Author(s): Tim Kam Lee
Cutaneous melanocytic lesions, commonly known as moles, are mostly benign; however, some of them are malignant melanomas, the most fatal form of skin cancer. Because the survival rate of melanoma is inversely proportional to the thickness of the tumor, early detection is vital to the treatment process. Many dermatologists have advocated the development of computer-aided diagnosis systems for early detection of melanoma. One of the important clinical features differentiating benign nevi from malignant melanomas is the lesion border irregularity. There are two types of border irregularity: texture and structure irregularities. Texture irregularities are the small variations along the border, while structure irregularities are the global indentations and protrusions that may suggest either the unstable growth in a lesion or regression of a melanoma. An accurate measurement of structure irregularities is essential to detect the malignancy of melanoma. This thesis extends the classic curvature scale-space filtering technique to locate all structure irregular segments along a melanocytic lesion border. An area-based index, called irregularity index, is then computed for each segment. From the individual irregularity index, two important new measures, the most significant irregularity index and the overall irregularity index, are derived. These two indices describe the degree of irregularity along the lesion border . A double-blind user study is performed to compare the new measures with twenty experienced dermatologists' evaluations. Forty melanocytic lesion images were selected and their borders were extracted automatically after dark thick hairs were removed by a preprocessor called DullRazor. The overall irregularity index and the most significant irregularity index were calculated together with three other common shape descriptors. All computed measures and the dermatologists' evaluations were analysed statistically. The results showed that the overall irregularity index was the best predictor for the clinical evaluation, and both the overall irregularity index and the most significant irregularity index outperformed the other shape descriptors. The new method has great potential for computer-aided diagnosis systems.