Towards automated skin lesion diagnosis

Author: 
Date created: 
2011-06-02
Identifier: 
etd6667
Keywords: 
Automated Skin Lesion Diagnosis (ASLD)
Computer Aided Diagnosis (CAD)
Conditional Random Fields (CRF)
Dermoscopy
Calibration
Segmentation
Hair Detection
Pigment Network Detection
Abstract: 

Melanoma, the deadliest form of skin cancer, must be diagnosed early in order to be treated effectively. Automated Skin Lesion Diagnosis (ASLD) attempts to accomplish this using digital dermoscopic images. This thesis investigates several areas in which ASLD can be improved. Typically, the ASLD pipeline consists of 5 stages: 1) image acquisition, 2) artifact detection, 3) lesion segmentation, 4) feature extraction and 5) classification. The main focus of the thesis is the development of two probabilistic models which are sufficiently general to perform several key tasks in the ASLD pipeline, including: artifact detection, lesion segmentation and feature extraction. We then show how all parameters of these two models can be inferred automatically using supervised learning and a set of examples. Additionally, we present methods to: 1) evaluate the experts’ perception of texture in images of dermoscopic skin lesions, 2) calibrate acquired digital dermoscopy images for color, lighting and chromatic aberration, and 3) digitally remove detected occluding artifacts. Our general probabilistic models’ ability to detect occluding hair and segment lesions performs comparably to other, less general, methods. Perceptually, we conclude that the textural information in skin lesions exists independently of color. Calibrating, for colour and lighting, we achieve results consistent with previous work; calibrating for chromatic aberration, we are able to reduce distortions by 47%. Furthermore, our method to digitally remove occluding artifacts outperforms previous work.

Document type: 
Thesis
Rights: 
Copyright remains with the author. The author granted permission for the file to be printed and for the text to be copied and pasted.
File(s): 
Supervisor(s): 
M. Stella Atkins
Department: 
Applied Science: School of Computing Science
Thesis type: 
(Thesis) Ph.D.
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