Patent Publication Number: US-2006008174-A1

Title: Count adaptive noise reduction method of x-ray images

Description:
BACKGROUND OF INVENTION  
      The present invention relates generally to x-ray imaging systems. More particularly, the present invention relates to the reduction of noise within x-ray images at highly attenuated regions without affecting image contrast at relatively low attenuated regions.  
      Many techniques are known and are presently in use for the generation of digital image data. Such techniques range from the use of simple charge coupled device apparatuses, such as digital cameras, to more complex imaging systems, such as those used for part inspection and medical diagnostics purposes. The stated systems, in general, form a matrix of discrete picture elements or pixels that have individual values over a range of intensities. Raw image data acquired by the stated systems may be processed to clarify an image, to enhance image features, or to improve the image quality from various points of view. In general, the goal of image enhancement and quality improvement is to provide useful images that provide desired information for a user.  
      In the medical imaging context a number of imaging modalities are employed. Within the imaging modalities a scanner or other image acquisition system is typically used to acquire raw image data, which is then processed to form a useful set of data for image reconstruction and viewing. The raw data typically contains noise and must be processed to provide clear and useful images for evaluation.  
      Noise may result from a wide variety of sources, typically from the various components used to acquire the image data, but may also be a function of the physics of a system and the nature of the subject being imaged. Noise may be a mixture of random point noise, sometimes referred to as spike noise, and patterned noise. Modalities such as x-ray imaging and optical imaging, where image data is directly acquired, exhibit such noise in a readily visible manner. However, imaging methods requiring image reconstruction, such as MRI, CT, and ultrasound, convert point or spike noise into splotches or small streaks that are usually hidden with the patterned noise. It is desirable that the point noise and the patterned noise be detected and appropriately mitigated. Unfortunately, current methods designed to mitigate patterned noise do not adequately mitigate point noise without blurring or decreasing the contrast of the useful information in the processed image.  
      During the display processing of the x-ray images, the image noise at highly attenuated regions in the images becomes more noticeable, and can therefore decrease the perceived quality of the final images. This perceived image degradation is mainly due to the contrast enhancement that occurs in the highly attenuated regions of the image. Currently, noise reduction techniques based on image properties alone can improve the perceived quality of highly attenuated regions of such images. However, this noise reduction decreases the contrast at lowly attenuated regions of the images, such as in lung parenchyma regions.  
      In the filtering and/or processing of the images it is generally desirable to filter areas of an image with low counts heavily and areas with high counts lightly. The term “counts” refers to the intensity level of a pixel representing a structure, such as a bone of a patient. This filtering provides a clearer view of the structures of interest. Some structures that are generally not desired for evaluation have such a low count that all that is visible on an image is noise; these structures are generally removed with the noise from the image. However, certain structures that are desired for evaluation and that have relatively low counts, such that they are partially visible, when heavily filtered are also undesirably removed from an image. The undesirably removed structures may be within the stated lung parenchyma regions or, as another example, within the jaw regions of a patient.  
      Thus, there exists a need for an improved filtering technique that improves the image quality, that reduces point noise and pattern noise, and that reduces noise in high attenuated regions of x-ray images, without affecting the image contrast at relatively low attenuated regions. It is also desirable that the technique account for detected signal properties in a noise reduction framework, where the framework is adaptive to the detected signal.  
     SUMMARY OF INVENTION  
      The present invention provides methods and systems for adaptively reducing noise within an x-ray image. In one embodiment of the present invention, a method is provided that includes the receiving of raw data representing a detected x-ray signal from an object. A counts-based modulation mask is generated in response to the raw data. A structure dependent noise filtered image is generated in response to the raw data. A noise-reduced image is generated in response to the counts-based modulation mask and the structure dependent noise filtered image.  
      In another embodiment of the present invention, a similar method is provided that includes the receiving of raw data and the generation of a counts-based modulation mask, but rather includes the generation of a structure gradient mask in response to the raw data. Subsequent to the generation of a structure gradient mask, a noise-reduced image is generated in response to the counts-based modulation mask and the structure gradient mask.  
      The embodiments of the present invention provide several advantages. One such advantage is the provision of generating a noise reduced image that is structure dependent and count dependent. This dependency prevents the removal or filtering of at least somewhat visible structures with relatively low counts or pixel intensities.  
      Another advantage that is provided by an embodiment of the present invention is the provision of smoothing highly attenuated x-ray image regions more than relatively low attenuated regions. Noise reduction is performed while minimizing contrast degradation in high signal-to-noise regions of the x-ray images.  
