3D surface roughness measurement for scaliness scoring of psoriasis lesions.
Psoriasis is an incurable skin disorder affecting 2-3% of the world population. The scaliness of psoriasis is a key assessment parameter of the Psoriasis Area and Severity Index (PASI). Dermatologists typically use visual and tactile senses in PASI scaliness assessment. However, the assessment can be subjective resulting in inter- and intra-rater variability in the scores. This paper proposes an assessment method that incorporates 3D surface roughness with standard clustering techniques to objectively determine the PASI scaliness score for psoriasis lesions. A surface roughness algorithm using structured light projection has been applied to 1999 3D psoriasis lesion surfaces. The algorithm has been validated with an accuracy of 94.12%. Clustering algorithms were used to classify the surface roughness measured using the proposed assessment method for PASI scaliness scoring. The reliability of the developed PASI scaliness algorithm was high with kappa coefficients>0.84 (almost perfect agreement).
Centre for Intelligent Signal and Imaging Research, Department of Electrical & Electronic Engineering, Universiti Teknologi PETRONAS, Perak, Malaysia. Electronic address: fadzmo@petronas.com.my.
M Hani
MH
Ahmad Fadzil
Esa
E
Prakasa
Vijanth Sagayan
VS
Asirvadam
Hermawan
H
Nugroho
Azura Mohd
AM
Affandi
Suraiya Hani
SH
Hussein
D000465:Algorithms; D016000:Cluster Analysis; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D011565:Psoriasis / Q000145:classification* / Q000473:pathology*; D015203:Reproducibility of Results; D012867:Skin / Q000473:pathology; D013499:Surface Properties
Agreement analysis; Fuzzy c-means clustering; Polynomial surface fitting; Skin surface roughness; k-means clustering