Coronary heart disease is a major cause of death in developed countries. There are some factors like cholesterol, high blood pressure, diabetes or obesity that increase the risk of having coronary heart disease. Early detection of the disease may have a better treatment response. Cardiac imaging methods require new processing analysis approaches. In this paper, new segmentation system has been studied in order to develop a semi-automated methodology for left ventricular myocardium segmentation from cardiac MR CINE images based on figure detection and active contours. Different image preprocessing techniques like the Hough Transform, centroid detection and snakes were integrated in a pipeline that permits the analysis and generation of structured reports with imaging biomarkers of the cardiac function in different cardiomyopathies. To implement the new segmentation method, a new segmentation and image analysis software has been created. Cardiac reports are made automatically with this tool using bull eyes and biomarkers like ejection fraction (EF), myocardial mass, cardiac output or thickening.