期刊
PATTERN RECOGNITION
卷 42, 期 6, 页码 1017-1028出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2008.09.006
关键词
Automatic skin lesion segmentation; Melanoma detection; Border detection; Active contour; Snake; Narrow band energy
资金
- NSF [521527]
- Grants to Enhance and Advance Research program
- Texas Learning and Computation Center at the University of Houston
Accurate skin lesion segmentation is critical for automated early skin cancer detection and diagnosis. In this paper, we present a novel multi-modal skin lesion segmentation method based on region fusion and narrow band energy graph partitioning. The proposed method can handle challenging characteristics of skin lesions, such as topological changes, weak or false edges, and asymmetry. Extensive testing demonstrated that in this method complex contours are detected correctly while topological changes of evolving curves are managed naturally. The accuracy of the method was quantified using a lesion similarity measure and lesion segmentation error ratio, Our results were validated using a large set of epiluminescence microscopy (ELM) images acquired using Cross-polarization ELM and side-transillumination ELM. Our findings demonstrate that the new method can achieve improved robustness and better overall performance compared to other state-of-the-art segmentation methods. Published by Elsevier Ltd.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据