A review on brain tumor segmentation of MRI images

被引：204

作者：

Wadhwa, Anjali ^{[1
]}

Bhardwaj, Anuj ^{[1
]}

Verma, Vivek Singh ^{[2
]}

机构：

[1] Jaypee Inst Informat Technol, Noida 201309, India

[2] Ajay Kumar Garg Engn Coll, Ghaziabad 201009, India

来源：

MAGNETIC RESONANCE IMAGING | 2019年 / 61卷

关键词：

MRI; Segmentation; Brain tumor; Classification; Ensemble learning; MEANS CLUSTERING-ALGORITHM; ACTIVE CONTOUR MODELS; CONVOLUTIONAL NEURAL-NETWORKS; GRADIENT VECTOR FLOW; K-MEANS; NEIGHBORHOOD ATTRACTION; AUTOMATIC SEGMENTATION; DEFORMABLE MODEL; FEATURES; SELECTION;

D O I：

10.1016/j.mri.2019.05.043

中图分类号：

R8 [特种医学]; R445 [影像诊断学];

学科分类号：

1002 ; 100207 ; 1009 ;

摘要：

The process of segmenting tumor from MRI image of a brain is one of the highly focused areas in the community of medical science as MRI is noninvasive imaging. This paper discusses a thorough literature review of recent methods of brain tumor segmentation from brain MRI images. It includes the performance and quantitative analysis of state-of-the-art methods. Different methods of image segmentation are briefly explained with the recent contribution of various researchers. Here, an effort is made to open new dimensions for readers to explore the concerned area of research. Through the entire review process, it has been observed that the combination of Conditional Random Field (CRF) with Fully Convolutional Neural Network (FCNN) and CRF with DeepMedic or Ensemble are more effective for the segmentation of tumor from the brain MRI images.

引用

页码：247 / 259

页数：13

共 156 条

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