Body surface area measurement and soft clustering for PASI area assessment

Hani, A.F.M. and Prakasa, E. and Nugroho, H. and Affandi, A.M. and Hussein, S.H. (2013) Body surface area measurement and soft clustering for PASI area assessment. In: UNSPECIFIED.

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Abstract

Psoriasis is a common skin disorder with a prevalence of 0.6 - 4.8 around the world. The most common are plaques psoriasis and it appears as red scaling plaques. Psoriasis is incurable but treatable in a long term treatment. Although PASI (Psoriasis Area and Severity Index) scoring is recognised as gold standard for psoriasis assessment, this method is still influenced by inter and intra-rater variation. An imaging and analysis system called α-PASI is developed to perform PASI scoring objectively. Percentage of lesion area to the body surface area is one of PASI parameter. In this paper, enhanced imaging methods are developed to improve the determination of body surface area (BSA) and lesion area. BSA determination method has been validated on medical mannequin. BSA accuracies obtained at four body regions are 97.80 (lower limb), 92.41 (trunk), 87.72 (upper limb), and 83.82 (head). By applying fuzzy c-means clustering algorithm, the membership functions of lesions area for PASI area scoring have been determined. Performance of scoring result has been tested with double assessment by α-PASI area algorithm on body region images from 46 patients. Kappa coefficients for α-PASI system are higher than 0.72 for all body regions (Head - 0.76, Upper limb - 0.81, Trunk - 0.85, Lower limb - 0.72). The overall kappa coefficient for the α-PASI area is 0.80 that can be categorised as substantial agreement. This shows that the α-PASI area system has a high reliability and can be used in psoriasis area assessment. © 2013 Springer-Verlag.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: cited By 0; Conference of World Congress on Medical Physics and Biomedical Engineering ; Conference Date: 26 May 2012 Through 31 May 2012; Conference Code:96476
Uncontrolled Keywords: Body surface; Determination methods; Fuzzy C means clustering; Fuzzy c-means clustering algorithms; High reliability; Kappa coefficient; lesion area assessment; psoriasis, Biomedical engineering; Clustering algorithms; Fuzzy clustering; Physics, Dermatology
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 09 Nov 2023 15:51
Last Modified: 09 Nov 2023 15:51
URI: https://khub.utp.edu.my/scholars/id/eprint/3667

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