Patent Document

BACKGROUND OF THE INVENTION 
     The present invention relates generally to segmentation masks resulting from nuclear magnetic resonance imaging and, in particular, relates to a method and apparatus for fitting a smooth boundary to a segmentation mask. 
     When a substance such as human tissue is subjected to a uniform magnetic field (polarizing field B 0 ), the individual magnetic moments of the spins in the tissue attempt to align with this polarizing field, but precess about it in random order at their characteristic Larmor frequency. If the substance, or tissue, is subjected to a magnetic field (excitation field B 1 ) which is in the x-y plane and which is near the Larmor frequency, the net aligned moment, M z , may be rotated, or “tipped”, into the x-y plane to produce a net transverse magnetic moment M t . A signal is emited by the excited spins after the excitation signal B 1  is terminated. This signal may be received and processed to form an image. 
     When utilizing these signals to produce images, magnetic field gradients (G x  G y  and G z ) are employed. Typically, the region to be imaged is scanned by a sequence of measurement cycles in which these gradients vary according to the particular localization method being used. The resulting set of received NMR signals are digitized and processed to reconstruct the image using one of many well-known reconstruction techniques. 
     The prognosis of patients with a wide variety of cardiac diseases, for example, has been closely linked to the performance of the heart as indicated by measurements such as wall thickening, wall motion, and myocardial mass. Accurate quantitative measures of regional contractile function could therefore have significant prognostic and therapeutic importance. For example, many patients with severe coronary artery disease may have normal regional and global left ventricular function at rest but have abnormalities induced by stress. In clinical practice, patients with coronary artery disease can be detected by stress echocardiography based on new functional deficits during stress. However, interobserver variability of this type of qualitative measure is an inherent limitation that could be improved with quantitative measures. Thus, there is a need for high quality quantitative measures of regional cardiac function. 
     Image data of the epicardial boundary, for example, is currently acquired by applying a specific sequence of RF pulses to yield a NMR signal that provides information pertaining to the tissue under test. A particular pulse sequence can therefore be applied to obtain an image of, for example, a cross-section of the left ventricle tissue. 
     Segmentation methods that are currently available include snake-based techniques such as that described by A. Yezzi, et al. “A Geometric Snake Model for Segmentation of Medical Imagery,” IEEE Transaction on Medical Imaging, 16, 199-209 (April, 1997). Snakes, also known as active contours, have been used in an attempt to segment features of the left ventricle. Snakes are described by a parameterized curve whose evolution is determined by the minimization of an energy field. The equation of the energy field, as defined by J. C. Gardner et al. “A Semi-Automated Computerized System for Fracture Assessment of Spinal X-Ray Films,” Proceedings of the International Society for Optical Engineering, 2710, 996-1008 (1996), is:                E        [       x   →          (   s   )       ]       ≡     k          ∫   0   1                          s        [         1   2            α        (            x   →            s       )       2       +       1   2            β        (            2          x   →              s   2         )       2       -     γ                   H        (       x   →          (   s   )       )           ]                     (   1   )                                
     where s is the parameterization variable, {right arrow over (x)} is the parameterized curve, κ is the normalization constant, α is the H({right arrow over (x)})=|{right arrow over (∇)}/({right arrow over (x)})| tension of the snake, β is the rigidity of the snake, γ controls the attraction to image features, and I is the pixel intensity of the image. H (x) refers to a function which defines the features that attract the snake algorithm to the boundary and, typically, is chosen to be the magnitude of the gradient of the image intensity. 
     Because the magnitude of the gradient is used to attract the algorithm to the boundary of the left ventricle, the snake does not work well where the boundary is defined by edges that are weak in intensity. In order for the snake algorithm to attach to a boundary, a user must intervene and supply a boundary condition to define the proximity of the boundary for the snake. This is undesirable because user may need to interact with the segmentation algorithm while the images are being processed. 
     Snake based techniques can be used, as described by Yezzi, to produce a geometric snake model having a stopping term and a constant inflation term added to the evolution equation. The resulting evolution equation of the Yezzi active contour model is:                  ∂   Ψ       ∂   t       =       φ                     ∇   Ψ                       (     κ   +   v     )       +       ∇   φ     *     ∇   Ψ                 (   2   )                                
     where v is a constant inflation force,        κ   ≡     div        (       ∇   ψ                      ∇   ψ                      )                              
      the curvature of the level sets of ψ(x, y, t), φ is a function dependent on the type of image and is a stopping term for the curve evolution. Snake based techniques are additionally unfavorable because they rely primarily on edge information only, and therefore are subject to greater error and generally lack robustness, particularly in a clinical setting. S. Ranganath attempted unsuccessfully to segment an epicardium using a snake, as described in “Contour Extraction from Cardiac MRI Studies Using Snakes,” IEEE Transactions on Medical Imaging, 14(2), 328-338 (June, 1995). 