      Yet another advantage provided by an embodiment of the present invention is the provision of performing limiting noise reduction in low signal-to-noise regions where significant structures are present, thus maintaining clinical detail.  
      A further advantage provided by an embodiment of the present invention is the provision of modulating smoothing in response to visually relevant structures and graininess pertaining to noise of the x-ray images. This aids in inadvertently filtering desired structure data.  
      Moreover, the embodiments of the present invention provide an adaptive filtering technique that is easily implemented, computationally efficient, and offers options for image enhancement and time optimization.  
      The present invention itself, together with attendant advantages, will be best understood by reference to the following detailed description, taken in conjunction with the accompanying figures.  
    
    
     BRIEF DESCRIPTION OF DRAWINGS  
      For a more complete understanding of this invention reference should now be had to the embodiments illustrated in greater detail in the accompanying figures and described below by way of examples of the invention wherein:  
       FIG. 1  is a block schematic diagram of an x-ray system in accordance with an embodiment of the present invention;  
       FIG. 2  is a perspective and schematic diagram of a sample x-ray detector array;  
       FIG. 3  is a diagram illustrating an image having multiple structures and a corresponding pixel matrix;  
       FIG. 4  is a logic flow diagram illustrating a method of adaptively filtering x-ray image data in accordance with an embodiment of the present invention;  
       FIG. 5  is a plot of weights versus counts for received raw data collected by a detector operating at a standard dose sensitivity setting in accordance with an embodiment of the present invention;  
       FIG. 6  is a plot of weights versus counts for received raw data collected by a detector operating at a low dose sensitivity setting in accordance with an embodiment of the present invention;  
       FIG. 7  is a plot of count modulation curves utilized in the method of  FIG. 4 ;  
       FIG. 8  is a logic flow diagram illustrating a method of performing a structure dependent smoothing filter operation within the method of  FIG. 4 ;  
       FIG. 9  is a logic flow diagram illustrating a logic flow diagram illustrating a method of enhancing structural and non-structural regions in the method of  FIG. 8 ;  
       FIG. 10  is a logic flow diagram illustrating a method of identifying structures in the blending operation of  FIG. 9 ;  
       FIG. 11  is a gradient level masking plot in accordance with an embodiment of the present invention; and  
       FIG. 12  is a logic flow diagram illustrating a method of performing blending within the method of  FIG. 4 . 
    
    
     DETAILED DESCRIPTION  
      In the following figures, the same reference numerals will be used to refer to the same components. While the present invention is described with respect to the reduction of noise within x-ray images of an x-ray system, the present invention is capable of being adapted for various purposes and is not limited to the following applications: computed tomography systems, radiotherapy or radiographic systems, x-ray imaging systems, ultrasound imaging systems, magnetic resonance imaging systems, positron emission tomography systems, electron beam imaging systems, tomosynthesis systems, and other applications known in the art.  
      In the following description, various operating parameters and components are described for one constructed embodiment. These specific parameters and components are included as examples and are not meant to be limiting.  
      Also, in the following description the term “mask” may refer to a “look-up” table, a conversion scale, a function, or the like. A mask may for example be in the form of a weighted curve that provides a value between zero and one depending upon one or more look-up parameters or input values. A mask may be in various forms and may have any number of inputs and corresponding outputs.  
      Referring now to  FIG. 1 , a block schematic diagram of an x-ray system  10  in accordance with an embodiment of the present invention is shown. The x-ray system  10  includes an x-ray source  15 , an x-ray detector  22 , an x-ray detector controller  26  that contains electronics for operating the x-ray detector  22 , and an x-ray source controller  24  that contains electronics for operating the x-ray source  15 . During operation, x-rays  17  are directed from the x-ray source  15  towards the x-ray detector  22 , which may comprise a scintillator  23  and an amorphous silicon array  25 . An overall system controller  36  provides power and timing signals to the x-ray source controller  24  and the x-ray detector controller  26 , which then control the operation of the x-ray source  15  and x-ray detector  22 , respectively. After passing through an object  19 , such as a patient, the x-rays  17  impinge upon scintillator  23 , which converts the x-ray photons therein to visible light. The visible light is then converted to an electrical charge. The x-ray detector controller  26  samples analog electrical charge data from the x-ray detector  22  and converts that analog data into digital signals. The digital signals are then sent to an image processor  28 , where the image is processed and enhanced. The processed image or reconstructed image may then be viewed on a display  32 , or other the like, and stored in a mass storage  30  for later retrieval.  
