Left ventricle segmentation in echocardiography based on adaptive mean shift_Journal of Biomedical Engineering

Authors：

 ZHU Kai ^1,2 , FU Zhongliang ¹ , TAO Pan ^1,2 , ZHU Shuo ³

1. Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu 610041, P.R.China;
2. University of Chinese Academy of Sciences, Beijing 100049, P.R.China;
3. Guizhou Medcial University, Guiyang 550025, P.R.China;

Corresponding author：

ZHU Kai, Email: 578473077@qq.com

Keywords：

left ventricle segmentation; left ventricle localization; pixel clustering; mean shift segmentation; adaptive bandwidth

DOI：

10.7507/1001-5515.201702037

Video：

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Abstract Full text Figures/Tables Video References Cited by

The use of echocardiography ventricle segmentation can obtain ventricular volume parameters, and it is helpful to evaluate cardiac function. However, the ultrasound images have the characteristics of high noise and difficulty in segmentation, bringing huge workload to segment the object region manually. Meanwhile, the automatic segmentation technology cannot guarantee the segmentation accuracy. In order to solve this problem, a novel algorithm framework is proposed to segment the ventricle. Firstly, faster region-based convolutional neural network is used to locate the object to get the region of interest. Secondly, K-means is used to pre-segment the image; then a mean shift with adaptive bandwidth of kernel function is proposed to segment the region of interest. Finally, the region growing algorithm is used to get the object region. By this framework, ventricle is obtained automatically without manual localization. Experiments prove that this framework can segment the object accurately, and the algorithm of adaptive mean shift is more stable and accurate than the mean shift with fixed bandwidth on quantitative evaluation. These results show that the method in this paper is helpful for automatic segmentation of left ventricle in echocardiography.

Citation： ZHU Kai, FU Zhongliang, TAO Pan, ZHU Shuo. Left ventricle segmentation in echocardiography based on adaptive mean shift. Journal of Biomedical Engineering, 2018, 35(2): 273-279. doi: 10.7507/1001-5515.201702037 Copy

1.	纪祥虎, 高思聪, 黄志标, 等. 基于 Centripetal Catmull-Rom 曲线的经食道超声心动图左心室分割方法. 四川大学学报: 工程科学版, 2016, 48(5): 84-90.
2.	孙申申, 范立南, 康雁, 等. 基于改进主动形状模型的含胸壁粘连型肿块的肺区分割方法研究. 生物医学工程学杂志, 2016(5): 879-884.
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6.	Mignotte M, Meunier J, Tardif J C. Endocardial boundary e timation and tracking in echocardiographic images using deformable template and markov random fields. Pattern Analysis Applications, 2001, 4(4): 256-271.
7.	Mishra A, Dutta P K, Ghosh M K. A GA based approach for boundary detection of left ventricle with echocardiographic image sequences. Image Vis Comput, 2003, 21(11): 967-976.
8.	Sadek I, Elawady M, Stefanovski V. Automated breast lesion segmentation in ultrasound images. arXiv preprint. 2016. arXiv: 1609.08364.
9.	McClymont D, Mehnert A, Trakic A, et al. Fully automatic lesion segmentation in breast MRI using Mean-shift and graph-cuts on a region adjacency graph. Journal of Magnetic Resonance Imaging, 2014, 39(4): 795-804.
10.	ZHOU Zhuhuang, WU Weiwei, WU Shuicai, et al. Semi-automatic breast ultrasound image segmentation based on mean shift and graph cuts. Ultrason Imaging, 2014, 36(4): 256-276.
11.	Kim J H, Lee S, Lee G S, et al. Using a method based on a modified K-Means clustering and mean shift segmentation to reduce file sizes and detect brain tumors from magnetic resonance (MRI) images. Wireless Personal Communications, 2016, 89(3, SI): 993-1008.
12.	Mayer A, Greenspan H. An adaptive Mean-Shift framework for MRI brain segmentation. IEEE Trans Med Imaging, 2009, 28(8): 1238-1250.
13.	Comaniciu D, Meer P. Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell, 2002, 24(5): 603-619.
14.	Ren Shaoqing, He Kaiming, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks//Computer Vision and Pattern Recognition, 2015: 91-99.
15.	Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation//Proceedings of the IEEE conference on computer vision and pattern recognition, 2014: 580-587.
16.	Girshick R. Fast R-CNN//Proceedings of the IEEE International Conference on Computer Vision, 2015: 1440-1448.
17.	Zeiler M D, Fergus R. Visualizing and understanding convolutional networks//Computer Vision–ECCV 2014, Zurich, 2014: 818-833.
18.	Deng Jia, Dong Wei, Socher R, et al. Imagenet: a large-scale hierarchical image database//Computer Vision and Pattern Recognition, Miami, 2009: 248-255.

