Article: Human skin ringworm detection using wavelet and curvelet transforms: a comparative study Journal: International Journal of Computational Vision and Robotics (IJCVR) 2021 Vol.11 No.3 pp.245 - 263 Abstract: The common human skin disease called ringworm is investigated in the light of computer vision. Two distinct methodologies are developed for its detection. The first methodology implements three-level multi-wavelet decomposition of the skin images and subsequent evaluation of the approximation and detail subband energies which act as the texture characterising features. The second methodology incorporates the curvelet to segment the circular protrusion of the skin images especially with ringworms followed by statistical texture investigation by grey-level co-occurrence matrix (GLCM). After feature extraction by both the methodologies, binary classifier called the support vector machine (SVM) recognises the images as ringworm with detection accuracy of around 87% and 80% for the first and second methodologies respectively. In addition, the performance indexing parameters of SVM classification like sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) which are not previously addressed are evaluated. Both the methodologies are comprehensively demonstrated and compared to select the better one. The selected method is then compared with the available technique and commented upon. Inderscience Publishers - linking academia, business and industry through research

Title: Human skin ringworm detection using wavelet and curvelet transforms: a comparative study

Authors: Manas Saha; Mrinal Kanti Naskar; B.N. Chatterji

Addresses: Electronics and Communication Engineering Department, Siliguri Institute of Technology, Sukna-734009, West Bengal, India ' Electronics and Telecommunication Engineering Department, Jadavpur University, Kolkata-700032, India ' Department of Computer Science and Engineering, B.P. Poddar Institute of Management and Technology, 137, VIP Road, Kolkata-700052, India

Abstract: The common human skin disease called ringworm is investigated in the light of computer vision. Two distinct methodologies are developed for its detection. The first methodology implements three-level multi-wavelet decomposition of the skin images and subsequent evaluation of the approximation and detail subband energies which act as the texture characterising features. The second methodology incorporates the curvelet to segment the circular protrusion of the skin images especially with ringworms followed by statistical texture investigation by grey-level co-occurrence matrix (GLCM). After feature extraction by both the methodologies, binary classifier called the support vector machine (SVM) recognises the images as ringworm with detection accuracy of around 87% and 80% for the first and second methodologies respectively. In addition, the performance indexing parameters of SVM classification like sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) which are not previously addressed are evaluated. Both the methodologies are comprehensively demonstrated and compared to select the better one. The selected method is then compared with the available technique and commented upon.

Keywords: multiresolution; wavelet; curvelet; approximation subband; detail subband; energy signature.

DOI: 10.1504/IJCVR.2021.115158

International Journal of Computational Vision and Robotics, 2021 Vol.11 No.3, pp.245 - 263

Received: 28 Jan 2019
Accepted: 21 Sep 2019
Published online: 21 May 2021 *

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Title: Human skin ringworm detection using wavelet and curvelet transforms: a comparative study

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