      The image processor  28  can also produce a brightness control signal that can be applied to an exposure control circuit  34  to regulate the power supply  16 . The power supply  16  may thereby regulate the x-ray source  15  through x-ray source controller  24 . The overall operation of the x-ray system  10  may be governed by a system controller  36 , which may receive commands and/or scanning parameters from an operator via an input device or operator interface  38 . The operator interface  38  may be in the form of a keyboard, a touch pad, or other suitable input device. The operator supplied commands and parameters may be used by the system controller  36  to provide control signals and information to the image processor  28 , the x-ray detector controller  26 , the x-ray source controller  24 , and/or the exposure control circuit  34 .  
      Referring now to  FIG. 2 , a perspective and schematic diagram of a sample x-ray detector array  40  is shown. The detector array  40  is an amorphous silicon flat panel x-ray detector that may be utilized in the embodiments of the present invention. Generally, column electrodes  42  and row electrodes  44  are disposed on a single piece glass substrate  46  that define an amorphous silicon array  48 . The amorphous silicon array  48  comprises an array of photodiodes  50  and field effect transistors (FETs)  52 . A scintillator  54  is disposed over the amorphous silicon array  48 , and is optically coupled thereto. The scintillator  54 , which may comprise a dose-efficient cesium iodide scintillator, receives and absorbs x-ray radiation during operation, and converts the x-ray photons therein to visible light. The high fill factor amorphous silicon array  48  converts the detected visible light into an electrical charge. Each photodiode  50  therein within the array  48  represents a pixel. The charge at each pixel is then read out and digitized by low-noise electronics, via contact fingers  56  and contact leads  58 , and is thereafter sent to an image processor  28 .  
      Referring now to  FIG. 3 , a diagram illustrating a pixel image  60  having multiple structures or structural regions  62  and a corresponding pixel matrix  64  is shown. The image  60  is composed of the matrix  64  of discrete pixels  66  disposed adjacent to one another in a series of rows  68  and columns  70 . The rows  68  and columns  70  provide a pre-established matrix width  72  and matrix height  74 . Typical matrix dimensions may include 256×256 pixels; 512×512 pixels; 1,024×1,024 pixels, and others. The particular image matrix size may be selected via the input device  38  and may vary depending upon such factors as the subject to be imaged and the resolution desired.  
      The image  60  includes the structural regions  62 , illustrated as consisting of long, contiguous lines defined by adjacent pixels. The image  60  also includes non-structural regions  76  lying outside of the structural regions  62 . The image  60  may also include isolated artifacts  78  of various sizes (i.e., number of adjacent pixels), which may be defined as structural regions, or which may be eliminated from the definition of structure in accordance with the techniques described below.  
      Referring now to  FIG. 4 , a logic flow diagram illustrating a method of adaptively filtering x-ray image data in accordance with an embodiment of the present invention is shown.  
      In step  100 , raw image data R is received from the detector  22  by the image processor  28 . In step  102 , the image processor  28  normalizes the raw data R to generate normalized raw image data R N . The gain of the detector  22  may be adjusted depending upon the data acquisition speed. The higher detect or speeds may have a lower corresponding detector gain than lower detector speeds.  
      Referring now also to  FIGS. 5 and 6 , plots of weights versus counts for received raw data collected by a detector operating at a standard dose sensitivity setting or speed and operating at a low dose sensitivity setting or speed are shown. Note that the weight curve or look-up table  90  for the standard sensitivity is “stretched” such that the weight curve spans twice the number of counts as that for the weight curve  92  for the low sensitivity setting. The weight curve  90  is stretched by one over the ratio of the low dose sensitivity gain relative to the standard sensitivity gain, which in the provided example is equal to two.  
      In step  104 , the normalized data R N  is smoothed to generate smoothed raw image data D s . Local averaging of the normalized data R N  is performed using techniques known in the art. In step  106 , a counts-based modulation mask M CB  is generated. The counts-based mask M CB  is generated using count modulation curves  94 , some of which are shown in  FIG. 7 . The counts-based mask M CB  contains weighted values between zero and one. The counts-based mask M CB  includes weight values that are assigned each pixel location in response to absolute detected intensities or detected signal levels including the effects of imaging system gain at each pixel location. The count curves  94  are used to modulate the effect of image noise reduction in different regions of the image, without creating visible boundaries between the regions, and to simultaneously modulate smoothing based on visually relevant structures and graininess pertaining to noise.  
