Object classification in 3-D images using alpha-trimmed mean radial basis function network

• Published in: IEEE transactions on image processing Vol. 8; no. 12; pp. 1744 - 1756
• Main Authors: Bors, A.G., Pitas, I.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 1999; Institute of Electrical and Electronics Engineers
• Subjects: Algorithms; Applied sciences; Brain; Classification; Clustering algorithms; Ellipsoids; Exact sciences and technology; Image processing; Image segmentation; Information, signal and communications theory; Intelligent networks; Layout; Mathematical models; Networks; Pattern classification; Radial basis function networks; Segmentation; Signal processing; Solid modeling; Statistics; Telecommunications and information theory; Bayes estimation; Parameter estimation; Experimental result; Microscopy; Algorithm performance; Segmentation; Image processing; Hough transformation; Numerical simulation; Three dimensional space; Modeling
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/83.806620

We propose a pattern classification based approach for simultaneous three-dimensional (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 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. A new robust training algorithm for RBF networks based on /spl alpha/-trimmed mean statistics is employed in this study. The extension of the Hough transform algorithm in the 3-D space by employing a spherical coordinate system is used for ellipsoidal center estimation. We study the performance of the proposed algorithm and we present results when segmenting a stack of microscopy images.