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CLC number: TP181; R739.41

On-line Access: 2018-06-07

Received: 2016-07-27

Revision Accepted: 2016-12-23

Crosschecked: 2018-04-14

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Citations:  Bibtex RefMan EndNote GB/T7714


Ji-jun Tong


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Frontiers of Information Technology & Electronic Engineering  2018 Vol.19 No.4 P.471-480


Kernel sparse representation for MRI image analysis in automatic brain tumor segmentation

Author(s):  Ji-jun Tong, Peng Zhang, Yu-xiang Weng, Dan-hua Zhu

Affiliation(s):  School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China; more

Corresponding email(s):   jijuntong@zstu.edu.cn

Key Words:  Brain tumor segmentation, Kernel method, Sparse coding, Dictionary learning

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Ji-jun Tong, Peng Zhang, Yu-xiang Weng, Dan-hua Zhu. Kernel sparse representation for MRI image analysis in automatic brain tumor segmentation[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(4): 471-480.

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DOI - 10.1631/FITEE.1620342

The segmentation of brain tumor plays an important role in diagnosis, treatment planning, and surgical simulation. The precise segmentation of brain tumor can help clinicians obtain its location, size, and shape information. We propose a fully automatic brain tumor segmentation method based on kernel sparse coding. It is validated with 3D multiple-modality magnetic resonance imaging (MRI). In this method, MRI images are pre-processed first to reduce the noise, and then kernel dictionary learning is used to extract the nonlinear features to construct five adaptive dictionaries for healthy tissues, necrosis, edema, non-enhancing tumor, and enhancing tumor tissues. sparse coding is performed on the feature vectors extracted from the original MRI images, which are a patch of m×m×m around the voxel. A kernel-clustering algorithm based on dictionary learning is developed to code the voxels. In the end, morphological filtering is used to fill in the area among multiple connected components to improve the segmentation quality. To assess the segmentation performance, the segmentation results are uploaded to the online evaluation system where the evaluation metrics dice score, positive predictive value (PPV), sensitivity, and kappa are used. The results demonstrate that the proposed method has good performance on the complete tumor region (dice: 0.83; PPV: 0.84; sensitivity: 0.82), while slightly worse performance on the tumor core (dice: 0.69; PPV: 0.76; sensitivity: 0.80) and enhancing tumor (dice: 0.58; PPV: 0.60; sensitivity: 0.65). It is competitive to the other groups in the brain tumor segmentation challenge. Therefore, it is a potential method in differentiation of healthy and pathological tissues.


摘要:脑肿瘤分割在疾病辅助诊断、治疗方案规划以及手术导航中扮演重要角色。对脑肿瘤精确分割可以帮助临床医生获取肿瘤位置、尺寸和形状信息。提出一种基于核稀疏编码的全自动脑肿瘤分割方法,并在3D多模态磁共振成像图(magnetic resonance imaging, MRI)上验证。首先对MRI图像进行预处理以减少噪声,然后通过核字典学习提取非线性特征,用来构建坏死组织、水肿组织、非增强肿瘤组织、增强肿瘤组织和健康组织5个适应性字典。对从原始MRI图像上肿瘤像素点周边m×m×m的小区域提取的特征向量进行稀疏编码,并通过一种基于字典学习的核聚类方法对像素点进行编码。最后通过形态滤波填充在多个相连部分间的区域,提高分割质量。为评估分割表现,分割结果被上传到在线评估系统中,该评估系统使用dice系数、阳性预测值(positive predictive value, PPV)、灵敏度和kappa值作为评估指标。结果表明,该方法在完整肿瘤区域分割上具有良好表现(dice: 0.83; PPV: 0.84; sensitivity: 0.82),而在肿瘤核心区域(dice: 0.69; PPV: 0.76; sensitivity: 0.80)和增强肿瘤区域(dice: 0.58; PPV: 0.60; sensitivity: 0.65)上表现稍差。相较于脑肿瘤分割(BRATS)挑战中其他团队采用的方法,该方法具有竞争力。该方法在健康组织和病理组织区分上具有一定潜力。


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