Content Based Image Retrieval for Large Medical Image Corpus

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.

[1]  Cordelia Schmid,et al.  Combining attributes and Fisher vectors for efficient image retrieval , 2011, CVPR 2011.

[2]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[3]  Haejun Lee,et al.  Medical Image Retrieval: Past and Present , 2012, Healthcare informatics research.

[4]  Koen E. A. van de Sande,et al.  Evaluating Color Descriptors for Object and Scene Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Cordelia Schmid,et al.  Aggregating Local Image Descriptors into Compact Codes , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Fuhui Long,et al.  Fundamentals of Content-Based Image Retrieval , 2003 .

[7]  Cordelia Schmid,et al.  Aggregating local descriptors into a compact image representation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[8]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[9]  C. Dye,et al.  Research for Universal Health Coverage , 2013, Science Translational Medicine.

[10]  Cordelia Schmid,et al.  Product Quantization for Nearest Neighbor Search , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Antonio Torralba,et al.  Spectral Hashing , 2008, NIPS.