Abstract:
A computer-implemented method for vertebrae segmentation includes providing an image of a plurality of vertebrae, and determining a seed in each of at least two adjacent vertebrae in the image. The method further includes mapping a unit square to the seeds in the image as corresponding shape constraints on a segmentation, evolving the shape constraints to determine the segmentation of the adjacent vertebrae, wherein evolutions of the shape constraints interact, and outputting a segmented image indicating a location of the vertebra.

Description:
This application claims priority to U.S. Provisional Application Ser. No. 60/664,418, filed on Mar. 23, 2005, which is herein incorporated by reference in its entirety. 
    
    
     BACKGROUND OF THE INVENTION 
     1. Technical Field 
     The present invention relates to image processing, and more particularly to a system and method for vertebrae segmentation including a determination of disk orientation. 
     2. Discussion of Related Art 
     Magnetic resonance (MR) spine imaging has been widely used for non-invasive detection of different abnormalities and diseases in the spinal column, vertebrae, and inter-vertebral disks. In typical MR spine imaging cases, a patient is initially scanned to obtain a set of T2-weighted sagittal images or coronal localizer images. If an abnormality of an inter-vertebral disk is found, a transverse scan is performed. The orientation of the transverse images is planned parallel to the major axis of the disk and the center of the transverse images is located on where the disk joins the spinal cord. A saturation band  101  is placed to suppress strong MR signals from abdominal vessels and should not overlap with the spinal column (see  FIG. 1 ). 
     Referring to  FIG. 1 , in a sagittal view of the vertebral column, the general shape of a vertebra can be approximated by a rectangle making it easier to perform an efficient segmentation. The orientation of the inter-vertebral disk is used to set up the slice stack  102 . 
     Transverse imaging planning is done manually and is time-consuming and subject to intra- or inter-operator variation. 
     Therefore a need exists for a system and method for vertebrae segmentation including determination of disk orientation. 
     SUMMARY OF THE INVENTION 
     According to an embodiment of the present disclosure, a computer-implemented method for vertebrae segmentation includes providing an image of a plurality of vertebrae, and determining a seed in each of at least two adjacent vertebrae in the image. The method further includes mapping a unit square to the seeds in the image as corresponding shape constraints on a segmentation, evolving the shape constraints to determine the segmentation of the adjacent vertebrae, wherein evolutions of the shape constraints interact, and outputting a segmented image indicating a location of the vertebra. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Preferred embodiments of the present invention will be described below in more detail, with reference to the accompanying drawings: 
         FIG. 1  is a sagittal view of the vertebral column in which a general shape of a vertebra is approximated by a rectangle; 
         FIG. 2  is a diagram of a system according to an embodiment of the present disclosure; 
         FIG. 3  is a flow chart of a method according to an embodiment of the present disclosure; 
         FIG. 4  is an illustration of a transformation of the unit square into the MR image according to an embodiment of the present disclosure; 
         FIGS. 5A-C  are an illustration of an evolution of a single rectangle according to an embodiment of the present disclosure; 
         FIGS. 6A-C  are an illustration of an effect of an interaction force according to an embodiment of the present disclosure; 
         FIGS. 7A-C  are an illustration of a segmentation approach according to an embodiment of the present disclosure; 
         FIGS. 8A-F  are an illustration of a segmentation approach according to an embodiment of the present disclosure; 
         FIG. 9  is an illustration of a determination of disk orientation according to an embodiment of the present disclosure; and 
         FIG. 10  is an illustration of an edge weight determination according to an embodiment of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS 
     According to an embodiment of the present disclosure, a method for determining inter-vertebral disk orientation in a magnetic resonance (MR) image of a spine implements an active contour theory and enforces a shape constraint to avoid leaks through weak or non-existent boundaries. The method represents a vertebra as a rectangle, modeled as a transformation applied to the unit square. The method may be implemented for setting up transverse image acquisition for diagnosis of inter-vertebral disk pathologies. 
     A regional flow integrated along the rectangle&#39;s perimeter updates the rectangle&#39;s transformation to achieve the segmentation. Further constraints are added so that adjacent rectangles have similar orientation and scale. The inter-vertebral disk orientation is inferred by finding the bounding edges of adjacent vertebrae. Since each vertebra can be geometrically approximated by a rectangle, this a priori shape constraint is incorporated to increase robustness. 
     It is to be understood that the present invention may be implemented in various forms of hardware, software, firmware, special purpose processors, or a combination thereof. In one embodiment, the present invention may be implemented in software as an application program tangibly embodied on a program storage device. The application program may be uploaded to, and executed by, a machine comprising any suitable architecture. 
