An Improved Segmentation Approach for Skin Lesion Classification

  • Youssef Filali Department of computer science,Faculty of Sciences Dhar-Mahraz, Sidi Mohamed Ben Abdellah University
  • Sabri Abdelouahed University sidi Mohamed Ben Abdellah
  • Abdellah Aarab Department of physics,Faculty of Sciences Dhar-Mahraz, Sidi Mohamed Ben Abdellah University
Keywords: Skin cancer, PDE Multi-scale decomposition, Texture analysis, Features extraction, SVM.


Skin cancer is one of the most common types of cancer, its incidence reached epidemic proportions and caused many deaths. Skin cancer can be categorize into three main types; Actinic Keratoses, Basal cell carcinoma and Melanoma. The melanoma skin cancer is the most aggressive and the deadliest form of skin cancer compared to the others. Early Melanoma detection and diagnosis often allows for more treatment option and decreases significantly the number of deaths. Many researchers proposed to use image processing for skin lesion detection. The process can be divided into three main stages: lesion identification based on image segmentation, features extraction and lesion classification. Segmentation and features extraction are the key-steps and significantly influence the outcome of the classification results. In this paper, a new approach for automatic segmentation and classification for skin lesion has been proposed. The proposed approach consists on a preprocessing based on multiscale decomposition that’s separate the input image into two components. The geometrical component will be used in the segmentation stage and the texture component in features extraction. The classification performed using the Support Vector Machine (SVM) classifier. The efficiency and the performance of the proposed approach has been evaluated in comparison with recent and robust dermoscopic approaches from literature.

Author Biography

Sabri Abdelouahed, University sidi Mohamed Ben Abdellah
Professor of computer science in the Faculty of sciences Dhar El Mahraz of Fez. I am working in computer vision and medical image processing


D. N. L. Correa, L. R. B. Paniagua, J. L. V. Noguera, D. P. Pinto-Roa and L. A. S. Toledo, Computerized Diagnosis of Melanocytic Lesions Based on the ABCD Method, 2015 Latin American Computing Conference (CLEI), Arequipa, 2015, pp. 1-12.

S. Jain, V. Jagtap, and N. Pise, Computer aided melanoma skin cancer detection using image processing, Procedia Comput. Sci., International Conference on Computer, Communication and Convergence (ICCC 2015), vol. 48, no. C, pp. 736C741, 2015.

K. Norton, H. Iyatomi, M. E. Celebi, G. Schaefer, M. Tanaka, and K. Ogawa, Development of a Novel Border Detection Method for Melanocytic and Non-Melanocytic Dermoscopy Images, Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society pp. 5403C5406, 2010.

Abbas and Qaisar et al., NA perceptually oriented method for contrast enhancement and segmentation of dermoscopy images, Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging 19 1 (2013): e490-7.

Celebi ME, Aslandogan YA, Stoecker WV, Iyatomi H, Oka H, Chen X., Unsupervised border detection in dermoscopy images. Skin research and technology, official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI). 2007;13(4):454-462. doi:10.1111/j.1600-0846. 2007.00251.x.

R. Garnavi, M. Aldeen, M. E. Celebi, A. Bhuiyan, C. Dolianitis, and G. Varigos, Automatic Segmentation of Dermoscopy Images Using Histogram Thresholding on Optimal Color Channels, Int. J. Med. Med. Sci., pp. 126C134, 2010.

R. Garnavi, M. Aldeen, M. E. Celebi, G. Varigos, S. Finch, A. Bhuiyan, and C. Dolianitis, Automatic segmentation of dermoscopy images using histogram thresholding on optimal color channels, Conputerized Med. Imaging Graph., vol. 35, no. January, pp.105C115, 2011.

A. Chopra and B. R. Dandu, Image Segmentation Using Active Contour Model, Int. J. Comput. Eng. Res., vol. 2, no. 3, pp. 819C822, 2012.

H. Zhou, G. Schaefer, M. Celebi, H. Iyatomi, K.-A. Norton, T. Liu, and F. Lin, Skin lesion segmentation using an improved snake model, JConf. Proc. IEEE Eng. Med. Biol. Soc., vol. 2010, no. 1, pp. 1974C7, 2010.

S. N. Deepa and B. Aruna Devi, A survey on artificial intelligence approaches for medical image classification, Indian J. Sci.Technol., vol. 4, no. 11, pp. 1583C1595, 2011.

S. Mathew and D. Sathyakala, Segmentation of skin lesions and classification by neural network, International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE) Volume 4, Issue 2, February 2015.

J. Ko, S. M. Swetter, H. M. Blau, A. Esteva, B. Kuprel, R. A. Novoa, and S. Thrun, with deep neural networks, Nature, pp. 1C11,2017.

