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Fuzzy C Mean Thresholding based Level Set for Automated Segmentation of Skin Lesions
School of Electrical, Mechanical and Mechatronic Engineering, University of Technology, Sydney, Australia.
School of Electrical, Mechanical and Mechatronic Engineering, University of Technology, Sydney, Australia.
- 1 School of Electrical, Mechanical and Mechatronic Engineering, University of Technology, Sydney, Australia.
- 2 School of Electrical, Mechanical and Mechatronic Engineering, University of Technology, Sydney, Australia.
Journal of Signal and Information Processing·Volume 04 (2013)·Pages 66–71·Published 1 August 2013·DOI10.4236/jsip.2013.43B012
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Abstract
Accurate segmentation is an important and challenging task in any computer vision system. It also plays a vital role in computerized analysis of skin lesion images. This paper presents a new segmentation method that combines the advan-tages of fuzzy C mean algorithm, thresholding and level set method. 3-class Fuzzy C mean thresholding is applied to initialize level set automatically and also for estimating controlling parameters for level set evolution. Parameters for performance evaluation are presented and segmentation results are compared with some other state-of-the-art segmentation methods. Increased true detection rate and reduced false positive and false negative errors confirm the effectiveness of proposed method for skin cancer detection.
KeywordsSkin CancerSegmentationDiagnosisFuzzyThresholdingLevel Sets
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