PATENT ABSTRACT
A technique for reducing noise in pixel images includes modifying initial image data to prevent subsequent image data loss, shrinking the modified image, processing the shrunken image with known segmentation-based filtering techniques which identify and differentially processing structures within the image. After processing, the shrunken image is enlarged to the dimensions of the initial data and subsequently processed to correct intensity irregularities and to preserve CT numbers. The resulting technique is versatile and provides greatly improved computational efficiency while maintaining image quality, allowing for dose reduction during imaging.

PATENT DESCRIPTION
FIELD OF THE INVENTION 
   This invention relates to discrete picture element or pixel imaging techniques, and, more particularly, to an improved technique for use with computed tomography. The invention relates to analyzing and modifying values representative of pixels which significantly increases computational efficiency while maintaining overall image quality. 
   BACKGROUND OF THE INVENTION 
   A variety of discrete pixel imaging techniques are known and are presently in use. In general, such techniques rely on the collection or acquisition of data representative of each discrete pixel composing an image matrix. Particular examples abound in the medical imaging field where modalities such as computed tomography (CT) are available for producing the data represented by the pixels. Depending upon the particular modality employed, the pixel data is detected and encoded, such as in the form of digital values. The values are linked to particular relative locations of the pixels in the reconstructed image. 
   The utility of a processed image is often largely dependent upon the degree to which it can be interpreted by users or processed by subsequent automation. For example, in the field of medical diagnostics imaging, CT images are most useful when they can be easily understood and compared by an attending physician or radiologist. Typically one impediment to interpretation or further processing is the pixel to pixel variation which is attributable to acquisition noise. While acquisition noise is usually random, there may also be additional structured noise as well which may be observed as artifacts in the image. To mitigate the effects of random noise, many forms of noise reduction filters to improve the final image presentation have been proposed. 
   Moreover, while a number of image processing parameters may control the final image presentation, it is often difficult to determine which of these parameters, or which combination of the parameters, may be adjusted to provide the optimal image presentation. Often, the image processing techniques must be adjusted in accordance with empirical feedback from an operator, such as a physician or technician. 
   The facility with which a reconstructed discrete pixel image may be interpreted by an observer may rely upon intuitive factors of which the observer may not be consciously aware. In medical imaging, a physician or radiologist may seek specific structures or specific features in an image such as bone, soft tissue or fluids. Such structures may be physically defined in the image by contiguous edges, contrast, texture, and so forth. 
   The presentation of such features often depends heavily upon the particular image processing technique employed for converting the detected values representative of each pixel to modified values used in the final image. The image processing technique employed can therefore greatly affect the ability of an observer or an analytical device to recognize salient features of interest. The technique should carefully maintain recognizable structures of interest, as well as abnormal or unusual structures, while providing adequate textural and contrast information for interpretation of these structures and surrounding background. Ideally the technique will perform these functions in a computationally efficient manner so that processing times, as well as hardware requirements, can be minimized. 
   Known signal processing systems for enhancing discrete pixel images suffer from certain drawbacks. For example, such systems may not consistently provide comparable image presentations in which salient features or structures may be easily visualized. Differences in the reconstructed images may result from particularities of individual scanners and circuitry, as well as from variations in the detected parameters such as received radiation. Differences can also result from the size, composition, position, or orientation of a subject or item being scanned. Signal processing techniques employed in known systems are often difficult to reconfigure or adjust, owing to the relative inflexibility of hardware or firmware devices in which they are implemented or to the coding approach employed in software. 
   In addition, current techniques may result in highlighting of small, isolated areas which are not important for interpretation and may be distracting to the viewer. Conversely, in techniques enhancing images by feature structure recognition, breaks or discontinuities may be created between separate structural portions, such as along edges. Such techniques may provide some degree of smoothing or edge enhancement, but may not provide satisfactory retention of textures at ends of edges or lines. 
   Finally, known signal processing techniques often employ inefficient computational noise reduction algorithms, resulting in delays in formulation of the reconstituted image or under-utilization of signal processing capabilities. More computationally efficient algorithms would allow both quicker image display. Further, more computationally efficient noise reduction algorithms are more suitable for diagnostic imaging systems due to both the increased speed and the less stringent equipment requirements. 
   In addition to the general problems associated with image processing certain specific problems are associated with the processing of images derived from CT systems. First, using current techniques, spatial domain filters are computationally intensive and alter the CT numbers, making the current spatial domain filters unsuited for diagnostic imaging CT scanners. In addition, the nature of the circular CT field produces degraded performance along the periphery which requires different levels of correction. 
   There is a need, therefore, for a more computationally efficient technique for enhancing discrete pixel images which addresses these concerns. Ideally such a technique would be robust in its implementation and would address not only the general image processing problems but also those specific to CT imaging. 
