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Erschienen in:
2016 | OriginalPaper | Buchkapitel
Feature Sensitive Label Fusion with Random Walker for Atlas-Based Image Segmentation
verfasst von : Siqi Bao, Albert C. S. Chung
Erschienen in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016
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Abstract
In this paper, a novel label fusion method is proposed and formulated on a graph, which embraces both label priors from atlases and anatomical priors from target image. To represent a pixel in a comprehensive way, three kinds of feature vectors are generated, including intensity, gradient and structural signature. Feature Sensitive Label Prior (FSLP), which takes both the consistency and variety of different features into consideration, is proposed to gather atlas priors. As FSLP is a non-convex problem, one heuristic approach is further designed to solve it efficiently. Moreover, based on anatomical knowledge, parts of the target pixels are also employed as graph seeds to assist the label fusion process. The experiments carried out on two publicly available databases give results to demonstrate that the proposed method can obtain better segmentation quality.