Scheduled System Maintenance on May 29th, 2015:
IEEE Xplore will be upgraded between 11:00 AM and 10:00 PM EDT. During this time there may be intermittent impact on performance. We apologize for any inconvenience.
By Topic

A knowledge discovery approach to diagnosing myocardial perfusion

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)

Discusses applying a six-step discovery process to a database of SPECT bull's-eye maps of the heart. Visual assessment of clinical diagnostic images is observer-dependent. Thus, much effort is expended to computerize the process of diagnosis so it is less dependent on the observer, especially when the observer is not experienced. A large number of images to be evaluated (as in SPECT myocardial perfusion studies: approximately 15 oblique "slices," 15 oblique/sagittal, and 15 oblique/coronal, both in stress and rest, which comes to nearly 100 2-D images per patient) forced the creation of more "comprehensive" images; namely, the bull's-eye perfusion maps. Using these maps, the authors showed that it is possible to differentiate the patients with coronary artery disease (one- or two-vessel) from the patients with low probability of the disease (normals). In the future, features other than those used in this work will be used; for instance, a feature representing the area of "abnormal" myocardium, available in most previously mentioned algorithms for "normative" evaluation of bull's-eye maps. In the course of this work, the authors also came up with methods that can accurately extract the ROIs from an image where a thresholding method cannot be used.

Published in:

Engineering in Medicine and Biology Magazine, IEEE  (Volume:19 ,  Issue: 4 )