   SUMMARY OF THE INVENTION 
   The invention provides an improved technique for enhancing discrete pixel images in a CT imaging system which is computationally efficient and which maintains image quality. The technique provides a means of combining multi-resolution decomposition (wavelet based processing) with segmentation based techniques which identify structures within an image and separately process the pixels associated with the structures. This combination allows the technique to exploit the redundancies of an image, as with wavelet based techniques, while also allowing the separate processing of structures and non-structures, as in segmentation-based techniques. The combination of these techniques results in a computationally efficient, yet robust, noise reduction filter which may be applied to a variety of pixel based images. 
   The technique also allows for the introduction of a filter dependent offset which acts to keep CT numbers intact and, further, processes image data using CT masks which allow differential processing of image data within the CT circle. The technique also allows for virtual dose improvements since a subject can be exposed to lower levels of a radiation source while still achieving the same quality of images. Any CT image acquisition system may benefit from the application of the present technique. 
   In an exemplary embodiment an image is pre-processed to introduce an image offset to ensure that all pixel values are positive. Multi-resolution decomposition is then performed whereby the image is shrunk by a given factor, allowing for the exploitation of redundancies in the image during subsequent processing. The shrunken image is then processed using segmentation based techniques which begin by identifying the structure elements within a blurred or smoothed image. Segmentation processing renders the structural details more robust and less susceptible to noise. A scalable threshold may serve as the basis for the identification of structural regions, making the enhancement framework inherently applicable to a range of image types and data characteristics. While small, isolated regions may be filtered out of the image, certain of these may be recuperated to maintain edge and feature continuity. 
   Following identification of the structures, portions of the image, including structural and non-structural regions, are smoothed. Structural regions may be smoothed to enhance structural features in dominant orientations, which may be identified to provide consistency both along and across structures. Non-structural regions may be homogenized to provide an understandable background for the salient structures. The structures may be further sharpened, and minute regions may be identified which are considered representative of noise. Such artifacts may be smoothed, removed or selectively sharpened based upon a predetermined threshold value. 
   Upon completion of the segmentation based processing, the image is expanded by the same factor it was originally shrunk by to return it to its original size. Original texture may be added back to non-structural regions to further facilitate interpretation of both the non-structural and structural features. Following restoration of texture, CT masks are created and the image intensity values within the CT circle are corrected using the CT masks. A post-processing step is then performed to remove the image offset introduced at the beginning of the process. Finally, a filter strength dependent bias value is subtracted from all pixels to correct for small biases in the CT numbers. 
   The ability of the present technique to increase computational efficiency, due to exploitation of the image redundancies, while maintaining image quality is particularly noteworthy since a reduction in image quality might be expected as a result of the image resizing. Surprisingly, however, no such decrease in image quality is observed. 
   The technique is useful in processing pixel based images derived by a CT imaging system. Due to the efficiencies introduced by the technique, lower dose images may be acquired without perceived image quality degradation. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
       FIG. 1  is a diagrammatical representation of an imaging system adapted to enhance discrete pixel images of a subject; 
       FIG. 2  is a diagram of an exemplary discrete pixel image made up of a matrix of pixels having varying intensities defining structures and non-structures; 
       FIG. 3  is a flow chart illustrating the progression of an image through the present technique; 
       FIG. 4  is a flow chart illustrating steps in exemplary control logic for processing an image by the present technique; 
       FIG. 5  is a flow chart illustrating steps in exemplary control logic for identifying structural features in a discrete pixel image; 
       FIG. 6  is a diagram of elements or modules used in the steps of  FIG. 4  for generating gradient components for each discrete pixel of the image; 
       FIG. 7  is a gradient histogram of an image used to identify gradient thresholds for dividing structure from non-structure in the image; 
       FIG. 8  is a flow chart of steps in exemplary control logic for selectively eliminating small or noisy regions from the structure definition; 
       FIG. 9  is a flow chart of steps in exemplary control logic for processing structural features identified in the image by binary rank order filtering; 
       FIG. 10  is a flow chart illustrating steps in exemplary control logic for orientation smoothing of structure identified in an image; 
       FIG. 11  is a flow chart illustrating steps in exemplary control logic for performing dominant orientation smoothing in the process summarized in  FIG. 9 ; 
       FIG. 12  is a diagram of directional indices employed in the orientation smoothing process of  FIG. 10 ; 
       FIG. 13  is a flow chart of steps in exemplary control logic for performing local orientation smoothing through the process of  FIG. 9 ; 
       FIG. 14  is a flow chart of steps in exemplary control logic for homogenization smoothing of non-structural regions of a discrete pixel image; 
       FIG. 15  is a flow chart of steps in exemplary control logic for orientation sharpening of structural regions in a discrete pixel image; 
       FIG. 16  is a flow chart illustrating steps in exemplary control logic for reintroducing certain textural features of non-structural regions in a discrete pixel image; 
       FIG. 17  is a flow chart of steps in the creation of CT masks; and 
       FIG. 18  is a schematic of a CT mask. 
   

