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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

Segment a slice image using LBF model: (a) ROI of the gray image, (b) result of LBF model, (c) contour of blood pool by LBF model, (d) segment result, (e) segmented blood pool.
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pone-0114760-g002: Segment a slice image using LBF model: (a) ROI of the gray image, (b) result of LBF model, (c) contour of blood pool by LBF model, (d) segment result, (e) segmented blood pool.

Mentions: The energy is minimized to find the object boundary by an iterative optimization method gradient descent flow. Thus the boundary of blood pool can be obtained by the overlap of the contour area derived by LBF model and a binary mask (see Fig. 2a–2c). The binary object which has the maximal overlap area is the blood pool we are looking for. For the mid-slice image, the mask is a binary circle at the center of the image; for a non-mid-slice image, the mask is the endocardial region segmented from previous slice. Thus, the binary object derived by LBF model is used as the contour mask for LV image thresholding segmentation.


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)

Segment a slice image using LBF model: (a) ROI of the gray image, (b) result of LBF model, (c) contour of blood pool by LBF model, (d) segment result, (e) segmented blood pool.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0114760-g002: Segment a slice image using LBF model: (a) ROI of the gray image, (b) result of LBF model, (c) contour of blood pool by LBF model, (d) segment result, (e) segmented blood pool.
Mentions: The energy is minimized to find the object boundary by an iterative optimization method gradient descent flow. Thus the boundary of blood pool can be obtained by the overlap of the contour area derived by LBF model and a binary mask (see Fig. 2a–2c). The binary object which has the maximal overlap area is the blood pool we are looking for. For the mid-slice image, the mask is a binary circle at the center of the image; for a non-mid-slice image, the mask is the endocardial region segmented from previous slice. Thus, the binary object derived by LBF model is used as the contour mask for LV image thresholding segmentation.

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