
0.92397661





36.43159850 )
C4


0.07602339


198766
99383
left
(leaf)
NOT
7
0.00000000
1.00000000






C4


0.00000000


198767
99383
right
( RedStd <
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44.94667250 )
C4


0.40476190


202324
101162
left
( BlueMean >=
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259
177.43650113
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24.16304400 )
C4


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202325
101162
right
(leaf)
NOT
31
37.35129634
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C4


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216392
108196
left
(leaf)
NOT
31
0.00000000
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C4


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216393
108196
right
( BlueMean >=
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141.41804604
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21.16735350 )
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216660
108330
left
( BlueStd >=
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25.27611950 )
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216661
108330
right
(leaf)
C4
2
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216664
108332
left
(leaf)
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C4


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216665
108332
right
( Black % <
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78.90671950 )
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266978
133489
left
( RedMean <
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52.41380900 )
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266979
133489
right
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C4
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307802
153901
left
( Black % <
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33.49176400 )
C4


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307803
153901
right
(leaf)
C4
2
0.00000000
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1.00000000


397506
198753
left
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C4


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397507
198753
right
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397534
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left
( BlueMean <
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41.45530350 )
C4


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397535
198767
right
(leaf)
C4
3
0.00000000
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404648
202324
left
( RGBMean <
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24.47212150 )
C4


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404649
202324
right
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NOT
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C4


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432786
216393
left
(leaf)
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27
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C4


0.03703704


432787
216393
right
( BlueMean <
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20.85527150 )
C4


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433320
216660
left
( BlueMean <
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10.15660150 )
C4


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433321
216660
right
(leaf)
C4
10
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0.40000000









0.60000000


433330
216665
left
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C4


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216665
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C4


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533957
266978
right
( RedMean >=
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52.49773800 )
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615605
307802
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( BlueMean <
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51.00749950 )
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397534
left
( BlueMean >=
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41.19118150 )
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795069
397534
right
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42
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C4


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right
( RedMean >=
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24.22689100 )
C4


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865574
432787
left
( BlueMean >=
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115
109.32502807
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20.25724800 )
C4


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865575
432787
right
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0.37500000









0.62500000


866640
433320
left
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38
15.67059585
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C4


0.05263158


866641
433320
right
( Black % <
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82.35655600 )
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1067914
533957
left
(leaf)
NOT
316
88.74329473
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C4


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1067915
533957
right
(leaf)
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0.66666667






C4


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615605
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( Red % <
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34.90112650 )
C4


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1231211
615605
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1590136
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1590137
795068
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( Red % >=
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47.11168700 )
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1618594
809297
left
( RGBMean <
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25.65376000 )
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1618595
809297
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0.66666667






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1731148
865574
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1731149
865574
right
( RedMean <
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30.41704200 )
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1733283
866641
right
( GreenStd <
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2462420
1231210
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0.00000000
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C4


0.00000000


2462421
1231210
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34.97943300 )
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3180274
1590137
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C4


0.30769231


3180275
1590137
right
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3
0.00000000
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1618594
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C4


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3237189
1618594
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1731149
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34.00269100 )
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3462299
1731149
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1733282
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3466565
1733282
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3466566
1733283
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3466567
1733283
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33.68111600 )
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4924843
2462421
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( RGBMean >=
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30.32782150 )
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55396781
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1.00000000

## Claims

1. A method of identifying a threat object of interest in X-ray image data, wherein the image data comprises a plurality of grey-scale or color pixel values, comprising:
receiving the X-ray image data; and
applying at least one predetermined bifurcation transform to the X-ray image data to effect divergence of the threat object of interest from other objects, wherein said at least one predetermined bifurcation transform is determined by applying at least one initial bifurcation transform to X-ray image data containing a known threat object that is substantially similar to said threat object of interest, and adjusting said at least one initial bifurcation transform so that the known threat object diverges from other objects in the X-ray image data once said predetermined bifurcation transform is applied to said X-ray image data.

2. The method of claim 1, wherein the at least one predetermined bifurcation transform comprises a series of predetermined bifurcation transforms.

3. The method of claim 1, wherein the at least one predetermined bifurcation transform comprises at least one point operation.

4. The method of claim 3, wherein the at least one point operation is at least partially a non-linear point operation.

5. The method of claim 3, wherein the at least one point operation comprises at least one nodal point.

6. The method of claim 5, wherein the at least one nodal point is adjusted so as to effect the divergence of the threat object of interest from other objects.

7. The method of claim 1, wherein the at least one predetermined bifurcation transform is adapted to maintain an integrity of the threat object of interest during divergence of the threat object of interest from other objects.

8. The method of claim 1, further comprising generating a threat output image based on the results of the at least one predetermined bifurcation transform.

9. The method of claim 8, wherein the threat object of interest is distinguished from other objects in the output image by adjusting a visual parameter of the threat object of interest and other objects based on the results of the at least one predetermined bifurcation transform.

10. The method of claim 1, wherein the X-ray image data comprises nonparametric image data.

11. The method of claim 1, wherein the X-ray image data comprises parametric image data.

12. The method of claim 1, wherein the threat object of interest is statistically indistinguishable from other objects in the X-ray image data.

13. The method of claim 1, wherein the threat object of interest comprises a plurality of types of threat objects, wherein each type of threat object comprises at least one member.

14. The method of claim 13, wherein the at least one predetermined bifurcation transform is generated for each member of the threat objects.

15. The method of claim 13, wherein the type of threat objects include explosives, weapons, restricted items, biological agent or materials, or chemical materials.

16. The method of claim 1, wherein the X-ray image data is scanner data.

17. The method of claim 1, wherein the X-ray image data includes a portion of an interior of a container.

18. The method of claim 17, wherein the container includes baggage, clothing or a person.

19. The method of claim 1, wherein the X-ray data comprises results from dual energy X-rays, at least one X-ray source or multiple scans of an input object.

20. An apparatus configured to identify a threat object of interest in X-ray image data wherein the image data comprises a plurality of grey-scale or color pixel values, comprising:
an input device configured to receive the X-ray image data; and
an image transformation recognition system configured to apply at least one predetermined bifurcation transform to the X-ray image data to effect divergence of the threat object of interest from other objects, wherein said at least one predetermined bifurcation transform is determined by applying at least one initial bifurcation transform to X-ray image data containing a known threat object that is substantially similar to said threat object of interest, and adjusting said at least one initial bifurcation transform so that the known threat object diverges from other objects in the X-ray image data once said predetermined bifurcation transform is applied to said X-ray image data.

21. A method of creating a bifurcation transform for a class of threat objects, comprising:
selecting a point operation;
performing said point operation on a subset of images, wherein said subset of images includes at least one image comprising a plurality of grey-scale or color pixels and containing an object in said class of threat objects; and
adjusting said point operation and repeating said selecting and said performing step until said point operation bifurcates said object.

22. The method of claim 21, wherein said point operation bifurcates said object by increasing differentials in response characteristics among objects in said class of threat objects relative to other objects in said subset of images.

23. The method of claim 21, wherein said class of threat objects comprises a plurality of types of threat objects, wherein each type of threat object comprises at least one member.