Recently, the problem of automatic left ventricular boundary detection has yielded to methods that rely on the matching of the image data to a model of the heart. The problem of matching such a model to data is further complicated by the fact that heart position, orientation and size can vary widely for various patient studies. In this paper, we propose a method for generation of the left ventricular boundary model for both normal or abnormal (interior and inferior) shapes based on a general notion of the average shape, size and orientation of the left ventricular boundary obtained from a clinical data set with 30 patients in each category. The general description is invariant to position and contains the mean and limits of the size and orientation of the heart boundaries as well as descriptors for different shapes. A total of 100 frames/cycle is assumed for a global model with the ES frame being at frame 50. The descriptor of each frame of the 100 samples is obtained by interpolation between adjacent original frames. A particular model for a patient cycle with known number of frames and known position of ED and ES is carried on by an interpolation over the global model. This general model is employed for the partial matching against boundary fragments derived from Canny edge detection. The mean square error between the slope angle (shape descriptor) of the model and that of the boundary provides a cost function. The minimum of the function, corresponding to the best match, is obtained by varying the size of the model and the relative offset between the beginning of the model and the boundary fragment. After the parameters of the model are determined, it can be placed in the x-y plane to select more boundary fragments from the edge image, or it can be used for ranking the boundary fragments or complete boundaries. This matching is specially useful for open contours with preferences on their size and/or on their orientation, or even bounds on those values.