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Title: Computer Aided Diagnosis of Melanoma - A Photometric Stereo Based Approach
Author: Zhou, Yu
ISNI:       0000 0004 2695 8671
Awarding Body: University of the West of England, Bristol
Current Institution: University of the West of England, Bristol
Date of Award: 2010
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This thesis describes a new melanoma diagnosii:s framework based on photometric stereo - a linear and computationally efficient method for extracting 3D data. The primary goal of this thesis is to investigate texture analysis of skin, including 3D based techniques, to realize a relatively reliable and accurate diagnosis of melanoma. In this thesis, a novel semi-automatic segmentation algorithm based on the normalized cut is proposed at first to realize fast segmentatioID. of melanoma images. As classic normalized cut involves generalized eigenvalue decompositioru, it can be extremely slow in segmenting large skin images even if the results are usually very impressive. Here a hierarchical normalized cut is proposed to conduct fast segmentation of skim lesion images with final results as good as the classic normalized cut in most cases. In addition, a new border analysis framework called centroid distance diagram is formulated to describe the border irregularity of melanoma images. This Fourier transform based approach gives a series of border irregularity descriptors insensitive to tire scate and rotation of tire images. Descriptors generated by using this method are proved to be effective features in describing malignant melanomas. The relationship between classic convexity and centroid convexity of border curves is examined in detail and the non-centroid -convexity index is generated to measure the irregularity of border curves. Moreover, statistical principal curvature patterns of skin surfaces are formulated to describe 3D melanoma shapes. The principal curvatures of skin surfaces are extracted by using the normal vector data obtained from photometric stereo. A robust estimator of these curvature parameters is embedded in this algorithm by using Gaussian kernels. Finally, an ensemble classifier for melanoma diagnosis is formulated by combining classifiers designed with various 2D /3D descriptors, e.g., border irregularity descriptors, colour variation descriptors and 3D principal curvature pattern descriptors. Even when the performance of the classifier obtained from one single group of descriptors is relatively poor, the ensemble classifier can offer highly impressive performances. Two novel schemes for designing this ensemble I classifier are proposed and compared with other well-known ensemble classifier designing methods, including Boosting and majority voting. Experiment studies suggest that the method named Bayesian II gives the best mean sensitivity (91.08 percent) and the best consistency level of sensitivity while the mean specificity can achieve 90.89 percent. The present work thus makes a novel contribution to the existing methods for computer aided diagnosis of melanoma. However, there are still many intriguing directions for further research works such as enhancing the accuracy of 3D data, recognition of subtypes of melanoma, introducing other descriptors in diagnosis and so on.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available