Abstract

A number of active contour models have been proposed that unify the curve evolution framework with classical energy minimization techniques for segmentation, such as snakes. The essential idea is to evolve a curve (in two dimensions) or a surface (in three dimensions) under constraints from image forces so that it clings to features of interest in an intensity image. The evolution equation has been derived from first principles as the gradient flow that minimizes a modified length functional, tailored to features such as edges. However, because the flow may be slow to converge in practice, a constant (hyperbolic) term is added to keep the curve/surface moving in the desired direction. We derive a modification of this term based on the gradient flow derived from a weighted area functional, with image dependent weighting factor. When combined with the earlier modified length gradient flow, we obtain a partial differential equation (PDE) that offers a number of advantages, as illustrated by several examples of shape segmentation on medical images. In many cases the weighted area flow may be used on its own, with significant computational savings.

Keywords

Image segmentationBalanced flowSegmentationPartial differential equationEnergy functionalWeightingMathematicsActive contour modelFlow (mathematics)Vector flowMinificationAlgorithmGeometric flowArc lengthMathematical optimizationArtificial intelligenceComputer scienceGeometryMathematical analysis

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2001 IEEE Transactions on Image Processing 10188 citations

Publication Info

Year
1998
Type
article
Volume
7
Issue
3
Pages
433-443
Citations
260
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Closed

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Cite This

Kaleem Siddiqi, Y.B. Lauziere, Allen Tannenbaum et al. (1998). Area and length minimizing flows for shape segmentation. IEEE Transactions on Image Processing , 7 (3) , 433-443. https://doi.org/10.1109/83.661193

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DOI
10.1109/83.661193