Abstract:
Many image processing problems are concerned with determining measurements of an anomalous area in an image. Most automated systems suffer from low specificity, which may reduce their acceptance. An example embodiment of the present invention relates to a method and corresponding apparatus for providing measurement data of a region of interest in an image in a graphical user interface environment. The example embodiment locates a pair of edges in multiple dimensions of a region of interest selected by a user, calculates a center position between respective edges, and iterates until a convergence or divergence is determined. Linear calculation may be employed for rapid results, allowing an advance in speed of image processing over current techniques. In a case of convergence, the measurement data is reported. In a case of divergence, a failure state is reported. By reporting divergence, the example embodiment achieves high specificity, thereby reducing the number of false positive reports.

Description:
BACKGROUND OF THE INVENTION 
       [0001]    A large number of image processing problems are concerned with determining the structure of an anomalous area in a larger field of regard. In the context of medical imaging, there is great interest in characterizing already identified regions of interest. Image processing methods, such as computer aided diagnosis (CAD), may also be used to aid diagnosis and confirm or facilitate interpretation and findings by an image screener, such as a radiologist. 
         [0002]    Most available CAD methods are designed to have high sensitivity. However, while having high sensitivity, most CAD systems suffer from low specificity. Thus, while these systems may highlight anomalous regions correctly (i.e., correct diagnosis or true positive), they may also incorrectly highlight healthy sections (i.e., incorrect diagnosis or false positive). Unfortunately, having low specificity may reduce the acceptance of CAD methods by the medical community since a medical professional (e.g., radiologist) may need to review the findings of a CAD module to identify false positives. 
       SUMMARY OF THE INVENTION 
       [0003]    A method or corresponding apparatus in an example embodiment of the present invention provides measurement data of a region of interest of an image in a graphical user interface (GUI) environment by locating a pair of edges in first and second dimensions of the region of interest. The region of interest is selected by the user in a pixel field presented in the GUI. The example embodiment calculates a center position between respective edges in the first and second dimensions and continues to locate updated edges and calculate updated center positions until a convergence or divergence is determined. In the event that convergence is determined, the example embodiment computes measurement data based on a final center position relative to edges associated with the final center position. The example embodiment reports the measurement data in the event that convergence is detected or reports a failure state in the event that divergence is detected. 
         [0004]    It should be understood that the region of interest may be selected by a user or may be selected by a computer in an automated or semi-automated manner, such as through user-configured criteria. If selected by a computer, the computer may select the region of interest in a predetermined or arbitrary manner. Convergence or divergence is determined thereafter as though the user selected the region of interest. 
         [0005]    The example embodiment may compensate for noise during the locating of the pairs of edges. In order to compensate for noise, the example embodiment may employ at least one of the following: averaging multiple pixels at each pixel position along each dimension during the locating of the edges or smoothing multiple pixels at each pixel position along each dimension during the locating of the edges. 
         [0006]    The example embodiment may locate the pair of edges as a function of identifying a pixel value deviation corrected for noise. In order to locate the pair of edges, the example embodiment may transition outward from the center position along a single pixel line in the first and second dimensions until the respective pairs of edges are identified. The example embodiment may locate the pair of edges using a gray-scale of the GUI and not a gray-scale of pixel values of the pixel field. The example embodiment may locate the pair of edges as a function of employing information stored in a Digital Imaging and Communications in Medicine (DICOM) image. 
         [0007]    The example embodiment may compute the measurement data based on at least one of the following: computing a size of the region of interest, computing a density of the region of interest optionally by computing the density of the region of interest as a function of using a gray-scale of the GUI, computing a texture of the region of interest, computing a position of the region of interest within the pixel field, computing a distance of the region of interest (e.g., located center position) from the initial center position, or computing a distance of the region of interest from a second region of interest (e.g., center position-to-center position). 
         [0008]    The pixel field presented in the GUI may be a radiological image, computed tomography image, or X-ray image and the region of interest selected by the user may be an anomaly detected in a radiological image. 
