Abstract
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 language | English |
|---|---|
| Pages (from-to) | 1744-1756 |
| Number of pages | 13 |
| Journal | IEEE Transactions on Image Processing |
| Volume | 8 |
| Issue number | 12 |
| DOIs | |
| Publication status | Published - Dec 1999 |
Bibliographical note
Copyright © 1999 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.Keywords
- alpha-trimmed mean
- radial basis function networks
- 3-D Hough transform
- RESONANCE BRAIN IMAGES
- 3-DIMENSIONAL SEGMENTATION
- MODEL
- RECONSTRUCTION