1. 纪祥虎, 高思聪, 黄志标, 等. 基于 Centripetal Catmull-Rom 曲线的经食道超声心动图左心室分割方法. 四川大学学报: 工程科学版, 2016, 48(5): 84-90.
2. 孙申申, 范立南, 康雁, 等. 基于改进主动形状模型的含胸壁粘连型肿块的肺区分割方法研究. 生物医学工程学杂志, 2016(5): 879-884.
3. Carneiro G, Nascimento J C, Freitas A. The segmentation of the left ventricle of the heart from ultrasound data using deep learning architectures and derivative-based search methods. IEEE Transactions on Image Processing, 2012, 21(3): 968-982.
4. Milletari F, Ahmadi S A, Kroll C A, et al. Hough-CNN: deep learning for segmentation of deep brain regions in MRI and ultrasound. Computer Vision and Image Understanding, 2017, 164(SI): 92-102.
5. Gao Mingchen, Xu Ziyue, Lu Le, et al. Segmentation label propagation using deep convolutional neural networks and dense conditional random field//2016 IEEE 13th international symposium on biomedical imaging (ISBI), 2016: 1265-1268.
6. Mignotte M, Meunier J, Tardif J C. Endocardial boundary e timation and tracking in echocardiographic images using deformable template and markov random fields. Pattern Analysis Applications, 2001, 4(4): 256-271.
7. Mishra A, Dutta P K, Ghosh M K. A GA based approach for boundary detection of left ventricle with echocardiographic image sequences. Image Vis Comput, 2003, 21(11): 967-976.
8. Sadek I, Elawady M, Stefanovski V. Automated breast lesion segmentation in ultrasound images. arXiv preprint. 2016. arXiv: 1609.08364.
9. McClymont D, Mehnert A, Trakic A, et al. Fully automatic lesion segmentation in breast MRI using Mean-shift and graph-cuts on a region adjacency graph. Journal of Magnetic Resonance Imaging, 2014, 39(4): 795-804.
10. ZHOU Zhuhuang, WU Weiwei, WU Shuicai, et al. Semi-automatic breast ultrasound image segmentation based on mean shift and graph cuts. Ultrason Imaging, 2014, 36(4): 256-276.
11. Kim J H, Lee S, Lee G S, et al. Using a method based on a modified K-Means clustering and mean shift segmentation to reduce file sizes and detect brain tumors from magnetic resonance (MRI) images. Wireless Personal Communications, 2016, 89(3, SI): 993-1008.
12. Mayer A, Greenspan H. An adaptive Mean-Shift framework for MRI brain segmentation. IEEE Trans Med Imaging, 2009, 28(8): 1238-1250.
13. Comaniciu D, Meer P. Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell, 2002, 24(5): 603-619.
14. Ren Shaoqing, He Kaiming, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks//Computer Vision and Pattern Recognition, 2015: 91-99.
15. Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation//Proceedings of the IEEE conference on computer vision and pattern recognition, 2014: 580-587.
16. Girshick R. Fast R-CNN//Proceedings of the IEEE International Conference on Computer Vision, 2015: 1440-1448.
17. Zeiler M D, Fergus R. Visualizing and understanding convolutional networks//Computer Vision–ECCV 2014, Zurich, 2014: 818-833.
18. Deng Jia, Dong Wei, Socher R, et al. Imagenet: a large-scale hierarchical image database//Computer Vision and Pattern Recognition, Miami, 2009: 248-255.

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