Bayesian network classification using spline-approximated kernel density estimation
Pattern Recognition Letters
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MRI tagging is a technique for measuring heart deformations. In tagged MR images, a set of dark lines is encoded within the deforming Left-Ventricle (LV) tissue for quantitative measurement of motion. The points along tag lines measured at different frames and different directions carry important information for measuring the 3D motion of LV. However, these measurements are sparse and therefore multidimensional interpolation techniques are needed to reconstruct a dense displacement field. In this paper, a novel spline technique is used to accomplish this task. We formulate the displacement estimation as a variational problem and then use B-spline bases to approximate the solution. Taking advantages of B-spline properties derives efficient numerical methods. The proposed techniques can improve the results significantly with respect to computational time as well as the computational accuracy. The new methods have been validated on both simulated and in-vivo heart data.