<title>Interactive relaxation labeling for 3D cardiac image analysis</title>

Abstract

Image segmentation remains one of the major challenges in 3D medical image analysis. We describe a generally applicable 3D image-segmentation technique that combines operator interaction and automatic processing, with a particular focus on 3D cardiac image analysis. For a given 3D image, the method works as follows. First, the operator interactively defines region cues that either give region 'tissue samples' or that impose spatial constraints on where regions can and cannot lie. Next, a three-step relaxation-labeling algorithm is applied. For the first step, each image voxel gets an initial probability vector assigned to it. This vector, computed using the previously defined region cues, contains the initial probabilities that a voxel belongs to various regions of interest. Next, a true 3D relaxation-labeling process is performed to update the probability vectors. Relaxation labeling concludes by assigning region labels to image voxels. Results for 3D cardiac image segmentation demonstrate the method's efficacy. A major advantage of the method is that the operator, who understands what he sees but has less understanding of the `numbers' defining the image, can apply the technique without having to set parameters.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.