         [0009]    The example embodiment may locate the pair of edges in response to a selection of a point in the region of interest by the user, repeat the locating and calculating for a selectable number of times, repeat the locating and calculating in the first or second dimensions but not both, or allow divergence after repeating the locating and calculating for a predetermined number of times. The first and second dimensions may be orthogonal dimensions, and the region of interest may be non-homogenous. 
         [0010]    The example embodiment may calibrate the measurement data based on a resolution of the screen. The example embodiment may report the measurement data using methods available in the art such as: superimposing the measurement data on the pixel field, transmitting the measurement data via a data network, or storing the measurement data to a storage unit and optionally storing the measurement data in an electronic format. 
         [0011]    Another example embodiment of the present invention relates to a method and corresponding apparatus for training an image screener. The example embodiment presents the image screener with an image in a GUI that includes a pixel field having multiple potential regions of interest. The example embodiment determines whether convergence or divergence occurs in an attempt to locate a center of a region of interest selected by the user in an automated manner. The example embodiment reports to the user whether the selected region of interest resulted in a convergence or divergence to enable the user to make future selections of regions of interest that will result in an automated convergence. The image screener may be a radiologist, and the image may be a radiological image, including a computed tomography image. 
         [0012]    Another example embodiment of the present invention relates to a computer-readable medium having computer-readable code stored thereon, which, when executed by a processor, causes the processor to present an image in a GUI including a pixel field having multiple potential regions of interest. The example embodiment determines whether convergence or divergence occurs in an attempt to locate a center of a region of interest selected by the user in an automated manner and reports to the user whether the selected region of interest resulted in a convergence or divergence. This feedback trains the user to make future selections of regions of interest that will result in an automated convergence. 
         [0013]    Yet another example embodiment of the present invention relates to a method and corresponding apparatus for detecting edges surrounding a point in an image. The example embodiment determines the edges as a function of linear walks around a starting point in a plurality of dimensions in the image. The example embodiment computes a center of a region bounded by the edges, updates the starting point to be a center for the region bounded by the edges, and repeats the finding, computing, and updating until a convergence or divergence of the center point is determined. The example embodiment computes features of the region bounded by the edges and reports the computed features in the event convergence is detected or reports an ill-defined border or failure state in an event divergence is detected. 
         [0014]    The example embodiment may locate the pair of edges as a function of displayed pixel data on a graphical user interface (gray scale value or color) or raw image data in computer memory. The region bounded by the edges may be a subset of a larger image. The example embodiment may incrementally increase the size of the region bounded by the edges to include the larger image. The starting point may be in a region of interest initially detected by a user or computer program such as a radiologist or computer-assisted diagnosis program for the case of a medical image. 
         [0015]    The example embodiment may filter and compensate for noise during the linear walk. The example embodiment may perform a single linear walk for a 2-dimensional image. The number of pixels filtered or processed may be lower in number than a total number of pixels in the image. 
         [0016]    The features of the region of interest include diameter, area, density, extrema, texture, centroid, and convex hull. The example embodiment may calculate the features of the region of interest along fewer dimensions than number of dimensions in the image. 
         [0017]    The image may be a medical image including a Digital Imaging and Communications in Medicine (DICOM), any photographic image, a video frame, or any numerical data that may be displayed as an image. 
         [0018]    The example embodiment may report the measurement data by displaying the data as image markup, transmitting the measurement data via a data network, or storing the measurement data locally in electronics or on paper. 
         [0019]    The example embodiment may display the edges or features, if found, on a screen and periodically update the edges or features as a function of mouse motion or changing image. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0020]    The foregoing will be apparent from the following more particular description of example embodiments of the invention, as illustrated in the accompanying drawings in which like reference characters refer to the same parts throughout the different views. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating embodiments of the present invention. 
           [0021]      FIG. 1A  illustrates an example embodiment of the present invention for providing measurement data of a region of interest of an image in a graphical user interface (GUI). 