   DETAILED DESCRIPTION OF THE INVENTION 
   A highly abstracted rendition of image processing by the present technique is illustrated in  FIG. 3 , beginning with the input of the raw signal data as input image  70 . Input image  70  is initially processed to calculate a minimum intensity value. This constant is algebraically added to all the pixel intensity values in the initial image data to make the image intensity positive. The result of this pre-processing step is pre-processed image  71 . 
   Pre-processed image  71  is shrunk by a user configurable parameter, X, to create shrunken image  72 . Shrunken image  72  undergoes normalization to create normalized image  74 . Threshold criteria are applied to identify structures within normalized image  74 . The structures identified are used to generate a structure mask  76  which is used in subsequent processing to distinguish both structure and non-structure regions, allowing differential processing of these regions. Normalized image  74  is filtered to reduce noise via structure mask  76  to create an intermediate filtered image  78  which is subsequently normalized to form renormalized image  80 . Renormalized image  80  and structure mask  76  are expanded to form expanded image  82  and expanded structure mask  83 . Differential blending of expanded image  82  and input image  70  is accomplished via the application of expanded structure mask  83 . The product of the blending process is blended image  84 . 
   Next CT mask  86  is extracted. Based upon application of CT mask  86  corrections are made to blended image  84  resulting in corrected image  88 . The minimum intensity value which was added to offset initial image  70  is subtracted from the image data of corrected image  88  to form offset image  90 . Finally, a filter strength dependent bias value is subtracted from the image data of offset image  90  to form final image  92 . More particular descriptions of this technique follows. 
   Referring to  FIG. 1 , a CT image acquisition system  10  is illustrated as including a CT scanner  12  coupled to circuitry for acquiring and processing discrete pixel data. Signals sensed by scanner  12  are encoded to provide digital values representative of the signals associated with specific locations on or in the subject, and are transmitted to signal acquisition circuitry  22 . Signal acquisition circuitry  22  also provides control signals for configuration and coordination of scanner operation during image acquisition. Signal acquisition circuitry  22  transmits the encoded image signals to an signal processing circuit  24 . Signal processing circuit  24  executes pre-established control logic routines stored within a memory circuit  26  to filter and condition the signals received from signal acquisition circuitry  22  to provide digital values representative of each pixel in the acquired image. These values are then stored in memory circuit  26  for subsequent processing and display. Alternately, signal acquisition circuitry  22  may transmit the encoded image signals to memory circuit  26 . Signal processing circuit  24  may subsequently acquire the signals from memory circuit  26  for the filtering and conditioning steps described above. 
   Signal processing circuit  24  receives configuration and control commands from an input device  28  via an input interface circuit  30 . Input device  28  will typically include an operator&#39;s station and keyboard for selectively inputting configuration parameters and for commanding specific image acquisition sequences. Signal processing circuit  24  is also coupled to an output device  32  via an output interface circuit  34 . Output device  32  will typically include a monitor or printer for generating reconstituted images based upon the image enhancement processing carried out by circuit  24 . 
   In the embodiment described, signal processing circuit  24 , memory circuit  26 , and input and output interface circuits  30  and  34  are included in a programmed digital computer. However, circuitry for carrying out the techniques described herein may be configured as appropriate coding in application-specific microprocessors, analog circuitry, or a combination of digital and analog circuitry. 
     FIG. 2  illustrates an exemplary discrete pixel image  50  produced via CT system  10 . Image  50  is composed of a matrix of discrete pixels  52  disposed adjacent to one another in a series of rows  54  and columns  56 . These rows and columns of pixels provide a pre-established matrix width  58  and matrix height  60 . Typical matrix dimensions may include 256×256 pixels; 512×512 pixels; 1,024×1,024 pixels, and so forth. The particular image matrix size may be selected via input device  28  (see  FIG. 1 ) and may vary depending upon such factors as the subject to be imaged and the resolution desired. 
   Illustrated in  FIG. 2 , exemplary image  50  includes structural regions  62 , illustrated as consisting of long, contiguous lines defined by adjacent pixels. Image  50  also includes non-structural regions  64  lying outside of structural regions  62 . Image  50  may also include isolated artifacts  66  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. It should be understood that the structures and features of exemplary image  50  are also features of the specific and modified images discussed above in relation to FIG.  3 . 
   Structural regions  62  and non-structural regions  64  are identified and enhanced in accordance with control logic summarized generally in FIG.  4 . This control logic is preferably implemented by signal processing circuit  24  based upon appropriate programming code stored within memory circuit  26 . The control logic routine, designated generally by reference numeral  120  in  FIG. 4 , begins at step  116  with the pre-processing of initial image  70 . Pre-processing step  116  involves the calculation of a minimum intensity value for initial image  70 . The minimum intensity value is then algebraically added to all of the intensity values of initial image  70  to make the image intensity positive and to obtain pre-processed image  71 . 
   Next, at step  121  the filter parameters employed in the image enhancement process are initialized. The filter parameters are tuned iteratively at step  118  using a testing image database that contains images of various body parts from different patients of various thickness with and without various pathologies. The iterative tuning of filter parameters is illustrated by the dashed line connecting steps  118  and  121 . Initialization step  121  may also includes the reading of default and operator-selected values for parameters described in the following discussion, such as the size of small regions to be eliminated from structure, a “focus parameter” and so forth. Where desired, certain of these parameters may be prompted via input device  28 , requiring the operator to select between several parameter choices, such as image matrix size. 
   Next, at step  123 , signal processing circuit  24  collects the pre-processed image data  71 , represented as I pre  and shrinks the image. The shrinking of step  123  may be accomplished by various sub-sampling techniques, including pixel averaging, which read the digital values representative of intensities at each pixel and then shrink the image by some 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 3×1 or 1×3. In order to obtain a non-overlapping average, pixels may be mirrored at the boundaries when needed. A shrunken image  72 , I shrunk , is the product of the sub-sampling technique. 
