Mining histopathological images via hashing-based scalable image retrieval

Abstract

Automatic analysis of histopathological images has been widely investigated using computational image processing and machine learning techniques. Computer-aided diagnosis (CAD) systems and content-based image retrieval (CBIR) systems have been successfully developed for diagnosis, disease detection, and decision support in this area. In this paper, we focus on a scalable image retrieval method with high-dimensional features for the analysis of histopathology images. Specifically, we present a kernelized and supervised hashing method. With a small amount of supervised information, our method can compress a 10, 000-dimensional image feature vector into only tens of binary bits with informative signatures preserved, and these binary codes are then indexed into a hash table that enables real-time retrieval. We validate the hashing-based image retrieval framework on several thousands of images of breast microscopic tissues for both image classification (i.e., benign vs. actionable categorization) and retrieval. Our framework achieves high search accuracy and promising computational efficiency, comparing favorably with other commonly used methods.