An automated myocardial segmentation in cardiac MRI

Abstract

Abstract-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 thirtynine 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.

Automatic cardiac ventricle segmentation in MR images: a validation study

“…The great need for automated methods has led to the development of a wide variety of segmentation methods [4], among which thresholding [5], pixel classification [6][7][8], deformable models. This latter family of methods have been greatly used thanks to their flexibility, especially for this application [9][10][11][12], either on the form of 2D active contours or 3D deformable surfaces, which are more computationally expensive [13,14]. Shape prior information can also be used to guide the segmentation process, under the form of a statistical model, in a variational framework [15], by using active shape and appearance [16][17][18][19][20] or via an atlas, using registration-based segmentation [21,22].…”

A curvature-based approach for left ventricular shape analysis from cardiac magnetic resonance imaging

“…Also, existing automatic segmentation methods for CMR images are often based on algorithms that are sensitive to image quality and frequently depend on the specific imaging protocol [1,10,19,29]. Therefore, in this work, the segmentation of LV endocardial contours has been performed manually by a trained expert using CMRtools, which is a software package for the display and quantitative analysis of cardiac MR images [32].…”

Myocardium segmentation using a priori knowledge of shape and a spatial relation

“…Then, we decided to compare our contribution ASM+d with the ground truth, the original model ASM and the method proposed by Berbari et al for the detection of myocardial contours [4]. The results of this comparison are presented in Figure 4.…”

“…This criterion is defmed due to a prior modeling of the image noise and the mean shape of the structure to be segmented. In another work, Berbari et al [4] have chosen to apply, at fust, a cardiac MRI filtering step using connected morphological filters in order to solve the problems caused by the presence of papillary muscles. Then, a segmentation step based on the GVF snake [5] is applied to detect the endocardium and later the epicardium.…”