Abstract:
A method for determining the location, shape and orientation of a tumor in a medical image includes finding a plurality of spatial extrema μ of a D-dimensional spatial signal f for a set of bandwidths H by performing mean shift-based gradient-ascent iterations for a set of bandwidths H and then determining a D-dimensional spread and orientation of the signal about each extrema μ by estimating a covariance Σ of the signal f for each extrema μ. The optimal estimate of μ and Σ is determined by performing a Jensen-Shannon divergence on the full set of μ and Σ.

Description:
CROSS REFERENCE TO RELATED UNITED STATES APPLICATIONS 
     This application claims priority from “Robust Scale-Space Analysis of 3D Local Structures in medical Images”, U.S. Provisional Application No. 60/488,603 of Okada, et al, filed Jul. 18, 2003, the contents of which are incorporated herein by reference. 
    
    
     BACKGROUND OF THE INVENTION 
     A problem in the volumetric medical image analysis is to characterize the 3D local structure of tumors across various scales, because the size and shape of tumors varies largely in practice. Such underlying scales of tumors also provide useful clinical information, correlating highly with probability of malignancy. There are a number of previously proposed approaches addressing this problem. However, these prior art approaches are prone to be sensitive to signal noises and their accuracy degrades when the target shapes differ largely from an isolated Gaussian. In the medical domain, these constraints are too strong since many tumors appear as irregular shapes within noisy background signals. 
     SUMMARY OF THE INVENTION 
     This invention is directed to methods for the robust estimation of the covariance matrix that describes the spread and 3-dimensional (3D) orientation of the structure of interest. Given an input signal, a mean shift-based gradient ascent is performed for an extended mean-shift vector using all the available data points for each analysis bandwidth. The data points that converge to the same point are grouped together, forming a set of local structure candidates. These convergence candidates can be interpreted as spatial extrema of the signal. Then, for each candidate, the underlying scale is determined by estimating a covariance matrix by a constrained least-squares method. Finally, for each candidate, a stability test is performed across the analysis scales, resulting in an optimal scale estimate for each local target. As a result, one can find a signal&#39;s local scales that can vary spatially. 
     These methods utilize an algorithm, referred to herein as a mean shift-based bandwidth selection algorithm, for analyzing general discretized signals and estimating fully parameterized covariance matrices. With the methods of the invention, it becomes possible to address the problem of representing local structures in images. The robust mode and scale selection of the mean shift-based bandwidth selection algorithm together with the consideration of the fully parameterized covariance matrix also enables one to estimate a tumor&#39;s scale in more flexible and robust manner, mitigating the aforementioned shortcomings of the previous methods. Since many target objects in the medical domain possess complex 3D structures, the methods of the invention can be deployed for a number of different application scenarios. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a flow chart depicting a method of a preferred embodiment of the invention. 
         FIG. 2  is a flow chart depicting a method of another preferred embodiment of the invention. 
         FIGS. 3-4  are images depicting results of analyses performed using a preferred method of the invention. 
         FIG. 5  depicts a preferred embodiment of a computer system for implementing a preferred method of the invention. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
     1. Fixed-bandwidth Mean Shift Vector for Continuous Signals 
     A medical image can be represented by a D-dimensional continuous signal f:R D →R evaluated at n d-dimensional points x i , and the uncertainty associated with each point x i  can be represented by a D×D matrix H i , for an i∈1, . . . n. The matrices H i  are referred to as bandwidth matrices. The signal can have one or more extrema. An extrema of the signal can be associated with a location of a tumor or other target object. Referring now to  FIG. 1 , a first step in the analysis of the medical image represented by f is to determine, at step  11 , the spatial extrema μ of the signal. To find the extrema, one can first define a function, m (x; H), where x is a spatial location corresponding to a signal measurement and H is the corresponding bandwidth, referred to herein as an extended mean shift vector, by 
                     m   ⁡     (     x   ;   H     )       ≡         ∫     μ   ⁢           ⁢     Φ   ⁡     (       x   -   μ     ;   H     )       ⁢     f   ⁡     (   μ   )       ⁢     ⅆ   μ           ∫       Φ   ⁡     (       x   -   μ     ;   H     )       ⁢     f   ⁡     (   μ   )       ⁢     ⅆ   μ           -     x   .               (   3   )               
where a Gaussian kernel Φ(x−μ; H) can be defined as
 
             exp   ⁡     (       -     1   2       ⁢       D   2     ⁡     (     x   ,     μ   ;   H       )         )           
with D 2 (x,μ; H)=(x−μ) T  H −1 (x−μ) and H −1  is a weighted harmonic mean of the bandwidth matrices,
 
