Volumetric segmentation using Weibull E-SD fields

Jiuxiang Hu, Anshuman Razdan, Gregory M. Nielson, Gerald E. Farin, Debra Baluch, David Capco

Research output: Contribution to journalArticle

18 Scopus citations

Abstract

This paper presents a coarse-grain approach for segmentation of objects with gray levels appearing in volume data. The input data is on a 3D structured grid of vertices v(i, j, k), each associated with a scalar value. In this paper, we consider a voxel as a κ × κ × κ cube and each voxel is assigned two values: expectancy and standard deviation (E-SD). We use the Weibull noise index to estimate the noise in a voxel and to obtain more precise E-SD values for each voxel. We plot the frequency of voxels which have the same E-SD, then 3D segmentation based on the Weibull E-SD field is presented. Our test bed includes synthetic data as well as real volume data from a confocal laser scanning microscope (CLSM). Analysis of these data all show distinct and defining regions in their E-SD fields. Under the guide of the E-SD field, we can efficiently segment the objects embedded in real and simulated 3D data.

Original languageEnglish (US)
Pages (from-to)320-328
Number of pages9
JournalIEEE Transactions on Visualization and Computer Graphics
Volume9
Issue number3
DOIs
StatePublished - Jul 1 2003

Keywords

  • 3D segmentation
  • CLSM
  • Confocal laser scanning microscope
  • Noise index
  • Weibull E-SD field

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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