Object segmentation in 3-D images based on alpha-trimmed mean radial basis function network

This paper presents a new approach for 3-D object segmentation. Objects from a stack of images are represented as overlapping ellipsoids. Graylevel statistics and shape features are simultaneously employed for object modeling in an unsupervised approach. The extension of the Hough Transform in the 3-D space is used for finding the ellipsoid centers. Each ellipsoid is modeled by a Radial Basis Function (RBF) and the entire structure is represented by means of an RBF network. The proposed algorithm is applied for blood vessel segmentation from tooth pulp in a stack of microscopy images.