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

A G Bors, I Pitas

Research output: Contribution to journalArticlepeer-review


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, 4 new robust training algorithm for RBF networks based on alpha-trimmed mean statistics is employed in this study. The extension of the Hough transform algorithm in the 3-D space by employing 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.

Original languageEnglish
Pages (from-to)1744-1756
Number of pages13
JournalIEEE Transactions on Image Processing
Issue number12
Publication statusPublished - Dec 1999

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  • alpha-trimmed mean
  • radial basis function networks
  • 3-D Hough transform

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