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Proceedings Paper

Content-based image retrieval for pulmonary computed tomography nodule images
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Paper 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%.

Paper Details

Date Published: 21 March 2007
PDF: 12 pages
Proc. SPIE 6516, Medical Imaging 2007: PACS and Imaging Informatics, 65160N (21 March 2007);
Show Author Affiliations
Michael Lam, James Madison Univ. (United States)
Tim Disney, Seattle Pacific Univ. (United States)
Mailan Pham, Mt. Holyoke College (United States)
Daniela Raicu, DePaul Univ. (United States)
Jacob Furst, DePaul Univ. (United States)
Ruchaneewan Susomboon, DePaul Univ. (United States)

Published in SPIE Proceedings Vol. 6516:
Medical Imaging 2007: PACS and Imaging Informatics
Steven C. Horii; Katherine P. Andriole, Editor(s)

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