Version 1
: Received: 15 March 2018 / Approved: 16 March 2018 / Online: 16 March 2018 (05:26:31 CET)
How to cite:
Singh, S.; Urooj, S. Orthogonal Moment Extraction and Classification of Melanoma Images. Preprints2018, 2018030128. https://doi.org/10.20944/preprints201803.0128.v1
Singh, S.; Urooj, S. Orthogonal Moment Extraction and Classification of Melanoma Images. Preprints 2018, 2018030128. https://doi.org/10.20944/preprints201803.0128.v1
Singh, S.; Urooj, S. Orthogonal Moment Extraction and Classification of Melanoma Images. Preprints2018, 2018030128. https://doi.org/10.20944/preprints201803.0128.v1
APA Style
Singh, S., & Urooj, S. (2018). Orthogonal Moment Extraction and Classification of Melanoma Images. Preprints. https://doi.org/10.20944/preprints201803.0128.v1
Chicago/Turabian Style
Singh, S. and Shabana Urooj. 2018 "Orthogonal Moment Extraction and Classification of Melanoma Images" Preprints. https://doi.org/10.20944/preprints201803.0128.v1
Abstract
This paper provides orthogonal moments (OM) such as, Zernike Moments(ZM), Psuedo Zernike Moments(PZM) and Orthogonal Fourier Mellin Moments(OFMM) for the analysis of melanoma images. The moment invariants may vary with respect to geometric variations. For the analysis of orthogonal moments hundred random melanoma images and hundred non-melanoma images have been taken into consideration from the database of 570 melanoma images and 250 non-melanoma images respectively. Orthoganal moments have been computed by varying the phase angles from 10° to 40° with an equal interval of 10° degree for the orders 2, 4,8,16,32,64,128,256 respectively. For the optimal OMs Particle Swarm Optimization (PSO) technique have been used. These set of extracted optimal OMs have been further applied to classify melanoma images. Support Vector Machine (SVM) has been used for the classification of [1]sensitivity=88.78%.
Keywords
moments invariants; ZM,PZM; OFMM; SVM; PSO
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