           [0022]      FIG. 1B  illustrates an example embodiment of the present invention for training an image screener in making future selections of regions of interest of an image to obtain automated convergence. 
           [0023]      FIG. 2  illustrates an example of a user-selected point within a region of interest in a pixel field according to an example embodiment of the present invention. 
           [0024]      FIG. 3  illustrates an example embodiment of the present invention for providing measurement data of a region of interest. 
           [0025]      FIG. 4  illustrates procedures that may be used by an example embodiment of the present invention to locate a pair of edges of a region of interest. 
           [0026]      FIG. 5  is a detailed description of the procedures that may be used by an example embodiment of the present invention to locate a center position and a pair of edges of a region of interest. 
           [0027]      FIG. 6  is an illustration of an example embodiment of the present invention for providing measurement data of a region of interest of an image. 
           [0028]      FIG. 7  is a high-level flow diagram of an example embodiment of the present invention. 
           [0029]      FIG. 8  is a high-level flow diagram of an example embodiment of the present invention for providing measurement data. 
           [0030]      FIG. 9  is a flow diagram of an example embodiment of the present invention for providing measurement data. 
           [0031]      FIG. 10  is an example embodiment of the present invention for reporting measurement data. 
       
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       [0032]    A description of example embodiments of the invention follows. 
         [0033]      FIG. 1A  illustrates an example embodiment  100  of the present invention for providing measurement data of a region of an interest of a pixel field (e.g., an image) in a graphical user interface (GUI). The pixel field includes images such as radiological images, computed tomography images, or X-ray images. In this example embodiment, a screener  115  (e.g., radiologist or physician) monitors images (e.g., radiological images) and detects a region (or regions) of interest  125  in these images. The region of interest may be an anomaly (or anomalies), such as tumors, aneurisms, etc., found in radiological images. In one example embodiment, the screener may monitor radiological images such as Computed Tomography (CT) images. Other images, such as X-ray tomography, Magnetic Resonance Imaging, Digital Imaging and Communications in Medicine (DICOM) images, may also be employed. The images may be presented to the screener  115  on a display screen, such as a computer monitor  110 . The images may be presented to the screener in a GUI. In this example embodiment, the screener  115  employs a peripheral device (e.g., a computer mouse or a computer keyboard  130 ) to select an initial point  140  positioned within the region of interest  125 , as understood in the art. 
         [0034]    Various other methods may be used to select the initial point  140  within the region of interest  125 . For example, the screener  115  may press a key on a computer keyboard  130  or receive the initial point  140  from other modules or applications. In another example, an automated application that detects a region of interest  125  and automatically selects an initial point  140  within the region of interest  125  may be employed. 
         [0035]    The example embodiment employs the selected initial point  140  to determine features of the region of interest  125 , such as size, density, texture, diameter, area, centroid, convex hull, and so forth. In order to look for the features of the region of interest  125 , the example embodiment employs an iterative method that starts at the selected initial point  140  and searches for boundaries of the region of interest  125 . The region of interest  125  may be homogenous or non-homogenous. After performing the search, the example embodiment may reach a state of convergence or divergence. In the event a state of convergence  150  is reached, the example embodiment reports measurement data  152  and determined features  154  of the region of interest  125 . In the event a state of divergence is reached  160 , the example embodiment reports divergence and may prompt the screener  115  to select an alternative initial point within the region of interest  125 . The reporting of convergence  150  or divergence  159  may be done using a reporting module  160 . 
         [0036]    As used herein, convergence  150  is obtained when the determined features  154  of the region of interest  125  remain within a certain limit. In order to determine convergence, the example embodiment may determine the differences between the measurement data or determined features obtained from any given iteration to the measurement data or determined features obtained from that iteration&#39;s previous or subsequent iteration. If the determined differences between the measurement data from consecutive iterations is within a predetermined range or less than a predetermined threshold, the example embodiment may continue to iterate or declare convergence. If the change between the measurement data from consecutive iterations is outside of the predetermined limit over a set number of iterations, the example embodiment declares divergence. 
