We present an automatic segmentation method using the Maximum a posterior (MAP)-Markov random field (MRF) framework that possesses regional adaptive�...
We present an automatic segmentation method using the Maximum a posterior (MAP)-Markov random field (MRF) framework that possesses regional adaptive capability�...
The paper is an extension of previous work on spatial-varying Gaussian mixture and Markov random field (SVGM-MRF) from 2D to 3D to segment the MR brain�...
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In this paper, we propose a powerful fully automated classification method, which is based on Gaussian-mixture model with Markov random field (MRF). First,�...
The Gaussian Mixture Model (GMM) is one of the most widely used models for statistical segmentation of brain Magnetic Resonance (MR) images.
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Nov 15, 2019 � We propose a unified brain MR image segmentation method based on the MMFRF model. The evaluation is performed on both real and simulated brain MR images.
Feb 25, 2022 � A robust modified Gaussian mixture model with rough set for image segmentation. ... Segmentation of brain MR images through a hidden Markov random�...
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In this paper, a spatially constrained mixture model for the segmentation of MR brain images is presented.
May 13, 2019 � The proposed GMMD-U considers the local spatial relationships by assuming that the prior probability obeys the Dirichlet distribution.
Abstract— An automated algorithm for tissue segmentation of noisy, low contrast magnetic resonance (MR) images of the brain is presented. A mixture model�...