An automated myocardial segmentation in cardiac MRI

In this paper we present an automatic approach to segment cardiac magnetic resonance (CMR) images. A preprocessing step that consists in filtering the image using connected operators (area opening and closing filters) is applied in order to homogenize the cavity and solve the problems due to the papillary muscles. Thereby the GVF snake algorithm is applied with one point clicked in the cavity as initialization and an optimized tuning of parameters for the endocardial contour extraction. The epicardial border is then obtained using the endocardium as initialization. The performance of the proposed method was assessed by experimentation on thirty- nine CMR images. A high agreement between manual and automatic contours was obtained with correlation scores of 0.96 for the endocardium and 0.90 for the epicardium. Overlapping percentage, mean and maximum distances between the two contours show a good performance of the method.

[1]  Michael H. F. Wilkinson,et al.  A Comparison of Algorithms for Connected Set Openings and Closings , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  José M. F. Moura,et al.  STACS: new active contour scheme for cardiac MR image segmentation , 2005, IEEE Transactions on Medical Imaging.

[3]  Marcel Breeuwer,et al.  Myocardial Delineation via Registration in a Polar Coordinate System , 2002, MICCAI.

[4]  Isabelle Bloch,et al.  Automated Segmentation of the Left Ventricle Including Papillary Muscles in Cardiac Magnetic Resonance Images , 2007, FIMH.

[5]  Jyrki Lötjönen,et al.  A 3-D model-based registration approach for the PET, MR and MCG cardiac data fusion , 2003, Medical Image Anal..

[6]  M.J. Ledesma-Carbayo,et al.  Semi automatic estimation and visualization of left ventricle volumes in cardiac MRI , 2005, Computers in Cardiology, 2005.

[7]  Junaed Sattar Snakes , Shapes and Gradient Vector Flow , 2022 .

[8]  Isabelle Bloch,et al.  Using cine MR images to evaluate myocardial infarct transmurality on delayed enhancement images , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[9]  Kenya Murase,et al.  Quantification of left ventricular volumes from cardiac cine MRI using active contour model combined with gradient vector flow. , 2005, Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine.

[10]  M. Graves,et al.  A multicenter validation of an active contour‐based left ventricular analysis technique , 2000, Journal of magnetic resonance imaging : JMRI.

[11]  V Positano,et al.  Automated cardiac MR image segmentation: theory and measurement evaluation. , 2003, Medical engineering & physics.

[12]  Paul F. Whelan,et al.  Automatic segmentation of the left ventricle cavity and myocardium in MRI data , 2006, Comput. Biol. Medicine.

[13]  Marcel Breeuwer,et al.  Myocardial delineation via registration in a polar coordinate system1 , 2003 .

[14]  Jens von Berg,et al.  Automated Segmentation of the Left Ventricle in Cardiac MRI , 2003, MICCAI.

[15]  Marie-Pierre Jolly,et al.  Automatic Segmentation of the Left Ventricle in Cardiac MR and CT Images , 2006, International Journal of Computer Vision.