   At step  124 , signal processing circuit  24  normalizes the image values acquired for the pixels defining the shrunken image  72 . In the illustrated embodiment, this step includes reading digital values representative of intensities at each pixel, and scaling these intensities values 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 output device  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. Thus, at step  124 , circuit  24  processes I shrunk  in  FIG. 4 , to produce normalized image  74 , I normal . Normalized image  74  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. 
   It should be noted that while reference is made in the present discussion to intensity values within an image, such as input image  70 , shrunken image  72 , normalized image  73 , or exemplary image  50 , the present technique may also be used to process other parameters encoded for individual pixels  52  of an image. Such parameters might include frequency or color, not merely intensity. 
   At step  126 , signal processing circuit  24  executes a predetermined logic routine for identifying structure  62  within normalized image  74 , 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.  5 . The structure identified at step  126  is used to generate a structure mask  76 , M structure , which is used in subsequent steps. Step  128  uses structure mask  76  to identify structure which is then orientation smoothed as summarized below with reference to  FIGS. 10-13 . While various techniques may be employed for this orientation smoothing, in the embodiment described, 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 carried out in step  128  thus transforms normalized image  74  to a filtered image  78 , I filtered , which will be further refined by subsequent processing. After the structure identified at step  126  has been orientation smoothed, the structure regions are then orientation sharpened at step  132  to further refine filtered image  78 . The process of orientation sharpening is described more fully below with reference to FIG.  15 . 
   In parallel with the processing of the structure regions described in steps  128  and  132 , the non-structure regions of normalized image  74  are further processed as follows to also contribute to filtered image  78 . At step  130 , signal processing circuit  24  performs homogenization smoothing on non-structure regions of normalized image  74 . As described more fully below with reference to  FIG. 14 , this homogenization smoothing is intended to blend features of non-structural regions into the environment surrounding the structure identified at step  126 . At step  134  the filtered image  78  is then renormalized based upon the intensity values after filtering and the original normalized intensity range to produce renormalized image  80 . 
   At step  135 , both structure mask  76  and renormalized image  80  are expanded by the same factor by which raw image  70  was originally shrunk in step  123 . The products of step  135  are thus an expanded structure mask  83  and expanded image  82 , both of which are the same dimensions as input image  70 . Finally, at step  136  texture present in input image  70  is blended back into the expanded image  82 , I expanded , to provide texture for blended image  84 . The blending process of step  136  utilizes expanded structure mask  83  to allow differential blending of structure and non-structure regions. The texture blending process is described below with reference to FIG.  16 . 
     FIG. 5  illustrates steps in control logic for identifying structural regions  62  within normalized image  74  and for eliminating small or noisy isolated regions from the definition of the structural regions. As indicated above, the logic of  FIG. 5 , summarized as step  126  in  FIG. 4 , begins with pixel data of the normalized image  74 . 
   At step  150  a blurred or smoothed version of normalized image  74  is preferably formed. It has been found that by beginning the steps of  FIG. 5  with this smoothed image, structural components of the image may be rendered more robust and less susceptible to noise. While any suitable smoothing technique may be employed at step  150 , 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 (3 or 5 pixels in length) which is moved horizontally and vertically along the image until each pixel has been processed. 
   At step  152 , X and Y gradient components for each pixel are computed based upon the smoothed version of normalized image  74 . While several techniques may be employed for this purpose, in the presently preferred embodiment, 3×3 Sobel modules or operators  180  and  182 , illustrated in  FIG. 6 , are employed. As will be appreciated by those skilled in the art, module  180  is used for identifying the X gradient component, while module  182  is used for identifying the Y gradient component of each pixel. In this process, modules  180  and  182  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. 
   With these gradient components thus computed, at step  154  the gradient magnitude, Gmag, and gradient direction, Gdir, are computed. In the presently preferred technique, the gradient magnitude 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 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  180  and  182 , other modules such as the Roberts or Prewitt operators may be employed. Moreover, the gradient magnitude 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. 
   Based upon the gradient magnitude values determined at step  154 , a gradient histogram is generated as indicated at step  156 .  FIG. 7  illustrates an exemplary gradient histogram of this type. The histogram, designated by reference numeral  190 , is a bar plot of specific populations of pixels having specific gradient values. These gradient values are indicated by positions along a horizontal axis  192 , while counts of the pixel populations for each value are indicated along a vertical axis  194 , with each count falling at a discrete level  196 . The resulting bar graph forms a step-wise gradient distribution curve  198 . Those skilled in the art will appreciate that in the actual implementation the histogram of  FIG. 7  need not be represented graphically, but may be functionally determined by the image processing circuitry operating in cooperation with values stored in memory circuitry. 
   Histogram  190  is used to identify a gradient threshold value for separating structural components of the image from non-structural components. The threshold value is set at a desired gradient magnitude level. Pixels having gradient magnitudes at or above the threshold value are considered to meet a first criterion for defining structure in the image, while pixels having gradient magnitudes lower than the threshold value are initially considered non-structure. The threshold value used to separate structure from non-structure is preferably set by an automatic processing or “autofocus” routine as defined below. However, it should be noted that the threshold value may also be set by operator intervention (e.g. via input device  28 ) or the automatic value identified through the process described below may be overridden by the operator to provide specific information in the resulting image. 
   As summarized in  FIG. 5 , the process for identification of the threshold value begins at step  158  by selecting an initial gradient threshold. This initial gradient threshold, designated  200  in  FIG. 7  is conveniently set to a value corresponding to a percentile of the global pixel population, such as 30 percent. The location along axis  192  of the IGT value  200  is thus determined by adding pixel population counts from the left-hand edge of histogram  190  of  FIG. 7 , adjacent to axis  194  and moving toward the right (i.e., ascending in gradient values). Once the desired percentile value is reached, the corresponding gradient magnitude is the value assigned to the IGT. 
