Human ringworm detection using wavelet energy signature

Author

Saha, Manas ; Naskar, Mrinal Kanti ; Chatterji, Biswa Nath

Author_Institution

Dept. of Electron. & Commun. Eng., Siliguri Inst. of Technol., Siliguri, India

fYear

2015

fDate

9-11 July 2015

Firstpage

178

Lastpage

182

Abstract

We propose an application based experimental work to identify the ringworm images from a set of human skin images. Our approach deals with the 3-level decomposition of the skin images by the Daubechies (DB), Coiflet (CF), Biorthogonal (BO) and Discrete Meyer (DM) wavelets and extraction of the corresponding energy signatures. The discriminatory energy signatures from the different wavelet decomposed approximation and detail subbands at each level of resolution are used to tabulate the training and testing databases. The binary classifier, Support Vector Machine (SVM) is then deployed to detect the ringworm images successfully.

Keywords

image classification; image resolution; medical image processing; support vector machines; wavelet transforms; 3-level decomposition; Biorthogonal wavelets; Coiflet wavelets; Daubechies wavelets; Discrete Meyer wavelets; binary classifier; human ringworm detection; human skin images; support vector machine; wavelet decomposed approximation; wavelet energy signature; Databases; Discrete wavelet transforms; Diseases; Skin; Support vector machines; Training; Approximation subband; Detail subband; Energy signature; Sensitivity; Specificity; Wavelet transform;

fLanguage

English

Publisher

ieee

Conference_Titel

Recent Trends in Information Systems (ReTIS), 2015 IEEE 2nd International Conference on

Conference_Location

Kolkata

Type

conf

DOI

10.1109/ReTIS.2015.7232874

Filename

7232874