Locality-constrained group lasso coding for microvessel image classification

Abstract

Image-based classification of histology sections plays an important role in predicting clinical outcomes. In this paper, we propose a Locality-Constrained Group Lasso Coding (LCGLC) method for microvessel image classification, which realizes the automatic “hot spot” detection of angiogenesis for human liver carcinoma. First, we extract Scale-Invariant Feature Transform (SIFT) descriptors on the Single-Opponent (SO) feature map, which simulates the biological functionality of human visual systems. Then, we present the feature-biased dictionary learning to effectively generate the dictionary of SIFT descriptors. With the learned dictionary, our LCGLC method introduces the locality constraint in classical group lasso problem to encode SIFT descriptors. Furthermore, we apply the Spatial Pyramid Matching (SPM) for the code pooling of microvessel images. Finally, we use Support Vector Machine (SVM) to classify a tissue image as having angiogenesis or not. Comprehensive experiments on the microvessel dataset show that the proposed LCGLC method achieves better performance compared with other representative approaches.