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.
Highlights
Cardiac magnetic resonance imaging (MRI) is a noninvasive imaging methodology, which is used to obtain the anatomical data of a heart for clinical diagnosis of cardiovascular analysis [1]
Segmentation of left ventricle (LV) and right ventricle (RV) plays a crucial role in the detection and prevention of the heart attacks, which is a common cause of mortality in this century
The proposed method is validated on 15 training and 15 validation datasets from the MICCAI 2009 [36] for left ventricle segmentation
Summary
Cardiac MRI is a noninvasive imaging methodology, which is used to obtain the anatomical data of a heart for clinical diagnosis of cardiovascular analysis [1]. A region-based signed pressure force (SPF) function is introduced which utilizes image global intensity means from Chan-Vese method [23] This method adapts similar approach from geodesic active contour (GAC) model. In [34], Wang et al introduced a new energy formulation in which image local and global information from LBF and Chan-Vese methods are incorporated in an additive manner This method is capable of handling intensity inhomogeneities and yields better segmentation results compared to the stateof-the-art methods. A segmentation method is proposed which is able to segment intensity inhomogeneous regions and delineate weak object boundaries It helps to segment endocardium and epicardium walls for both left and right ventricles.
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