MELANOMA IMAGE CLASSIFICATION SYSTEM BY NSCT FEATURES AND BAYES CLASSIFICATION
DOI:
https://doi.org/10.29284/ijasis.2.2.2016.27-33Keywords:
Melanoma Image Classification, Skin Cancer, NSCT, Bayes classifierAbstract
The knowledge obtained from a classification system is increasingly important for making a final decision. In this paper, a skin cancer classification system using Non-Sub sampled Contourlet Transform (NSCT) is presented. It uses double iterated filter banks to detect point discontinuities by a Laplacian pyramid and directional features by a directional filter bank. It allows the approximation of given image into a smooth contour at various level of decomposition. The Bayesian classifier is utilized in this work to classify the dermoscopic images in the PH2 database into normal or abnormal. From the results of the system, the melanoma image classification system can be used as a tool to make a final decision for the physicians.
Downloads
References
R. Garnavi, M. Aldeen, and J. Bailey, Computer-aided diagnosis of melanoma using border-and wavelet-based texture analysis, IEEE Transactions on Information Technology in Biomedicine, Vol. 16, No. 6, 2012, pp. 1239-1252.
A. Saez, J. Sanchez-Monedero, P.A. Gutierrez, and C. Hervas-Martínez, Machine learning methods for binary and multiclass classification of melanoma thickness from dermoscopic images, IEEE transactions on medical imaging, Vol. 35, No. 4, 2015, pp. 1036-1045.
F. Topfer, S. Dudorov, and J. Oberhammer, Millimeter-wave near-field probe designed for high-resolution skin cancer diagnosis, IEEE Transactions on Microwave Theory and Techniques, Vol. 63, No. 6, 2015, pp. 2050-2059.
O. Abuzaghleh, B.D. Barkana, and M. Faezipour, Noninvasive real-time automated skin lesion analysis system for melanoma early detection and prevention, IEEE journal of translational engineering in health and medicine, Vol. 3, 2015, pp. 1-12.
N. Alfed, F. Khelifi, A. Bouridane, and H. Seker, Pigment network-based skin cancer detection, IEEE 37th Annual International Conference on Engineering in Medicine and Biology Society, 2015, pp. 7214-7217.
D. Choudhury, A. Naug, and S. Ghosh, Texture and color feature based WLS framework aided skin cancer classification using MSVM and ELM, IEEE Annual India Conference, 2015, pp. 1-6.
Y.K. Jain, and M. Jain, Skin cancer detection and classification by using Wavelet Transform and Probabilistic Neural Network, 2012, pp. 250-252.
A. Kardynal, and M. Olszewska, Modern non-invasive diagnostic techniques in the detection of early cutaneous melanoma, Journal of dermatological case reports, Vol. 8, No. 1, 2014, pp. 1-8.
A. Masood, A. Al-Jumaily, and K. Anam, Self-supervised learning model for skin cancer diagnosis, IEEE 7th International IEEE/EMBS Conference on Neural Engineering, 2015, pp. 1012-1015.
M. Mete, and N.M. Sirakov, Optimal set of features for accurate skin cancer diagnosis, IEEE International Conference on Image Processing, 2014, pp. 2256-2260.
H.R. Mhaske, and D.A. Phalke, Melanoma skin cancer detection and classification based on supervised and unsupervised learning, IEEE International conference on Circuits, Controls and Communications, 2013, pp. 1-5.
N. Minh Do, and M. Vetterli, The contourlet transform: An efficient directional multiresolution image representation, IEEE Transactions on Image Processing, IEEE Transactions on Image Processing, Vol. 14, 2004, pp. 2091-2106.
T. Mendonca, P.M. Ferreira, J.S. Marques, A.R. Marcal, and J. Rozeira, PH 2-A dermoscopic image database for research and benchmarking, 35th Annual International Conference of the IEEE in Engineering in Medicine and Biology Society, 2013, pp. 5437-5440.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.