This paper proposes a texture feature which is applied on human breast Optical Coherence Microscopy (OCM) images to classify different types of breast tissues. Inspired by local binary pattern (LBP) texture features, a new variant of LBP feature, block based LBP (BLBP) is proposed. Instead of representing intensity differences between neighbors and a center pixel, BLBP feature extracts the intensity differences among certain blocks of the neighborhood around a pixel. Two different ways are proposed to organize the blocks: the spokes and the rings. By integrating spoke BLBP with ring BLBP features, very high classification accuracy is achieved using a neural network classifier. In one of our experiments which classifies 4310 OCM images into five tissue types, the classification accuracy increased from 81.7% to 92.4% when new features are used instead of the traditional LBP feature. In another experiment which classifies 46 large field OCM images as either benign or containing tumor, a classification accuracy of 91.3% is reached by using multi-scale BLBP features.