Automatic skin tumour border detection for digital dermoscopy using a new digital image analysis scheme.

Malignant melanoma and basal cell carcinoma are common skin tumours. For skin lesion classification it is necessary to determine and calculate different attributes such as exact location, size, shape and appearance. It has been noted that illumination, dermoscopic gel and features such as blood vessels, hair and skin lines can affect border detection. Thus, there is a need for approaches that minimise the effect of such features. This study aims to detect multiple borders from dermoscopy with increased sensitivity and specificity for the detection of early melanoma and other pigment lesions. An automated border detection method based on minimising geodesic active contour energy and incorporating homomorphic, median and anisotropic diffusion (AD) filtering, as well as top-hat watershed transformation is used. Extensive experiments on various skin lesions were conducted on real dermoscopic images and proved to enhance accurate border detection and improve the segmentation result by reducing the error rate from 12.42% to 7.23%. The results have validated the integrated enhancement of numerous lesion border detections with the noise removal algorithm which may contribute to skin cancer classification.