     Another such method is disclosed herein with reference to pending U.S. patent application Ser. No. 09/652,739, filed by the present assignee and entitled “Method and Apparatus for Segmentation of a Left Ventricular Epicardium.” The disclosure of the referenced pending application is hereby incorporated by reference. 
     When segmenting a left ventricular epicardium, as well as other internal body parts, it is typical to represent the area of interest with a binary mask. Pixels inside the area of interest are marked “on” and pixels outside the area are marked “off.” Many times it is of interest to show the boundary of an organ or region that has been segmented. Conventional segmentation operations have been unsuccessful in producing an accurate representation of the boundary between the organ to be segmented and its surroundings. What is therefore needed is a method and apparatus for transforming a segmentation mask into a smooth, closed contour that is representative of the body part under examination. 
     SUMMARY OF THE INVENTION 
     The present invention relates to a method and apparatus for fitting a smooth boundary curve to a segmented image of a human organ or tissue. 
     In accordance with a first aspect of the invention, boundary points of a mask produced using, for example, a magnetic resonance imaging system each are at least partially defined by a corresponding radius. The length of each radius is compared to a predetermined interval, and those falling outside of the interval are removed. The remaining radii define corresponding remaining boundary points of the image. Next, a moving window encapsulates a portion of the remaining radii, which are then examined to determine whether a given radius falls within a second predetermined interval. Those radii falling outside of the predetermined interval are adjusted such that the adjusted lengths fall within the interval. The remaining boundary points are then curve fit to produce a smooth and continuous contour. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     Reference is hereby made to the following figures in which like reference numerals correspond to like elements, and in which: 
     FIG. 1 is a block diagram of an MRI system which employs the preferred embodiment of the present invention; 
     FIG. 2 is a schematic map corresponding generally to a nuclear magnetic resonance image of a chest cavity in accordance with the preferred embodiment; 
     FIG. 3 is a flow chart of the steps performed with the MRI system to carry out the preferred embodiment; 
     FIG. 4 is a flow chart of the steps performed to carry out the intensity segmentation process that forms part of the method of FIG. 3; 
     FIG. 5 is a representation of a kernel used in combination with the dilation step in the process of FIG. 4; 
     FIG. 6 is a graphical representation of a blood pool mask in accordance with the preferred embodiment; 
     FIG. 7 is the blood pool mask of FIG. 6 once dilated in accordance with the preferred embodiment; 
     FIG. 8 is a dilation mask obtained by the subtraction of the mask illustrated in FIG. 6 from the mask illustrated in FIG. 7; 
     FIG. 9 is a graph of the mean and standard deviations plotted against the natural log of the corresponding dilation iteration number in accordance with the preferred embodiment; 
     FIG. 10 is an intensity map in accordance with the preferred embodiment; 
     FIG. 11 is a diagram of a plurality of compass operators used in conjunction with the “create edge map” step of FIG. 7; 
     FIG. 12 is a flow chart of the process performed to carry out the “create edge map” step that forms part of the method of FIG. 3; 
     FIG. 13 is a flow chart of the process performed to carry out the “refine intensity map” step in the method of FIG. 3; 
     FIG. 14 is a mask representing an image containing islands in accordance with the preferred embodiment; 
     FIG. 15 is a mask corresponding to FIG. 14 with the “on” pixels labeled; 
     FIG. 16 is a mask corresponding to FIG. 15 with the islands joined; 
     FIG. 17 is a mask corresponding to FIG. 16 with the island removal process completed in accordance with the preferred embodiment; 
     FIG. 18 is the intensity map of FIG. 10 having the edges, islands, and blood pool removed in accordance with the preferred embodiment; 
     FIG. 19 is a graphical representation illustrating a center of mass calculation in accordance with the preferred embodiment; 
     FIG. 20 is a flow chart of the process performed to carry out the boundary smoothing step in the method of FIG. 3; 
     FIG. 21 is an illustration of the boundary smoothing process of an epicardial boundary in accordance with the preferred embodiment; 
     FIG. 22 is the boundary of FIG. 21 with the boundary smoothing process completed; 
     FIG. 23 is a graphical representation of the smoothing effects on actual radii data in accordance with the preferred embodiment; and 
     FIG. 24 is a flow chart of the process performed to carry out the window averaging step of FIG. 20 in accordance with the preferred embodiment. 
