Brain tumor segmentation from multimodal magnetic resonance images via sparse representation

	Brain tumor segmentation from multimodal magnetic resonance images via sparse representation
	Yuhong Li; Fucang Jia; Jing Qin
刊名	Artificial Intelligence in Medicine
	2016
英文摘要	Objective Accurately segmenting and quantifying brain gliomas from magnetic resonance (MR) images remains a challenging task because of the large spatial and structural variability among brain tumors. To develop a fully automatic and accurate brain tumor segmentation algorithm, we present a probabilistic model of multimodal MR brain tumor segmentation. This model combines sparse representation and the Markov random field (MRF) to solve the spatial and structural variability problem. Methods We formulate the tumor segmentation problem as a multi-classification task by labeling each voxel as the maximum posterior probability. We estimate the maximum a posteriori (MAP) probability by introducing the sparse representation into a likelihood probability and a MRF into the prior probability. Considering the MAP as an NP-hard problem, we convert the maximum posterior probability estimation into a minimum energy optimization problem and employ graph cuts to find the solution to the MAP estimation. Results Our method is evaluated using the Brain Tumor Segmentation Challenge 2013 database (BRATS 2013) and obtained Dice coefficient metric values of 0.85, 0.75, and 0.69 on the high-grade Challenge data set, 0.73, 0.56, and 0.54 on the high-grade Challenge LeaderBoard data set, and 0.84, 0.54, and 0.57 on the low-grade Challenge data set for the complete, core, and enhancing regions. Conclusions The experimental results show that the proposed algorithm is valid and ranks 2nd compared with the state-of-the-art tumor segmentation algorithms in the MICCAI BRATS 2013 challenge.
收录类别	SCI
原文出处	http://www.sciencedirect.com/science/article/pii/S0933365716301051
语种	英语
内容类型	期刊论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/10386]
专题	深圳先进技术研究院_医工所
作者单位	Artificial Intelligence in Medicine
推荐引用方式 GB/T 7714	Yuhong Li,Fucang Jia,Jing Qin. Brain tumor segmentation from multimodal magnetic resonance images via sparse representation[J]. Artificial Intelligence in Medicine,2016.
APA	Yuhong Li,Fucang Jia,&Jing Qin.(2016).Brain tumor segmentation from multimodal magnetic resonance images via sparse representation.Artificial Intelligence in Medicine.
MLA	Yuhong Li,et al."Brain tumor segmentation from multimodal magnetic resonance images via sparse representation".Artificial Intelligence in Medicine (2016).