Skip to main content

Automated segmentation of MS lesions from multi-channel MR images

van Leemput, Koen, Maes, Frederik, Bello, Fernando, Vandermeulen, Dirk, Colchester, Alan C. F., Suetens, Paul (1999) Automated segmentation of MS lesions from multi-channel MR images. In: Taylor, Chris and Colchester, Alan C. F., eds. Medical Image Computing and Computer-Assisted Intervention, MICCAI'99, Proceedings. Lecture Notes in Computer Science , 1679. pp. 11-21. Springer-Verlag Berlin ISBN 3-540-66503-X. (doi:10.1007/10704282_2) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1007/10704282_2

Abstract

Quantitative analysis of AIR images is becoming increasingly important as a surrogate marker in clinical trials in multiple sclerosis (MS). This paper describes a fully automated model-based method for segmentation of MS lesions from multi-channel AIR images. The method simultaneously corrects for AIR field inhomogeneities, estimates tissue class distribution parameters and classifies the image voxels. MS lesions are detected as voxels that are not well explained by the model. The results of the automated method are compared with the lesions delineated by human experts, showing a significant total lesion load correlation and an average overall spatial correspondence similar to that between the experts.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1007/10704282_2
Additional information: Proceedings Paper
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
T Technology > TA Engineering (General). Civil engineering (General) > TA1637 Image Analysis, Image Processing
Divisions: Faculties > Sciences > School of Engineering and Digital Arts
Depositing User: F.D. Zabet
Date Deposited: 19 Mar 2009 13:22 UTC
Last Modified: 28 May 2019 13:54 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/16705 (The current URI for this page, for reference purposes)
  • Depositors only (login required):