     Referring to  FIG. 2 , according to an embodiment of the present invention, a computer system  201  for implementing a method for vertebrae segmentation including a determination of disk orientation can comprise, inter alia, a central processing unit (CPU)  202 , a memory  203  and an input/output (I/O) interface  204 . The computer system  201  is generally coupled through the I/O interface  204  to a display  205  and various input devices  206  such as a mouse and keyboard. The support circuits can include circuits such as cache, power supplies, clock circuits, and a communications bus. The memory  203  can include random access memory (RAM), read only memory (ROM), disk drive, tape drive, etc., or a combination thereof. The present invention can be implemented as a routine  207  that is stored in memory  203  and executed by the CPU  202  to process the signal from the signal source  208 . As such, the computer system  201  is a general-purpose computer system that becomes a specific purpose computer system when executing the routine  207  of the present invention. 
     The computer platform  201  also includes an operating system and micro-instruction code. The various processes and functions described herein may either be part of the micro-instruction code or part of the application program (or a combination thereof), which is executed via the operating system. In addition, various other peripheral devices may be connected to the computer platform such as an additional data storage device and a printing device. 
     It is to be further understood that, because some of the constituent system components and method steps depicted in the accompanying figures may be implemented in software, the actual connections between the system components (or the process steps) may differ depending upon the manner in which the present invention is programmed. Given the teachings of the present invention provided herein, one of ordinary skill in the related art will be able to contemplate these and similar implementations or configurations of the present invention. 
     Referring to  FIG. 3 , a method according to an embodiment of the present disclosure performs segmentation through registration of the multiple objects in an image, wherein each object is modeled using a shape constraint, e.g., a rectangle, is imposed by mapping a unit square into the images using a semi-affine transformation. One skilled in the art would recognize that other transformations may be used, including but not limited to rigid body, similarity, projective, and non-rigid/elastic transformations. 
     Rectangles are used to segment adjacent vertebrae on the same image. In addition, interaction forces are implemented that are designed to penalize larger variations in scale and rotation, under the assumption that adjacent vertebrae have a similar size and orientation. A mathematical model of the method is based on ordinary differential equations (ODEs), allowing larger time steps than partial differential equations (PDES) in the numerical implementation. 
     It should be noted that the multiple images may be acquired by different imaging modalities, e.g., computed tomography (CT) and MR images. 
     According to an embodiment of the present disclosure, an image of vertebra/vertebrae is provided  300  and a transformation initialization  301  sets a starting point {circumflex over (x)} in the MR image, a rotation angle, e.g., to 0, and the scale parameters, e.g., to 1. The transformation may be initialized using different values, e.g., the rotation angle parameter of 90 and the scale parameter of 0.5. 
     A user selects an area of interest, e.g., by clicking a point in the image; seed points are generated based on the selected area  302 . Two adaptive thresholds are applied to the image; one to separate the vertebrae from the background and another to separate the vertebrae from the spinal column and other similarly dense structures. This allows the region of interest to be insensitive to the user&#39;s click point and increase the consistency of the vertebrae shape for later detection and selection. An intersection of a binary threshold image with a binary threshold image from at least two neighboring slices is determined, wherein an erosion and dilation morphological operation are performed to separate the vertebrae from surrounding structures. 
     The resulting binarized image can be labeled into regions. Regions are measured for shape characteristics and those having the size and compactness typical of vertebrae are labeled as such, with at least two vertebrae of interest retained. A seed point is generated as the centroid of a vertebra. 
     Given the transformation initialization  301  and at least two seed points  302 , an active rectangle representation is utilized  303 : Let I:Ω⊂R 2 →R denote the image of the unit square  401 , formed as a closed polyline with an outward oriented normal N (see  FIG. 4 ), and let Î:{circumflex over (Ω)}⊂R 2 →R be the target MR image  402 . The unit square C is mapped from I to Î as Ĉ using a transformation g:R 2 →R 2 , e.g.,
 
 Ĉ=g ( C ),  (1)
 
The mapping g includes registration parameters, g 1  . . . g n , which are a set of n=5 parameters from a finite-dimensional group represented by a rotation angle θ, non-uniform scale parameters M x , M y  and displacement parameters D x  and D y . These are used in a semi-affine transformation given as
 
 {circumflex over (x)}=g ( x )= RMx+D,   (2)
 
with rotation matrix
 
               R   =     ⌊           cos   ⁢           ⁢   θ           sin   ⁢           ⁢   θ                 -   sin     ⁢           ⁢   θ           cos   ⁢           ⁢   θ           ⌋       ,         
scaling matrix
 
               M   =     ⌊           M   x         0           0         M   y           ⌋       ,         
and translation vector [D x ,D y ] T , and x is a point on the unit square.  FIG. 4  depicts the transformation of the unit square  401  into the MR image  402 .
 
