CBIR for mammograms using medical image similarity

David Tahmoush

doi:10.1117/12.844247

11 March 2010 CBIR for mammograms using medical image similarity

David Tahmoush

Proceedings Volume 7628, Medical Imaging 2010: Advanced PACS-based Imaging Informatics and Therapeutic Applications; 76280A (2010) https://doi.org/10.1117/12.844247
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

Abstract

One fundamental problem remains in the area of medical image analysis and retrieval: how to measure radiologist's perception of similarity between two images. This paper develops a similarity function that is learned from medical annotations and built upon extracted medical features in order to capture the perception of similarity between images with cancer. The technique first extracts high-level medical features from the images to determine a local contextual similarity, but these are unordered and unregistered from one image to the next. Second, the feature sets of the images are fed into the learned similarity function to determine the overall similarity for retrieval. This technique avoids arbitrary spatial constraints and is robust in the presence of noise, outliers, and imaging artifacts. We demonstrate that utilizing unordered and noisy higher-level cancer detection features is both possible and productive in measuring image similarity and developing CBIR techniques.

Citation Download Citation

David Tahmoush "CBIR for mammograms using medical image similarity", Proc. SPIE 7628, Medical Imaging 2010: Advanced PACS-based Imaging Informatics and Therapeutic Applications, 76280A (11 March 2010); https://doi.org/10.1117/12.844247

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available