JSIP  Vol.4 No.3 B , August 2013
Fuzzy C Mean Thresholding based Level Set for Automated Segmentation of Skin Lesions
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.
Cite this paper: A. Masood and A. Al-Jumaily, "Fuzzy C Mean Thresholding based Level Set for Automated Segmentation of Skin Lesions," Journal of Signal and Information Processing, Vol. 4 No. 3, 2013, pp. 66-71. doi: 10.4236/jsip.2013.43B012.

[1]   R. Siegel, et al., “Cancer statistics, 2011”, CA: A Cancer Journal for Clinicians, Vol. 61, No. 4, 2011, pp. 212-236. doi:10.3322/caac.20121

[2]   Society, A.C., Cancer Facts & Figures 2012, 2012.

[3]   “Causes of Death 2010,” C.W.O. Australia, Editor, Australian Bureau of Statistics, Canberra, Australia.

[4]   G. Argenziano and H.P. Soyer, “Dermoscopy of pigmented skin lesions, a valuable tool for early diag-nosis of Melanoma,” The Lancet Oncology, Vol. 2, No. 7, 2001, pp. 443-449. doi:10.1016/S1470-2045(00)00422-8

[5]   D. Piccolo, et al., “Dermoscopic Diagnosis by A Trained Clinician vs. A Clinician with Minimal Dermoscopy Training vs. Computer-aided Diagnosis of 341 Pigmented Skin Lesions: A Comparative Study,” British Journal of Dermatology, Vol. 147, No. 3, 2002, pp. 481-486. doi:10.1046/j.1365-2133.2002.04978.x

[6]   S. Ben Chaabane, et al., “Color Image Segmentation Using Automatic Thresholding and the Fuzzy C-means Techniques”, in Proceedings 14th IEEE Mediterranean Electro technical Conference, 2008, pp. 857-861.

[7]   L. Dongju and Y. Jian. , “Otsu Method and K-means,” in Proceedings Ninth International Conference on Hybrid Intelligent Systems, China, 2009, pp. 344-349.

[8]   M. Emre Celebi, et al., “Border Detection in Dermoscopy Images Using Statistical Region Merging”, Skin Research and Technology, Vol. 14, No. 3, 2008, pp. 347-353. doi:10.1111/j.1600-0846.2008.00301.x

[9]   M. E. Celebi, G. S. H. Iyatomi and W. V. Stoecker, “Lesion Border Detection in Dermoscopy Images,” Computerized Medical Imaging & Graphics, Vol. 33, 2009, pp. 148-153.

[10]   T. Mendonca, et al., “Comparison of Segmentation Methods for Automatic Diagnosis of Dermoscopy Images”, in Proceedings of 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007, pp. 6572-6575.

[11]   M. Silveira, et al., “Comparison of Segmentation Methods for Melanoma Diagnosis in Dermoscopy Images,” IEEE Journal of Selected Topics in Signal Processing, Vol. 3, No. 1, 2009, pp. 35-45. doi:10.1109/JSTSP.2008.2011119

[12]   Isard, A. B. A. M., Active Contours 1998: Springer Verlag.

[13]   Mahmoud, M. K. A. and A. Al-Jumaily, “Segmentation of Skin Cancer Images Based on Gradient Vector Flow Snake,” in Proceedings of 2011 International Conference on Mechatronics and Automation, 2011, pp. 216-220. doi:10.1109/ICMA.2011.5985659

[14]   Bulent Erkol, R. H. M., R. Joe Stanley, William V. Stoecker and Erik Hvatum, “Automatic Lesion Boundary Detection in Dermoscopy Images Using Gradient Vector Flow Snakes,” Skin Research and Technology, Vol. 11, No. 1, 2005, pp. 17-26. doi:10.1111/j.1600-0846.2005.00092.x

[15]   P. Perona, and J. Malik, “Scale-space and Edge Detection Using Aniso-tropic Diffusion,” IEEE Transactions on Pattern Analy-sis and Machine Intelligence, Vol. 12, No. 7, 1990, pp. 629-639. doi:10.1109/34.56205

[16]   J. C. Nascimento, et al., “Adaptive Snakes Using the EM Algorithm,” IEEE Transactions on Image Processing, Vol. 14, No. 11, 2005, pp.1678-1686. doi:10.1109/TIP.2005.857252

[17]   M. N. M. Babu, V. K. Hanmandlu, M. Vasikarla, S., “Histo-pathological Image Analysis Using OS-FCM and Level Sets”, in Proceedings of IEEE 39th Applied Imagery Pattern Recognition Workshop 2010, pp. 1-10.

[18]   Li, B. N. et al., “Integrating Spatial Fuzzy Clustering with Level Set Methods for Automated Medical Image Segmentation”, Computers in Biology and Medicine, Vol. 41, pp. 1-10, 2011. doi:10.1016/j.compbiomed.2010.10.007

[19]   B. N. Li, et al., “Integrating FCM and Level Sets for Liver Tumor Segmentation,” in Proceedings 13th Int. Conference on Biomedical Engineering, Singapore, 2009, pp. 202-205.

[20]   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, 2010, pp. 4752-4755.

[21]   S. Sookpotharom, “Border Detection of Skin Lesion Images Based on Fuzzy C-Means Thresholding,” in Proceedings of 3rd Int. Conference on Genetic and Evolutionary Computing, China, 2009, pp. 777-780.

[22]   M. Silveira and J. S. Marques, “Level Set Segmentation of Dermoscopy Images,” presented at 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, May 14-17, 2008.

[23]   M. Kamali and G. Samei, “Border Preserving Skin Lesion Segmentation,” in Proceedings of SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 2008.

[24]   L. Chunming, 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, 2005, pp. 430-436.

[25]   S. Osher and R. Fedkiw, “Level Set Methods and Dynamic Implicit Surfaces2002,” New York: Springer-Verlag.

[26]   T. F. Chan and L. A. Vese, “Active contours without edges,” IEEE Transactions on Image Processing, Vol. 2, pp.266-277, 2001. doi:10.1109/83.902291

[27]   V. Caselles, R. Kimmel, and G. Sapiro, “Geodesic Active Contours”, International Journal of Computer Vision, Vol. 22, No. 1, 1997, pp. 61-79. doi:10.1023/A:1007979827043

[28]   Huiyu, Z., et al., “Anisotropic Mean Shift Based Fuzzy C-Means Segmentation of Dermoscopy Images,” IEEE Journal of Selected Topics in Signal Processing, Vol. 3, No. 1, 2009, pp. 26-34. doi:10.1109/JSTSP.2008.2010631

[29]   Q. Abbas, I. Fondón and M. Rashid, “Unsupervised Skin Lesions Border Detection via Two-dimensional Image Analysis,” Computer Methods and Programs in Biomedicine, Vol. 104, No. 3, 2011, pp. 1-15. doi:10.1016/j.cmpb.2010.06.016