Variational approaches based on level set representation have become some of the most important methodologies used to handle the segmentation tasks of biological structures in medical images. Because the segmentation is one of the most challenging processes in medical applications, all the methods fail to achieve perfect results. The major problems are due to noise, poor contrast and high variation of the structure shapes. In this paper, we review the principal level set – based methods that have been designed for image segmentation applications. These approaches include: Geodesic Active Contour, Chan-Vese Functional and Geodesic Active Regions. We also shortly analyze the first method proposed for shape extraction in images by using level set representation. We make a comparative study of the performance obtained for each method applied on cardiac CT images which present strong and very marked differences about the contrast and shape variation. Left ventricle is selected as structure of analysis. Measures of similarity are used to evaluate the performance of the methods.