         [0037]      FIG. 1B  illustrates an example embodiment  101  of the present invention for training an image screener  115  in making future selections of regions of interest  125  in an image to obtain automated convergence. 
         [0038]    The image may include radiological images, computed tomography images, or X-ray images. In this example embodiment  101 , a screener  115  (e.g., radiologist or physician) monitors images (e.g., radiological images) and detects region (or regions) of interest  125  in these images. The region of interest may be an anomaly (or anomalies), such as tumors, aneurisms, etc., found in radiological images. The images may be presented to the screener  115  on a display screen, such as a computer monitor  110 . The images may be presented to the screener in a GUI. In this example embodiment  101 , the screener  115  employs a peripheral device (e.g., a computer mouse or a computer keyboard  130 ) to select an initial point  140  positioned within the region of interest  125 , as understood in the art. 
         [0039]    The user  115 , after being presented with the image, selects an initial center point  140  within the region of interest  125 . The example embodiment  101  employs the selected point to search for a center position of the region on interest  125 . The example embodiment  101  determines if convergence  150  or divergence  159  is obtained. The example embodiment  101  reports to the user  115  if convergence  150  or divergence  159  is obtained to train the user to make future selections of the region of interest  125 . For example, if the user  115  selects an initial center point  140  close to the center of the region of interest  125  and convergence is obtained, the user is trained to make his/her future selections at or near the center of the region of interest  125 . Whereas, if the user  115  selects an initial center point  140  near the boundary of the region of interest  125  and divergence is obtained, the user is trained to make his/her future selections away from the boundaries of the region of interest  125 . 
         [0040]      FIG. 2  illustrates an example of a user-selected point  230  within a region of interest  225  in a pixel field  210  according to an example embodiment  200  of the present invention. The pixel field  210  may include a small portion of pixels from a larger pixel field or image, such as a medical image (i.e, radiological), semiconductor or steel screening images, or environmental monitoring images (e.g., remote sensing image). The pixel field  210  may include a plurality of regions of interest  225  (e.g., anomalous regions or tumors in medical images). Regardless of the nature of these images, one possible goal of processing is to extract, from the images, features that describe the structure of the region of interest. 
         [0041]    In order to obtain the features from a region of interest, a screener (not shown), such as a radiologist, selects a point  230  within the region of interest  225 . The example embodiment  200  employs the selected point  230  as an initialization point. The example embodiment  200  employs the selected point  230  and iterates until a state of convergence or divergence is reached.  FIGS. 3-5  graphically illustrate an embodiment that reaches a state of convergence. 
         [0042]      FIG. 3  illustrates an example embodiment  300  of the present invention for providing measurement data of a region of interest  320 . The region of interest  320  may include a plurality of pixels (in two dimensions (2D)) or voxels (in three dimensions (3D)). In a 2D imaging case, the pixels forming the region of interest  320  pixels may be a subset of a pixel field  310  such as a radiological image. 
         [0043]    The example embodiment  300  provides the measurement data of the region of interest  320  by using an initial user-selected (or computer-selected) point  340  to locate a pair of edges  342 ,  344  in first and second dimensions  362 ,  364  (e.g., X and Y) of the region of interest  320 . The first and second dimensions  362 ,  364  may be orthogonal dimensions. In one embodiment, the edges in the pair of edges  342 ,  344  is found through linear searches, described in detail below in reference to  FIGS. 3 and 4 . A center position between the located pair of edges  342 ,  344  is calculated, and the process repeats to use the calculated center positions to find a next position from which to identify a next pair of edges. The example embodiment continues to make iterations and locates updated pairs of edges and updated center locations. 
