Level Set Initialization Based on Modified Fuzzy C Means Thresholding for Automated Segmentation of Skin Lesions
Segmentation of skin lesion is an important step in the overall automated diagnostic systems used for early detection of skin cancer. Skin lesions can have various different forms which makes segmentation a difficult and complex task. Different methods are present in literature for improving results for skin lesion segmentation. Each method has some pros and cons and it is observed that none of them can be regarded as a generalized method working for all types of skin lesions. The paper proposes an algorithm that combines the advantages of clustering, thresholding and active contour methods currently being used independently for segmentation purposes. A modified algorithm for thresholding based on fusion of Fuzzy C mean clustering and histogram thresholding is applied to initialize level set automatically and also for estimating controlling parameters for level set evolution. The performance of level set segmentation is subject to appropriate initialization, so the proposed algorithm is being compared with some other state-of-the-art initialization methods. The work has been tested on clinical database of 270 images. Parameters for performance evaluation are presented in detail. Increased true detection rate and reduced false positive and false negative errors confirm the effectiveness of the proposed method for skin cancer detection.
KeywordsSkin cancer Segmentation Diagnosis Thresholding Fuzzy Active contours
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- 2.Society, A.C., Cancer Facts & Figures (2012), http://www.cancer.org/acs/groups/content/epidemiologysurveilance/documents/document/acspc-031941.pdf
- 4.Ben Chaabane, S., et al.: Color image segmentation using automatic thresholding and the fuzzy C-means techniques. In: Proceedings of the 14th IEEE Mediterranean Electro technical Conference, pp. 857–861 (2008)Google Scholar
- 5.Dongju, L., Jian, Y.: Otsu Method and K-means. In: Proceedings of the Ninth International Conference on Hybrid Intelligent Systems, China, pp. 344–349 (2009)Google Scholar
- 9.Blake, A., Isard, M.: Active Contours. Springer (1998)Google Scholar
- 14.Aja-Fernandez, et al.: Soft thresholding for medical image segmentation. In: Proceedings 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Argentina, pp. 4752–4755 (2010)Google Scholar
- 15.Jun, Z., Jinglu, H.: Image Segmentation Based on 2D Otsu Method with Histogram Analysis. In: Proceedings 2008 International Conference on Computer Science and Software Engineering, pp. 105–108 (2008)Google Scholar
- 17.Sookpotharom, S.: Border Detection of Skin Lesion Images Based on Fuzzy C-Means Thresholding. In: Proceedings of 3rd Int. Conference on Genetic and Evolutionary Computing, China, pp. 777–780 (2009)Google Scholar
- 18.Chunming, L., et al.: Level set evolution without re-initialization: A new variational formulation. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, pp. 430–436 (2005)Google Scholar
- 21.Osher, S., Fedkiw, R.: Level Set Methods and Dynamic Implicit Surfaces. Springer, New York (2002)Google Scholar