Content based image retrieval for large medical image corpus

Abstract

In this paper we address the scalability issue when it comes to Content based image retrieval in large image archives in the medical domain. Throughout the text we focus on explaining how small changes in image representation, using existing technologies leads to impressive improvements when it comes to image indexing, search and retrieval duration. We used a combination of OpponentSIFT descriptors, Gaussian Mixture Models, Fisher kernel and Product quantization that is neatly packaged and ready for web integration. The CBIR feature of the system is demonstrated through a Python based web client with features like region of interest selection and local image upload.