Multi-parameter segmentation of brain images

Atam Dhawan, Brian D'Alessandro

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recent advances in multi-parameter MR brain imaging has enabled multi-class tissue characterization for better quantitative analysis and understanding brain disorders and pathologies. This paper presents a maximum likelihood based method for multi-class segmentation that utilizes spatio-frequency features obtained from wavelet analysis along with the multi-parameter measurements. Results on MR brain images of a patient with stroke are presented.

Original languageEnglish (US)
Title of host publication2009 4th International IEEE/EMBS Conference on Neural Engineering, NER '09
Pages222-225
Number of pages4
DOIs
StatePublished - Oct 27 2009
Event2009 4th International IEEE/EMBS Conference on Neural Engineering, NER '09 - Antalya, Turkey
Duration: Apr 29 2009May 2 2009

Other

Other2009 4th International IEEE/EMBS Conference on Neural Engineering, NER '09
CountryTurkey
CityAntalya
Period4/29/095/2/09

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Clinical Neurology
  • Neuroscience(all)

Fingerprint Dive into the research topics of 'Multi-parameter segmentation of brain images'. Together they form a unique fingerprint.

Cite this