<title>Parallel algorithm for volumetric segmentation</title>

Abstract

Developing techniques for interpreting the structure of volumetric data is useful in many applications. A key initial stage is volumetric segmentation, involving the processes of pariilionirig and idenificaion. Here, we present a parallel algorithm which, through the use of a-partitioning and volume filtering, segments volumetric image data 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.