TY - GEN
T1 - Fusing speed and phase information for vascular segmentation in phase contrast MR angiograms
AU - Chung, Albert C S
AU - Alison Noble, J.
AU - Summers, Paul
PY - 2000
Y1 - 2000
N2 - 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.
AB - 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.
KW - Medical image processing
KW - Statistical segmentation and medical information fusion
UR - http://www.scopus.com/inward/record.url?scp=84945550444&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84945550444&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84945550444
SN - 3540411895
SN - 9783540411895
VL - 1935
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 166
EP - 175
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Verlag
T2 - 3rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2000
Y2 - 11 October 2000 through 14 October 2000
ER -