A novel level set method for segmentation of left and right ventricles from cardiac MR images

In this paper, we propose a novel level set method for segmentation of cardiac left and right ventricles based on the distance regularized level set evolution (DRLSE) framework [7] and the distance regularized two-layer level set (DR2LS) model [17]. First, DRLSE is applied to obtain a preliminary segmentation of left and right ventricles, which is then used to initialize the endocardial contour, which is represented by the zero level contour of the level set function in our method. Then, the epi-cardial contour is represented by a different level contour of the same level set function. These two level sets are optimized by an energy minimization process to best fit the true endocardium and epicardium. In order to ensure smoothly varying distance between the two level contours, we introduce a distance regu-larization constraint in the energy function. With the region-scalable fitting (RSF) energy [8] as the data term, our method is able to deal with intensity inhomogeneities in the images, which is a main source of difficulty in image segmentation. Our method has been tested on cardiac MR images with promising results.

[1]  A. Rahmouni,et al.  Segmentation of cardiac cine-MR images and myocardial deformation assessment using level set methods. , 2005, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[2]  Chunming Li,et al.  Minimization of Region-Scalable Fitting Energy for Image Segmentation , 2008, IEEE Transactions on Image Processing.

[3]  Gabriel P Krestin,et al.  Accurate automatic papillary muscle identification for quantitative left ventricle mass measurements in cardiac magnetic resonance imaging. , 2008, Academic radiology.

[4]  Caroline Petitjean,et al.  Automatic cardiac ventricle segmentation in MR images: a validation study , 2011, International Journal of Computer Assisted Radiology and Surgery.

[5]  Chunming Li,et al.  Distance Regularized Level Set Evolution and Its Application to Image Segmentation , 2010, IEEE Transactions on Image Processing.

[6]  Christos Davatzikos,et al.  Segmentation of the Left Ventricle Using Distance Regularized Two-Layer Level Set Approach , 2013, MICCAI.

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

[8]  Milan Sonka,et al.  Multistage hybrid active appearance model matching: segmentation of left and right ventricles in cardiac MR images , 2001, IEEE Transactions on Medical Imaging.

[9]  Caroline Petitjean,et al.  Cardiac MRI assessment of right ventricular function in acquired heart disease: factors of variability. , 2012, Academic radiology.

[10]  Daniel Rueckert,et al.  Atlas-Based Segmentation and Tracking of 3D Cardiac MR Images Using Non-rigid Registration , 2002, MICCAI.

[11]  Alejandro F. Frangi,et al.  Three-dimensional modeling for functional analysis of cardiac images, a review , 2001, IEEE Transactions on Medical Imaging.

[12]  Ioannis A. Kakadiaris,et al.  Automated left ventricular segmentation in cardiac MRI , 2006, IEEE Transactions on Biomedical Engineering.

[13]  Wiro J Niessen,et al.  Automatic image‐driven segmentation of the ventricles in cardiac cine MRI , 2008, Journal of magnetic resonance imaging : JMRI.

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

[15]  Caroline Petitjean,et al.  A review of segmentation methods in short axis cardiac MR images , 2011, Medical Image Anal..

[16]  Luminita A. Vese,et al.  Energy Minimization Based Segmentation and Denoising Using a Multilayer Level Set Approach , 2005, EMMCVPR.