Ventricular shape visualization using selective volume rendering of cardiac datasets
In this paper, we present a novel technique of improving volume rendering quality and speed by integrating original volume data and global model information attained by segmentation. The segmentation information prevents object occlusions that may appear when volume rendering is based on local image features only. Thus the presented visualization technique provides meaningful visual results that enable a clear understanding of complex anatomical structures. In the first part, we describe a segmentation technique for extracting the region of interest based on an active contour model. In the second part, we propose a volume rendering method for visualizing the selected portions of fuzzy surfaces extracted by local image processing methods. We show the results of selective volume rendering of left and right ventricle based on cardiac datasets from clinical routines. Our method offers an accelerated technique to accurately visualize the surfaces of segmented objects.
selective volume rendering segmentation cardiology medical imaging direct volume rendering distance transform