Unsupervised border detection in dermoscopy images

Abstract

Background: As a result of the advances in skin imaging technology and the development of suitable image processing techniques, during the last decade, there has been a significant increase of interest in the computer-aided diagnosis of skin cancer. Automated border detection is one of the most important steps in this procedure as the accuracy of the subsequent steps crucially depends on the accuracy of this step.

Methods: In this article, we present an unsupervised approach to border detection in dermoscopy skin lesion images based on a modified version of the JSEG algorithm.

Results: The method is tested on a set of 100 dermoscopy images. The border detection error is quantified by a metric that uses manually determined borders from a dermatologist as the ground truth. The results are compared with three other automated methods and manually determined borders by a second dermatologist.

Conclusion: The results demonstrate that the presented method achieves both fast and accurate border detection in dermoscopy images.

Fig. 1

Melanoma (a) original image (62,201 colors), (b) GLA quantization result (five colors).

Fig. 2

Lentigo segmented at scales (a) 4, (b) 3, (c) 2, and (d) 1.

Fig. 3

Final border detection result (error = 9.64%) (blue, Automaticborder; green, Manualborder).