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.
CITATION STYLE
Chung, A. C. S., Alison Noble, J., & Summers, P. (2000). Fusing speed and phase information for vascular segmentation in phase contrast MR angiograms. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1935, pp. 166–175). Springer Verlag. https://doi.org/10.1007/978-3-540-40899-4_17
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