eprintid: 2455
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/24/55
datestamp: 2023-11-09 15:50:41
lastmod: 2023-11-09 15:50:41
status_changed: 2023-11-09 15:43:34
type: conference_item
metadata_visibility: show
creators_name: Hani, A.F.M.
creators_name: Prakasa, E.
creators_name: Nugroho, H.
creators_name: Asirvadam, V.S.
title: Implementation of fuzzy c-means clustering for Psoriasis Assessment on lesion erythema
ispublished: pub
keywords: Clinical practices; Cluster boundaries; erythema assessment; Fuzzy C-means algorithms; Fuzzy-C-Means clustering; psoriasis; Scoring performance; Visual assessments, Algorithms; Fuzzy systems; Industrial electronics; Skin, Dermatology
note: cited By 11; Conference of 2012 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2012 ; Conference Date: 23 September 2012 Through 26 September 2012; Conference Code:96680
abstract: Psoriasis is a skin disease that causes the appearance of reddish and scaly skin lesions. Lesion erythema, which refers to the inflammation (colour) of psoriasis lesion, is defined as one of Psoriasis Area and Severity Index (PASI) parameters. However, visual assessment by dermatologists is subjective and results in inter-rater variations. In this paper, an objective PASI erythema-scoring algorithm has been developed. The colour of lesion erythema was found to be dependent on the normal skin tone of the affected person. Normal skin tones are categorised into four groups (dark, brown, light brown and fair skins). A soft clustering is applied to solve the ambiguity problems at cluster boundaries. CIE L*a*b* data of lesions and their surrounding normal skin are used to calculate lesion erythema. The hue difference between lesion and normal skin corresponds to the lesion erythema. Two dedicated fuzzy c-means (FCM) algorithms are applied consecutively to classify normal skin tone and to score PASI erythema. 2,322 normal skin and 1,462 lesions samples from 204 recruited patients at Hospital Kuala Lumpur are used to build skin tone and PASI erythema score classifiers respectively. Agreement values between first and second assessments of 430 lesions for PASI erythema are determined to evaluate scoring performance. Kappa coefficients are found � 0.70 for all skin tones (fair-0.70, light brown-0.8, brown-0.79, and dark skin-0.90). These agreement results show that the proposed method is reliable and objective, and thus can be used for clinical practices. © 2012 IEEE.
date: 2012
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84876775044&doi=10.1109%2fISIEA.2012.6496654&partnerID=40&md5=f4b580235008bb55d63af63ba553c288
id_number: 10.1109/ISIEA.2012.6496654
full_text_status: none
publication: ISIEA 2012 - 2012 IEEE Symposium on Industrial Electronics and Applications
place_of_pub: Bandung
pagerange: 331-335
refereed: TRUE
isbn: 9781467330046
citation:   Hani, A.F.M. and Prakasa, E. and Nugroho, H. and Asirvadam, V.S.  (2012) Implementation of fuzzy c-means clustering for Psoriasis Assessment on lesion erythema.  In: UNSPECIFIED.