Left ventricle segmentation in echocardiography based on adaptive mean shift.
10.7507/1001-5515.201702037
- Author:
Kai ZHU
1
,
2
;
Zhongliang FU
3
;
Pan TAO
1
,
4
;
Shuo ZHU
5
Author Information
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.578473077@qq.com.
3. Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu 610041, P.R.China.
4. University of Chinese Academy of Sciences, Beijing 100049, P.R.China.
5. Guizhou Medcial University, Guiyang 550025, P.R.China.
- Publication Type:Journal Article
- Keywords:
adaptive bandwidth;
left ventricle localization;
left ventricle segmentation;
mean shift segmentation;
pixel clustering
- From:
Journal of Biomedical Engineering
2018;35(2):273-279
- CountryChina
- Language:Chinese
-
Abstract:
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, -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.
