Skin lesion segmentation using clustering techniques

M. Emre Celebi, Wenzhao Guo, Y. Alp Aslandogan, Paul R. Bergstresser

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005 - Recent Advances in Artifical Intelligence
EditorsI. Russell, Z. Markov
Pages364-369
Number of pages6
StatePublished - 2005
EventRecent Advances in Artifical Intelligence - Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005 - Clearwater Beach, FL, United States
Duration: May 15 2005May 17 2005

Other

OtherRecent Advances in Artifical Intelligence - Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005
CountryUnited States
CityClearwater Beach, FL
Period5/15/055/17/05

Fingerprint

Skin
Clustering algorithms
Cluster analysis
Computer vision
Pattern recognition
Data mining
Feature extraction
Tumors
Color
Experiments

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Emre Celebi, M., Guo, W., Alp Aslandogan, Y., & Bergstresser, P. R. (2005). Skin lesion segmentation using clustering techniques. In I. Russell, & Z. Markov (Eds.), Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005 - Recent Advances in Artifical Intelligence (pp. 364-369)

Skin lesion segmentation using clustering techniques. / Emre Celebi, M.; Guo, Wenzhao; Alp Aslandogan, Y.; Bergstresser, Paul R.

Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005 - Recent Advances in Artifical Intelligence. ed. / I. Russell; Z. Markov. 2005. p. 364-369.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Emre Celebi, M, Guo, W, Alp Aslandogan, Y & Bergstresser, PR 2005, Skin lesion segmentation using clustering techniques. in I Russell & Z Markov (eds), Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005 - Recent Advances in Artifical Intelligence. pp. 364-369, Recent Advances in Artifical Intelligence - Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005, Clearwater Beach, FL, United States, 5/15/05.
Emre Celebi M, Guo W, Alp Aslandogan Y, Bergstresser PR. Skin lesion segmentation using clustering techniques. In Russell I, Markov Z, editors, Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005 - Recent Advances in Artifical Intelligence. 2005. p. 364-369
Emre Celebi, M. ; Guo, Wenzhao ; Alp Aslandogan, Y. ; Bergstresser, Paul R. / Skin lesion segmentation using clustering techniques. Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005 - Recent Advances in Artifical Intelligence. editor / I. Russell ; Z. Markov. 2005. pp. 364-369
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