A. A. I. Mohamed, M. M. Ali, K. Nusrat, J. Rahebi, and A. Sayiner, Melanoma Skin Cancer Segmentation with Image Region Growing Based on Fuzzy Clustering Mean, International Journal of Engineering Innovations and Research , vol. 6, no. 2, pp. 91C95,2017

M. A. Sabri, A. Ennouni, and A. Aarab, Automatic estimation of clusters number for K-means, 2016 4th IEEE International Colloquium on Information Science and Technology (CiSt). Pages: 450 - 454, DOI: 10.1109/CIST.2016.7805089. Electronic ISSN:2327-1884. 24-26 Oct. 2016.

M. A. Sabri; A. Ennouni; A. Aarab, Robust approach for textured image clustering, 2016 4th IEEE International Colloquium on Information Science and Technology (CiSt). Pages: 465 - 470, DOI: 10.1109/CIST.2016.7805089. Electronic ISSN: 2327-1884.24-26 Oct. 2016. Tangier, Morocco

P. Mohanaiah, P. Sathyanarayana, and L. Gurukumar, Image Texture Feature Extraction Using GLCM Approach, International Journal of Scientific and Research Publications, Volume 3, Issue 5,May 2013 1 ISSN 2250-3153.

Y. Filali; M. A. Sabri; A. Aarab.An improved approach for skin lesion analysis based on multiscale decomposition.2017 International Conference on Electrical and Information Technologies (ICEIT). ISBN: 978-1-5386-1516-4. DOI:10.1109/EITech.2017.8255250. 15-18 Nov. 2017. Rabat, Morocco.

Z. Waheed, An Efficient Machine Learning Approach for the Detection of Melanoma using Dermoscopic Images, pp. 316C319, 2017.

Y. Filali; A. Ennouni; M. A. Sabri; A. Aarab. A study of lesion skin segmentation, features selection and classification approaches.2018 International Conference on Intelligent Systems and Computer Vision (ISCV). Pages: 1-4. ISBN: 978-1-5386-4396-9. DOI: 10.1109/ISACV.2018.8354069. 2-4 April 2018. Fez, Morocco

M. E. Celebi, H. A. Kingravi, B. Uddin, H. Iyatomi, Y. A. Aslandogan, W. V. Stoecker, and R. H. Moss, A methodological approach to the classification of dermoscopy images Comput. Med. Imaging Graph., vol. 31, no. 6, pp. 362C373, 2007.

L. I. Rudin, S. Osher, and E. Fatemi, Nonlinear total variation based noise removal algorithms, Phys. D Nonlinear Phenom., vol.60, no. 1C4, pp. 259C268, 1992.

J. F. Aujol, G. Aubert, L. Blanc-Fraud, and A. Chambolle, Image decomposition into a bounded variation component and an oscillating component, J. Math. Imaging Vis., vol. 22, no. 1, pp. 71C88, 2005.

Y. Filali, A. Ennouni, and M. A. Sabri, Multiscale approach for skin lesion analysis and classification, International Conference on Advanced Technologies for Signal and Image Processing (ATSIP). ISBN: 978-1-5386-0551-6. DOI: 10.1109/ATSIP.2017.8075545.22-24 May 2017. Fez, Morocco.

R. M. Haralick, K. Shanmugam, and I. Dinstein, Textural Features for Image Classification, 1973.

U. T. a. Rahman, Gabor filters and gray level co occurrence matrices in texture classification, Citeseer, 2007.

L. Ruiz, a Fdez-Sarrła, and J. Recio, Texture feature extraction for classification of remote sensing data using wavelet decomposition:a comparative study, Int. Arch. Photogramm. Remote Sens., vol. XXXV, no. 1, pp. 1682C1750, 2004.

A. Marghoob, R. Braun, and A. Kopf, Interactive CD-ROM of Dermoscopy. London, U.K.: Informa Healthcare; 2007.

D. A. L and M. I. M, K-Means Clustering and Ensemble of Regressions: An Algorithm for the ISIC 2017 Skin Lesion Segmentation Challenge. pp. 1C5, 2017.

R. Amelard, S. Member, J. Glaister, S. Member, A. Wong, D. A. Clausi, and S. Member, High-Level Intuitive Features ( HLIFs ) for Intuitive Skin Lesion Description. vol. 62, no. 3, pp. 820C831, 2015.

A. M. Solomon, A. Murali, R. B. Sruthi, M. K. Sreekavya, S. Sasidharan, and L. Thomas, Identification of Skin Cancer based on Colour , Subregion and Texture. vol. 6, no. 7, pp. 8331C8334, 2016.

E. Almansour and M. A. Jaffar, Classification of Dermoscopic Skin Cancer Images Using Color and Hybrid Texture Features. vol.16, no. 4, pp. 135C139, 2016.

B. V Gohila and A. Selvaraj, Automated Diagnosis of Pigmented Skin Lesions Based on Texture Characteristics., vol. 3, no. 2, pp. 938C945, 2015

How to Cite
Filali, Y., Abdelouahed, S., & Aarab, A. (2019). An Improved Segmentation Approach for Skin Lesion Classification. Statistics, Optimization & Information Computing, 7(2), 456-467.
Research Articles