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Proceedings Paper

AWM: Adaptive Weight Matting for medical image segmentation
Author(s): Jieyu Cheng; Mingbo Zhao; Minquan Lin; Bernard Chiu
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Paper Abstract

Image matting is a method that separates foreground and background objects in an image, and has been widely used in medical image segmentation. Previous work has shown that matting can be formulated as a graph Laplacian matrix. In this paper, we derived matting from a local regression and global alignment view, as an attempt to provide a more intuitive solution to the segmentation problem. In addition, we improved the matting algorithm by adding a weight extension and refer to the proposed approach as Adaptive Weight Matting (AWM), where an adaptive weight was added to each local regression term to reduce the bias caused by outliers. We compared the segmentation results generated by the proposed method and several state-of-the-art segmentation methods, including conventional matting, graph-cuts and random walker, on medical images of different organs acquired using different imaging modalities. Experimental results demonstrated the advantages of AWM on medical image segmentation.

Paper Details

Date Published: 24 February 2017
PDF: 6 pages
Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101332P (24 February 2017); doi: 10.1117/12.2254774
Show Author Affiliations
Jieyu Cheng, City Univ. of Hong Kong (Hong Kong, China)
Mingbo Zhao, Donghua Univ. (China)
Minquan Lin, City Univ. of Hong Kong (Hong Kong, China)
Bernard Chiu, City Univ. of Hong Kong (Hong Kong, China)

Published in SPIE Proceedings Vol. 10133:
Medical Imaging 2017: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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