A novel iterative optimizing quantization technique and its application to X-ray tomographic microscopy for three-dimensional reconstruction from a limited number of views

Heung Rae Lee, Luiz DaSilva, Gary Ford, Waleed Haddad, Ian McNulty, James Trebes, Yin Yeh

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

The iterative optimizing quantization technique (IOQT) is a novel method in reconstructing three-dimensional (3D) images from a limited number of 2D projections. IOQT can reduce the artifacts and image distortion due to a limited number of projections and limited range of viewing angles. Equivalently, by reducing the number of projections required for reconstruction, the use of IOQT can reduce the dose delivered to the specimen, simplify the complexity of an experimental setup, and consequently support the development of techniques to nondestructively image microstructures of materials. In this article, we will demonstrate the capability of IOQT to reconstruct an accurate 30 image of an object from a limited number of views, using a computer simulation and an actual 3D test pattern experiment with submicrometer features. In addition, we will introduce a promising application of IOQT to X-ray tomographic microscopy to study microbiological specimens by presenting the 3D reconstructions of the two different-conditioned human sperm cells from six projections.

Original languageEnglish (US)
Pages (from-to)204-213
Number of pages10
JournalInternational Journal of Imaging Systems and Technology
Volume8
Issue number2
StatePublished - 1997
Externally publishedYes

Keywords

  • Iterative optimization technique
  • Limited number of views
  • Tomography
  • X-ray tomographic microscopy

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Atomic and Molecular Physics, and Optics

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