<?xml version="1.0" encoding="UTF-8"?> <article key="pdf/10000824" mdate="2015-02-03 00:00:00"> <author>G. Murugeswari and A. Suruliandi</author> <title>Fuzzy Based Visual Texture Feature for Psoriasis Image Analysis</title> <pages>1931 - 1938</pages> <year>2014</year> <volume>8</volume> <number>10</number> <journal>International Journal of Computer and Information Engineering</journal> <ee>https://publications.waset.org/pdf/10000824</ee> <url>https://publications.waset.org/vol/94</url> <publisher>World Academy of Science, Engineering and Technology</publisher> <abstract>This paper proposes a rotational invariant texture feature based on the roughness property of the image for psoriasis image analysis. In this work, we have applied this feature for image classification and segmentation. The fuzzy concept is employed to overcome the imprecision of roughness. Since the psoriasis lesion is modeled by a rough surface, the feature is extended for calculating the Psoriasis Area Severity Index value. For classification and segmentation, the Nearest Neighbor algorithm is applied. We have obtained promising results for identifying affected lesions by using the roughness index and severity level estimation. </abstract> <index>Open Science Index 94, 2014</index> </article>