A divide and conquer deformable contour method with a model based searching algorithm.
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions(2003)
Abstract
A divide and conquer deformable contour method is presented with an initial inside closed contour being divided into arbitrary segments, and these segments are allowed to deform separately preserving the segments' connectivity. A maximum area threshold, A/sub max/, is used to stop these outward contour segments' marching. Clear and blur contour points are then identified to partition the whole contour into clear and blur segments. A bi-directional searching method is then recursively applied to each blur segment including a search for contour-within-contour segment to reach a final close contour. Further improvements are provided by a model based searching algorithm. It is a two-step process with step 1 being a linked contour model matching operation where landmarks are extracted, and step 2 being a posteriori probability model matching and correction operation where large error segments are fine tuned to obtain the final results. The experiments include ultrasound images of pig heart, MRI brain images, MRI knee images having complex shapes with or without gaps, and inhomogeneous interior and contour region brightness distributions. These experiments have shown that the method has the capability of moving a contour into the neighboring region of the desired boundary by overcoming inhomogeneous interior, and by adapting each contour segment searching operation to different local difficulties, through a contour partition and repartition scheme in searching for a final solution.
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Key words
whole contour,image segmentation,blur contour point,outward contour segment,contour partition,divide and conquer approach,blur segment,contour segment,contour model,final close contour,model based approach.,contour region brightness distribution,deformable contour method,index terms—deformable contour method,energy based method,divide and conquer,search algorithm,brain imaging,indexing terms
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