    
    
     GENERAL DESCRIPTION OF A SEGMENTATION OPERATION 
     An epicardial detection process is performed on an acquired MR image by an image processor. Specifically, a blood pool mask is created and subsequently dilated to obtain an expanded, dilated, mask. Next, the blood pool mask is subtracted from the dilated mask to produce a boundary mask. The dilation process is repeated so as to produce a plurality of boundaries, which represent the radially outwardly advancing boundary of the dilated mask toward the epicardium during successive iterations. The mean and standard deviation of the resulting intensity values corresponding to the boundaries are calculated and stored in an array. The dilations repeat until the dilation boundary grows beyond the epicardium, and into the other areas surrounding the heart. As the boundary moves beyond the outer wall of the left ventricle, the boundary will encounter areas of vastly different pixel intensities due to the different tissue compositions of the regions beyond the heart. The behavior of the calculated standard deviation will reflect the boundary advancing from the endocardium to the epicardium and also will display predictable behavior when the boundary moves away from the epicardium into other areas surrounding the heart. The changes in standard deviation as each iteration is performed provides a relatively accurate approximation of the region containing the epicardial boundary. Finally, the process computes an intensity range for the mask, and an intensity map is created. 
     The next step is to generate an edge map for the intensity map by histogramming a gradient map of the image and discarding values in the gradient map that fall below a predetermined threshold. The gradient values which remain are defined as the edge map. Next, the edge map is subtracted from the intensity map. The intensity map with edge map removed is then altered to remove the blood pool as well as any additional islands in the image that might exist as the result of noise, for example, so as to produce a final classification map. 
     The mask for the blood pool is then used to determine an approximate location for the center of the left ventricle. To determine the boundary points of the epicardium, rays are cast outwardly from the center in search of a transition from a non-mask intensity value (indicative of the rays traversing the location formerly occupied by the blood pool) to a mask intensity value (indicative of the rays traversing the myocardium) and again to a non-mask value (indicative of the rays traversing the epicardium). Once the boundary is produced, a smoothing process is performed to create a smooth curve representing the epicardial boundary of the left ventricle. 
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
     Referring initially to FIG. 1, there is shown the major components of a preferred magnetic resonance imagine (MRI) system which incorporates the present invention. The operation of the system is controlled from an operator console  100  which includes a keyboard and control panel  102  and a display  104 . The console  100  communicates through a link  116  with a separate computer system  107  that enables an operator to control the production and display of images on the screen  104 . The computer system  107  includes a number of modules which communicate with each other through a backplane  118 . These include an image processor module  106 , a CPU module  108  and a memory module  113 , known in the art as a frame buffer for storing image data arrays. The computer system  107  is linked to a disk storage  111  and a tape drive  112  for storage of image data and programs, and it communicates with a separate system control  122  through a high speed serial link  115 . 
     The system control  122  includes a set of modules connected together by a backplane. These include a CPU module  119  and a pulse generator module  121  which connects to the operator console  100  through a serial link  125 . It is through this link  125  that the system control  122  receives commands from the operator which indicate the scan sequence that is to be performed. The pulse generator module  121  operates the system components to carry out the desired scan sequence. It produces data which indicates the timing, strength and shape of the RF pulses which are to be produced, and the timing of and length of the data acquisition window. The pulse generator module  121  connects to a set of gradient amplifiers  127 , to indicate the timing and shape of the gradient pulses to be produced during the scan. The pulse generator module  121  also receives patient data from a physiological acquisition controller  129  that receives signals from a number of different sensors connected to the patient, such as ECG signals from electrodes or respiratory signals from a bellows. And finally, the pulse generator module  121  connects to a scan room interface circuit  133  which receives signals from various sensors associated with the condition of the patient and the magnet system. It is also through the scan room interface circuit  133  that a patient positioning system  134  receives commands to move the patient to the desired position for the scan. 
     The gradient waveforms produced by the pulse generator module  121  are applied to a gradient amplifier system  127  comprised of G x , G y  and G z , amplifiers. Each gradient amplifier excites a corresponding gradient coil in an assembly generally designated  139  to produce the magnetic field gradients used for position encoding acquired signals. The gradient coil assembly  139  forms part of a magnet assembly  141  which includes a polarizing magnet  140  and a whole-body RF coil  152 . A transceiver module  150  in the system control  122  produces pulses which are amplified by an RF amplifier  151  and coupled to the RF coil  152  by a transmit/receive switch  154 . The resulting signals radiated by the excited nuclei in the patient may be sensed by the same RF coil  152  and coupled through the transmit/receive switch  154  to a preamplifier  153 . The amplified NMR signals are demodulated, filtered, and digitized in the receiver section of the transceiver  150 . The transmit/receive switch  154  is controlled by a signal from the pulse generator module  121  to electrically connect the RF amplifier  151  to the coil  152  during the transmit mode and to connect the preamplifier  153  during the receive mode. The transmit/receive switch  154  also enables a separate RF coil (for example, a head coil or surface coil) to be used in either the transmit or receive mode. 
