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Commenced in January 2007 Frequency: Monthly Edition: International Publications Count: 29414

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Fuzzy Based Visual Texture Feature for Psoriasis Image Analysis
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.
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[1] Ahmed Fadzil M. H., Esa Prakasa, Hurriyatul Fitriyah, Hermawan Nugroho, Azura Mohd Affandi and S. H. Hussein, “Validation on 3D Surface Roughness Algorithm for Measuring Roughness of Psoriasis Lesion,” World Academy of Science, Engineering and Technology Vol.4 2010-03-25
[2] Ahmad Fadzil M Hani, Esa Prakasa, Hermawan Nugroho, Azura M Affandi and Suraiya H Hussein, “Body Surface Area Measurement and Soft Clustering for PASI Area Assessment,” Conf Proc IEEE Eng Med Biol Soc 2012:4398-401
[3] Ahmad Fadzil M. Hani, Esa Prakasa, Hurriyatul Fitriyah, Hermawan Nugroho, Azura Mohd Affandi and Suraiya Hani Hussein, “High Order Polynomial Surface Fitting for Measuring Roughness of Psoriasis Lesion,” Lecture Notes in Computer Science Volume 7066, 2011, pp 341-351
[4] Alexandru Caliman, Mihai Ivanovici and Noel Richard, “Colour Fractal Dimension for Psoriasis Image Analysis,” Proceedings of SPAMEC 2011, Cluj-Napoca, Romania, Pages 113-115
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[19] Nidhal K. Al Abbadi, Nizar Saadi Dahir, Muhsin A. AL-Dhalimi and Hind Restom, “Psoriasis Detection Using Skin Color and Texture Features,” Journal of Computer Science 6 (6): 648-652, 2010, ISSN 1549-3636
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