A high performance algorithm to diagnosis of skin lesions deterioration in dermatoscopic images using new feature extraction

Abstract

Differentiation of pigmented skin lesions is difficult even for expert. In previous work, we proposed an algorithm for segmentation the dermoscopic images. In this paper, a feature extraction based algorithm is proposed which diagnose benignity or malignancy of the pigmented skin lesions in dermatoscopic images, to develop the previous work. In the proposed scheme the shape features are extracted from the binary segmented image according to ABCD rule. Subsequentely, after tracing the obtained binary image with the original dermatoscopic image, color and texture features are achieved according the same rule. The obtained features (shape, color and texture) are normalized to reach a high performance. Finally, classification is performed using SVM classifier to diagnose the deterioration of pigmented skin lesions (benignity or melanoma). The experimental results show that the proposed approach has specificity 90.03%, sensitivity 79.89% and accuracy 84.09% and improves the related results in existing works.

A Feature Selection Framework for Granular Parakeratosis Lesion Detection

“…Over the past few decades, several researchers in the computer vision and medical image analysis field are trying to develop automated techniques for skin lesion detection to achieve high performance. The work of [5] extracted 12 shape features, 35 texture features, and 75 color features from the binary image based on the ABCD rule. The extracted features are normalized and then SVM classifier is applied to obtain a high performance leading to 90% specificity, 80% sensitivity, and 84% accuracy.…”