         [0044]    In order to locate the updated center locations, the example embodiment  300  may calculate a center position  360  between the respective edges in the first  362  and second  364  dimensions. The example embodiment  300  may find the updated center position  360  based on discovering a structure, such as an ellipse  350  or a circle, that best fits the region contained between the located edges in the first  362  and second  364  dimensions. The example embodiment  300  may determine the dimension of the ellipse  350  based on calculating the largest dimensions of the area surrounded by the first  362  and second  364  dimensions and designing an ellipse  350  whose horizontal and vertical axes are similar to the largest dimensions. The example embodiment  300  determines the updated center location as a function of the center (i.e., intersection point of the axes) of the ellipse. The example embodiment  300  continues to search for the first and second pair of edges and updates the center location until convergence or divergence is determined. The example embodiment  300  computes the measurement data based on a final center position  360  relative to edges associated with the final center position  360  in the event convergence is determined. In the event divergence is detected, the example embodiment reports a failure state. 
         [0045]      FIG. 4  illustrates procedures that may be used by an example embodiment  400  of the present invention to locate a pair of edges in first  450  and second  460  dimensions of the region of interest  420  selected by a user in a pixel field  410  presented in a GUI. 
         [0046]    Edge detection algorithms have been introduced and used in the art. These algorithms generally assume that a sharp change in the intensity of the pixels (or voxels in 3D) of an image most likely represents image features, such as discontinuities in depth and surface orientations and changes in material properties. In the case of medical images, a sharp change in the value of the pixels may represent discontinuities in shape or material properties. 
         [0047]    In order to locate the pair of edges, the example embodiment  400  may employ the discontinuities and deviations in the intensity of the pixels. The example embodiment  400  may locate the pair of edges based on transitioning outward from the center position  440  and determining the deviations in the neighboring pixel intensities. If a sharp change in the intensity of the pixels is detected (i.e., deviation in pixel intensities is larger than a predetermined intensity), the example embodiment  400  employs the position of the pixel responsible for the sharp discontinuity to determine an edge in the first dimension  450 . Similarly, the example embodiment  400  may employ the discontinuities in the pixel intensities to determine a second edge in the second dimension  460 . 
         [0048]    The example embodiment  400  compensates for noise during the locating of the pairs of edges (not shown). The example embodiment  400  employs methods known in the art such as averaging the intensities of multiple pixels at each pixel position along each dimension or smoothing the intensities of multiple pixels at each pixel position along each dimension to compensate for noise. The example embodiment locates the pair of edges based on identifying discontinuities and deviations in the value of the pixels corrected for noise. 
         [0049]    The example embodiment  400  may locate the pair of edges based on the pixel values obtained from the gray scale of the image presented in the GUI. The example embodiment  400  may locate the pair of edges based on other image information (i.e., other than pixel values), such as information stored in a Digital Imaging and Communications in Medicine (DICOM) image. 
         [0050]      FIG. 5  is a detailed illustration of the procedures that may be used by an example embodiment  500  of the present invention to locate a center position  540  and a pair of edges  503 A,  503 B of a region of interest  501 . The example embodiment  500  provides measurement data of the region of interest  501  in an image in a GUI environment by locating a pair of edges  503 A,  503 B in first  510  and second  520  dimensions of the region of interest  501  selected by a user in a pixel field presented in a GUI (not shown). The user may select the region of interest  501  by selecting an initial center position  530  within the region of interest. Various methods in the art may be used to select the initial center position including, but not limited to, click of a mouse, pressing a key on a keyboard, and so forth. The initial center point  530  selected by the user (or computer) may be located anywhere within the region of interest  501 . In one example embodiment, an image screener, such as a radiologist, may select a point within a region of interest  501  such as an area suspected to be an anomaly (e.g., tumor, aneurism, etc.). 
         [0051]    The example embodiment  500  starts by transitioning outwards in the first  510  and second  520  dimensions from the initial center position  530 , where transitioning outward may be a linear walk on a pixel-by-pixel basis, skipping multiple pixels and determining whether an edge has been reached at each pixel tested. Additionally, the example embodiment  500  may perform only a single dimension linear walk for a 2-dimensional image. 
