MRI image segmentation using multiscale autoregressive model and 3D Markov random fields

Pierre Martin Tardif; Andre Zaccarin

doi:10.1117/12.274082

25 April 1997 MRI image segmentation using multiscale autoregressive model and 3D Markov random fields

Pierre Martin Tardif, Andre Zaccarin

Author Affiliations +

Proceedings Volume 3034, Medical Imaging 1997: Image Processing; (1997) https://doi.org/10.1117/12.274082
Event: Medical Imaging 1997, 1997, Newport Beach, CA, United States

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.

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Pierre Martin Tardif and Andre Zaccarin "MRI image segmentation using multiscale autoregressive model and 3D Markov random fields", Proc. SPIE 3034, Medical Imaging 1997: Image Processing, (25 April 1997); https://doi.org/10.1117/12.274082

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