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Proceedings Paper

MRI image segmentation using multiscale autoregressive model and 3D Markov random fields
Author(s): Pierre Martin Tardif; Andre Zaccarin
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Paper Abstract

Texture segmentation applied to magnetic resonance image (MRI) is investigated using a multiscale autoregressive model (M-AR). Since M-AR models need large region for good parameter estimation, a mixture model using M-AR and constant gray level value is developed. Region uniformity is obtained using a 3D Markov random field. The segmentation is given by its maximum a posteriori estimate. The segmentation is computed using iterated conditional modes. Two initial segmentation choices are studied: MLE segmentation with multiple resolution segmentation and human atlas. Human atlas initial segmentation proves to be closer to desired segmentation, even if the image from the atlas is not precise.

Paper Details

Date Published: 25 April 1997
PDF: 12 pages
Proc. SPIE 3034, Medical Imaging 1997: Image Processing, (25 April 1997); doi: 10.1117/12.274082
Show Author Affiliations
Pierre Martin Tardif, Univ. Laval (Canada)
Andre Zaccarin, Univ. Laval (Canada)


Published in SPIE Proceedings Vol. 3034:
Medical Imaging 1997: Image Processing
Kenneth M. Hanson, Editor(s)

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