Abstract
Despite the impressive performance, Active Contour Model (ACM) is yet to be verified for automatic segmentation of Region-of-Interest (ROI) in medical images (MIs). Different modality MIs have unique properties and requirements that constitute the automatic segmentation of ROI even more challenging than that for natural images. In this paper, we investigate the performance of popular ACM based segmentation methods of ROI in multi-modality MIs. Three different ACM based segmentation methods that rely on the object's regions and edges are examined. Performances of the methods are evaluated and compared in terms of popular evaluation metrics with commonly used modalities of MIs. Our experimental results demonstrate that region-based ACM generally has the best segmentation performance and computational efficiency over the edge-based ACMs for all modalities of MI. Region-based segmentation thus can be promising for automatic segmentation of ROIs in MI-based understanding, detection and recognition applications.
Published Version
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