Classification of Dermoscopic Skin Cancer Images Using Color and Hybrid Texture Features

Malignant Melanoma is one of the rare and the deadliest form of skin cancer if left untreated. Death rate due to this cancer is three times more than all other skin-related malignancies combined. Incidence rates of melanoma have been increasing, especially among young adults, but survival rates are high if detected early. Unfortunately, the time and costs required for dermatologists to screen all patients for melanoma are prohibitively expensive. There is a need for an automated system to assess a patient's risk of melanoma using digital dermoscopy, that is, a skin imaging technique widely used for pigmented skin lesion inspection. In this research, we aim to propose an intelligent automated method for identification of the type of skin lesions using machine-learning techniques. Two types of texture feature have been used to perform classification of melanoma and non-melanoma. First local information through Local Binary Pattern (LBP) on different scales and Gray Level Co-Occurrence Matrix (GLCM) at different angles has been extracted as a texture features. These features are robust due to scale invariant property of LBP and rotation invariant property of GLCM features. Global information of different colors channels has been incorporated through four different moments extracted in six different color spaces like RGB, HSV, YCbCr, NTSc, CIE L*u*v and CIE L*a*b. Thus a fused hybrid texture local and color as global features has been proposed to classify the melanoma and non-melanoma. Support vector machine has been used as a classifier to classify melanoma and non-melanoma. Experiments have been tested on well-known dataset dermis that is freely available on the Internet. The proposed method has been compared with state of the art methods and shows better performance in comparison to the existing methods.