   At step  160 , a search is performed for edges of the desired structure. The edge search proceeds by locating the pixels having gradient magnitudes greater than the IGT value selected in step  158  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 IGT 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 presently preferred embodiment, an angle of 0.35 radians is used in this comparison step. If the 5×5 neighborhood count is greater than a preset number, 3 in the present embodiment, the pixel of interest is identified as a relevant edge pixel. At step  162 , a binary mask image is created wherein pixels identified as relevant edge pixels in step  160  are assigned a value of 1, while all other pixels are assigned a value equal to zero. 
   At step  164  small or noisy segments identified as potential candidates for structure are iteratively eliminated. Steps in control logic for eliminating these segments are summarized in FIG.  8 . Referring to  FIG. 8 , the process begins at step  210  where a binary image is obtained by assigning a value of 1 to pixels having a gradient magnitude value equal to or greater than a desired value, and a value of zero to all other pixels. This binary image or mask is substantially identical to that produced at step  162  (see FIG.  5 ). At step  212  each pixel having a value of 1 in the binary mask is assigned an index number beginning with the upper-left hand corner of the image and proceeding to the lower right. The index numbers are incremented for each pixel having a value of 1 in the mask. At step  214  the mask is analyzed row-by-row beginning in the upper left by comparing the index values of pixels within small neighborhoods. For example, when a pixel is identified having an index number, a four-connected comparison is carried out, wherein the index number of the pixel of interest is compared to index numbers, if any, for pixels immediately above, below, to the left, and to the right of the pixel of interest. The index numbers for each of the connected pixels are then changed to the lowest index number in the connected neighborhood. The search, comparison and reassignment then continues through the entire pixel matrix, resulting in regions of neighboring pixels being assigned common index numbers. In the preferred embodiment the index number merging step of  214  may be executed several times, as indicated by step  216  in FIG.  8 . Each subsequent iteration is preferably performed in an opposite direction (i.e., from top-to-bottom, and from bottom-to-top). 
   Following the iterations accomplished through subsequent search and merger of index numbers, the index number pixel matrix will contain contiguous regions of pixels having common index numbers. As indicated at step  218  in  FIG. 8 , a histogram is then generated from this index matrix by counting the number of pixels having each index number appearing in the index matrix. As will be apparent to those skilled in the art, each separate contiguous region of pixels having index numbers will have a unique index number. At step  220 , regions represented by index numbers having populations lower than a desired threshold are eliminated from the definition of structure as determined at step  162  of FIG.  5 . In a presently preferred embodiment, regions having a pixel count lower than 50 pixels are eliminated in step  220 . The number of pixels to be eliminated in this step, however, may be selected as a function of the matrix size, and the amount and size of isolated artifacts to be permitted in the definition of structure in the final image. 
   Returning to  FIG. 5 , with pixels for small segments eliminated from the binary mask created at step  162 , the number of pixels remaining in the binary mask are counted as indicated at step  166 . While the resulting number may be used to determine a final gradient threshold, 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, in a presently preferred embodiment a value of 4,000 is added to the binary mask count resulting from step  164  to arrive at a desired number of pixels in the image structure definition. This parameter 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 parameter, referred to in the present embodiment as the “focus parameter” may thus be varied to redefine the classification of pixels into structures and non-structures. 
   With the desired number of structure pixels thus identified, a final gradient threshold or FGT is determined as illustrated at step  168  in  FIG. 5 , based upon the histogram  190  as shown in FIG.  7 . In particular, the population counts for each gradient magnitude value beginning from the right-hand edge of histogram  190  are summed moving to the left as indicated by reference number  202 . Once the desired number of structural pixels is reached (i.e., the number of pixels counted at step  166  plus the focus parameter), the corresponding gradient magnitude value is identified as the final gradient threshold  204 . In the presently preferred embodiment, the FGT value is then scaled by 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 FGT, depending upon the image characteristics, the type and features of the structure viewable in the image, and so forth. 
   Based upon this scaled final gradient threshold, a new binary mask is defined by assigning pixels having values equal to or greater than the FGT a value of 1, and all other pixels a value of zero. At step  170  the resulting binary mask is filtered to eliminate small, isolated segments in a process identical to that described above with respect to step  164  and FIG.  8 . However, at step  170  rather than a four-connected neighborhood, a 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. 
   At step  172 , certain of the isolated regions may be recuperated to provide continuity of edges and structures. In the present embodiment, for example, if a pixel in the gradient image is above a second gradient threshold, referred to as GFT, and is connected (i.e. immediately adjacent) to a pixel which is above the FGT, the corresponding pixel in the binary image is changed from a 0 value to a value of 1. The value of the GFT may be set to a desired percentage of the FGT, and may be determined empirically to provide the desired degree of edge and structure continuity. This gradient following step is preferably carried out recursively to determine an initial classification of the pixels. 
   At step  174  in  FIG. 5 , the feature edges identified through the previous steps, representative of candidate structures in the image, are binary rank order filtered. 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. Steps in exemplary control logic for implementing the binary rank order filtering of step  174  are illustrated in FIG.  9 . 