      The count curves  94  may include a low noise reduction curve  95 , a medium noise reduction curve  96 , and high noise reduction curve  97 . The count curves  94  may include a primary offset segment with a constant weighting, a primary roll-off segment with a decreasing weighting, a secondary offset segment with a constant weighting, a secondary roll-off segment with a decreasing weighting, all of which are shown as part of the high noise reduction curve and denoted as Offset 1 , Sigma 1 , Offset  2 , and Sigma 2 .  
      Referring now to step  108  of  FIG. 4  and to the logic flow diagram of  FIG. 8 .ln step  108 , the normalized data R N  is structure dependent smooth filtered. The tasks performed in step  108  are shown in and described with respect to  FIGS. 8-10 .  
      Referring to  FIG. 8 , in step  150  the normalized data R N  is filtered via the processor  28 . In order to account for and reduce the spike noise in the ultimate image, the spike noise in the input images are characterized. The processor  28  may shrink or sub-sample the normalized data R N  by a shrink parameter. As will be appreciated by those skilled in the art, such shrinking may be accomplished by various sub-sampling techniques, including a pixel averaging, in which the digital values representative of intensities at each pixel are read and the image is shrunk by some factor X which is generally greater than 1. In a present embodiment, a 2×2 or 3×3 boxcar filter may be applied to obtain a non-overlapping average. Multi-dimensional factors may also be employed, such as 2×3 or 3×2 filters. A multi-dimensional factor must be greater than 1 in at least one of the dimensions, such as in a 3×1or a 1×3 filter. To obtain non-overlapping averages, the pixels of the image may be mirrored at the boundaries when needed.  
      In step  154 , the normalized data R N  is rank order filtered. For characterization of spike noise, the input image is processed through a rank-order filter (not shown). The extent of the rank-order filtering may depend upon the definition of a spike for a particular image, imaging modality, and the like. For example, if spike noise is defined as a single pixel, then a 3×3 rank-order filtering kernel may suffice. As will be appreciated by those skilled in the art, for rank-order filtering each pixel of interest is replaced by a value selected from a rank-ordered listing of neighboring pixels. Thus, for a 3×3 kernel, 9 pixels, including the pixel of interest are rank-ordered and one of the values is selected to replace the pixel of interest value in the rank-ordered filtered image. Other kernel sizes may, of course, be utilized. In one embodiment, the pixel of interest is replaced by a value near but necessarily in the middle of the range of neighboring pixel values.  
      In step  156 , an absolute difference image is computed based upon the rank-order filtered image. The absolute difference image is computed by subtracting pixel values from the rank-order filtered image from correspondingly located pixel values in the input image. The absolute difference image may include relatively low values owing to the neighborhoods considered in the rank-ordering of step  154  performed.  
      In step  158 , a spike noise-based mask M s  is created having the same dimensions as the input image, the rank-order filtered image, and the absolute difference image. In this process, a histogram of the values contained in the absolute difference image is first compiled. A threshold intensity value on the histogram is selected based upon some criterion, such that it is more likely that the spike noise differences are above the intensity value. The threshold intensity value may depend upon the particular image characteristics and the imaging modality. By way of example, the threshold intensity value may be set to a value that is a percentage of counts of the first non-zero difference bin of the histogram.  
      The spike noise mask M s  may be developed such that the pixels of the input image are tagged as spike noise. Blending of the input image with the filtered and expanded image data may be governed by the spike noise mask M s .  
      The spike noise mask M s  may be a binary or multi-level mask. To create such a mask, a multi-level threshold criterion may be used having many levels of likelihood of spike noise. By way of example, six levels in the spike noise mask M s  may be created by using difference percentage multipliers with the base account described above. Pixels within these multiple levels may be identified the spike noise mask M s  as relatively more or less likely to represent spike noise. The pixels at the various levels will then be associated with different blending parameters as described below.  
      In step  160 , the processor  28  determines whether each individual pixel of the input image is likely to represent spike noise. As noted above, this likelihood may be based upon a binary mask or a multi-level mask. Where a pixel is not identified as likely to represent spike noise, normal blending with the expanded image I expanded , generated in step  218  of  FIG. 9 , may be performed as indicated at step  162 . However, where the pixel is likely to represent spike noise, the expanded image I expanded  may be blended differently, as indicated at step  164 . In step  166 , structural and non-structural regions of the normalized data R N  are enhanced in response to the selected blending technique determined in steps  160 - 164 .  
