25 March 2016 Building high dimensional imaging database for content based image search
Author Affiliations +
Abstract
In medical imaging informatics, content-based image retrieval (CBIR) techniques are employed to aid radiologists in the retrieval of images with similar image contents. CBIR uses visual contents, normally called as image features, to search images from large scale image databases according to users’ requests in the form of a query image. However, most of current CBIR systems require a distance computation of image character feature vectors to perform query, and the distance computations can be time consuming when the number of image character features grows large, and thus this limits the usability of the systems. In this presentation, we propose a novel framework which uses a high dimensional database to index the image character features to improve the accuracy and retrieval speed of a CBIR in integrated RIS/PACS.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Qinpei Sun, Qinpei Sun, Jianyong Sun, Jianyong Sun, Tonghui Ling, Tonghui Ling, Mingqing Wang, Mingqing Wang, Yuanyuan Yang, Yuanyuan Yang, Jianguo Zhang, Jianguo Zhang, "Building high dimensional imaging database for content based image search", Proc. SPIE 9789, Medical Imaging 2016: PACS and Imaging Informatics: Next Generation and Innovations, 97890V (25 March 2016); doi: 10.1117/12.2216451; https://doi.org/10.1117/12.2216451
PROCEEDINGS
12 PAGES


SHARE
Back to Top