A learned distance function for medical image similarity retrieval

Dave Tahmoush

doi:10.1117/12.811365

13 March 2009 A learned distance function for medical image similarity retrieval

Dave Tahmoush

Author Affiliations +

Proceedings Volume 7264, Medical Imaging 2009: Advanced PACS-based Imaging Informatics and Therapeutic Applications; 726406 (2009) https://doi.org/10.1117/12.811365
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

One fundamental problem remains in the area of image analysis and retrieval: how to measure perceptual similarity between two images. Most researchers employ a Minkowski-type metric, which does not reliably find similarities in objects that are obviously alike. This paper develops a similarity function that is learned in order to capture the perception of similarity. The technique first extracts high-level landmarks in the images to determine a local contextual similarity, but these are unordered and unregistered. Second, the point sets of the two images are fed into the learned similarity function to determine the overall similarity. This technique avoids arbitrary spatial constraints and is robust in the presence of noise, outliers, and imaging artifacts.

Citation Download Citation

Dave Tahmoush "A learned distance function for medical image similarity retrieval", Proc. SPIE 7264, Medical Imaging 2009: Advanced PACS-based Imaging Informatics and Therapeutic Applications, 726406 (13 March 2009); https://doi.org/10.1117/12.811365

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