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{"references": ["Ahmed Fadzil M. H., Esa Prakasa, Hurriyatul Fitriyah, Hermawan\nNugroho, Azura Mohd Affandi and S. H. Hussein, \"Validation on 3D\nSurface Roughness Algorithm for Measuring Roughness of Psoriasis\nLesion,\" World Academy of Science, Engineering and Technology\nVol.4 2010-03-25", "Ahmad Fadzil M Hani, Esa Prakasa, Hermawan Nugroho, Azura M\nAffandi and Suraiya H Hussein, \"Body Surface Area Measurement and\nSoft Clustering for PASI Area Assessment,\" Conf Proc IEEE Eng Med\nBiol Soc 2012:4398-401", "Ahmad Fadzil M. Hani, Esa Prakasa, Hurriyatul Fitriyah, Hermawan\nNugroho, Azura Mohd Affandi and Suraiya Hani Hussein, \"High Order\nPolynomial Surface Fitting for Measuring Roughness of Psoriasis\nLesion,\" Lecture Notes in Computer Science Volume 7066, 2011, pp\n341-351", "Alexandru Caliman, Mihai Ivanovici and Noel Richard, \"Colour Fractal\nDimension for Psoriasis Image Analysis,\" Proceedings of SPAMEC\n2011, Cluj-Napoca, Romania, Pages 113-115", "J. Arrault, A. 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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.
Fuzzy texture feature, roughness feature, psoriasis, skin disease.
Fuzzy texture feature, roughness feature, psoriasis, skin disease.
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