Abstract
The model-based technique offers a unique and efficient approach toward medical image segmentation and analysis due to its power to unify image information within a physical framework. Of the model-based techniques, the deformable model is most effectively used for its ability to unify image statistics — both local and global — in a geometrically constrained framework. The geometric constraint imparts a compact form of shape information. This chapter reviews one of the most promising and highly used deformable approaches: the active contour model in medical image analysis. The active contour model is one of the most effective approaches due to its flexibility to adapt to various anatomical shapes while constraining the local geometric shape constraint. Within the geometric paradigm, local image statistics and regional information has been effectively used in segmentation purposes. In addition, various forms of a-priori information can be incorporated into this model. Active contour models are capable of accommodating a wide range of shape variability over time and space. The active contour also has to overcome the limitation of topological adaptibility by introducing a topology adaptive model. This chapter details the development and evolution of the active contour model with the growing sophistication of medical images.
Published Version
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