The study population consisted of 52 consecutive patients suffering from cardiac arrhythmias or dyspnea, pacemakers or implanted defibrillators, or suffering from claustrophobia, were excluded from the study population. In all cases, echocardiography was previously performed and all patients gave written informed consent before the cardiac MRI imaging examination. The study was carried out in accordance with the guidelines of the local ethics committee: the work was approved by the local (Galician) ethics committee. Informed consent was also obtained from all patients. [1]Say no to plagiarism. Get a tailor-made essay on “Why violent video games should not be banned”?Get an original essayAs a first step and to define a single set of images to be used for successful segmentation evaluation, short-axis ventricular slices were selected for analysis, starting with the highest basal slice, selected from simultaneous display of a long axis and short axis view, in which at least 50% of the LV myocardial circumference was visible in all phases cardiac. Frames visually showing the maximum and minimum ventricular cross-sectional areas at the mid-ventricular level were considered to be in end-diastole (ED) and end-systole (ES), respectively. The ventricular contours were traced in each slice, for these two frames, using two segmentation methods (manual and semi-automatic). A difference of one slice position was allowed between the most basal slice at end-diastole and ES due to the influence of cross-plane motion. The papillary muscles and trabeculae were considered as part of the ventricular volumes. End-diastolic volume (EDV) and end-systolic volume (ESV) were calculated by adding the area surrounded by the endocardium multiplied by the slice thickness, in all sections imaged at end-diastole and end-systole, respectively. (Simpson's method). Ejection fraction (EF) was calculated as follows: (EDV − ESV)•100/EDV. Functional parameters derived from the semi-automatic contours were calculated using the Simpson method. Ventricular analysis was also performed on a high-performance personal computer (2 AMD Opteron Dual-Core 2.80 GHz processors, 8 GB RAM) with a specially designed semi-automatic segmentation method based on edge detection, iterative thresholding and region growth techniques. A brief description of the segmentation scheme is given below. Edge detection: Region boundaries were roughly extracted from the original grayscale image, based on the gradient existing along the edge of an object. These operators are based on the idea that edge information is found by examining the relationship of a given pixel to its neighbors. In other words, a border was defined by a discontinuity in the grayscale values. Implementation details for these operators can be found elsewhere. Iterative threshold: In order to remove the noise of the filtered edge information, a square kernel with a given threshold value k0 (0 ≤ k0 ≤ 255) was automatically configured in each short circuit. axial view, around the position of the “mouse click” entered by the user in a mid-ventricular end-diastolic setting. Then, all the pixels of this kernel were scanned and the threshold value k1 was calculated according to the following expression: k1 = (1/2) × (average gray scale less than k0 + average gray scale greater than k0) . If k0 ≠ k1, k0 is. [2]