Pattern classification for dermoscopic images based on structure textons and Bag-of-Features model

An effective method of pattern classification for dermoscopic images based on structure textons and Bag-of-Features (BoFs) model is proposed in this paper. Firstly, the pattern structures of images were enhanced. Secondly, images with obvious directivity were rotated to align their principal directions with horizontal axis, and Otsu method was used to obtain interesting regions. The intensity values of each pixel in the interesting region and its neighborhood composed patch vector. For each pattern, patch vectors of training images were clustered to generate K structure textons and a dictionary with 5K elements was obtained. Then BoFs model was applied to obtain texton histograms for training and testing images respectively. Finally, a nearest neighbor classifier with chi-square distance was adopted to classify. The experimental results shows that our enhancement method is beneficial to pattern classification and correct classification rate achieves 91.87%.