Approach to the motion estimation of cardiac image sequences: Active contours motion tracking based on the generalized fuzzy gradient vector flow

Using the active contours model (ACM) to estimate the cardiac motion, the new concept of generalized fuzzy gradient vector flow (GFGVF) is presented. The GFGVF is referred as a component of external force and associated with optical flow field (OFF) to build a set of Snake equations. After the GFGVF and OFF are accurately calculated respectively, the initial outline can gradually approach to the region of interest (ROI) edges in the images under the constraint of Snake equations and track the ROI from frame to frame. Under some constrained conditions, the motion states of some feature points in the edge of ROI can be found by the Maximum a Posteriori Probability (MAP) during a period of cardiac motion. Then, the motion estimation is well optimized. Another, the coefficients in the set of ACM equations can be found using the prior information, which avoids giving them by experience and improves the capability of edge tracing of ROI. By a period of motion tracking for CT and MR cardiac image sequences, it is shown that the method can robustly simulate the motion of the cardiac left ventricle (LV) and left atria (LA), moreover, the simulation result of them using GFGVF is obviously better than the one using GVF.