Multiple-Feature Kernel Hashing for Mass Detection
Xin Lv, Ying Wang · 2016
Medical image retrieval is very important for multimedia applications. Precision of abnormity detection is the key factor of improving retrieval accuracy. This paper proposed a novel detection algorithm based on multiple-feature kernel hashing. To comprehensively describe breast image, different specific features of each suspicious region are extracted, such as CNN deep features, hierarchy weigh Gist features and Histogram of Oriented Gradient (HOG) features. Integrating multiple features and kernel-based supervised hashing algorithm, we achieve an efficient mass detection system for digital mammo grams. Large scale experimental results on Digital Database for Screening Mammography (DDSM) show that the proposed method in this paper can effectively improve the whole detection performance by maintaining the high sensitivity and low false positive rate at the same time.