Maximum Entropy Based Image Segmentation of Human Skin Lesion
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.
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segmented natural images and its application to evaluating segmentation
algorithms and measuring ecological statistics,” in Computer Vision,
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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-
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[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:69704", author = "Sheema Shuja Khattak and Gule Saman and Imran Khan and Abdus Salam", title = "Maximum Entropy Based Image Segmentation of Human Skin Lesion ", abstract = "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.", volume = "9", number = "5", pages = "1103-5", }