   Referring to  FIG. 9 , the binary rank order filtering begins at step  230  with the binary mask generated and refined in the foregoing steps. At step  230 , circuit  24  determines whether each pixel in the binary mask has a value of 1. If the pixel found to have a value of 1 in the mask, a neighborhood count is performed at step  232 . In this neighborhood count, pixels in the binary mask having values of 1 are counted within a 3×3 neighborhood surrounding the structural pixel of interest. This count includes the pixel of interest. At step  234 , circuit  24  determines whether the count from step  232  exceeds a desired count m. In the present embodiment, the value of m used at step  234  is 2. If the count is found to exceed the value m the value of 1 is reassigned to the pixel of interest, as indicated at step  236 . If, however, the count is found not to exceed the value of m the pixel of interest is assigned the value of 0 in the mask as indicated at step  238 . Following steps  236  and  238 , or if the pixel is found not to have an original value of 1 in the mask at step  230 , control proceeds to step  240 . 
   At step  240 , circuit  24  reviews the structure mask to determine whether each pixel of interest has a value of 0. If a pixel is located having a value of 0, circuit  24  advances to step  242  to compute a neighborhood count similar to that described above with respect to step  232 . In particular, a 3×3 neighborhood around the non-structure pixel of interest is examined and a count is determined of pixels in that neighborhood having a mask value of 1. At step  244  this neighborhood count is compared to a parameter n. If the count is found to exceed the parameter n, the mask value for the pixel is changed to 1 at step  246 . If the value is found not to exceed n, the mask pixel retains its 0 value as indicated at step  248 . In the present embodiment, the value of n used in step  244  is 2. Following step  246  or step  248 , the resulting structure mask  76 , M structure , contains information identifying structural features of interest and non-structural regions. Specifically, pixels in structure mask  76  having a value of 1 are considered to identify structure, while pixels having a value of 0 are considered to indicate non-structure. 
   With the structure of the image thus identified, orientation smoothing of the structure, as indicated at step  128  of  FIG. 4 , is carried out through logic such as that illustrated diagrammatically in FIG.  10 . As shown in  FIG. 10 , the orientation smoothing of image structure begins by looking at the structure pixels of normalized image  74 , as determined via application of structure mask  76 , and may proceed in different manners depending upon the type of smoothing desired. In particular, based upon an operator input designated  260  in  FIG. 10 , a logical decision block  262  directs signal processing circuit  24  to either dominant orientation smoothing as indicated at reference numeral  264  or local orientation smoothing as indicated at  266 . If dominant orientation smoothing is selected, the intensity values for the structural pixels are processed as summarized below with respect to  FIG. 11 , to generate a binary mask M′. Following iterations of the procedure outlined below with reference to  FIG. 11 , the values of mask M′ are evaluated at step  268 , and smoothing is performed on the structure intensity values by use of multipliers α and β resulting in values which are then summed as indicated at blocks  270 ,  272  and  274  of FIG.  10  and as summarized in greater detail below. 
   To explain the dominant orientation smoothing step of  264 , reference is now made to FIG.  11 . As illustrated in  FIG. 11 , the dominant orientation smoothing begins with assigning directional indices to each pixel identified as a structural pixel in structure mask  76 . In the present embodiment, one of four directional indices is assigned to each structural pixel in accordance with the statistical variances for each pixel, as shown in FIG.  12 . As illustrated in  FIG. 12 , within a local neighborhood  300  surrounding each structural pixel, statistical variances for pixel kernels in four directions are computed by reference to the normalized intensity values of the surrounding pixels. The direction of the minimum variance is selected from the four computed values and a corresponding directional index is assigned as indicated by reference numeral  302  in FIG.  12 . In the present embodiment these directional indices are assigned as follows: “1” for 45 degrees; “2” for 135 degrees; “3” for 90 degrees; and “4” for 0 degrees. These steps are summarized as  282  and  284  in FIG.  11 . At step  286  a local area threshold value is assigned based upon the image matrix size. In the present embodiment, a local area threshold of 6 is used for 256×256 pixel images, a value of 14.25 is used for 512×512 pixel images, and a value of 23 is used for 1024×1024 pixel images. 
   At step  288 , a binary mask M′ is initialized with zero values for each pixel. At step  290  a dominant orientation is established for each structural pixel by examining the directional indices set in step  284  within a local neighborhood surrounding each structural pixel. In this process, the directional indices found in the local neighborhood are counted and the pixel of interest is assigned the directional index obtaining the greatest count (or the lowest index located in the case of equal counts). 
   In the present embodiment, both the dominant direction and its orthogonal direction are considered to make a consistency decision in the dominant orientation smoothing operation. In terms of  FIG. 12 , these directions are 1 and 2, or 3 and 4. It has been found that considering such factors substantially improves the robustness of the dominant orientation determination in the sense of being consistent with the human visual system (i.e. providing reconstructed images which are intuitively satisfactory for the viewer). 
   The consistency decision made at step  290  may be based upon a number of criteria. In the present embodiment, the image is smoothed along the dominant direction (i.e. the direction obtaining the greatest number of counts in the neighborhood) if any one of the following criteria is met: (1) the number of counts of the orientation obtaining the greatest number is greater than a percentage (e.g. 67%) of the total neighborhood counts, and the orthogonal orientation obtains the least counts; (2) the number of counts of the orientation obtaining the maximum counts is greater than a smaller percentage than in criterion (1) (e.g. 44%) of the total neighborhood counts, and the orthogonal direction obtains the minimum number, and the ratio of the counts of the dominant direction and its orthogonal is greater than a specified scalar (e.g. 5); or (3) the ratio of the dominant direction counts to its orthogonal direction counts is greater than a desired scalar multiple (e.g. 10). 
   In the present embodiment, the neighborhood size used to identify the direction of dominant orientation in step  290  is different for the series of image matrix dimensions considered. In particular, a 3×3 neighborhood is used for 256×256 pixel images, a 5×5 neighborhood is used for 512×512 pixel images, and a 9×9 neighborhood is used for 1024×1024 pixel images. 
   At step  292 , the count determined in the searched neighborhood for each pixel is compared to the local area threshold. If the count is found to exceed the local area threshold, signal processing circuit  24  advances to step  294 . At that step, the intensity value for each structural pixel is set equal to the average intensity of a 3×1 kernel of pixels in the dominant direction for the pixel of interest. Subsequently, at step  296 , the value of a corresponding location in the binary matrix M′ is changed from 0 to 1. If at step  292 , the count is found not to exceed the local area threshold for a particular pixel, the intensity value for the pixel of interest is set equal to a weighted average as indicated at step  298 . This weighted average is determined by the relationship:
 
