Brain tumor segmentation from multimodal magnetic resonance images via sparse representation
作者：
Highlights：
• An automated brain tumor segmentation model based on maximum a posteriori probabilistic (MAP) estimation is presented.
• The likelihood probability of the model is estimated by sparse coding and dictionary learning.
• The Markov random field (MRF) is introduced into the prior probability.
• The MAP is converted into a minimum energy optimization problem and graph cuts is used to find its solution.
摘要
Highlights•An automated brain tumor segmentation model based on maximum a posteriori probabilistic (MAP) estimation is presented.•The likelihood probability of the model is estimated by sparse coding and dictionary learning.•The Markov random field (MRF) is introduced into the prior probability.•The MAP is converted into a minimum energy optimization problem and graph cuts is used to find its solution.
论文关键词：Sparse representation,Dictionary learning,Graph cuts,Markov random field,Multimodal magnetic resonance images,Brain tumor segmentation
论文评审过程：Received 23 March 2016, Revised 24 July 2016, Accepted 30 August 2016, Available online 6 September 2016, Version of Record 14 September 2016.
论文官网地址：https://doi.org/10.1016/j.artmed.2016.08.004
