Computer-aided analysis of four-dimensional tomography data plays an increasingly vital role in the field of diagnosis and treatment of heart function deficiencies. A key task for understanding the dynamics involved within a recorded cardiac cycle is to segment the acquired data to identify objects of interest, like the heart muscle (or myocardium) and the left ventricle. In this paper, a new robust and fast semi-automatic algorithm for segmentation of the myocardium from a CT data set is presented. The user marks the myocardium by placing a poly-line on one slice of the data volume. This poly-line forms a skeleton representing the cross-section of the myocardium on this slice. The skeleton is then automatically propagated and adjusted to the other slices in order to create a three-dimensional skeleton of the entire heart muscle. Then each voxel is assigned a value which denotes the voxel's connectivity to the skeleton. The boundaries of the myocardium can then be extracted as an iso-surface in the volume of connectivity values.
This paper is concerned with the estimation of underlying discontinuous functions from error-contaminated and blurred observations. Such problems occur in a number of important applications, particularly in inverse problems and signal and image reconstruction. We compare three different regularization techniques for estimating discontinuous signals.
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