Segmentation of Left and Right Ventricles in Cardiac MRI Using Active Contours

Abstract

Segmentation of left and right ventricles plays a crucial role in quantitatively analyzing the global and regional information in the cardiac magnetic resonance imaging (MRI). In MRI, the intensity inhomogeneity and weak or blurred object boundaries are the problems, which makes it difficult for the intensity-based segmentation methods to properly delineate the regions of interests (ROI). In this paper, a hybrid signed pressure force function (SPF) is proposed, which yields both local and global image fitted differences in an additive fashion. A characteristic term is also introduced in the SPF function to restrict the contour within the ROI. The overlapping dice index and Hausdorff-Distance metrics have been used over cardiac datasets for quantitative validation. Using 2009 LV MICCAI validation dataset, the proposed method yields DSC values of 0.95 and 0.97 for endocardial and epicardial contours, respectively. Using 2012 RV MICCAI dataset, for the endocardial region, the proposed method yields DSC values of 0.97 and 0.90 and HD values of 8.51 and 7.67 for ED and ES, respectively. For the epicardial region, it yields DSC values of 0.92 and 0.91 and HD values of 6.47 and 9.34 for ED and ES, respectively. Results show its robustness in the segmentation application of the cardiac MRI.

Fully automatic segmentation of 4D MRI for cardiac functional measurements

“…The active contour methods have been used on RV segmentation from 2D/3D MR images 57,58 . However, segmentation of the RV from 4D MR images is more challenging because of thinner imaging slice thickness and lower contrast between the myocardium and blood.…”

An Image Segmentation Method Using an Active Contour Model Based on Improved SPF and LIF

“…The GAC model utilizes the image gradient to construct an ESF, which can stop the contour evolution on object boundaries. When images have weak boundaries or the initial contour is far from the desired object boundary, the GAC model will fail to find the target [18,22].…”

“…where µ, ν, λ 1 , and λ 2 denote the corresponding coefficients, all of which are positive constants; ∇ is the gradient operator; µ controls the smoothness of the zero level set; ν increases the propagation speed; and λ 1 and λ 2 control the image data driving force inside and outside the contour, respectively. Because c 1 and c 2 are related to the global information inside and outside the curve, this model can segment blurred images and images with weak gradients more effectively than the edge-based model can, and it is insensitive to the initialization location [22,35]. However, when the internal and external intensities of the curve are inhomogeneous, c 1 and c 2 cannot express the local information precisely, which leads to the failure of image segmentation [2].…”

“…However, this assumption does not suit the intensity of inhomogeneous images, which limits the method's further applications [20,21].Recently, hybrid methods have gained popularity among region-based methods. These methods combine region (local or global) and edge information in their energy formulations [22]. Zhang et al [23] proposed the selective binary and Gaussian filtering regularized level set (SBGFRLS) model.…”

“…This traditional method uses only global image intensity information. Therefore, the method is unable to analyze intensity-inhomogeneous images [21,22]. Li et al[24] investigated a local binary fitting (LBF) model, which is an efficient region-based level set method.…”