Cart (Loading....) | Create Account
Close category search window
 

Localization of epileptogenic zones in F-18 FDG brain PET of patients with temporal lobe epilepsy using artificial neural network

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

7 Author(s)
Jae Sung Lee ; Interdisciplinary Program in Med. & Biol. Eng., Seoul Nat. Univ., South Korea ; Dong Soo Lee ; Seok-Ki Kim ; Sang-Kun Lee
more authors

For an objective interpretation of cerebral metabolic pattern to find epileptogenic zones in patients with temporal lobe epilepsy (TLE), the authors developed a computer-aided classifier using an artificial neural network (ANN). They studied 261 epilepsy patients diagnosed as no abnormal findings (NA, n=64), left TLE (n=116), or right TLE (n=81) on interictal brain F-18-fluorodeoxyglucose positron emission tomography (FDG PET) by the consensus of two expert physicians. Seventeen asymmetry indexes between the mean counts of the 34 mirrored regions were extracted from the spatially normalized images and used as input parameters. The three diagnoses of NA, left TLE, and right TLE were used as outputs of the ANN. The structure of the ANN was optimized with variable error goals and the number of hidden units. On the criteria of agreement of diagnoses with those of expert viewers, the best classifier was chosen, which yielded a maximum average agreement of 85% for the test set when the authors used an error goal of 20 (sum of squared error) and ten hidden units. The authors could devise an ANN that performed as well in diagnosing left or right TLE on FDG PET as human experts and could be used as a clinical decision support tool.

Published in:

Medical Imaging, IEEE Transactions on  (Volume:19 ,  Issue: 4 )

Date of Publication:

April 2000

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.