By Topic

Medical image segmentation using analysis of isolable-contour maps

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

3 Author(s)
Shiffman, S. ; Dept. of Psychiatry & Radiol., Stanford Univ., CA, USA ; Rubin, G.D. ; Napel, S.

A common challenge for automated segmentation techniques is differentiation between images of close objects that have similar intensities, whose boundaries are often blurred due to partial-volume effects. The authors propose a novel approach to segmentation of two-dimensional images, which addresses this challenge. Their method, which they call intrinsic shape for segmentation (ISeg), analyzes isolabel-contour maps to identify coherent regions that correspond to major objects. ISeg generates an isolabel-contour map for an image by multilevel thresholding with a fine partition of the intensity range. ISeg detects object boundaries by comparing the shape of neighboring isolabel contours from the map. ISeg requires only little effort from users; it does not require construction of shape models of target objects. In a formal validation with computed-tomography angiography data, the authors showed that ISeg was more robust than conventional thresholding, and that ISeg's results were comparable to results of manual tracing.

Published in:

Medical Imaging, IEEE Transactions on  (Volume:19 ,  Issue: 11 )