Medical Image Segmentation Using Topologically Adaptable Snakes

This paper presents a technique for the segmentation of anatomic structures in medical images using a topologically adaptable snakes model The model is set in the framework of domain subdivi sion using simplicial decomposition This framework allows the model to maintain all of the strengths associated with traditional snakes while overcoming many of their limitations The model can ow into complex shapes even shapes with signi cant protrusions or branches and topo logical changes are easily sensed and handled Multiple instances of the model can be dynamically created can seamlessly split or merge or can simply and quickly detect and avoid collisions Finally the model can be easily and dynamically converted to and from the traditional parametric snakes model representation We apply a D model to segment structures from medical images with complex shapes and topologies such as arte rial trees that cannot easily be segmented with traditional deformable models