                 H     -   1       ⁡     (   x   )       =       ∑     i   =   1     n     ⁢         w   i     ⁡     (   x   )       ⁢       H   i     .               
The weights can be defined as
 
                 w   i     ⁡     (   x   )       =         1            H   i            1   2         ⁢     exp   ⁡     (       -     1   2       ⁢       D   2     ⁡     (     x   ,     μ   ;     H   i         )         )             ∑     i   =   1     n     ⁢       1            H   i            1   2         ⁢     exp   ⁡     (       -     1   2       ⁢       D   2     ⁡     (     x   ,     μ   ;     H   i         )         )                   
and can be normalized to unity.
 
     Eq. (3) can be used to locate spatial extrema μ of f given a fixed analysis bandwidth H as follows. First, make an estimate of an extrema, μ 1 , and then evaluate m 1  (x; H) for this extrema from Eq. (3). If y 1  is used to denote the result of the first term of Eq. (3) for the initial estimate of μ 1 , then, for the next iteration of Eq. (3), x is replaced with y 1  and μ 1  is replaced with m 1  (x; H), denoted as μ 2 . This process can be repeated, each time replacing the second term of Eq. (3) with the result of the first term from the previous iteration, and evaluating the first term on the previous evaluation of m (x; H). For each iteration k of Eq. (3), the resulting difference will converge to zero. The value of μ k  for which the extended mean shift vector m (x; H) is sufficiently close to zero can be taken as an extrema of the signal f. The data space of the signal can be partitioned by grouping data points that converge into the same extrema. 
     2. Constrained Least-squares Solution of Covariance Matrices 
     The next step, step  12  of  FIG. 1 , is to estimate the D-dimensional spread and orientation of the tumors whose center μ as a spatial extremum was found in step  11 . The geometrical information of a D-dimensional local surface can be characterized by a covariance matrix Σ estimated at the extrema. 
     The covariance Σ can be defined by the equation m(x)≈−H(Σ+H) −1 (x−μ) when f can be approximated by a Gaussian. This can be rewritten in the following simple form,
 
Σ H   −1   m   i   ≈b   i   ≡μ−x   i   −m   i   (4)
 
     Considering all the trajectory points {x i : i=1, . . . , t u } that converge to an extremum μ, an over-complete set of linear equations can be contructed:
 
AΣ≈B,  (5)
 
where
 
 A= ( m   1   ; . . . ; m   t     u   ) T   H   −T ,
 
 B= ( b   i   ; . . . ; b   t     u   ) T ,
 
and where Σ is a symmetric, positive definite matrix in R D×D . The covariance can be estimated by a constrained least-squares solution of Eq. (5). This solution yields the following closed form,
 
Σ*= U   p Σ P   −1   U   {tilde over (Q)} Σ {tilde over (Q)}   U   {tilde over (Q)}   T Σ P   −1   U   P    T ;  (6)
 
where the solution involves the following symmetric Schur decompositions: A T A=U P Σ P   2 U P   T  and B T B≡Q with {tilde over (Q)}=Σ P U P   T QU P Σ P =U {tilde over (Q)} Σ {tilde over (Q)}   2 U {tilde over (Q)}   T . This closed form can be found by determining the unique minimizer for an area, g(Y)≡∥AY−BY −T ∥ F   2 , where Σ=YY T .
 
