Zhang, Mingjun; Yang, Fumeng; Xie, Zhenzhu; Wang, Haoyu; Wei, Xueqi Medical image retrieval based on binary code learning. (Chinese. English summary) Zbl 1389.92025 J. Anhui Norm. Univ., Nat. Sci. 40, No. 1, 43-47 (2017). Summary: In recent years, the incidence of breast cancer increased year by year. Many computer aided diagnostic techniques have been successfully developed to automatically analysis breast mammographic image to assist the doctor making a diagnosis. However, since the difference between images is subtle, and a small database, most traditional methods are limited in diagnosis accuracy and fall short of scalability. To solve the mentioned problems, we propose a hashing-based large-scale mammographic image retrieval method for the image-guided diagnosis of breast cancer. In this method, local features are extracted from each query image and diagnosed image. The iterative quantization (ITQ) hashing method is used to compress the mammogram features into compact binary codes which preserve the similarity in original space, and then perform searching in the Hamming space. Finally, a diagnosis result can be obtained according to the returned images. Extensive experiments demonstrate that our system can be used in large-scale database and achieve excellent performance. MSC: 92C55 Biomedical imaging and signal processing 68T05 Learning and adaptive systems in artificial intelligence 68P20 Information storage and retrieval of data Keywords:computer aided diagnosis; large-scale image retrieval; binary code learning; mammogram PDFBibTeX XMLCite \textit{M. Zhang} et al., J. Anhui Norm. Univ., Nat. Sci. 40, No. 1, 43--47 (2017; Zbl 1389.92025) Full Text: DOI