Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/75905
Title: Computerized methods for classification and severity assessment of skin pigmentation disorders
Authors: Liang, Yunfeng
Keywords: DRNTU::Engineering::Computer science and engineering::Computer applications::Computer-aided engineering
Issue Date: 2018
Source: Liang, Y. (2018). Computerized methods for classification and severity assessment of skin pigmentation disorders. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: Skin pigmentation disorders are widely spread around the world and can bring huge negative emotional impact on the patients. To meet the high demand of proper diagnoses and treatments of the non-tumorous skin pigmentation disorders, image processing and machine learning techniques are investigated in the thesis. In particular, two problems during the treatment process, namely the classification and the severity assessment of the pigmentation disorders, are addressed. In the classification problem, five most commonly seen skin pigmentation disorders are studied. A voting based probabilistic linear discriminant analysis algorithm (V-PLDA) is proposed to tackle the large within-class variance problem in the image dataset. In the severity assessment problem, a complete severity assessment system for a common pigmentation disease, melasma, is developed. An optimal hybrid thresholding algorithm (OHYTA) is also proposed to segment the melasma pigmentation region properly in the image. To the best of the author’s knowledge, this is the first study that applies computerized methods to the classification and severity assessment problems for the non-tumorous skin pigmentation image dataset. The proposed algorithms are tested on real-world image data collected by the National Skin Centre of Singapore and the experimental results are verified by the dermatologists. It is shown that the results obtained are satisfactory and the proposed classification and segmentation algorithms outperform other state-of-the-art methods.
URI: http://hdl.handle.net/10356/75905
DOI: 10.32657/10356/75905
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:IGS Theses

Files in This Item:
File Description SizeFormat 
Thesis_2.pdf2.65 MBAdobe PDFThumbnail
View/Open

Page view(s)

238
Updated on Apr 19, 2021

Download(s) 50

80
Updated on Apr 19, 2021

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.