weighted avg=(1/1 +p )(input)+( p/ 1 +p )(smoothed value); 
 
where the input value is the value for the pixel of interest at the beginning of routine  264 , p is a weighting factor between 1 and 200, and the smoothed value is the average intensity of a 3×1 kernel in the dominant direction of the pixel of interest. From either step  296  or  298 , circuit  24  returns to step  268  of FIG.  10 . Referring again to  FIG. 10 , at step  268 , the values of each pixel in the binary mask M′ are evaluated. If the value is found to equal zero, the corresponding intensity value is multiplied by a weighting factor α at step  270 . In the present embodiment, factor α is set equal to 0.45. At block  272  the resulting value is summed with the product of the normalized intensity value for the corresponding pixel and a weighting factor β as computed at step  274 . In the present embodiment, the factors α and β have a sum equal to unity, resulting in a value of β equal to 0.55.
 
   If at step  268  the value for a particular pixel is found to equal 1 in the binary mask M′, control advances to decision block  276 . Decision block  276  is also reached following the summation performed at block  272  as described above. In the present embodiment, the foregoing dominant orientation smoothing steps are performed over a desired number of iterations to provide sufficient smoothing and bridging between structural regions. At step  276 , therefore, circuit  24  determines whether the desired number of iterations have been completed, and if not, returns to step  264  to further smooth the structural regions. In the present embodiment, the operator may select from 1 to 10 such iterations. 
   As noted above, the orientation smoothing can proceed through an alternative sequence of steps for local orientation smoothing as noted at block  266  in FIG.  10 .  FIG. 13  illustrates exemplary steps in control logic for such local orientation smoothing. As with the dominant orientation smoothing, the local orientation smoothing begins with the normalized intensity values for the structural pixels. At step  310 , statistical variances for 3×1 pixel kernels about each structural pixel are calculated for each indexed direction (see  FIG. 12 ) as described above for the dominant orientation smoothing process. At step  312 , a ratio of the maximum/minimum statistical variances identified for each pixel in step  310  is computed. At step  314  this ratio for each structural pixel is compared to a parameter R, referred to as a relaxation factor for the local orientation filtering. In the present embodiment, the value of R can be set between 1 and 200. If at step  314  the variance ratio is found to exceed R, local orientation filtering is accomplished as indicated at step  316  by setting the intensity value for the structural pixel of interest equal to an average value for the 3×1 pixel kernel in the direction of the minimum variance. If at step  314  the ratio between the maximum and minimum variances for the pixel of interest is found not to exceed R, no local orientation smoothing is performed and circuit  24  advances to a point beyond step  316 . From this point, control returns to block  270  of FIG.  10 . 
   As illustrated in  FIG. 10 , at block  270  the intensity value for each structural pixel is multiplied by a weighting factor α, and combined at block  272  with the product of the normalized intensity value for the corresponding pixel and a weighting factor β produced at block  274 . As summarized above, at step  276 , circuit  24  determines whether the desired number of iterations has been completed and, if not, returns to the local orientation smoothing block  266 , to repeat the steps of  FIG. 13  until the desired number of iterations is complete. Once the desired iterations have been performed, the filtered image  78  resulting from the orientation smoothing is further filtered by the processes described below. 
   As summarized above with reference to  FIG. 4 , in parallel with orientation smoothing of the structure identified within the image, homogenization smoothing of non-structure is performed. The steps in a process for such homogenization smoothing are summarized in FIG.  14 . As shown in  FIG. 14 , the normalized intensity values for non-structural pixels are considered in this process. At step  330 , the mean neighborhood intensity value for each non-structural pixel is computed (taking into account the normalized values of structural pixels where these are included in the neighborhood considered). In the present embodiment, step  330  proceeds on the basis of a 3×3 neighborhood surrounding each non-structural pixel. This mean value is assigned to the pixel of interest and control advances to step  334 . At step  334  circuit  24  determines whether a desired number of iterations has been completed. If not, control returns to step  330  for further homogenization of the non-structural pixel intensity values. Once the desired number of iterations has been completed the homogenization smoothing routine of  FIG. 14  is exited. In the present embodiment, the operator may set the number of homogenization smoothing iterations from a range of 1 to 10. 
   The filtered image is further processed by orientation sharpening of the identified structure pixels as mentioned above with regard to  FIG. 4 , and as illustrated in greater detail in FIG.  15 . As shown in  FIG. 15 , in the present embodiment, the sharpening is performed only for pixel values which are above a preset lower limit, as indicted at decision block  340 . This limit, which may be set to a multiple of the FGT (e.g. 2×FGT), thus avoids enhancement of structural pixels which should not be sharpened. If a structural pixel has a value above the limit, the orientation sharpening sequence begins at step  342  where Laplacian values for each such structural pixel are computed in the indexed directions shown in FIG.  12  and described above. The Laplacian values may be computed from the formula
 
