A narrow band graph partitioning method for skin lesion segmentation

Authors:
Xiaojing Yuan;Ning Situ;George Zouridakis
Affiliations:
Department of Engineering Technology, University of Houston, Houston, TX 77204, USA;Department of Computer Science, University of Houston, Houston, TX 77204, USA;Department of Engineering Technology, University of Houston, Houston, TX 77204, USA and Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77204, USA and Departm ...
Venue:
Pattern Recognition
Year:
2009

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Abstract

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.