     In  FIG. 4 , the atlas shape in image I is the unit square  401 , transformed as a rectangle  403  into the image Î  402  by a semi-affine transformation g(x). 
     According to an embodiment of the present disclosure, the method includes an energy function and curve evolution  303 . Segmentation can be achieved by following a gradient descent procedure to minimize a region-based energy functional of the form:
 
 E ( g )=∫ Ĉ     in   {circumflex over (ƒ)} in ( {circumflex over (x)} ) d{circumflex over (x)}+∫   Ĉ     out   {circumflex over (ƒ)} out ( {circumflex over (x)} ) d{circumflex over (x)}   (3)
 
where {circumflex over (ƒ)} is a function that best represents a certain characteristic of the image such as the mean or variance. For example, a piecewise constant segmentation model, for which {circumflex over (ƒ)} in =(I−û) 2  and {circumflex over (ƒ)} out =(I−{circumflex over (v)}) 2 , where û and {circumflex over (v)} are the mean values inside and outside the segmenting curve respectively. This functional is re-expressed on the domain Ω as:
 
 E ( g )=∫ C     in   (| g′|{circumflex over (ƒ)}   in   ◯g )( x ) dx+∫   C     out   (| g′|{circumflex over (ƒ)}   out   ◯g )( x ) dx   (4)
 
where |g′| is the determinant of the Jacobian of g, and ◯ denotes functional composition.
 
     Taking the derivative of Equation (4) with respect to the registration parameter g i  gives the following gradient descent minimization: 
                         ⅆ     g   i         ⅆ   t       =         ∂   E       ∂     g   i         =       ∫   C     ⁢         f   ^     ⁡     (     g   ⁡     (   x   )       )       ⁢     〈         ∂     g   ⁡     (   x   )           ∂     g   i         ,       mRM     -   1       ⁢   N       〉     ⁢           ⁢     ⅆ   s             ,           (   5   )               
where g i  indicates one element of g, m=M x M y , {circumflex over (ƒ)}=({circumflex over (ƒ)} in −{circumflex over (ƒ)} out ), and            ) indicates an inner product. Equation (5) is an ODE whose solution includes a traversal of the contour of the unit square  401 , shown in  FIG. 4 , finding its new transformed pose in the image  402 , and updating the pose function g until convergence. That is, the segmentation occurs by updating the registration parameters g i  . . . g n . There is no contour update

               ∂   C       ∂   t           
since the contour in domain Ω is fixed as the unit square.
 
     Once the segmentation is complete, an orientation of the disk is identified  306  by determining a line that bisects a box connecting the two adjacent rectangles. This can be determined using the corners of the adjacent vertebra: the point P 1  is found, which is the centroid of points A 1  and B 1 , and the point P 2  is found, which is the centroid of points A 2  and B 2 . The line can then be formed between P 1  and P 2 , as shown in  FIG. 9 . 
     An image or data corresponding to the segmentation and/or inter-vertebrae disk orientation can be output  307 , e.g., to a display or storage device. 
     To avoid misalignment due to salient features away from the disk, a weighting can be applied  304 , which is empirically set, e.g., as 4.0, to the edges of the transformed shape constraint, e.g., square, that are closest to the inter-vertebral disk. Referring to  FIG. 10 , the weighting is applied to edges of the rectangles having their normals aligned with a vector going between centroids of two adjacent rectangles. The weighting may be expressed as follows: 
     Recall that the i th  rectangle is described by its transformation parameters including the rotation angle θ, the scale parameters [M x ,M y ] T  and translation or displacement parameters [D x ,D y ] T . An orientation vector for the i th  rectangle is formed using the angle of the rectangle&#39;s transformation, e.g., n=[cos(θ i )sin(θ i )] T . A vector is formed from the rectangle&#39;s centroid to the centroid of an adjacent rectangle, e.g., v=[D xn ,D yn ]−[D xi ,D yi ], where [D xn ,D yn ] is the neighbor&#39;s translation, and [D xi ,D yi ] is the i th  rectangle&#39;s translation. v is normalized to have a unit length. The dot product between these vectors is determined as: •Product=v•n. If the dot product is between [0.7071 and 1] or [−1 and −0.7071], then the angle between the two vectors is between [−45 and 45] degrees, or [135 and 215] degrees respectively. In this case the weighting is applied to edges  1  and  3 , otherwise, the weighting is applied to edges  2  and  4  (see  FIG. 10 ). One of ordinary skill in the art would recognize that other values of the dot product and angles may be used. An exemplary evolution for a single rectangle appears in  FIGS. 5A-C . 
     Referring to  FIGS. 5A-C , an evolution of a single rectangle is shown wherein, from left to right: 0, 25, and 100 iterations, using time step Δt=0.5. 
     While it is possible to independently evolve rectangles in each vertebra adjacent to an inter-vertebral disk, the similarity of adjacent vertebrae can be leveraged as a further constraint. Under the assumption that adjacent vertebrae have a similar size and orientation, an interaction energy between adjacent rectangles is applied  305 . The interaction energy penalizes large orientation and scale differences, and can take the form:
 
