Probabilistic multiscale image segmentation by the hyperstack

In the medical community, images from different modalities have found their way to a variety of medical disciplines. Multidimensional images have become indispensable in clinical diagnosis, therapy planning and evaluation. Image segmentation -- dividing an image into meaningful objects -- is a subfield of image processing that is of crucial importance for quantitative analysis and, in the case of 3D images, volume visualization. We present an approach to segmentation of multidimensional images called the hyperstack. This method is based on the scale space theory, where an image is considered at multiple levels of scale (resolution) simultaneously. The original image represents the highest scale (detail information), while succesively low-pass filtering of the image produces the larger scales (global information). By establishing linkages between these levels of scale, the global information can efectively be used to guide the segmentation of the pixels in the original image. In particular, the research has focused on the segmentation of partial volume voxels. It is shown that this leads to an improvement of both quantitative analysis and 3D volume renderings. Furthermore, it has been investigated if the segmentations could be further improved by using nonlinear filtering techniques to generate the scale space. The hyperstack has basically been applied to neurological MR T1 images, but is not restricted to a particular type of image or modality.