Multiresolution segmentation of 3D images by the hyperstack

An improvement in the design of the hyperstack, a three-dimensional image segmentation tool, is described. It is based on a multiresolution approach which is mathematically supported by the diffusion equation. The blurring strategy, used to build the scale space, is outlined, including some difficulties that occur in view of the transition from 2-D to 3-D. The existing prototype, a hyperstack grounded on isointensity following, is extended by two novel ideas: weighted linking and stand-alone parents. The result of a segmented 3D SPECT image (of a liver) is shown. Theoretical considerations concerning the addition of feature information to guide the segmentation process are briefly mentioned. A flexible way to obtain several output images from one single hyperstack is outlined and the reduction of the sampling rate by means of interpolation, which will decrease the total amount of processing time, is investigated