Competitive Medical Image Segmentation: A Competitive Framework for the Fast Marching Method

Continuous advancements in medical data acquisition and 3D display technologies have made them increasingly significant tools in modern medical treatment. However, these technologies are only effective with the use of innovative data processing methods that produce accurate 3D models of biological data, bridging the gap between acquisition and presentation. The most successful existing methods to accomplish this make use of significant data dependent input and user manipulations. This work introduces an innovative approach to solving this image segmentation problem through the integration of the popular Fast Marching method into a competitive framework. The technique''s design is centered around minimizing user interaction and maximizing generality, making it appropriate for use on various bodies of data without substantial modification. Analysis of the algorithm''s performance makes clear its strengths and weaknesses and demonstrates a promising future in surgical simulation and endoscopic studies.

Jonathan W. Hearn, MS: Studied Computer Engineering at Case Western Reserve University