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.
Chapter PDF
Similar content being viewed by others
References
Andersen, A.H., Kirsch, J.E.: Analysis of noise in phase contrast MR imaging. Med. Phy. 23(6) (June 1996)
Besag, J.: On the Statistical Analysis of Dirty Pictures. J. Royal Statistical Society (B) 48(3), 259–302 (1986)
Bishop, C.M.: Neural Networks for Pattern Recognition. Clarendon Press, Oxford (1995)
Brummer, M.E., Mersereau, R.M., Eisner, R.L., Lewine, R.R.J.: Automatic Detection of Brain Contours in MRI Data Sets. TMI 12(2), 153–166 (1993)
Burleson, A.C., et al.: Computer Modeling of Intracranial Saccular and Lateral Aneurysms for the Study of Their Hemodynamics. Neurosurgery 37(4), 774–784 (1995)
Chung, A.C.S., Noble, J.A.: Statistical 3D vessel segmentation using a Rician distribution. In: Taylor, C., Colchester, A. (eds.) MICCAI 1999. LNCS, vol. 1679, pp. 82–89. Springer, Heidelberg (1999); In: MIUA 1999, pp.77–80 (1999)
Geman, S., Geman, D.: Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images. PAMI 6(6), 721–741 (1984)
Gobin, Y.P., Counord, J.L., Flaud, P., Duffaux, J.: In Vitro study of haemodynamics in a giant saccular aneurysm model. Neuroradiology 36, 530–536 (1994)
Held, K., Kops, E.R., Krause, B.J., Wells, W.M., Kikinis, R., Muller-Gartner, H.W.: Markov Random Field Segmentation of Brain MR Images. TMI 16(6), 878–886 (1997)
Herman, J.L., Hesselink, L.: Visualization of Vector Field Topology in Fluid Flows. IEEE Comp. Graphics and Appl. 11(3), 36–46 (1991)
Kapur, T., Grimson, W.E.L., Kikinis, R., Wells, W.M.: Enhanced Spatial Priors for Segmentation of Magnetic Resonance Imagery. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998, vol. 1496, pp. 457–468. Springer, Heidelberg (1998)
Krissian, K., Malandain, G., Ayache, N.: Model Based Detection of Tubular Structures in 3D Images, INRIA-Technical Report 3736 (1999)
Lathi, B.P.: Modern Digital and Analog Communication Systems, ch. 11. Hault-Saunders Intl. Ed.(1983)
Lorigo, L.M., Faugeras, O., Grimson, W.E.L., Keriven, R., Kikinis, R., Westin, C.F.: Co-dimension 2 Geodesic Active Contours for MRA Segmentation. In: IPMI 1999, pp. 126–139 (1999)
Mclnerney, T., Terzopoulos, D.: Medical Image Segmentation Using Topologically Adaptable Surface. In: Troccaz, J., Mösges, R., Grimson, W.E.L. (eds.) CVRMed-MRCAS 1997, CVRMed 1997, and MRCAS 1997, vol. 1205, pp. 23–32. Springer, Heidelberg (1997)
Pelc, N.J., Bernstein, M.A., et al.: Encoding Strategies for Three-Direction Phase-Contrast MR Imaging of Flow. JMRI 1, 405–413 (1991)
Steiger, H.J., et al.: Computational Simulation of Turbulent Signal Loss in 2D Time-of-FlightMagnetic Resonance Angiograms. MRM 37, 609–614 (1997)
Summers, P., Chung, A.C.S., Noble, J.A.: Impact of Image Processing Operations on MR Noise Distributions. In: Proc. of 8th ISMRM (2000)
Summers, P., Bhalerao, A.H., Hawkes, D.J.: Multiresolution, Model-Based Segmentation of MR Angiograms. JMRI 7, 950–957 (1997)
Zhang, Y., Smith, S., Brady, M.: Segmentation of brain MR images using Markov random fields. In: MIUA 1999, pp. 65–68 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chung, A.C.S., Noble, J.A., Summers, P. (2000). Fusing Speed and Phase Information for Vascular Segmentation in Phase Contrast MR Angiograms. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2000. MICCAI 2000. Lecture Notes in Computer Science, vol 1935. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-40899-4_17
Download citation
DOI: https://doi.org/10.1007/978-3-540-40899-4_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41189-5
Online ISBN: 978-3-540-40899-4
eBook Packages: Springer Book Archive