     The NMR signals picked up by the RF coil  152  are digitized by the transceiver module  150  and transferred to a memory module  160  in the system control  122 . When the scan is completed and an entire array of data has been acquired in the memory module  160 , an array processor  161  operates to Fourier transform the data into an array of image data. This image data is conveyed through the serial link  115  to the computer system  107  where it is stored in the disk memory  111 . In response to commands received from the operator console  100 , this image data may be archived on the tape drive  112 , or it may be further processed by the image processor  106  and conveyed to the operator console  100  and presented on the display  104 . 
     For a more detailed description of the transceiver  150 , reference is made to U.S. Pat. Nos. 4,952,877 and 4,922,736, which are incorporated herein by reference. 
     The MRI system of FIG. 1 performs a series of suitable pulse sequences to collect sufficient NMR data so as to produce an image of the left ventricle, as is well known in the art. FIG. 2 illustrates a schematic representation of a typical chest cavity image identifying a human heart  169  having a left ventricle  172 , a blood pool  174 , and an epicardium  182 . A lung field  170  surrounds or partially surrounds the heart  169 . 
     Referring now to FIG. 3, an epicardial detection process  200  is performed on the acquired image data by the image processor  106 . The first step indicated at process block  202  determines a pixel intensity range in the image for the muscle comprising the left ventricle. This intensity segmentation process  202  is illustrated in more detail in FIG. 4, which begins at step  214  using the blood pool mask  228  illustrated in FIG.  6 . It should be appreciated that the mask  228  could be generated by the user or produced using another segmentation algorithm. The blood pool mask  228 , as illustrated, is defined by a rectilinear grid  230  having shaded pixels  232  that are turned “on” to represent the structure of interest (blood pool), and clear pixels  234  that are turned “off” to represent space not occupied by the blood pool. Next, at step  214 , the blood pool mask  228  is dilated, using a morphological operator that defines a method for expanding a binary mask, and is defined by the following equation: 
     
       
           X⊕B≡{x:B   x   ∩X≠φ}   (3) 
       
     
     where X is the image; B is a structuring element represented by kernel  236  illustrated in FIG. 5; B x  is the translation of B such that its origin is at x; and x is a specific pixel. The structuring element B is moved across the blood pool mask  228 . When a pixel  238  in B is “on” and the corresponding pixel in the image mask is “on,” ( 232 ) then the pixel in the blood pool mask  228  corresponding to the center of B  240  is turned “on.” FIG. 7 illustrates a mask  242  of the blood pool mask  228  once dilated, and having an outer boundary  244  surrounding the image  232 . 
     Once the original blood pool mask  228  has been dilated, the blood pool mask  228  is subtracted from the dilated mask  242  at step  216  to produce a one-pixel wide boundary  246  illustrated in FIG.  8 . The subtraction is defined by the following operation: 
     
       
           X   n   −X   n−1   ≡{x:X   n   ∩X   n−1 =φ}  (4) 
       
     
     where X n  is the blood pool mask dilated n times, and X n−1  is the blood pool mask dilated n−1 times. The boundary  246  therefore represents the advancing boundary of the dilated mask  242  as shown in FIG.  8 . 
     The intensity segmentation process  202  then continues at step  218  and performs a statistics calculation of the {overscore (x)} dilation boundary  246 . In particular, first order statistics are calculated for the dilation boundary  246 . The mean of the sample is defined by the equation:                x   _     =       1   n            ∑     i   =   1     n                     x   i                 (   5   )                                
     where n is the number of pixels defining the dilation boundary  246 ; and x i  is the intensity of a pixel in the dilation boundary. The standard deviation metric SD of the intensity of the sample is defined by the equations:                v   _     =       1     n   -   1              ∑     i   =   1     n                       (       x   i     -     x   _       )     2                 (   6   )                                
     See A. Papoulis, “Probability, Random Variables, and Stochastic Processes,” (Third Edition), New York, NY: McGraw Hill, Inc. (1991), citing equations (5) and (6), and 
     
       
           SD={square root over ({overscore (v)})}   (7) 
       
     
     where {overscore (v)} is variance. At step  220 , the intensity segmentation process repeats steps  214 - 218  to once again dilate the once dilated mask  242 , perform the subtraction of the previous mask from the newly dilated mask, and finally perform the statistics calculation. These dilation iterations are performed N times, where N is chosen to be sufficiently large to ensure that the dilation boundary  246  grows beyond the epicardium  182  and into the areas surrounding the heart  169 . Thus, N depends primarily on the field of view of the image. If the field of view is small, resulting in the heart occupying a large portion of the image, N would increase. If, however, the field of view is large, resulting in the heart occupying a smaller portion of the image, N may be decreased to save computation time. N is set to 15 in accordance with the preferred embodiment. 
