Fusing speed and phase information for vascular segmentation in phase contrast MR angiograms

Albert C S Chung, J. Alison Noble, Paul Summers

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

Abstract

This paper presents a statistical approach to aggregating speed and phase (directional) information for vascular segmentation in phase contrast magnetic resonance angiograms (PC-MRA), and proposes a Maxwell-Gaussian finite mixture distribution to model the background noise distribution. In this paper, we extend our previous work [6] to the segmentation of phase-difference PC-MRA speed images. We demonstrate that, rather than relying on speed information alone, as done by others [12,14,15], including phase information as a priori knowledge in a Markov random field (MRF) model can improve the quality of segmentation, especially the region within an aneurysm where there is a heterogeneous intensity pattern and significant vascular signal loss. Mixture model parameters are estimated by the Expectation-Maximization (EM) algorithm [3], In addition, it is shown that a Maxwell-Gaussian finite mixture distribution models the background noise more accurately than a Maxwell distribution and exhibits a better fit to clinical data.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages166-175
Number of pages10
Volume1935
ISBN (Print)3540411895, 9783540411895
Publication statusPublished - 2000
Event3rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2000 - Pittsburgh, United States
Duration: Oct 11 2000Oct 14 2000

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1935
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other3rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2000
CountryUnited States
CityPittsburgh
Period10/11/0010/14/00

Keywords

  • Medical image processing
  • Statistical segmentation and medical information fusion

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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