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Maximum Entropy Based Image Segmentation of Human Skin Lesion

Authors: Sheema Shuja Khattak, Gule Saman, Imran Khan, Abdus Salam


Image segmentation plays an important role in medical imaging applications. Therefore, accurate methods are needed for the successful segmentation of medical images for diagnosis and detection of various diseases. In this paper, we have used maximum entropy to achieve image segmentation. Maximum entropy has been calculated using Shannon, Renyi and Tsallis entropies. This work has novelty based on the detection of skin lesion caused by the bite of a parasite called Sand Fly causing the disease is called Cutaneous Leishmaniasis.

Keywords: shannon, maximum entropy, Renyi, Tsallis entropy

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[1] D. Martin, C. Fowlkes, D. Tal, and J. Malik, “A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,” in Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on, 2001, vol. 2, pp. 416–423.
[2] M. Portes de Albuquerque, I. Esquef, A. Gesualdi Mello, and M. Portes de Albuquerque, “Image thresholding using Tsallis entropy”, Pattern Recognition Letters, vol. 25, no. 9, pp. 1059-1065, 2004.
[3] Y. J. Zhang, “A survey on evaluation methods for image segmentation”, Pattern recognition, vol. 29, no. 8, pp. 1335-1346, 1996.
[4] S. Satorres Martínez, J. Gómez Ortega, J. Gámez García, and A. Sánchez García, “A machine vision system for defect characterization on transparent parts with non-plane surfaces,” Machine Vision and Applications, vol. 23, no. 1, pp. 1–13, Jan. 2012.
[5] T. S. Nguyen, S. Begot, F. Duculty, and M. Avila, “Free-form anisotropy: A new method for crack detection on pavement surface images,” in Image Processing (ICIP), 2011 18th IEEE International Conference on, 2011, pp. 1069–1072.
[6] K. K. Singh and A. Singh,” A study of image segmentation algorithms for different types of images”, Int. J. Comput. Sci. Issues, vol. 7, pp. 41- 47, 2010.
[7] V. A. Cardenas, M. Price, M. A. Infante, E. M. Moore, S. N. Mattson, E. P. Riley, and G. Fein, “Automated cerebellar segmentation: validation and application to detect smaller volumes in children prenatally exposed to alcohol”, NeuroImage: Clinical, vol. 4, pp. 295-301, 2014
[8] B. Peng and D. Zhang, “Automatic image segmentation by dynamic region merging, Image Processing”, IEEE Transactions on, vol. 20, no. 12, pp. 3592-3605, 2011.
[9] M. A. Balafar, A. R. Ramli, M. I. Saripan, and S. Mashohor, “Review of brain mri image segmentation methods”, Artificial Intelligence Review, vol. 33, no. 3, pp. 261-274, 2010.
[10] G. Liu, H. Bian, and H. Shi, “Sonar image segmentation based on an improved level set method”, Physics Procedia, vol. 33, pp. 1168-1175, 2012.
[11] S. Arora, J. Acharya, A. Verma, and P. K. Panigrahi, “Multilevel thresholding for image segmentation through a fast statistical recursive algorithm”, Pattern Recognition Letters, vol. 29, no. 2, pp. 119-125, 2008
[12] M.-H. Horng, “Multilevel thresholding selection based on the artificial bee colony algorithm for image segmentation”, Expert Systems with Applications, vol. 38, no. 11, pp. 13785-13791, 2011
[13] C. Yan, N. Sang, and T. Zhang, “Local entropy-based transition region extraction and thresholding”, Pattern Recognition Letters, vol. 24, no. 16, pp. 2935-2941, 2003
[14] J.-C. Yen, F.-J. Chang, and S. Chang, “A new criterion for automatic multilevel thresholding, Image Processing”, IEEE Transactions on, vol. 4, no. 3, pp. 370-378, 1995.
[15] P. Gupta, V. Malik, and M. Gandhi, “Implementation of multilevel threshold method for digital images used in medical image processing”, Int. J. Adv. Res. Comput. Sci. Softw. Eng, vol. 2, no. 2, 2012.
[16] M. G. Mahore, V. V. Dhanrale, H. R. Borde, P. G. Lahoti, and S. B. Borge, “Automatic segmentation of digital images applied in cardiac medical images”, IJCSMC, vol. 3, Issue. 4, pg.121 – 124, 2014.
[17] K. Wang, S. Zhang, Z. Wang, Z. Liu, and F. Yang, “Mobile smart device-based vegetable disease and insect pest recognition method,” Intelligent Automation & Soft Computing, vol. 19, no. 3, pp. 263–273, Aug. 2013.
[18] J. Cartwright, “Roll over, Boltzmann”, Physics World, pp. 31-35, 2014.
[19] P. Bromiley, N. Thacker, and E. Bouhova-Thacker,” Shannon entropy, Renyi entropy, and information”, Statistics and Inf. Series (2004-004), 2004.