Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

Segmenting the human brain from magnetic resonance images is a challenging task due to the convoluted shape of the cortex, noise, intensity non-uniformity and partial volume effects. We propose a new way to overcome part of the biasvariance tradeoff existent in any segmentation technique by locally varying the behaviour of the model. We developed a novel metric based on the Laplacian of the geodesic distance to localise and iteratively modify the prior information and Markov random field weights, leading to a better delineation of deep sulci and narrow gyri. Experiments performed on 20 Brainweb datasets show statistically significant improvements in Dice scores and partial volume estimation when compared to two well established techniques. © 2010 IEEE.

Original publication

DOI

10.1109/ISBI.2010.5490146

Type

Conference paper

Publication Date

09/08/2010

Pages

956 - 959