     Because it is desirable to calculate an intensity map (as described below) which includes the left ventricle  172  and excludes as many surrounding areas as possible, it is of interest to calculate the statistics indicative of the point at which the boundary  246  has moved beyond the epicardium  182 . Therefore, once N is satisfied, the intensity segmentation process  202  proceeds to step  222 , where the point at which the outer boundary  246  crosses the outer wall of the left ventricle  172  is determined. As this occurs, the boundary  246  will encounter vastly differing pixel intensities, which are the result of the different tissue compositions of the regions beyond the heart  169 , for example the lungs and the diaphragm (not shown). FIG. 9 illustrates a graph of the mean and standard deviation of intensity values corresponding to the boundary  246  plotted against the natural log of the iteration number. As illustrated, as the boundary  246  advances from the endocardium, the standard deviation gradually decreases, as indicated in FIG. 9 at  248 , until the boundary  246  begins to reach the epicardium  182 . At this point, the standard deviation begins to increase significantly, as indicated in FIG. 9 at  250 . The standard deviation then begins to decrease once again as the boundary  246  moves away from the epicardium  182  and begins to move through more homogeneous materials. The boundary  246  defined by the maximum change in standard deviation yields a good approximation of the region containing the epicardial boundary, and is definad by the following equation:                Δ                   SD   n       =              SD   n     -     SD     n   -   1             In        (   n   )       -     In        (     n   -   1     )                        (   8   )                                
     where SD n  is the standard deviation metric for n; and SD n−1  is the standard deviation for iteration n−1. 
     The statistical data from the iteration having the largest ΔSD are used to calculate the intensity range for an intensity map  227 , as illustrated in FIG.  10 . 
     The intensity segmentation process is completed at step  224 , whereby the intensity map  227  is defined by the following equation: 
     
       
           M≡{p:SD−α{overscore (x)}≦I ( p )≦ SD+β{overscore (x)}}   (9) 
       
     
     where p is a specific pixel; I(p) is the intensity of p; SD is the standard deviation from equation (7); x is the sample mean from equation 6; and α and β are constants. In accordance with the preferred embodiment, α and β are empirically derived to yield a map having a predetermined intensity range. The intensity map  227  as illustrated in FIG. 10 contains a blood pool  174 , edges  229 , and islands  237 . 
     The intensity map  227  that was produced by the intensity segmentation block  202  represents an image having a plurality of “off” and “on” pixels representing non-mask values and mask values, respectively, according to whether a given portion of the MR image meets the intensity threshold requirements of the intensity segmentation block  202 . Some of the “on pixels” in the mask will define the left ventricular epicardial boundary while others define images such as the blood pool  174 , edges  229  and islands  237 . Accordingly, once the intensity map  227  is refined to produce a classification map lacking the blood pool  174 , edges  229 , and islands  237 , boundary points that define the epicardial boundary will be detected based on the remaining “on” pixels, as will be described below. 
     Referring again to FIG. 3, the epicardial detection process  200  next generates a one-pixel wide edge map for the image at step  204  using a plurality of compass operators on the input image data. Compass operators are functions which measure gradients in intensity for a selected number of directions, and were chosen in accordance with the preferred embodiment due, in part, to their low computational requirements. Step  204  is illustrated in detail in FIG. 12 in which the first step  270  calculates the intensity gradient at a given location (m,n) as defined by the following equation:                g        (     m   ,   n     )       ≡       max   k          {            g   k          (     m   ,   n     )            }               (   10   )                                
     where g k (m,n) is the compass operation in the direction θ k  for k=0, . . . 7; and θ k  is the gradient direction for a given compass operator. The compass operators can be calculated from a weighted average of the pixel values in the acquired image. A full set of compass operators can be represented by the following kernels  254 ,  256 ,  258 ,  260 ,  262 ,  264 ,  266 , and  268  representing kernels positioned north, northwest, west, southwest, south, southeast, east, and northeast, respectively, as shown in FIG.  11 . It can be observed that pixel values are positive in the direction representing the overall position of the kernel, negative in the direction opposite that representing the kernel separated by pixels of 0. For example, kernel  254  includes positive pixels in the north direction, negative kernels in the south, separated by a row of kernels of 0. 
