Lesion Border Detection in Dermoscopy Images

TL;DR

This paper reviews recent computational methods for automated lesion border detection in dermoscopy images, highlighting challenges and the importance of incorporating domain knowledge for improved accuracy.

Contribution

It provides a systematic overview of border detection techniques, emphasizing computational issues, evaluation challenges, and the potential of domain knowledge integration.

Findings

Existing methods face challenges with image acquisition and evaluation.

Inadequate description of methods hampers reproducibility.

Incorporating domain knowledge could improve detection performance.

Abstract

Background: Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, computerized analysis of dermoscopy images has become an important research area. One of the most important steps in dermoscopy image analysis is the automated detection of lesion borders. Methods: In this article, we present a systematic overview of the recent border detection methods in the literature paying particular attention to computational issues and evaluation aspects. Conclusion: Common problems with the existing approaches include the acquisition, size, and diagnostic distribution of the test image set, the evaluation of the results, and the inadequate description of the employed methods. Border determination by dermatologists appears to depend upon higher-level knowledge, therefore it is likely that the incorporation of domain knowledge in automated methods will enable them to perform better, especially in sets of images with a variety of diagnoses.