      Referring now to  FIG. 9 , a logic flow diagram illustrating a method of enhancing structural and non-structural regions in response to the selected blending technique, as depicted in step  166  of the method of  FIG. 8 . The logic flow diagram of  FIG. 9  illustrates a sequence of processing stages applied to image data and as a progression of modified images resulting from the stages of the present techniques. The structural regions  62  and non-structural regions  76  are identified and enhanced in accordance with summarized control logic.  
      In step  200 , the control logic routine begins with the initialization and tuning of parameters, either default or operator selected, and employed in the image enhancement process. This initialization typically includes the amount of shrinking, the interpolation type, and the parameters specific to noise reduction, i.e. thresholds related to focus, edge determination, and blending.  
      Where desired, entry or selection of some of these parameters may be prompted via the input device  38 , allowing the operator to select between several parameter choices, such as the desired field-of-view of an image. Assignment of certain downstream parameters may then be automated in response to the operator-selected parameters. For example, the parameter associated with the amount of shrinking to be used in processing, i.e. the shrink factor, may be set automatically in response to the operator&#39;s selection of a field-of-view which directly determines the amount of area described by a pixel. For example, in one embodiment, the shrink factor may be set to 1 for images of size 256×256 or smaller, 2 for images larger than 256×256 but less than or equal to 512×512, and 4 for images larger than 512×512.  
      Automated determination of this shrink factor, or other initialization parameters, in response to operator-selected parameters, allows the production of uniform images that possess the same appearance or quality, even when different fields of view are selected. In this manner, an operator need only select the field-of-view and the processing circuitry  28  will assign the other appropriate parameters to produce images of uniform quality and appearance. While the assignment of parameters, such as the shrink factor, need not be automated, thereby allowing the operator to assign all image enhancement parameters, it is generally desirable to provide such automation to enhance the uniformity of the filtering process and to minimize the time and effort required of the operator.  
      In step  202 , the processor  28  pre-processes the adjusted input image or adjusted smoothed normalized data D s . Pre-processing of the input image data D s  typically involves augmenting the size of the image to prevent the loss of data during the subsequent shrinking of the image. In particular, the appropriate boundaries of the input image D s  are padded by mirroring the boundary image data in order to allow the subsequent shrink function to process and produce integer values. The result of pre-processing is a pre-processed image, represented as I pre .  
      In step  204 , the pre-processed image I pre  is then shrunk by the processor  28  using a sub-sampling technique. The shrinking of the pre-processed image I pre  may be accomplished by various sub-sampling techniques, including pixel averaging, in which the digital values, representative of intensities at each pixel, are read and then the image is shrunk by some shrink factor X which is generally greater than one. In the preferred embodiment, a 2×2 or 3×3 boxcar filter may be applied to obtain a non-overlapping average. Multi-dimensional factors may also be employed, such as 2×3 or 3×2 filters. A multi-dimensional factor must be greater than one in at least one of the dimensions, such as in 3×1 or 1×3 filters. In order to obtain a non-overlapping average, pixels may be mirrored at the boundaries when needed. A shrunken image I shrunk  is the product of the sub-sampling technique.  
      In step  206 , the processor  28  normalizes the image values acquired for the pixels defining the shrunken image I shrunk  to generate a normalized image I normal . Digital values representing intensities at each pixel are read and scaled over a desired dynamic range. For example, the maximum and minimum intensity values in the image may be determined, and used to develop a scaling factor over the full dynamic range of the display  32 . Moreover, a data offset value may be added to or subtracted from each pixel value to correct for intensity shifts in the acquired data. The normalized image I normal  includes pixel values filtered to span a desired portion of a dynamic range, such as 12 bits, independent of variations in the acquisition circuitry or subject.  
      In step  208 , the processor  28  executes a predetermined logic routine for identifying the structure  62  within the normalized image I normal , as defined by data representative of the individual pixels of the image. Exemplary steps for identifying the structure in accordance with the present technique are described below with reference to  FIG. 10 . The structure identified in step  208  is used to generate a structure mask M struct .  
      Referring now to  FIG. 10 , a logic flow diagram illustrating a method of identifying structures in the operation of the method of  FIG. 9  is shown in accordance with an embodiment of the present invention.  
