Content-based image retrieval for pulmonary computed tomography nodule images

Michael Lam; Tim Disney; Mailan Pham; Daniela Raicu; Jacob Furst; Ruchaneewan Susomboon

doi:10.1117/12.710297

21 March 2007 Content-based image retrieval for pulmonary computed tomography nodule images

Michael Lam, Tim Disney, Mailan Pham, Daniela Raicu, Jacob Furst, Ruchaneewan Susomboon

Proceedings Volume 6516, Medical Imaging 2007: PACS and Imaging Informatics; 65160N (2007) https://doi.org/10.1117/12.710297
Event: Medical Imaging, 2007, San Diego, CA, United States

Abstract

Research studies have shown that advances in computed tomography (CT) technology allow better detection of pulmonary nodules by generating higher-resolution images. However, the new technology also generates many more individual transversal reconstructions, which as a result may affect the efficiency and accuracy of the radiologists interpreting these images. The goal of our research study is to build a content-based image retrieval (CBIR) system for pulmonary CT nodules. Currently, texture is used to quantify the image content, but any other image feature could be incorporated into the proposed system. Unfortunately, there is no texture model or similarity measure known to work best for encoding nodule texture properties or retrieving most similar nodules. Therefore, we investigated and evaluated several texture models and similarity measures with respect to nodule size, number of retrieved nodules, and radiologist agreement on the nodules' texture characteristic. The results were generated on 90 thoracic CT scans collected by the Lung Image Database Consortium (LIDC). Every case was annotated by up to four radiologists marking the contour of nodules and assigning nine characteristics (including texture) to each identified nodule. We found that Gabor texture descriptors produce the best retrieval results regardless of the nodule size, number of retrieved items or similarity metric. Furthermore, when analyzing the radiologists' agreement on the texture characteristic, we found that when just two radiologists agreed, the average precision increased from 88% to 96% for both Gabor and Markov texture features. Moreover, once three or four radiologists agreed the precision increased to nearly 100%.

Citation Download Citation

Michael Lam, Tim Disney, Mailan Pham, Daniela Raicu, Jacob Furst, and Ruchaneewan Susomboon "Content-based image retrieval for pulmonary computed tomography nodule images", Proc. SPIE 6516, Medical Imaging 2007: PACS and Imaging Informatics, 65160N (21 March 2007); https://doi.org/10.1117/12.710297

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