         [0052]    Some existing image processing techniques are linear in the number of pixels processed (i.e., also referred to as O(n), where O(•) is a complexity function and n denotes the total number of pixels in the image) and hence require that every pixel in the image be processed. In contrast, the example embodiment  500  need not process every pixel. Specifically, the example embodiment can produce useful results by processing O(a log b (n)) pixels, where a is a constant and b denotes the number of dimensions of the image. 
         [0053]    Alternatively, the method may select a pixel far away from the initial selected pixel  530  to be in an area expected to be outside the region of interest  501  and iteratively having or otherwise selecting an in-between point until an edge between the initial and “far away” points are found. In this latter case, if the in-between position has a gray-scale color closer to the initial positions gray-scale color, then a next position is selected between the in-between position and the “far away” position, and vice-versa. Using either technique, the example embodiment may employ methods known in the art, such as pixel intensity deviation, edge detection, image gradient intensity determination, etc., to locate the pair of the edges  501 A,  501 B in the first  510  and second  520  dimensions. 
         [0054]    The example embodiment  500  may use a gray-scale of the GUI instead of the gray-scale of the pixel intensities in the image to locate the pair of edges. 
         [0055]    When using a method such as pixel intensity deviation, the example embodiment  500  may determine the pair of edges  501 A,  501 B by calculating the differences between the intensities of the pixels in the first  510  and second  520  dimensions and determining an edge point (i.e., an edge pixel or an edge voxel) in the region of interest where a sharp change in the pixel intensities is detected. 
         [0056]    After determining the edge points in the first  510  and second  520  dimensions, the example embodiment determines an ellipse  555  whose dimensions best fit the area contained by the edge points  501 A,  501 B. In order to discover the ellipse used for fitting, the example embodiment may find an ellipse whose boundary contains both discovered edges  501 A and  501 B. The example embodiment  500  finds an updated center location based on finding the intersection point of the major and the minor axes of the ellipse  555 . The example embodiment continues to iterate and look for updated center locations until convergence or divergence is determined. 
         [0057]    The example embodiment  500  may determine convergence based on computing a difference (i.e., a residual value) between the center position measurements obtained in consecutive iterations. The residual value is compared against a threshold or a threshold range. If the residual value is within the threshold range (or is smaller than the threshold value), the example embodiment  500  either continues to calculate new center positions or declares convergence. Convergence may be declared based on comparing the residual value to a predetermined limit and declaring convergence if the residual value is negligible. If the residual value falls outside of the threshold range (or is larger than the threshold value), the example embodiment  500  declares divergence after some pre-set number of iterations. 
         [0058]      FIG. 6  is an illustration of an example embodiment  600  of the present invention for providing measurement data of a region of interest  615  of an image. In this example embodiment  600  a user, such as an image screener  610  or a radiologist, selects a region of interest within an image  605 . The user  610  may select the region of interest  615  by selecting a point  619  within the image, such that the selected point is a subset of the region of interest  615 . The example embodiment  600  employs the point  619  selected by the user  610  to obtain a pair of edges  621 ,  623  in first  618  and second  619  dimensions in a manner presented above and presented in detail immediately below. 
         [0059]    In one example embodiment, in order to find the pair of edges  621 ,  623 , a transition (also referred to as “walk”) from the selected pixel in the first  618  and second  619  dimensions is made. Specifically, the example embodiment  600  starts from the selected pixel  617 , walks to the next pixel in the first dimension  621  (e.g., the next pixel being the selected pixel&#39;s  617  immediate neighbor in the first dimension  618 ), and determines the pixel intensity deviation between the two pixels. Factors other than pixel intensity (e.g., texture) may also be used. The example embodiment  600  continues to walk in the first dimension and determine pixel intensity deviations. If a sharp change in value of deviation is determined, the example embodiment employs the point responsible for the sharp deviation to determine a first edge  621  in the first dimension  618 . A similar approach is taken to determine a second edge  623  in the second dimension  619 . Based on the discovered pair of edges  621 ,  623 , the example embodiment determines an ellipse  630  whose dimension best fits the region contained by the pair of edges  621  and  623 . An updated center location  635  is discovered based on the center of the ellipse  630  used for fitting. 
