Combined Features in Region of Interest for Brain Tumor Segmentation

被引:20
作者
Alqazzaz, Salma [1 ,2 ]
Sun, Xianfang [3 ]
Nokes, Len Dm [1 ]
Yang, Hong [4 ]
Yang, Yingxia [5 ]
Xu, Ronghua [6 ]
Zhang, Yanqiang [7 ]
Yang, Xin [1 ]
机构
[1] Cardiff Univ, Sch Engn, Cardiff CF24 3AA, Wales
[2] Baghdad Univ, Dept Phys, Coll Sci Women, Baghdad, Iraq
[3] Cardiff Univ, Sch Comp Sci & Informat, Cardiff CF24 3AA, Wales
[4] Second Peoples Hosp Guangxi Zhuang Autonomous Reg, Dept Radiol, Guilin 541002, Peoples R China
[5] Peoples Hosp Guangxi Zhuang Autonomous Reg, Dept Radiol, Nanning 530021, Peoples R China
[6] Peoples Hosp Guangxi Zhuang Autonomous Reg, Ctr Informat & Network Management, Nanning 530021, Peoples R China
[7] State Informat Ctr China, Beijing 100045, Peoples R China
关键词
Brain tumor segmentation; Multi-modal MRI; Convolutional neural networks; Gray-level co-occurrence matrix; Decision tree;
D O I
10.1007/s10278-022-00602-1
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
Diagnosis of brain tumor gliomas is a challenging task in medical image analysis due to its complexity, the less regularity of tumor structures, and the diversity of tissue textures and shapes. Semantic segmentation approaches using deep learning have consistently outperformed the previous methods in this challenging task. However, deep learning is insufficient to provide the required local features related to tissue texture changes due to tumor growth. This paper designs a hybrid method arising from this need, which incorporates machine-learned and hand-crafted features. A semantic segmentation network (SegNet) is used to generate the machine-learned features, while the grey-level co-occurrence matrix (GLCM)-based texture features construct the hand-crafted features. In addition, the proposed approach only takes the region of interest (ROI), which represents the extension of the complete tumor structure, as input, and suppresses the intensity of other irrelevant area. A decision tree (DT) is used to classify the pixels of ROI MRI images into different parts of tumors, i.e. edema, necrosis and enhanced tumor. The method was evaluated on BRATS 2017 dataset. The results demonstrate that the proposed model provides promising segmentation in brain tumor structure. The F-measures for automatic brain tumor segmentation against ground truth are 0.98, 0.75 and 0.69 for whole tumor, core and enhanced tumor, respectively.
引用
收藏
页码:938 / 946
页数:9
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