TY - GEN
T1 - Skin lesion segmentation using clustering techniques
AU - Emre Celebi, M.
AU - Guo, Wenzhao
AU - Alp Aslandogan, Y.
AU - Bergstresser, Paul R.
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=32844469684&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:32844469684
SN - 1577352343
T3 - Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005 - Recent Advances in Artifical Intelligence
SP - 364
EP - 369
BT - Recent Advances in Artifical Intelligence - Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005
A2 - Russell, I.
A2 - Markov, Z.
T2 - Recent Advances in Artifical Intelligence - Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005
Y2 - 15 May 2005 through 17 May 2005
ER -