         [0060]    The example embodiment  600  continues to search  640  for updated pairs of edges  645  in the first  618  and second  619  dimensions and updates the location of the center position. The example embodiment  600  compensates for noise  647  during the locating of the pairs of edges. The example embodiment  600  employs methods known in the art such as averaging the intensities of multiple pixels at each pixel position along each dimension  649  or smoothing the intensities of multiple pixels at each pixel position along each dimension  648  to compensate for noise. The example embodiment  600  locates the pair of edges based on identifying a pixel value deviation corrected for noise. 
         [0061]    At each iteration, the example embodiment determines if convergence  660  or divergence  650  has been reached. As stated above, a state of convergence  660  is defined to be the state in which the determined features of the region of interest  615  remain within a certain limit. In order to determine convergence  660 , the example embodiment  600  may determine the differences between the measurement data (such as location of the pair of edges) obtained from any given iteration to the measurement data obtained from that iteration&#39;s previous or consecutive iteration. If the determined differences between the measurement data from consecutive iterations is within a predetermined range or less than a predetermined threshold, the example embodiment  600  may continue to iterate or declare convergence. If the change between the measurement data from consecutive iterations is outside of the predetermined limit, the example embodiment  600  declares divergence  650 . 
         [0062]    In order to decide between declaring convergence  660  and continuing iterations  640 , the example embodiment  600  may compare the differences between measurement data from consecutive iterations. If the difference between measurement data from consecutive iterations is within the predetermined threshold for convergence and small and negligible (the negligibility factor is determined based on comparing to a pre-set value), the example embodiment declares convergence  660 . If the difference between measurement data from consecutive iterations is within the predetermined threshold for convergence but larger than the pre-set value for determining negligibility, the example embodiment  600  continues to search  640  for updated pairs of edges in the first  618  and second  619  dimensions and update the location of the center position. 
         [0063]    If convergence  660  is detected, the example embodiment  600  reports measurement data including features of the region of interest  670 . In order to compute the measurement data, the example embodiment  600  may compute measurements such as: a size of the region of interest  615 , a density of the region of interest  615  optionally by computing the density of the region of interest as a function of using a gray-scale of the GUI (not shown), convex hull (not shown), centroid (not shown), diameter (not shown), area (not shown), a texture of the region of interest  615 , a position of the region of interest within the pixel field  615 , a distance of the region of interest  615  from the initial center position  617 , or a distance of the region of interest  615  from a second region of interest (not shown). 
         [0064]    The example embodiment  600  may report the measurement data using methods available in the art such as: superimposing the measurement data on the pixel field, transmitting the measurement data via a data network, or storing the measurement data to a storage unit and optionally storing the measurement data in an electronic format. 
         [0065]    If divergence is detected  650 , the example embodiment  600  reports failure  680 . In this case, the example embodiment  600  may prompt the user to select a new initial center position within the region of interest  615  or a different region of interest (not shown). 
         [0066]      FIG. 7  is a high-level flow diagram of an example embodiment  700  of the present invention. In this example embodiment  700 , a user, such as an image screener or a radiologist, detects a region of interest, such as a tumor in a medical image  710 . The user then selects a point within the region of interest  720 . The example embodiment  700  employs the selected point to search for a pair of edges in the first and second directions  730 . In some embodiments, the first and the second dimensions include horizontal  741  and vertical  746  dimensions. The example embodiment  700  then determines if convergence or divergence has been detected  750 . If divergence has been detected (i.e., convergence is not detected)  752 , the example embodiment  700  reports a failure  790 . If convergence is detected  751 , the example embodiment obtains a final center position for the region of interest based on the detected edges  760 , determines measurement information such as size, density, etc. for the region of interest  770 , and reports the measurement information  780 . 
