Journal
PATTERN RECOGNITION
Volume 42, Issue 6, Pages 1017-1028Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2008.09.006
Keywords
Automatic skin lesion segmentation; Melanoma detection; Border detection; Active contour; Snake; Narrow band energy
Funding
- NSF [521527]
- Grants to Enhance and Advance Research program
- Texas Learning and Computation Center at the University of Houston
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available