Tim Lee

Associate Professor

Relevant Degree Programs

 

Graduate Student Supervision

Doctoral Student Supervision (Jan 2008 - Nov 2019)
Automated analysis of vascular structures of skin lesions : segmentation, pattern recognition and computer-aided diagnosis (2018)

Skin disorders are among the most common healthcare referrals in Canada, affecting a large population and imposing high healthcare costs. Early detection plays an essential role in efficient management and better outcome. However, restricted access to dermatologists and lack of education to other healthcare professionals pose a major challenge for early detection. Computer-aided systems have great potential as viable tools to identify early skin abnormalities. The initial clinical diagnosis phase of most skin disorders involves visual inspection of the lesion for specific features, associated with certain abnormalities. One of the main cutaneous features are vascular structures, which are significantly involved in pathogenesis, diagnosis, and treatment outcome of skin abnormalities. The presence and morphology of cutaneous vessels are suggestive clues for specific abnormalities. However, there has been no systematic approach for comprehensive analysis of skin vasculature. In this thesis, we propose a three-level framework to systematically detect, quantify and analyze the characteristics of superficial cutaneous blood vessels. First, we investigate the vessels at pixel-level. We propose novel techniques for detection (absence/presence) and segmentation of vascular structures in pigmented and non-pigmented lesions and evaluate the performance quantitatively. We develop a fully automatic vessel segmentation framework based on decomposing the skin into its component chromophores and accounting for shape. Furthermore, we design a deep learning framework based on stacked sparse auto-encoders for detection and localization of skin vasculature. Compared to previous studies, we achieve higher detection performance, while preserving clinical feature interpretability.Next, we analyze the vessels at lesion-level. We propose a novel set of architectural, geometrical and topological features to differentiate vascular morphologies. The defined feature set can effectively differentiate four major classes of vascular patterns.Finally, we investigate the vessels at disease-level. We analyze the relationship between vascular characteristics and disease diagnosis. We design and deploy novel features to evaluate total blood content and vascular characteristics of the lesion to differentiate cancerous lesions from benign ones. We also build a system upon integrating patient’s clinical information and lesion’s visual characteristics using deep feature learning, which achieves superior cancer classification performance compared to current techniques without the need for handcrafted high-level features.

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