Date of Award


Degree Name

Doctor of Philosophy


Engineering Science

First Advisor

LeAnder, Robert

Second Advisor

Gupta, Lalit


Purpose: Automatic border detection is the first and most crucial step for lesion segmentation and can be very challenging, due to several lesion characteristics. There are many melanoma border-detecting algorithms that perform poorly on dermoscopy images of basal cell carcinoma (BCC), which is the most common skin cancer. One of the reasons for poor lesion detection performance is that there are very few algorithms that detect BCC borders, because they are difficult to segment, even for dermatologists. This difficulty is due to low contrast, variation in lesion color and artifacts inside/outside the lesion. Segmentation that has adequate lesion-feature capture, with acceptable tolerance, will facilitate accurate feature segmentation, thereby maximizing classification accuracy. Methods: The main objective of this research was to develop an effective BCC border detecting algorithm whose accuracy is better than the existing melanoma border detectors that have been applied to BCCs. Fifteen auto-thresholding techniques were implemented for BCC lesion segmentation; but, only five were selected for use in algorithm development. A novel technique was developed to automatically expand BCC lesion borders, to completely circumscribe the lesion. Two error metrics were used that better measure Type II (false-negative) errors: Relative XOR error and Lesion Capture Ratio (a novel error metric). Results: On training and test sets of 1023 and 119 images, respectively, based on two error metrics, five thresholding-based algorithms outperformed two state-of-the-art melanoma segmentation techniques, in segmenting BCCs. Five algorithms generated borders that appreciably better matched dermatologists’ hand-drawn borders which were used as the “gold standard.” Conclusion: The five developed algorithms, which included solutions for image-vignetting correction and border expansion, to achieve dermatologist-like borders, provided more inclusive and therefore, feature-preserving border detection, favoring better BCC classification accuracy, for future work.




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