Atlas-based registration parameters in segmenting sub-cortical regions from brain MRI-images

Jyrki Lötjönen, Juha Koikkalainen, L. Thurfjell, D. Rueckert

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

6 Citations (Scopus)

Abstract

Multi-atlas segmentation has been proved to perform well in segmenting sub-cortical structures from images. In this work, we study different components of multi-atlas segmentation and propose new techniques to improve the segmentation accuracy. We found that the use of gradient information in addition to standard normalised mutual information increases the registration accuracy. We also studied different techniques to select atlases in the multi-atlas segmentation. In addition, the expectation maximisation algorithm was used to combine multi-atlas and intensity model information. The average similarity index obtained for six subcortical structures was 0.84. (11 refs.)
Original languageEnglish
Title of host publication2009 IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro
Place of PublicationPiscataway
PublisherIEEE Institute of Electrical and Electronic Engineers
Pages21-24
ISBN (Electronic)978-1-4244-3932-4
ISBN (Print)978-1-4244-3931-7
DOIs
Publication statusPublished - 2009
MoE publication typeA4 Article in a conference publication
EventIEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI - Boston, United States
Duration: 28 Jun 20091 Jul 2009

Conference

ConferenceIEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI
CountryUnited States
CityBoston
Period28/06/091/07/09

Keywords

  • Atlases
  • brain
  • registation
  • segmentation

Fingerprint Dive into the research topics of 'Atlas-based registration parameters in segmenting sub-cortical regions from brain MRI-images'. Together they form a unique fingerprint.

Cite this