Segmentation and ABCD rule extraction for skin tumors classification

TL;DR

This paper presents an automated skin lesion diagnosis system using segmentation and ABCD rule features, employing unsupervised segmentation and neural networks to improve melanoma detection accuracy.

Contribution

It introduces an unsupervised lesion segmentation method combined with ABCD feature extraction for improved melanoma classification.

Findings

Higher true detection rate in melanoma classification

Reduced false positive rate compared to previous methods

Effective segmentation with iterative thresholding and level set comparison

Abstract

During the last years, computer vision-based diagnosis systems have been widely used in several hospitals and dermatology clinics, aiming at the early detection of malignant melanoma tumor, which is among the most frequent types of skin cancer. In this work, we present an automated diagnosis system based on the ABCD rule used in clinical diagnosis in order to discriminate benign from malignant skin lesions. First, to reduce the influence of small structures, a preprocessing step based on morphological and fast marching schemes is used. In the second step, an unsupervised approach for lesion segmentation is proposed. Iterative thresholding is applied to initialize level set automatically. As the detection of an automated border is an important step for the correctness of subsequent phases in the computerized melanoma recognition systems, we compare its accuracy with growcut and mean shift algorithms, and discuss how these results may influence in the following steps: the feature extraction and the final lesion classification. Relying on visual diagnosis four features: Asymmetry (A), Border (B), Color (C) and Diversity (D) are computed and used to construct a classification module based on artificial neural network for the recognition of malignant melanoma. This framework has been tested on a dermoscopic database [16] of 320 images. The classification results show an increasing true detection rate and a decreasing false positive rate.