      In step  250 , a blurred or smoothed version of the normalized image I normal  is preferably formed. The structure identification process begins with this smoothed version such that structural components of the image may be rendered more robust and less susceptible to noise. While any suitable smoothing technique may be employed, in the present embodiment, a box-car smoothing technique is used, wherein a box-car filter smoothes the image by averaging the value of each pixel with values of neighboring pixels. As will be appreciated by those skilled in the art, a computationally efficient method for such filtering may be implemented, such as employing a separable kernel, which is moved horizontally and vertically along the image until each pixel has been processed.  
      In step  252 , X and Y gradient components for each pixel are computed based upon the smoothed version of the normalized image I normal . While several techniques may be employed for this purpose, one technique includes the use of 3×3 Sobel modules or operators. The modules are used for identifying the X gradient component and the Y gradient component of each pixel. The modules are superimposed over the individual pixel of interest, with the pixel of interest situated at the central position of the 3×3 module. The intensity values located at the element locations within each module are multiplied by the scalar value contained in the corresponding element, and the resulting values are summed to arrive at the corresponding X and Y gradient components.  
      In step  254 , the gradient magnitude Gmag and the gradient direction Gdir are determined in response to the X and Y gradient components. The gradient magnitude Gmag for each pixel is equal to the higher of the absolute values of the X and Y gradient components for the respective pixel. The gradient direction is determined by finding the arctangent of the Y component divided by the X component. For pixels having an X component equal to zero, the gradient direction is assigned a value of π/2. The values of the gradient magnitudes and gradient directions for each pixel are saved in the memory circuit  26 .  
      It should be noted that alternative techniques may be employed for identifying the X and Y gradient components and for computing the gradient magnitudes and directions. For example, those skilled in the art will recognize that in place of the Sobel gradient modules, other modules such as the Roberts or Prewitt operators may be employed. Moreover, the gradient magnitude Gmag may be assigned in other manners, such as a value equal to the sum of the absolute values of the X and Y gradient components. The gradient magnitude Gmag is the structure gradient mask depicted as being generated in box  110  of  FIG. 4 .  
      In step  256 , a gradient histogram is generated in response to the above-stated gradient values. The histogram is used to identify a gradient threshold value G TV  for separating structural components of the image from non-structural components. The gradient threshold value G TV  is set at a desired gradient magnitude level. The gradient threshold value G TV  is depicted as being generated in box  112  of  FIG. 4 . Pixels having gradient magnitudes at or above the gradient threshold value G TV  are considered to meet a first criterion for defining structure in the image, while pixels having gradient magnitudes lower than the threshold value G TV  are initially considered non-structure. The gradient threshold value G TV  is used to separate structure from non-structure and may be set by an automatic processing or “autofocus” routine. However, it should be noted that the gradient threshold value G TV  may also be set by operator intervention or may be over-ridden by the operator to provide specific information in the resulting image.  
      In step  258 , an initial gradient threshold value I GT  is selected. This initial gradient threshold I GT , is set to a value corresponding to a percentile of the global pixel population, such as 30%. Once the desired percentile value is acquired, the corresponding gradient magnitude Gmag is equal to the value assigned to the initial gradient threshold I GT .  
      In step  260 , a search is performed for edges of the desired structure. The edge search proceeds by locating the pixels having gradient magnitudes greater than the initial gradient threshold I GT  and considering a 5×5 pixel neighborhood surrounding the relevant pixels of interest. Within the 5×5 pixel neighborhood of each pixel of interest, pixels having gradient magnitudes above the initial gradient threshold I GT  and having directions which do not differ from the direction of the pixel of interest by more than a predetermined angle are counted. In the present embodiment, an angle of 0.35 radians is used in this comparison step. When the 5×5 neighborhood count is greater than a preset number the pixel of interest is identified as a relevant edge pixel.  
      In step  262 , a binary mask image I b  is created wherein pixels identified as relevant edge pixels in step  260  are assigned a value of 1, while all other pixels are assigned a value equal to zero.  
      In step  264 , small or noisy segments identified as potential candidates for structure are iteratively eliminated using techniques known in the art.  
      In step  266 , the number of pixels remaining in the binary mask image I b  are counted. While the resulting number may be used to determine a final gradient threshold F GT , it has been found that a convenient method for determining a final gradient threshold for the definition of structure includes the addition of a desired number of pixels to the resulting pixel count. For example, a value of 4,000 may be added to the binary mask count to arrive at a desired number of pixels in the image structure definition. The desired number of pixels may be set as a default value, or may be modified by an operator. In general, a higher additive value produces a sharper image, while a lower additive value produces a smoother image. This desired number of pixels, referred to in the present embodiment as the “focus parameter”, may be varied to redefine the classification of pixels into structures and non-structures.  
