Evaluation Optical-Flow based Methods for Estimation of Wall Motions 1-Soroor Behbahani and 2- Keivan Magholi Biomedical Engineering Department, Science and Research Branch, Islamic Azad University, Tehran, Iran. 1-Member of Young Researcher Club of Biomedical Engineering Department, Science and Research Branch, Islamic Azad University, Tehran, Iran. soroor_behbahani@yahoo.com Abstract Cardiac disease remains a major killer in the world. Improving the management of cardiovascular disease is one of the greatest challenges facing healthcare. It is valuable to study the electro-mechanics of the heart as any asynchrony of electrical activation and disorder of the mechanical properties can lead to abnormalities in heart function. Accurate quantitative of heart motion and deformation are of importance for evaluating normal and abnormal cardiac physiology and mechanics. However, the complexity of LV motion, the absence of internal landmarks in the myocardium and the influence of the right ventricle (RV) on the LV imply that the true motion trajectories of tissue elements are difficult to obtain from image analysis. In this paper we decided to compare two methods of heart motion estimation and consider the advantages or disadvantages of each method. Keywords: motion estimation, myocardial motion, optical flow, velocity field, medical imaging 1. Introduction Estimation techniques of heart wall motion could be categorized into invasive and non-invasive. Invasive techniques track markers physically implanted on the surface of the ventricle wall [9]. Movement of the markers is tracked in sequence of cardiac images and estimation of LV motion is possible. Such techniques are not appropriate for wide application because of the need for surgical intervention Non-invasive techniques overcome this problem. There are three different groups of approaches. The first group of approaches uses magnetic resonance tagging technique where magnetization of the tissue is altered such that grid of intersecting planes is produced [11][13]. Points of intersection are easily tracked as heart tissue moves, but motion of other points is not estimated. The second group of approaches analyzes shape of previously segmented cardiac wall extracting motion information from the changes in the shape [6][7]. Such approaches are usually limited to several characteristic points extracted from the LV boundary. In the third group of approaches are optical flow techniques [12] which detect changes in brightness intensity of every pixel in an image followed by estimation of movement from detected data. The optical flow approach shows some very good results in estimation of simple movements but for complex motion observed in cardiac images the algorithm need to be improved with additional constraints. Other approach uses optical flow results as input for further motion analysis [4]. Optical flow algorithms provide estimation of the motion fields. In general, optical flow information is not the same as the motion field. The motion field is represented by the field of vectors that show the displacement of points in the optical field relative to the observer. Optical flow shows a velocity field of pixels in the image. There are a number of ways to compute the optical flow. The basic classification of methods is of gradient-based ([16]), correlation-based and energy-based methods ([8]).In this paper we decided to compare the optical flow algorithm based on and energy-based a point-constrained methods. 2. Methods 2.1. Energy-Based Method This method is based on minimization of the velocity field energy function that is computed for two consecutive image frames. The minimal energy state corresponds to the optimal optical flow estimation. The energy function minimization is based on the steepest descent algorithm. The energy function E used in this algorithm consists of two terms, E1 and E2 are: E = E1 + E2 (1) Second International Multisymposium on Computer and Computational Sciences 0-7695-3039-7/07 $25.00 © 2007 IEEE DOI 10.1109/IMSCCS.2007.78 164