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

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.

I. Introduction

The incidence and mortality rates of melanoma in Caucasian populations have been increasing for many decades. Though only accounting for one tenth of the new cases of skin cancer, melanoma is associated with more than 90% of the skin cancer deaths [1]. The most dangerous characteristic of melanoma is that it can spread widely over the body via the lymphatic vessels and blood vessels. Thus, early diagnosis of melanoma is a key factor for the prognosis of the disease [2]. If detected at an early stage, the prognosis for the patient is excellent because the patient can be cured by simple excision of the tumor. However, early diagnosis is very challenging as melanomas are easily confused with benign skin lesions [1]. Australia has one of the highest rates of skin cancer in the world. Over 1,890 Australians die from skin cancer each year [2].