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Automatic segmentation of the left ventricle in cardiac MRI using local binary fitting model and dynamic programming techniques.

Hu H, Gao Z, Liu L, Liu H, Gao J, Xu S, Li W, Huang L - PLoS ONE (2014)

Bottom Line: The overlapping dice metric is about 0.91.The regression and determination coefficient between the experts and our proposed method on the LV mass is 1.038 and 0.9033, respectively; they are 1.076 and 0.9386 for ejection fraction (EF).The proposed segmentation method shows the better performance and has great potential in improving the accuracy of computer-aided diagnosis systems in cardiovascular diseases.

View Article: PubMed Central - PubMed

Affiliation: College of Biomedical Engineering, South-Central University for Nationalities, Wuhan, People's Republic of China.

ABSTRACT
Segmentation of the left ventricle is very important to quantitatively analyze global and regional cardiac function from magnetic resonance. The aim of this study is to develop a novel algorithm for segmenting left ventricle on short-axis cardiac magnetic resonance images (MRI) to improve the performance of computer-aided diagnosis (CAD) systems. In this research, an automatic segmentation method for left ventricle is proposed on the basis of local binary fitting (LBF) model and dynamic programming techniques. The validation experiments are performed on a pool of data sets of 45 cases. For both endo- and epi-cardial contours of our results, percentage of good contours is about 93.5%, the average perpendicular distance are about 2 mm. The overlapping dice metric is about 0.91. The regression and determination coefficient between the experts and our proposed method on the LV mass is 1.038 and 0.9033, respectively; they are 1.076 and 0.9386 for ejection fraction (EF). The proposed segmentation method shows the better performance and has great potential in improving the accuracy of computer-aided diagnosis systems in cardiovascular diseases.

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Workflow of our segmentation algorithm.LVOT: left ventricular outflow tract.
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pone-0114760-g001: Workflow of our segmentation algorithm.LVOT: left ventricular outflow tract.

Mentions: The whole procedure of one slice image segmentation consists of using a series of image processing techniques as depicted in Fig. 1. The algorithm starts from locating the region of interest (ROI) in the processing slice. Subsequently, local binary fitting model is used to find blood pool in the ROI. Then, threshold is obtained to get a refined blood pool. Two different types of slice image, i.e. with and without LVOT, are detected and corresponding techniques are processed to obtain the endocardial contour. To extract epicardial contour, the gradient image is calculated and a non-maxima gradient suppression technique is adopted to get an edge map, and region constrained dynamic programming is then employed. Details of our segmentation method are described in the following sections.


Automatic segmentation of the left ventricle in cardiac MRI using local binary fitting model and dynamic programming techniques.

Hu H, Gao Z, Liu L, Liu H, Gao J, Xu S, Li W, Huang L - PLoS ONE (2014)

Workflow of our segmentation algorithm.LVOT: left ventricular outflow tract.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4263664&req=5

pone-0114760-g001: Workflow of our segmentation algorithm.LVOT: left ventricular outflow tract.
Mentions: The whole procedure of one slice image segmentation consists of using a series of image processing techniques as depicted in Fig. 1. The algorithm starts from locating the region of interest (ROI) in the processing slice. Subsequently, local binary fitting model is used to find blood pool in the ROI. Then, threshold is obtained to get a refined blood pool. Two different types of slice image, i.e. with and without LVOT, are detected and corresponding techniques are processed to obtain the endocardial contour. To extract epicardial contour, the gradient image is calculated and a non-maxima gradient suppression technique is adopted to get an edge map, and region constrained dynamic programming is then employed. Details of our segmentation method are described in the following sections.

Bottom Line: The overlapping dice metric is about 0.91.The regression and determination coefficient between the experts and our proposed method on the LV mass is 1.038 and 0.9033, respectively; they are 1.076 and 0.9386 for ejection fraction (EF).The proposed segmentation method shows the better performance and has great potential in improving the accuracy of computer-aided diagnosis systems in cardiovascular diseases.

View Article: PubMed Central - PubMed

Affiliation: College of Biomedical Engineering, South-Central University for Nationalities, Wuhan, People's Republic of China.

ABSTRACT
Segmentation of the left ventricle is very important to quantitatively analyze global and regional cardiac function from magnetic resonance. The aim of this study is to develop a novel algorithm for segmenting left ventricle on short-axis cardiac magnetic resonance images (MRI) to improve the performance of computer-aided diagnosis (CAD) systems. In this research, an automatic segmentation method for left ventricle is proposed on the basis of local binary fitting (LBF) model and dynamic programming techniques. The validation experiments are performed on a pool of data sets of 45 cases. For both endo- and epi-cardial contours of our results, percentage of good contours is about 93.5%, the average perpendicular distance are about 2 mm. The overlapping dice metric is about 0.91. The regression and determination coefficient between the experts and our proposed method on the LV mass is 1.038 and 0.9033, respectively; they are 1.076 and 0.9386 for ejection fraction (EF). The proposed segmentation method shows the better performance and has great potential in improving the accuracy of computer-aided diagnosis systems in cardiovascular diseases.

Show MeSH
Related in: MedlinePlus