     The values of the elements of each kernel are used as multiplicative weights for the pixels in the neighborhood of interest to determine the gradients in each direction. After g k  is calculated for all k at a given pixel at step  270 , the maximum value of g k  is used to represent the gradient at that pixel. A gradient map is then calculated for the entire image at step  272 . The intensity values of the gradient map are histogrammed at step  274 . To generate the edge map at step  276 , a gradient threshold is selected whereby all values in the gradient map falling below the designated threshold are ignored. The gradient threshold is adjustable, and is set to 20% in the preferred embodiment, thereby retaining those pixels having intensity values falling within the top 20%, and discarding the remaining 80% of the pixels. The gradient values which remain after the thresholding step are defined to be the edge map corresponding to the edges  229  in the intensity map  227 . 
     Referring again to FIG. 3, once the intensity and edge maps are created, the epicardial detection process  200  refines the intensity map  227  at step  206 . As shown in more detail in FIG. 13, the intensity map  227  defines the areas that contain pixels in the intensity range of interest. The edge map defines strong edges in the image, some of which likely defining the epicardial boundary. The intensity map  227  and edge map are combined by subtracting the edge map from the intensity map to produce a classification map (not shown) at step  278 . The subtraction is performed according to the technique described above with reference to Equation  4 . 
     The classification map therefore defines the areas of proper intensity, with edges  229  of interest being cut out of the intensity map  227 . To further refine the classification map, the first dilation of the blood pool mask  228  is subtracted from a classification mask (corresponding to the classification map) at step  280  to produce a classification map with the blood pool  174  removed. Subtracting the blood pool mask  228  removes stray pixels in the blood pool  174  which may have been in the intensity range of the epicardium  182 , as a pixel cannot be a member of both the blood pool and the epicardium. 
     Next, with continuing reference to FIG. 3, an island removal process is performed at step  282 , whereby small groups of pixels are removed to reduce noise in the mask and to increase the probability of choosing a correct epicardial boundary. Such groups of pixels, or “islands,” are illustrated in FIG. 10 at  237 . The island removal process  282  is an iterative process which employs the mask  284  of FIG.  14  and identifies areas of structure (“on” pixels)  286 . In particular, the mask  284  is scanned from left to right in each row, starting with the upper left corner. Each non-adjacent “on” pixel  286  is labeled with successive numbers to produce a labeled image  288 , as illustrated in FIG.  15 . The labels are then merged, as shown in FIG. 16, by scanning the labeled image  288  and joining pixels that are connected. For example, when scanning the labeled image  288 , the first value encountered in the first row is a “1”. Connected to that pixel labeled “1”are two other pixels labeled “3”. The pixels labeled “3”are replaced with “1”since they are connected. A “merged labels” image  292 , illustrated in FIG. 16, is produced as a result of merging and labeling all of the islands  290  in the labeled image  288 . Finally, the island removal process histograms and thresholds the image  292 . If an island does not include enough labeled pixels (i.e. the island&#39;s pixel count value is not above a predetermined threshold), all pixels in that island are turned off. The threshold should be set so as to remove those islands which are small enough to be properly attributable to noise while retaining those that are representative of anatomy, and is set to 50 in accordance with the preferred embodiment. In the illustrated example, in FIG. 16, because the island labeled “2”did not meet the threshold, the pixels corresponding to that island have been turned “off” in FIG.  17 . FIG. 18 illustrates a final classification map  235  after the edges  229 , blood pool  174 , and islands  237  have been removed. 
