Automatic Detection of Blue-White Veil and Related Structures in Dermoscopy Images

TL;DR

This paper introduces a machine learning method using decision trees to automatically detect blue-white veil structures in dermoscopy images, aiding melanoma diagnosis with promising sensitivity and specificity.

Contribution

It presents a novel pixel classification approach for blue-white veil detection in dermoscopy images, enhancing automated melanoma feature recognition.

Findings

Sensitivity of 69.35% overall detection

Specificity of 89.97% overall detection

Sensitivity increases to 78.20% when blue veil is a primary feature

Abstract

Dermoscopy is a non-invasive skin imaging technique, which permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. One of the most important features for the diagnosis of melanoma in dermoscopy images is the blue-white veil (irregular, structureless areas of confluent blue pigmentation with an overlying white "ground-glass" film). In this article, we present a machine learning approach to the detection of blue-white veil and related structures in dermoscopy images. The method involves contextual pixel classification using a decision tree classifier. The percentage of blue-white areas detected in a lesion combined with a simple shape descriptor yielded a sensitivity of 69.35% and a specificity of 89.97% on a set of 545 dermoscopy images. The sensitivity rises to 78.20% for detection of blue veil in those cases where it is a primary feature for melanoma recognition.