      In step  268 , with the desired number of structure pixels thus identified, a final gradient threshold F GT  is determined based upon the histogram in step  256 . In particular, the population counts for each gradient magnitude value of the histogram are summed. Once the desired number of structural pixels is reached (i.e., the number of pixels counted at block  266  plus the focus parameter), the corresponding gradient magnitude value is identified as the final gradient threshold F GT . The final gradient threshold F GT  is then scaled through multiplication by a value, which may be automatically determined or which may be set by a user. For example, a value of 1.9 may be employed for scaling the final gradient threshold F GT , depending upon the image characteristics and the type and features of the structure viewable in the image. The use of a scalable threshold value also enables the technique to be adapted easily and quickly to various types of images, such as for MR image data generated in systems with different field strengths.  
      Based upon the scaled final gradient threshold, a new binary mask I bn  is defined by assigning pixels having values equal to or greater than the final gradient threshold F GT  a value of 1, and all other pixels a value of zero.  
      In step  270 , the resulting binary mask I bn  is filtered to eliminate small, isolated segments in a process identical to that described above with respect to step  264 . However, in step  270  rather than a four-connected neighborhood, an eight-connected neighborhood (i.e., including pixels having shared edges and corners bounding the pixel of interest) is considered in the index number merger steps.  
      In step  272 , certain isolated regions may be recuperated to provide continuity of edges and structures. When a pixel in the gradient image is above a second gradient threshold and is connected or adjacent a pixel which is above the final gradient threshold F GT , the corresponding pixel in the binary image is changed from a 0 value to a value of 1. The value of the second gradient threshold may be set to a desired percentage of the final gradient threshold F GT , and may be determined empirically to provide the desired degree of edge and structure continuity. Step  272  may be performed recursively to determine an initial classification of the pixels.  
      In step  274 , the feature edges identified through the previous processes, representative of candidate structures in the image, are binary rank order filtered to generate the structure mask M struct . While various techniques may be employed for this enhancing identified candidate structures, it has been found that the binary rank order filtering provides satisfactory results in expanding and defining the appropriate width of contiguous features used to define structural elements.  
      Referring again to  FIG. 9 , in step  210  the structure mask M struct  is used to identify structure, which is then orientation smoothed. The processor  28  in filtering or smoothing the normalized data R N  serves to identify, process, and differentiate structural features and non-structural features or regions. Various known techniques may be employed for this orientation smoothing. However, in the described embodiment, dominant orientation smoothing may be carried out, which tends to bridge gaps between spans of structure, or local orientation smoothing may be employed to avoid such bridging. Orientation smoothing transforms the normalized image I normal  to a filtered structure image I struct , which is further refined by subsequent processing. In step  212 , after the identified structure has been orientation smoothed, the structure regions are then orientation sharpened to further refine the filtered structure image I struct .  
      In step  214 , the image processing circuit  24  performs homogenization smoothing on non-structure regions of the normalized image I normal . This isotropic smoothing blends features of non-structural regions into the environment surrounding the identified structure to produce a filtered non-structure image I non-struct . The filtered non-structure image I non-struct , along with the filtered structure image I struct , are the components of the composite filtered image I filtered . Steps  210  and  214  may be iteratively performed.  
      In step  216 , the filtered image I filtered  is renormalized, based upon the post-filtering pixel intensity values and the original normalized intensity range, to produce a renormalized image I renormal .  
      In step  218 , both the structure mask M struct  and the renormalized image I renormal  are expanded by the same factor by which pre-processed image I pre  was shrunk or sub-sampled to produce an expanded structure mask M expanded  and an expanded image I expanded , both of which are the same dimensions as the pre-processed image I pre .  
      In step  220 , texture present in the pre-processed image I pre  is blended back into the expanded image I expanded , to produce a blended image I blended . The blending process of step  220  utilizes the expanded structure mask M expanded  to allow differential texture blending of structure and non-structure regions. The blended image I blended  is the structure dependent noise filtered image depicted in step  114  of  FIG. 4 .  
      Referring again to  FIG. 4 ,in step  116  an adjusted gradient threshold value G TA  is generated in response to the gradient threshold value G TV  and a gradient threshold scalar G TS . The gradient threshold scalar G TS  may be user selected, as sample of which is shown in the plot of  FIG. 11 . The adjusted gradient threshold value G TA  is set equal to the multiplied result of the gradient threshold value G T  and the gradient threshold scalar G TS .  