 E ( k )=2.0 *I ( k )− I ( k− 1)− I ( k +1); 
 
where k is the structural pixel of interest, “k−1” is the pixel preceding the pixel of interest in the indexed direction, and “k+1” is the pixel succeeding the pixel of interest in the indexed direction. E(k) is the edge strength and I(k) is the intensity value at the structural pixel of interest. It should be noted that the Laplacian values computed at step  342  are based upon the filtered intensity values (i.e., smoothed values for structure). At step  344 , the maximum of the four Laplacian values for each structural pixel is then saved to form an edge mask, M edge . In forming M edge  border pixels in a given image are set to 0 for the subsequent steps.
 
   At step  346 , for each structural pixel of M edge , the statistical variances and mean values for a 3×1 pixel kernel are computed in the indexed directions shown in  FIG. 12 , again using the filtered (i.e., homogenized and smoothed) values for each pixel in the relevant neighborhoods. The direction of minimum variance for each structural pixel is then identified from these values, and the mean value in the direction of minimum variance is saved for each pixel as indicated at step  348  to form a smoothed edge mask, M smooth edge . At step  350 , the mean value in the direction of minimum variance for each structural pixel of M smooth edge  is multiplied by a configurable parameter. In the present embodiment, the value of the configurable parameter may be set to any number greater than 0 depending on the application. In general, the higher the value of the configurable parameter selected, the greater the overall sharpness of strong edges in the final image. 
   At step  351 , each pixel, after multiplication, is compared to both a minimum and a maximum threshold value. Pixels which exceed the maximum threshold value are set equal to the maximum threshold value. Likewise, pixels which are less than the minimum threshold value are set equal to the minimum threshold value. At step  353 , the resulting weighted values, represented in M smooth edge , are added to the initial filtered values for the corresponding structural pixel to form a new filtered image  78 . If the resulting intensity for the structural pixel is less than 0, its intensity is set to 0. In the present preferred embodiment, if the resulting intensity for the structural pixel exceeds 4,095, its intensity is set to 4,095. This upper limit is configurable to any number greater than 0. The effect of the aforementioned operations is to more strongly enhance weaker edges while providing a more limited enhancement to edges which are already strong. The resulting filtered image values are then further processed as described below. 
   Following orientation sharpening of the structural features of the image and homogenization smoothing of non-structure regions, the entire image is again renormalized as indicated at step  134  in FIG.  4 . While various methods may be used for this renormalization, in the present embodiment the global average pixel intensity in filtered image  78  following steps  130  and  132  is computed, and a normalization factor is determined based upon the difference between this average value and the average value prior to the filtration steps described above. The new normalized intensity value for each pixel is then determined by multiplying this normalization factor by the filtered pixel intensity, and adding the global minimum intensity value from the original data to the product. 
   The resulting renormalized image  80 , denoted I renormal  in  FIG. 4 , is then expanded by the same factor, X, by which the input image  70  was shrunk. Structure mask  76  is also expanded by this time by the same factor. Various suitable interpolation techniques may be used to accomplish this expansion including cubic interpolation. The products of expansion step  135  are thus an expanded structure mask  83  and an expanded image  82 , each with the same dimensions as original input image  70 . 
   Expanded image  82  is processed to blend texture from input image  70  into expanded image  82  via expanded structure mask  83 , as can be seen in  FIG. 4  at step  136 . This texture blending step is summarized in FIG.  16 . Expanded structure mask  83  allows texture blended with pixels from expanded image  82  to be weighted differently depending upon whether a pixel is defined as structure or non-structure. 
   In general, the steps of  FIG. 16  tend to add more or less original texture depending upon the gradient magnitude of the pixels. In particular, at step  360 , the gradient magnitude for each pixel of interest is compared to a threshold value T. In the present embodiment, this threshold is set to a value of 300. If the gradient is found not to exceed the threshold, the pixel intensity value is multiplied by a value “a” at step  362 . The resulting product is added at step  364  to the product of the raw intensity value for the pixel (prior to the shrinking at step  123  of  FIG. 4 ) multiplied by a value equal to “1−a” at step  366 . The resulting weighted average is assigned to the pixel. 
   If at step  360 , the gradient magnitude value for a pixel is found to exceed the threshold value T, the pixel intensity is multiplied by a factor “b”, as noted at step  368 . The resulting product is then added at step  370  to the product of the raw intensity for that pixel and a multiplier equal to “1−b” determined at step  372 . In the present embodiment, the value of “b” may be set within a range from 0 to 1, with the value of “a” being set equal to 1.5 times the value of “b”. As will be apparent to those skilled in the art, the weighted averaging performed by the steps summarized in  FIG. 16  effectively adds texture to provide an understandable environment for the structures identified as described above. By performing the comparison at step  360 , the process effectively adds less original texture for pixels having low gradient values, and more original texture for pixels having higher gradient values. Where desired, the values of “a” and “b” may be set so as to increase or decrease this function of the process. The product of the differential textural blending of step  136  is blended image  84 . 
   Next, in step  140 , intensity irregularities are corrected in the vicinity of the CT circle using a CT mask created in step  138 , as illustrated in FIG.  17 . In step  380 , the first step in creating the CT mask is calculating the minimum intensity value in blended image  84 . All pixels equal to the minimum are labeled  0  in step  382 . Also in step  382 , all pixels greater than the minimum form mask A. Next, in step  384 , a binary erosion of radius  1  is performed on mask A to obtain eroded mask B. In step  386 , pixels in the difference ring between mask A and mask B are labeled  1 . Next, in step  388 , a binary erosion of radius  1  is performed on mask B to obtain eroded mask C. In step  390 , pixels in the difference ring between mask B and mask C are labeled  2 . Finally, in step  392 , those pixels in mask C are labeled  3 . 
   In the preferred embodiment, the resulting CT mask, illustrated as  FIG. 18  is composed of an inner circle, two larger concentric rings and an outer region, each labeled differently. Inner circle  396  is labeled  3 . Small ring  398  is labeled  2 . Large ring  400  is labeled  1  and outer region  402  is labeled  0 . 
   Returning to  FIG. 4 , in step  140  the extracted CT mask  86  is then used to correct intensity values in the vicinity of the CT circle. In the preferred embodiment each labeled area is differentially processed according to the following steps. Pixels in outer region  402  are set to the value of corresponding pixels from pre-processed image  71 . Pixels in large ring  400  are set to a value equal to (1−α)(corresponding pixel from pre-processed image  71 )+(α)(corresponding pixel from blended image  84 ) where 0&lt;α&lt;1. Pixels in small ring  398  are set to a value equal to (1−β)(corresponding pixel from pre-processed image  71 )+(β)(corresponding pixel from blended image  84 ) where 0&lt;β&lt;1. Generally β 0  will be greater than α. Pixels in inner circle  396  remain equal to the value of corresponding pixels from blended image  84 . The intensity value correction results in pixels near the center of the image retaining their full filtered values while pixels nearer to the edge of the circle are combined with their pre-filtered values in proportion to their distance from the center. The pixels farthest from the center of the image receive the greatest correction back to their pre-filtered values. The result of correction step  140  is corrected image  88 . 
   In step  142 , the minimum intensity value added to initial image  70  in step  116  is algebraically subtracted from all of the intensity values of corrected image  88 . Step  142  serves to return the image intensity back to the initial format and results in offset image  90 . Finally, in step  144 , a filter strength dependent constant is subtracted from all intensity values of offset image  90 . The constant is empirically determined based upon the filter settings. Step  144  is performed to avoid a CT number change in large uniform image regions as a result of the filtering process. Examples of filter strength types and their empirically determined constant values in the preferred embodiment may be seen in the following table: 
   
     
       
             
             
             
           
         
             
                 
                 
             
             
                 
               FILTER TYPE 
               VALUE 
             
             
                 
                 
             
           
           
             
                 
               Low Normal 
               0 
             
             
                 
               Medium Normal 
               1 
             
             
                 
               High Normal 
               2 
             
             
                 
               Low Special 
               1 
             
             
                 
               Medium Special 
               1 
             
             
                 
               High Special 
               2 
             
             
                 
                 
             
           
        
       
     
   
   Subtraction of the filter strength dependent constant in step  144  produces final image  92 . Final image  92  may be displayed, stored in memory, or further processes and analyzed.