         [0067]    If neither convergence nor divergence is detected  753  within a pre-selected or dynamically determined length of time or number of iterations, the example embodiment may continue to search for a pair of edges in the first and second directions  720  for an extended length of time or predetermined number of iterations, optionally with a mid-search notice of the extension being reported. The example embodiment  700  reports divergence if convergence is not met within the length of time or number of iterations. 
         [0068]    Given that the example embodiment  700  is arranged to iterate through a limited number of iterations and report convergence or divergence, the possibility of a false positive measurement report is significantly reduced. After each number of iterations, the example embodiment determines if convergence  751  or divergence  752  has been obtained. 
         [0069]      FIG. 8  is a high-level flow diagram of an example embodiment  800  of the present invention for providing measurement data. The example embodiment  800  provides measurement data of a region of interest in an image in a GUI environment by locating a pair of edges in first and second dimensions  820  of the region of interest selected by a user  810  in a pixel field presented in the GUI. The example embodiment  800  calculates a center position between respective edges in the first and second dimensions  830  and continues to locate an updated pair of edges as well as calculate updated center locations  840  until a convergence  850  or divergence  860  is determined. The example embodiment  800  computes measurement data based on a final center position relative to edges associated with the final center position in an event convergence is determined  870  and reports the measurement data  880  in the event convergence is detected or reports a failure state in an event divergence is detected  865 . 
         [0070]      FIG. 9  is a flow diagram of an example embodiment  900  of the present invention for providing measurement data of a region of interest in an image in a GUI environment  910 . In this example embodiment  900 , a locator module  920  locates a pair of edges in first and second dimensions of the region of interest selected by a user in a pixel field presented in a GUI. A calculation module  930  calculates an initial center position between respective edges in the first and second dimensions. The locator  910  and calculation  930  modules continue to locate and calculate until convergence or divergence is determined. A measurement data computation module  940  computes the measurement data based on a final center position relative to edges associated with the final center position in an event convergence is determined. A reporting module  950  reports the measurement data in the event convergence is detected or reports a failure state in the event divergence is detected. 
         [0071]      FIG. 10  is an example embodiment  1000  of the present invention for reporting measurement data. The example embodiment  1000  may receive images or imaging data (e.g., remote sensing images, medical images, screening images)  1050  from imaging sources such as imaging centers  1020 , and research centers  1030 . A module for reporting measurement data of a region of interest receives the image data  1050  and forwards the image data  1050  to image data processing centers  1040  for processing. The example embodiment may receive the imaging data  1050  in forms of unprocessed or processed images. The term unprocessed herein refers to images received at imaging centers or research centers without having been processed, whereas the term processed refers to images that have been processed to detect and select a region of interest. Regardless of its nature (i.e., processed or unprocessed), the imaging data  1050  is forwarded to an imaging data processing center  1040 . The imaging data processing center  1040  processes the imaging data  1050  and reports measurement data of region(s) of interest in the imaging data. The report information  1060  may include features of region(s) of interest such as convergence, divergence, failure state, density, size, location, etc. The example embodiment  1010  may report the measurement data using methods available in the art such as superimposing the measurement data on the imaging data, transmitting the measurement data via a data network, or storing the measurement data to a storage unit and optionally storing the measurement data in an electronic format. The image processing centers  1040  may store the imaging data  1050  or the reporting data  1060  in a storage device  1045  for future processing or reporting. The image processing centers  1040  transmit the measurement data to the module for reporting measurement data  1010 , which in turn reports the reporting data  1060  back to the imaging  1020  and research  1030  centers. 
         [0072]    It should be understood that procedures, such as those illustrated by flow diagram or block diagram herein or otherwise described herein, may be implemented in the form of hardware, firmware, or software. If implemented in software, the software may be implemented in any software language consistent with the teachings herein and may be stored on any computer-readable medium known or later developed in the art. The software, typically, in form of instructions, can be coded and executed by a processor in a manner understood in the art. 
         [0073]    While this invention has been particularly shown and described with references to example embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the invention encompassed by the appended claims.