By Topic

Thoracic image case retrieval with spatial and contextual information

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Yang Song ; Biomed. & Multimedia Inf. Technol. (BMIT) Res. Group, Univ. of Sydney, Sydney, NSW, Australia ; Weidong Cai ; Eberl, S. ; Fulham, M.J.
more authors

Positron emission tomography - computed tomography (PETCT) is now accepted as the best imaging technique to accurately stage lung cancer. The consistent and accurate interpretation of PET-CT images, however, is not a trivial task. We propose a content-based image retrieval system for retrieving similar cases from an imaging database as a reference dataset to aid the physicians in PET-CT scan interpretation. Problematic areas in diagnosis are the abnormal FDG uptake in the parenchymal lung tumor and in the regional nodes in the pulmonary hilar regions and the mediastinum. The primary tumor and the nodal disease are detected from the scans of thorax with learning-based techniques and a voting method for 3D object localization. Similar cases are then retrieved based on the similarity measure between the feature vectors of the cases. Our preliminary evaluation with clinical data from lung cancer patients suggests our approach is accurate with high retrieval precision.

Published in:

Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on

Date of Conference:

March 30 2011-April 2 2011