High Order Polynomial Surface Fitting for Measuring Roughness of Psoriasis Lesion

Ahmad Fadzil, Mohd Hani (2011) High Order Polynomial Surface Fitting for Measuring Roughness of Psoriasis Lesion. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , 7066 L (1 ). pp. 341-351. ISSN 0302-9743 (Print) 1611-3349 (Online)

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Scaliness of psoriasis lesions is one of the parameters to be determined during Psoriasis Area and Severity Index (PASI) scoring. Dermatologists typically use their visual and tactile senses to assess PASI scaliness. However, it is known that the scores are subjective resulting in inter- and intra-rater variability. In this paper, an objective 3D imaging method is proposed to assess PASI scaliness parameter of psoriasis lesions. As scales on the lesion invariably causes roughness, a surface-roughness measurement method is proposed for 3D curved surfaces. The method applies a polynomial surface fitting to the lesion surface to extract the estimated waviness from the actual lesion surface. Surface roughness is measured from the vertical deviations of the lesion surface from the estimated waviness surface. The surface roughness algorithm has been validated against 328 lesion models of known roughness on a medical mannequin. The proposed algorithm is found to have an error 0.0013 ± 0.0022 mm giving an accuracy of 89.30%. The algorithm is invariant to rotation of the measured surface. Accuracy of the rotated lesion models is found to be greater than 95%. System repeatability has been evaluated to successive measurements of 456 psoriasis lesions. The system repeatability can be accepted since 95.27% of the measurement differences are less than two standard deviation of measurement difference.

Item Type:Article
Impact Factor:SJR (2011): 0.034 SNIP (2011): 0.737
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RL Dermatology
Departments / MOR / COE:Centre of Excellence > Center for Intelligent Signal and Imaging Research
Departments > Electrical & Electronic Engineering
Research Institutes > Institute for Health Analytics
ID Code:7179
Deposited By: Prof Ir Dr Ahmad Fadzil Mohd Hani
Deposited On:12 Dec 2011 07:26
Last Modified:19 Jan 2017 08:22

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