Segmentation and Classification of Skin Lesions for Disease Diagnosis

TL;DR

This paper presents a new automated method for skin lesion segmentation and classification using region growing and machine learning classifiers, achieving promising results on a custom dataset.

Contribution

It introduces a novel segmentation approach with automatic seed initialization and combines classifiers for improved skin lesion diagnosis.

Findings

F-measure of 61% with classifier fusion

Effective removal of hairs and noise from skin images

Promising classification performance on a custom dataset

Abstract

In this paper, a novel approach for automatic segmentation and classification of skin lesions is proposed. Initially, skin images are filtered to remove unwanted hairs and noise and then the segmentation process is carried out to extract lesion areas. For segmentation, a region growing method is applied by automatic initialization of seed points. The segmentation performance is measured with different well known measures and the results are appreciable. Subsequently, the extracted lesion areas are represented by color and texture features. SVM and k-NN classifiers are used along with their fusion for the classification using the extracted features. The performance of the system is tested on our own dataset of 726 samples from 141 images consisting of 5 different classes of diseases. The results are very promising with 46.71% and 34% of F-measure using SVM and k-NN classifier respectively and with 61% of F-measure for fusion of SVM and k-NN.