Markov multiple feature random fields model for the segmentation of brain MR images

作者：

Highlights：

• We propose a novel Markov multiple feature random fields (MMFRF) model.

• A series of features are encoded through the MMFRF model using Bayesian framework.

• 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.

• The results demonstrate that our method outperforms the state-of-the-art approaches.

摘要

•We propose a novel Markov multiple feature random fields (MMFRF) model.•A series of features are encoded through the MMFRF model using Bayesian framework.•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.•The results demonstrate that our method outperforms the state-of-the-art approaches.

论文关键词：Markov random field,Multiple feature random fields,Segmentation,Magnetic resonance imaging

论文评审过程：Received 1 August 2018, Revised 9 May 2019, Accepted 25 May 2019, Available online 27 May 2019, Version of Record 6 June 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.05.038