Unsupervised Skin cancer detection by combination of texture and shape features in dermoscopy images

In this paper a novel unsupervised feature extraction method for detection of melanoma in skin images is presented. First of all، normal skin surrounding the lesion is removed in a segmentation process. In the next step، some shape and texture features are extracted from the output image of the first step: GLCM، GLRLM، the proposed directional-frequency features، and some parameters of Ripplet transform are used as texture features; Also، NRL features and Zernike moments are used as shape features. Totally، 63 texture features and 31 shape features are extracted. Finally، the number of extracted features is reduced using PCA method and a proposed method based on Fisher criteria. Extracted features are classified using the Perceptron Neural Networks، Support Vector Machine، 4-NN، and Naïve Bayes. The results show that SVM has the best performance. The proposed algorithm is applied on a database that consists of 160 labeled images. The overall results confirm the superiority of the proposed method in both accuracy and reliability over previous works.