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Integration of Multiple Segmentation Methods based on Evaluation Functions for Segmentation of Visible Human Color Images  

김한영 (숭실대학교 정보통신전자공학부)
김동성 (숭실대학교 정보통신전자공학부)
강흥식 (서울대학교 의과대학)
Publication Information
Journal of KIISE:Software and Applications / v.30, no.3_4, 2003 , pp. 308-315 More about this Journal
Abstract
This paper proposes an approach integrating multiple segmentation methods in a systematic way, which can improve overall accuracy without deteriorating accuracy of highly confident segments of boundaries generated by constituent methods. A segmentation method produces boundary segments, which are then evaluated with an evaluation function considering pros/cons of the current and next methods to apply. Boundary segments with low confidence are replaced by a next method while the other segments are kept. These steps are repeated until all segmentation methods are applied. The proposed approach is implemented for the segmentation of muscles in the Visible Human color images. A Balloon method, a minimum cost path finding method, and a Seeded Region Growing method are integrated. The final segmentation results showed improvements in both overall evaluation and segment-based evaluation.
Keywords
Integrated segmentation; Segmentation method; Medical Image; Visible human;
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