Color Image Decomposition and Fuzzy Clustering for Dermoscopic Image Segmentation

A. Ennaji; A. Aarab; G. Khaissidi

doi:10.15866/irecos.v9i11.4046

Color Image Decomposition and Fuzzy Clustering for Dermoscopic Image Segmentation

A. Ennaji^(1*), A. Aarab⁽²⁾, G. Khaissidi⁽³⁾

^(*) Corresponding author

Authors' affiliations

DOI: https://doi.org/10.15866/irecos.v9i11.4046

Abstract

This paper proposes a new approach for automatic and effective segmentation of dermoscopic images. The method of segmentation is based on a pre-processing using the color structure-texture image decomposition based on Partial Differential Equations, and a step of segmentation by fuzzy clustering based algorithm. The proposed approach has been implemented and compared with other recent algorithms for dermoscopic image segmentation in order to demonstrate sufficiently good results, the proposed technique was be evaluated as performance for lesion detection in dermoscopic images, and the results are 78% of specificity, 99% of sensitivity and 0.99 of the AUC metric.
Copyright © 2014 Praise Worthy Prize - All rights reserved.

Keywords

Clustering; Dermoscopic Images; Color Structure-Texture Image Decomposition; Unsupervised Fuzzy Segmentation

Full Text:

PDF

References

http://www.melanoma.org.au/about-melanoma/treatment.html.

L. Luccheseyz, "Color Image Segmentation: A State-of-the-Art Survey". Dept. of Electrical and Computer Eng., University of California, Santa Barbara; and S.K. Mitray, Dept. of Electronics and Informatics, University of Padua, Italy. 2001.

H. Ganster, P. Pinz, R. Rohrer, E. Wildling, M. Binder, and H. Kittler,“Automated melanoma recognition,” IEEE Trans. Med. Imag., vol. 20,pp. 233–239, Mar. 2001.
http://dx.doi.org/10.1109/42.918473

H.Castillejos, V.Ponomaryov, L.Nino-de-Rivera, and V.Golikov “Wavelet Transform Fuzzy Algorithms for Dermoscopic Image Segmentation” National Polytechnic Institute, Mexico city, Mexico. Computational and Mathematical Methods in Medicine Volume 2012, Article ID 578721, 2012.
http://dx.doi.org/10.1155/2012/578721

S.J. Osher, A.Sole, and L.A. Vese. “Image decomposition and restoration using minimization and the H-1 norm Multiscale and simulation” SIAM Interdisciplinary Journal 1(3) 349-370 2003.
http://dx.doi.org/10.1137/s1540345902416247

Y.Meyer “Oscillating patterns in image processing and in some nonlinear evolution equations”, March 2001 .The fifteenth Dean Jacquelines B.lewis Memorial Lectures.

L. Vese, S. Osher. “Modeling textures with total variation minimization and oscillating patterns in image processing”. Journal of scientific computing, 19 (1-3) :553-572, 2002.

J.C. bezdek, r.ehrlich, f.william “fcm: the fuzzy C-Means clustering algorithm “Computers & Geosciences Vol. 10, No. 2-3, pp. 191-203, 1984, u.s.a .
http://dx.doi.org/10.1016/0098-3004(84)90020-7

D.Q. Zhang, S.C. Chen, “Clustering incomplete data using kernel-based fuzzy C-Means algorithm,” Neural Processing Lett., vol.18, no.3, pp.155-162, 2003.
http://dx.doi.org/10.1023/b:nepl.0000011135.19145.1b

Ellappan, V., Samson Ravindran, R., An effective selection of DCT and DWT coefficients for an adaptive medical image compression technique using multiple kernel FCM, (2014) International Review on Computers and Software (IRECOS), 9 (4), pp. 628-637.

Ratna Babu, K., Sunitha, K.V.N., Enhancing hazy images with the aid of particle swarm optimization (PSO) and morphological operation, (2013) International Review on Computers and Software (IRECOS), 8 (1), pp. 21-28.

http://www.dermoscopyatlas.com

M. E. Celebi, G. Schaefer, and H. Iyatomi, “Objective evaluation of methods for border detection in dermoscopy images,” in Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS’08), pp. 3056–3059, Vancouver, Canada, August 2008.
http://dx.doi.org/10.1109/iembs.2008.4649848

Refbacks

There are currently no refbacks.

Username
Password
Remember me