3. Scale Selection
 
     The above two steps can result in pairs of center location and covariance estimates {μ h ; Σ h } for each analysis bandwidth H. The next step, step  13 , concerns finding the optimal estimate of the target structures analyzed across a range of bandwidths. This optimal estimate can be found by using a form of the Jensen-Shannon divergence, 
     
       
         
           
             
               
                 
                   
                     
                       
                         
                           
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     Given a neighborhood parameter a, the divergence can be computed for each analysis bandwidth h. The extremum of the divergences JS(h) across the bandwidths can provide a final scale estimate that is most stable over a range of scales. 
     The stability test described requires the set of analysis bandwidths a priori. In one embodiment of the invention, H=hI and h is varied with a constant step. In order to achieve higher performance for the scale selection, it is preferred to have more densely distributed analysis bandwidths. However, such dense sampling can prohibitively enlarge the search space, especially when a fully parameterized H is considered. 
     4. Algorithm for Local Multi-scale Analysis 
     In some application scenarios, the task to be solved is to represent the scale of local structure whose rough location is provided by another means. An example is the structural analysis of tumors whose locations in a volumetric image are provided manually by radiologist. The simplest strategy in such a case is to perform the mean shift iteration only from the given marker point. The convergence point serves as the tumor center estimate and all the trajectory points from the marker are used to estimate the scale. This naive strategy can fail when the provided locations are contaminated by uncertainties and when the iteration converges too soon, forcing the Eq.(4) to be under-complete and rank-deficient. These issues can be addressed by the following steps, depicted in  FIG. 2 . First, at step  21 , consider a set of starting points sampled from the neighborhood of the marker. At step  12 , after performing mean shift iterations, the point to which most starting points converged serves as a location estimate μ. Next, at step  13 , a regular sampling around the estimate μ can be performed. The scale estimate Σ can be given by solving, at step  14 , Eq. (4) using all the trajectory points that converged to μ. The same stability test of Eq. (7) can be used for the final estimate at step  15 . 
     6. EXAMPLES 
     A 3D domain implementation of the local multi-scale analysis algorithm described in Section 5 is evaluated with high-resolution computerized tomography (HRCT) images of 14 patients displaying pulmonary tumors. A total of 44 analysis scales with 0.25 interval h=(0.25 2 ; . . . ; 11 2 ) and a=1 were used. The rough location of the tumors were provided. As a pre-process, volumes of interest of size 32×32×32 are extracted using the markers.  FIGS. 3 and 4  show examples of the resulting center and part-solid nodules whose geometrical shapes are more deviated from the simple Gaussian structure. The correct estimation of the tumor locations, spreads, and 3D orientations for these difficult cases demonstrates the effectiveness of the methods of the invention. 
     It is to be understood that the present invention can be implemented in various forms of hardware, software, firmware, special purpose processes, or a combination thereof. In one embodiment, the present invention can be implemented in software as an application program tangible embodied on a computer readable program storage device. The application program can be uploaded to, and executed by, a machine comprising any suitable architecture. 
     Referring now to  FIG. 5 , according to an embodiment of the present invention, a computer system  101  for implementing the present invention can comprise, inter alia, a central processing unit (CPU)  102 , a memory  103  and an input/output (I/O) interface  104 . The computer system  101  is generally coupled through the I/O interface  104  to a display  105  and various input devices  106  such as a mouse and a keyboard. The support circuits can include circuits such as cache, power supplies, clock circuits, and a communication bus. The memory  103  can include random access memory (RAM), read only memory (ROM), disk drive, tape drive, etc., or a combinations thereof. The present invention can be implemented as a routine  107  that is stored in memory  103  and executed by the CPU  102  to process the signal from the signal source  108 . As such, the computer system  101  is a general purpose computer system that becomes a specific purpose computer system when executing the routine  107  of the present invention. 
     The computer system  101  also includes an operating system and micro instruction code. The various processes and functions described herein can either be part of the micro instruction code or part of the application program (or combination thereof) which is executed via the operating system. In addition, various other peripheral devices can 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 can be implemented in software, the actual connections between the systems 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. 
     While the present invention has been described in detail with reference to a preferred embodiment, those skilled in the art will appreciate that various modifications and substitutions can be made thereto without departing from the spirit and scope of the invention as set forth in the appended claims.