Skin Lesion Segmentation Using Clustering Techniques

M. Emre Celebi, Wenzhao Guo, and Y. Alp Aslandogan, The University of Texas at Arlington; Paul R. Bergstresser, University of Texas Southwestern Medical left at Dallas

Cluster analysis has been widely used in various disciplines such as pattern recognition, computer vision, and data mining. In this work we investigate the applicability of two spatial clustering algorithms, namely DBSCAN and STING, to a new problem domain: Color segmentation of skin lesion (tumor) images. Automated segmentation is a key step in the computerized analysis of skin lesion images since the accuracy of the subsequent steps (feature extraction, classification, etc.) crucially depends on the accuracy of this very first step. In this paper, we develop two unsupervised methods for segmentation of skin lesion images: one based on DBSCAN clustering algorithm and the other based on STING clustering algorithm. Experiments on a database of over hundred skin lesion images show that DBSCAN-based segmentation algorithm performs significantly better than the STING-based one.


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