Paper
19 August 2010 A novel automatic segmentation of the left ventricle cavity and myocardium in MSCT data
Xingjia Wang, Lina Dong, Yufeng Huang, Chuanfu Li, Huanqing Feng
Author Affiliations +
Proceedings Volume 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering; 78200K (2010) https://doi.org/10.1117/12.867463
Event: International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 2010, Xi'an, China
Abstract
The manual segmentation of 3D high resolution cardiac multi slice CT (MSCT) datasets is both labor intensive and time consuming. Therefore, it is necessary to provide a powerful automatic/semi-automatic method to segmentation the cardiac myocardium and cavities. In this paper a novel approach for the automatic 3D segmentation has been developed to extract the epicardium and endocardium boundaries of the left ventricle (LV) of the heart. The segmentation of the MSCT data is divided into three parts. The first part, which is based on nonlinear intensity transformation and bilateral filter, paints background and smoothes slice for all real CT images; The second part, applies a cavity template mask to extract the LV cavity coarse region from all slices using the threshold and morphologic operations; The last part performs improved coupled level set algorithm incorporating coarse cavity contours and priors for the final segmentation. Experimental results and 3D surface reconstruction show the efficacy and advantage of our method for the segmentation of the left ventricle from real CT data.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xingjia Wang, Lina Dong, Yufeng Huang, Chuanfu Li, and Huanqing Feng "A novel automatic segmentation of the left ventricle cavity and myocardium in MSCT data", Proc. SPIE 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 78200K (19 August 2010); https://doi.org/10.1117/12.867463
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KEYWORDS
Image segmentation

Nonlinear filtering

Chemical vapor deposition

Computed tomography

Control systems

Image filtering

Blood

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