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

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.

Citation Download Citation

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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