Object Segmentation and Modeling in Volumetric Images

We propose a pattern classification based approach for simultaneous 3-D object modeling and segmentation in image volumes. The 3-D objects are described as a set of overlapping ellipsoids. The segmentation relies on the geometrical model and graylevel statistics. The extension of the Hough Transform algorithm in the 3-D space by employing the spherical coordinate system is used for ellipsoidal center estimation. The characteristic parameters of the ellipsoids and of the graylevel statistics are embedded in a Radial Basis Function (RBF) network and they are found by means of unsupervised training. We propose a new robust training algorithm for RBF networks based on ff-Trimmed Mean statistics. The proposed algorithm is applied for tooth pulpal blood vessel segmentation in a stack of microscopy images. 1 Introduction Representation and recognition of 3-D objects is an important task in structure identification and visualization [1, 2]. In this study we consider a stack of images, each re..