Automatic Detection of Critical Dermoscopy Features for Malignant Melanoma Diagnosis

Improved methods for computer-aided analysis of identifying features of skin lesions from digital images of the lesions are provided. Improved preprocessing of the image that 1) eliminates artifacts that occlude or distort skin lesion features and 2) identifies groups of pixels within the skin lesion that represent features and/or facilitate the quantification of features are provided including improved digital hair removal algorithms. Improved methods for analyzing lesion features are also provided

Pigment network extraction and salient point analysis

Irregular networks are one of the characteristics of skin cancer such as malignant melanoma. Automoated diagnosis of skin cancer requires extraction of lesion features like color, texture, border irregularity, asymmetry, network structures, etc. This thesis presents an automated approach to determine salient points on pigment networks. The percentage of salient points contained in the total lesion area is used to distinguish malignant melanomas and Clarks nevi. The salient points are the candidate line points on a pigment network obtained by implementing a part of Steger\u27s 2D line extraction algorithm. --Abstract, page iii