An approach to three-dimensional image segmentation

Abstract

The development of techniques for interpreting the structure of three-dimensional images, f(x,y,z), is useful in many applications. A key initial stage in the signal to symbol conversion process, essential for the interpretation of the data, is three-dimensional image segmentation involving the processes of partitioning and identification. Most segmentation and grouping research in computer vision has addressed partitioning of 2D images, f(x,y). In this paper, we present a parallel 3D image segmentation algorithm which, through the use of [alpha]-partitioning and volume filtering, segments 3D images such that the greylevel variation within each volume can be described by a regression model. Experimental results demonstrate the effectiveness of this algorithm on several real-World 3D images.