 E ( g )=ƒ(∇ g   i )  (6)
 
where ƒ(z) is a differentiable function that penalizes the variation of the registration parameters of different active rectangles. Differentiation of Equation (6) with respect to g i  yields the interaction force
 
                       ⅆ     g   i         ⅆ   t       =         ∂   E       ∂     g   i         =         ∂   f       ∂   z       ⁢       ∂   z       ∂     g   i                     (   7   )               
Various forms of the penalty function may be implemented; for example, ƒ(z)=½z 2 , which provides sufficient regularization on the registration parameters. An evolution in the negative gradient direction is performed, yielding the update
 
                         ⅆ     g   i         ⅆ   t       =       -   αΔ     ⁢           ⁢     g   i         ,           (   8   )               
where Δ is the Laplacian operator and α is a constant used to weight the influence of the interaction force. In the experiments described herein, the weight is set to α=0.25. Other values of the penalty may be selected. The interaction force results in coupling between the active rectangles to jointly perform the segmentation. An example comparing independent and coupled segmentation is presented in  FIGS. 6A-C . For  FIGS. 6A-B , independent evolutions of the two rectangles were performed starting from different initial conditions (seed points)  302 , resulting in the active rectangles being attracted to undesirable local minima.  FIG. 6C  shows the coupled segmentation, which achieves a more robust segmentation.
 
     Referring to  FIGS. 6A-C , an effect of the interaction force is shown wherein,  FIGS. 6A-B  show uncoupled segmentation for two different initial conditions.  FIG. 6C  shows a coupled segmentation for the same initial conditions on the left or right figure. 
     In the following experiments disk orientation detection  306  results from different parts of the spine are reviewed. For initialization, a user clicks on the disk of interest and two seed points  701 - 702  were determined (see  FIG. 3 , block  302 ), one inside each of the upper and lower vertebrae. The seed points may be given by the user or automatically determined, e.g., by determining an approximate center of each vertebra.  FIGS. 7A-C  shows the initialization and the final detection of a disk in the lumbar region of the spine. In  FIG. 7A , copies of the unit square  703 - 704  are placed at each seed point. The segmentation is performed to get the result in  FIG. 7C . Notice how the rectangles align to the edges that are adjacent to the disk. From these results, the orientation of the disk is determined as shown by the line  705  in  FIG. 7C . The orientation is found by determining the line equally bisecting the bounding box connecting the detected vertebrae. 
       FIGS. 8A-C  show the result for a sagittal C-Spine image, and  FIGS. 8D-F  show an example for a coronal image. Determining the disk orientation in both the sagittal and coronal views defines a plane that is used for setting up the transverse slice stack. 
     According to an embodiment of the present disclosure, a method fits a rectangle to each adjacent vertebrae by minimizing an energy functional based on a shape constraint, image data, and coupling between adjacent rectangles. The shape constraint combined with the coupled segmentation results in vertebrae segmentation from which the inter-vertebral disk orientation can be determined. 
     Any shape representable by a closed polyline is supported. The method is applicable to other segmentation problems with different problem-specific shape constraints. 
     While embodiments have been described using two seed points, it is to be understood that a single point may be selected for segmenting a corresponding vertebra. In this case, a seed point is determined and an active rectangle or other shape constraint is deformed to segment the vertebra without using the interaction force. When the segmentation completes, it can be assumed that the disk orientation is the same as that the of the converged shape constraint. 
     Having described embodiments for a system and method for vertebrae segmentation including a determination of disk orientation, it is noted that modifications and variations can be made by persons skilled in the art in light of the above teachings. It is therefore to be understood that changes may be made in embodiments of the present disclosure which are within the scope and spirit thereof.