This paper presents a novel technique for the extraction of the left ventricle borders from echocardiograms with prior information. Although the literature includes many successful prior based methods, priors that include both image and non-image related features are rare for the contour extraction. We classify these features as local and global priors where the local priors refer to the locally definable features of the target borders and global priors refer to the geometric shape properties. The local priors, which include image, motion, and local shape information, are learned with AdaBoost. The scores produced by AdaBoost for the target images are combined with the global shape prior under a level set framework. The main contributions of this paper are to learn different types of local features efficiently with machine learning and to combine these features with the geometric shape information for the contour extraction task. The system is validated on the real echocardiograms and synthetic images. The results indicate that using local and global priors together produces better extraction results and the contours extracted by the proposed system are in accord with the expert delineated borders.