Automated extraction and description of dark areas in surface microscopy melanocytic lesion images

Abstract

Background: Identification of dark areas inside a melanocytic lesion (ML) is of great importance for melanoma diagnosis, both during clinical examination and employing programs for automated image analysis.

Objective: The aim of our study was to compare two different methods for the automated identification and description of dark areas in epiluminescence microscopy images of MLs and to evaluate their diagnostic capability.

Methods: Two methods for the automated extraction of 'absolute' (ADAs) and 'relative' dark areas (RDAs) and a set of parameters for their description were developed and tested on 339 images of MLs acquired by means of a polarized-light videomicroscope.

Results: Significant differences in dark area distribution between melanomas and nevi were observed employing both methods, permitting a good discrimination of MLs (diagnostic accuracy = 74.6 and 71.2% for ADAs and RDAs, respectively).

Conclusions: Both methods for the automated identification of dark areas are useful for melanoma diagnosis and can be implemented in programs for image analysis.