<title>MRI image segmentation using multiscale autoregressive model and 3D Markov random fields</title>

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.