title: Automatic segmentation of the left ventricle in cardiac MRI using local binary fitting model and dynamic programming techniques.
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.
authors.affiliation: College of Biomedical Engineering, South-Central University for Nationalities, Wuhan, People's Republic of China.
authors.forename: Huaifei
authors.initials: H
authors.lastname: Hu
authors.affiliation: College of Biomedical Engineering, South-Central University for Nationalities, Wuhan, People's Republic of China.
authors.forename: Zhiyong
authors.initials: Z
authors.lastname: Gao
authors.affiliation: College of Biomedical Engineering, South-Central University for Nationalities, Wuhan, People's Republic of China.
authors.forename: Liman
authors.initials: L
authors.lastname: Liu
authors.affiliation: College of Biomedical Engineering, South-Central University for Nationalities, Wuhan, People's Republic of China.
authors.forename: Haihua
authors.initials: H
authors.lastname: Liu
authors.affiliation: College of Biomedical Engineering, South-Central University for Nationalities, Wuhan, People's Republic of China.
authors.forename: Junfeng
authors.initials: J
authors.lastname: Gao
authors.affiliation: College of Computer Science, South-Central University for Nationalities, Wuhan, People's Republic of China.
authors.forename: Shengzhou
authors.initials: S
authors.lastname: Xu
authors.affiliation: College of Biomedical Engineering, South-Central University for Nationalities, Wuhan, People's Republic of China.
authors.forename: Wei
authors.initials: W
authors.lastname: Li
authors.affiliation: Department of Radiology of Tongji Hospital affiliated to Huazhong University of Science and Technology, Wuhan, People's Republic of China.
authors.forename: Lu
authors.initials: L
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D001331:Automation; D003936:Diagnosis, Computer-Assisted; D006331:Heart Diseases / Q000175:diagnosis; D006352:Heart Ventricles; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008962:Models, Theoretical; D012044:Regression Analysis