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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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Related in: MedlinePlus

Get endocardial contour of the basal slice image with LVOT in some cases: (a) ROI of the gray image, (b) binary image of the ROI, (c) blood pool of the preceding slice image, (d) endocardial contour of the current slice image, (e) segment result of the current slice image, the solid white curve indicates the endocardial contour.
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pone-0114760-g003: Get endocardial contour of the basal slice image with LVOT in some cases: (a) ROI of the gray image, (b) binary image of the ROI, (c) blood pool of the preceding slice image, (d) endocardial contour of the current slice image, (e) segment result of the current slice image, the solid white curve indicates the endocardial contour.

Mentions: For a slice image with LVOT, we use the above LBF model to convert the gray ROI image into binary image and obtain a binary image (see Fig. 3a–3b). The dilated blood pool in the previous slice image is used as a mask to obtain the current endocardial boundary (see Fig. 3c–3d). The center point from the center of blood pool in the previous slice image is used as pole, and pole coordinate of edge points from the current endocardial boundary is derived subsequently in the form: ; where n is the number of edge points.


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)

Get endocardial contour of the basal slice image with LVOT in some cases: (a) ROI of the gray image, (b) binary image of the ROI, (c) blood pool of the preceding slice image, (d) endocardial contour of the current slice image, (e) segment result of the current slice image, the solid white curve indicates the endocardial contour.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0114760-g003: Get endocardial contour of the basal slice image with LVOT in some cases: (a) ROI of the gray image, (b) binary image of the ROI, (c) blood pool of the preceding slice image, (d) endocardial contour of the current slice image, (e) segment result of the current slice image, the solid white curve indicates the endocardial contour.
Mentions: For a slice image with LVOT, we use the above LBF model to convert the gray ROI image into binary image and obtain a binary image (see Fig. 3a–3b). The dilated blood pool in the previous slice image is used as a mask to obtain the current endocardial boundary (see Fig. 3c–3d). The center point from the center of blood pool in the previous slice image is used as pole, and pole coordinate of edge points from the current endocardial boundary is derived subsequently in the form: ; where n is the number of edge points.

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