     Referring again to FIG. 3, once the intensity map is refined at step  206 , the epicardial detection process  200  executes step  208  to approximate the center point of the left ventricle using the blood pool mask  228 . The following mass equations are used to calculate the center of the blood pool  174 :                x   c     =       1   M            ∫   R                  ∫     x                   ρ        (     x   ,   y     )               A                     (   11   )                 y   c     =       1   M            ∫   R                  ∫     y                   ρ        (     x   ,   y     )               A                     (   12   )                                
     where x c  is the x coordinate of the center point; y c  is the y coordinate of the center point; R is the region of interest; ρ (x,y) is the density function; dA is an element of infinitesimal area; and M is the total mass, as defined by:                M   ≡       ∫   R                  ∫       ρ        (     x   ,   y     )               A                                (   13   )                                
     To find the center of the blood pool  174 , R is taken to be the blood pool mask. Because all pixels in the blood pool mask  228  are of equal value, ρ (x,y) can be taken as 1 to indicate constant density. M therefore reduces to the total area of the blood pool  174 . Using these simplifications, and recognizing that the image data is represented as discrete pixel values, equations (12) and (13) may be rewritten, respectively, as:                y   c     =       1   N            ∑   y                               ∑   x                           y                 (   14   )                 x   c     =       1   N            ∑   y                               ∑   x                           x                 (   15   )                                
     where N is the number of pixels in the blood pool mask  228 . It should be appreciated that equations (14) and (15) are simply the average values for x and y, respectively, for the points contained in the blood pool mask  228 . FIG. 19 illustrates an example calculation of center of mass  294  (x c ,y c )for an object  296  outlined in Cartesian space, having grid lines  298  representative of pixel location. In the illustrated example, x c =6.025 ( 388 / 64 ), and y c =5.0625 ( 324 / 64 ). 
     Referring again to FIG. 3, the calculated center point  294  is used with the intensity map  227  to find an approximate epicardial boundary in a radial boundary search process  210 . As shown in FIG. 21, rays (not shown) are cast radially outwardly from the center point  294  in search of a transition from a first non-mask intensity value to a mask intensity value, and then back to a second non-mask intensity value. The first non-mask value is representative of the rays traversing the location formerly occupied by the blood pool  174 ; the mask-value is representative of the rays crossing the myocardium; and the second non-mask value is representative of the rays crossing the epicardium  182 . Because the edges  229  have been removed during step  206 , many areas of the epicardium  182  are sufficiently defined. In some areas, however, where no strong edge was present and the intensity range is therefore that of the myocardium, a reliable approximation of the epicardial boundary  300  may not exist. In this case, the search will fail at decision block  211 , and the radial value stored for the search will be the final distance at which the search was attempted. A direct correlation exists between the number of radii having no reliable definition of the corresponding epicardial boundary  300  and the successful completion of the epicardial detection process  200 . The ability to detect failure, therefore, is particularly useful when providing a completely automated segmentation of a dataset. The failure may be communicated to the user at step  209  so that particular attention may be given to reviewing those images identified as having a failed boundary. Therefore, even though a failure may have been detected, the epicardial detection process  200  will proceed after notifying the user of the failure, as will now be described. 
     Once the epicardial boundary points  300  are determined, and after any failures have been communicated to the user, a boundary smoothing process  212  is performed to transform the boundary points into a smooth closed curve  308  as illustrated in FIG.  22 . The boundary smoothing process  212  is illustrated in detail in FIG.  20  and begins at step  302 , where any points having a small probability of actually being on or near the epicardial boundary are discarded, as defined by a thresholding operation:                R   ^     ≡     {     r        :          {               (     1   -   γ     )     *     r   ave       ≤   r   ≤       (     1   +   γ     )     *     r   ave                     r   ave2                   otherwise           }       }             (   16   )                                
     where {circumflex over (R)} is the set of all radii defining an estimate of the epicardial boundary; r is a specific radius; r ave  is the average value of all radii prior to thresholding; r ave2  is the average value of all radii within the threshold; and γ is the threshold coefficient. Once all radii exceeding the threshold have been removed, the new radial average (r ave2 ) is calculated at step  304 , and radii exceeding the threshold are replaced with r ave2  at step  306 . The boundary smoothing process  212  thereby produces a set of boundary points  300  having corresponding radii that are all within the empirically derived threshold. Once the refined estimate of the boundary points  300  is obtained, the radii values are further smoothed at step  309  to obtain a smooth, closed curve  308  representing the epicardial boundary using a window averaging technique. 
     Referring now to FIGS. 21 and 24, the window averaging process  309  begins at step  312 , where a window  310  is applied to a particular radius at step  312 , and encompasses a plurality of surrounding radii that comprise the refined boundary  300 . The window iteratively rotates radially about the center  294  until the entire boundary  300  has been smoothed. The radial orientation of the window is initially set to the 0° location, and the angle of the window is predetermined so as to encompass a predetermined number of radii at each iteration. The average length of all radii falling within the window at a given point in time is calculated at step  314 , and an interval is empirically derived so as to define an acceptable range above and below the calculated average. The length of the particular radius under examination is compared to the calculated average at decision block  316 . If the length falls within the interval surrounding the calculated average, then the process  309  will continue to step  320  and rotate the window to examine the next radius in succession. If, however, the length falls outside of the interval surrounding the calculated average, then the length of that radius will be replaced with the calculated window average at step  318  before rotating the window at step  320 . 
