A blockwise relaxation labeling scheme and its application to edge detection in cardiac MR image sequences

We present a segmentation scheme for magnetic resonance (MR) image sequences based on vector quantization of a block‐partitioned image followed by a relaxation labeling procedure. By first searching a coarse segmentation, the algorithm yields very fast and effective performance on images that are inherently noisy, and can effectively use the correlation in a sequence of images for robust performance and efficient implementation. The algorithm defines feature vectors by the local histogram on a block‐partioned image and approximates the local histograms by normal distributions. The relative entropy is chosen as the meaningful distance measure between the feature vectors and the templates. After initial computation of the normal distribution parameters, a blockwise classification maximization algorithm classifies blocks in the block‐partitioned image by minimizing their relative entropy distance for a coarse‐resolution segmentation; and finally, finer resolution is obtained by contextual Bayesian relaxation labeling in which label update is performed pixelwise by incorporating neighborhood information. Sequence processing is then performed to segment all images in the sequence. The scheme is applied to left ventricular boundary detection in short‐axis MR image sequences, and results are presented to show that the algorithm successfully extracts the endocardial contours and that sequence processing significantly improves edge detection performance and can avoid local minima problems. © 1998 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 9, 340–350, 1998