      In step  118 , weight function values are determined in response to the structure gradient mask Gmag and the adjusted gradient threshold value G TA . A gradient level masking look-up table or curve is utilized to generate the weight function values. The weight function values are values between zero and one. When the value of the structure gradient mask is less than the adjusted gradient threshold value G TA , a noise reduction weighting value corresponding to a respective point on the gradient level masking plot is assigned. When the value of the structure gradient mask is equal to or greater than the adjusted gradient threshold value G TA , a noise reduction weighting value corresponding to that of the threshold value G TA  is assigned.  
      In step  119 , the smoothed normalized raw image data generated in step  104  is used to generate a count based weighting mask M bcb . The values of the weighting mask M bcb  are determined in response to the smoothed data D s , a low count flat value LowCountFlat, a transition value T, and the weight function values W f . The values of the weighting mask M bcb  are represented by equation 1. 
 
 M   bcb =(( D   s −LowCountFlat)/ T )×(1 −W   f )   (1) 
 
      The low count flat value LowCountFlat is user selected.  
      The transition value T is equal to LowCountLimit minus LowCountFlat, where LowCountLimit is user selected and is higher than LowCountFlat.  
      In step  120 , a conditioned structure mask M cs  is generated. The conditioned structure mask M cs  is generated in response to the weight function values W f  and the counts-based weighting mask M bcb . The values of the conditioned structure mask M cs  are conditionally determined using equations 2-4. 
 
M cs =W f  if D s &lt;=LowCountFlat   (2) 
 
M cs =M bcb  if LowCountFlat&lt;D s &lt;LowCountLimit   (3) 
 
M cs =1 if D s &gt;=LowCountLimit   (4) 
 
      In step  122 , the normalized data R N  and the structure dependent noise filter data I blended  are blended in response to the multiplied result of the counts-based mask M cb  and the conditioned structure mask M cs . The tasks performed in step  122  are described with respect to  FIG. 12 .  
      Referring now to  FIG. 12 , a logic flow diagram illustrating a method of performing a blending operation for step  122  of the method of  FIG. 4  is shown.  
      In step  300 , the counts-based mask M cb  and the conditioned structure mask M cs  are multiplied to form a final mask M F . In step  302 , a blended noise reduced image I bnr  is generated utilizing equation 5. 
 
 I   bnr   =[M   F   ×B×I   blended ]+[1−( M   F   ×B )]× R   N    (5) 
 
      The blended noise reduced image I bnr  is high frequency noise reduced in low count regions, but since the counts of the raw data R have been considered low count areas are not filtered to such an extent to eliminate structures that are visible and have a relatively low count.  
      Referring again to  FIG. 4 , in step  124  the blended noise reduced image I bnr  is smoothed to form a smooth blended noise reduced image I sbnr . The blended noise reduced image I bnr  may be smoothed using a similar technique as that used in step  104 , using a low pass filter, or using some other technique known in the art.  
      In step  126 , the values of the blended noise reduced image I bnr  are intensity matched with the values in the normalized raw image data D s  to match low frequencies therein. The values may be intensity matched using equation 6 to generate a final noise reduced image I F . 
 
 I   F   =I   bnr   ×[LPF[D   s ]/( LPF[I   sbnr ]+0.1)]  (6) 
 
      LPF refers to a low pass filter function that is used to match low frequencies. Different low pass functions (e.g., a box-car filter) can be used for this purpose and the parameters controlling the low pass filter function can be used to vary the amount of matching. The above-described steps are meant to be an illustrative example; the steps may be performed synchronously, sequentially, simultaneously, or in a different order depending upon the application.  
      The present invention provides a method of adaptively reducing noise within an x-ray image. The present invention achieves a significant amount of noise reduction while minimizing contrast degradation in high signal-to-noise regions that need only minimal noise reduction based on the detected and or processed image intensities. Noise reduction is suppressed in low signal-to-noise regions that have significant “structure”, in order to avoid degrading clinical detail.  
      The above use of a detected signal in conjunction with image intensities after display processing ensures that the detection physics as well as the perceptual effects of display processing, such as edge enhancement, contrast enhancement, and others known in the art, are accounted for to achieve a desired noise reduction.  
      While the invention has been described in connection with one or more embodiments, it is to be understood that the specific mechanisms and techniques which have been described are merely illustrative of the principles of the invention, numerous modifications may be made to the methods and apparatus described without departing from the spirit and scope of the invention as defined by the appended claims.