     Once window  310  is rotated to the next radius at step  320 , it is determined at decision block  322  whether the window has rotated again to the 0° location, thereby signifying completion of a 360° revolution about the center point  294 . If a complete revolution has not yet been completed, steps  312 - 320  are repeated until decision block  322  determines that all radii have been examined and modified, if necessary. As illustrated in FIG. 22, once the window has returned to the 0° position, a smooth closed contour  308  is produced. 
     It should be appreciated that the threshold coefficient γ in Equation (16) may be decreased to produce a smoother curve incorporating a more refined approximation of the boundary  300 . Alternatively, the increasing the coefficient γ will yield a more accurate representation of the boundary  300  that was determined above. 
     The effects of the boundary smoothing process  212  and window averaging process  309  is illustrated with reference to FIG. 23, which depicts curve representing original radii lengths  326  that are thresholded to produce curve  328 , and that are subsequently smoothed to produce curve  330 , as described above. 
     It should be appreciated that the above described epicardial detection process  200 , while generally illustrated to segment a left ventricular epicardium in MR images, may also be used to segment cardiac images acquired with other imaging modalities such as x-ray, x-ray CT, ultrasound, and nuclear. Indeed, the above described technique may be expanded to segment other bodily organs. Accordingly, the present invention is not intended to be limited to fitting a smooth boundary to a segmentation mask of a left ventricular epicardial boundary. 
     While the steps performed in accordance with the preferred embodiment have been described, alternate embodiments may be implemented to improve the epicardial detection process  200 . In particular, factors such as speed and memory conservation are desirable for use in a clinical setting. This may be achieved by 1) combining the dilation and statistics calculation steps  214  and  218 , 2) simplifying the edge detection process  204 , and 3) reducing the number of floating point calculations. 
     The dilation and statistics calculation steps  214  and  218  may be improved by calculating the statistics during the dilation step. Calculating the statistics for each dilation iteration while the {overscore (x)} dilation kernel is moving through the image foregoes the need for additional passes through the image. Calculation of the mean {overscore (x)} may also be expedited by summing the values of each pixel added by the dilation kernel  236 . Once the dilation kernel  236  has passed completely through the image  228 , the only additional step necessary to calculate the mean {overscore (x)} is to divide the sum of the added pixels by the number of added pixels. 
     To increase the speed of calculating the standard deviation metric SD, Equation 6 may be rearranged to allow partial terms of the variance {overscore (V)} to be calculated as follows:                v   _     =       1     n   -   1                  [         ∑     i   =   1     n                     x   i   2       -     2        x   _                       ∑     i   =   1     n                     x   i         +     n          x   _     2         ]             (   17   )                                
     Equation 17 provides a way to calculate the variance during the dilation, or “on the fly.” Specifically, the first term is calculated by summing the squares of the pixel values for each pixel added by the dilation kernel  236 . The second term is twice the mean {overscore (x)} after the dilation kernel  236  has passed completely through the image  228 . The final term is calculated after {overscore (x)} the dilation is complete by squaring the mean and multiplying by the pixel counter used to compute the mean. 
     Because the statistics are being calculated on the fly, it is not necessary to store copies of the dilated masks  242  for later statistics calculations. However, a dilated blood pool is still needed later in the process to clean up the binary pixel mask. Therefore, in the dilation process, rather than adding every dilated pixel with the same intensity value in the dilation mask  242 , a pixel value may be used which is related to the iteration number. For example, the original blood pool mask  228  may be stored with a value of 1, with the values incrementing by 1 as successive iterations are performed. Accordingly, the results of all dilations may be stored in one image mask for future access while conserving memory space. 
     The edge detection process  204  may also be modified to increase the speed of the epicardial detection process  200 . For example, a mere sign difference separates those compass operators indicating north  254  and those indicating south  262 . Because only the magnitude of the operators is used when calculating the edge map, only one of the operators need be used. The result is a reduction of the number operators needed to calculate the edge map by a factor of two. Additionally, because the compass operators rotated at 45° ( 256 ,  260 ,  264 , and  268 ) provide little additional information, they may be eliminated altogether. While this step does minimally decrease the accuracy of the edge map, it provides benefits in time conservation. Accordingly, only two compass operators need to be used—one along the north-south direction, and one along the east-west direction. 
     The invention has been described in connection with what are presently considered to be the most practical and preferred embodiments. However, the present invention has been presented by way of illustration and is not intended to be limited to the disclosed embodiments. Accordingly, those skilled in the art will realize that the invention is intended to encompass all modifications and alternative arrangements included within the spirit and scope of the invention, as set forth by the appended claims.

Technology Category: 3