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Shape regularized active contour based on dynamic programming for anatomical structure segmentation
 

Summary: Shape regularized active contour based on dynamic programming
for anatomical structure segmentation
Tianli Yu1
, Jiebo Luo2
, Amit Singhal2
, Narendra Ahuja1
1
Department of Electrical and Computer Engineering, University of Illinois,
Urbana-Champaign, IL, USA 61801
2
Research and Development Laboratories, Eastman Kodak Company, Rochester, NY, USA 14650
ABSTRACT
We present a method to incorporate nonlinear shape prior constraints into segmenting different anatomical structures
in medical images. Kernel space density estimation (KSDE) is used to derive the nonlinear shape statistics and
enable building a single model for a class of objects with nonlinearly varying shapes. The object contour is coerced
by image-based energy into the correct shape sub-distribution (e.g., left or right lung), without the need for model
selection. In contrast to an earlier algorithm that uses a local gradient-descent search (susceptible to local minima),
we propose an algorithm that iterates between dynamic programming (DP) and shape regularization.
DP is capable of finding an optimal contour in the search space that maximizes a cost function related to the
difference between the interior and exterior of the object. To enforce the nonlinear shape prior, we propose two

  

Source: Ahuja, Narendra - Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign

 

Collections: Computer Technologies and Information Sciences; Engineering