Statistical image reconstruction for muon tomography using gaussian scale mixture model

Guobao Wang, Jinyi Qi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Muon tomographyis a novel imaging technique that uses background cosmic radiation to inspect cargo containers for detecting the transportation or smuggling of heavy nuclear materials. Empirically, muon scattering data are modeled as zero-mean Gaussian random variables with variance being a function of the atom number and density of the scattering material. However, a single Gaussian distribution cannot model the tail of the true distribution and hence results in inaccuracy in the reconstructed images. In this paper, we propose a Gaussian scale mixture (GSM) to approximate the true distribution of muon data. The GSM follows the true distribution more closely than a single Gaussian model. We have derived a maximum likelihood reconstruction algorithm using the optimization transfer principle. Receiver operating characteristics (ROC) studies were performed using computer simulated data to evaluate the new algorithm. The results show that the use of GSM improves the detection performance significantly over that of the traditional Gaussian model.

Original languageEnglish (US)
Title of host publicationProceedings - International Conference on Image Processing, ICIP
Pages2948-2951
Number of pages4
DOIs
StatePublished - 2008
Event2008 IEEE International Conference on Image Processing, ICIP 2008 - San Diego, CA, United States
Duration: Oct 12 2008Oct 15 2008

Other

Other2008 IEEE International Conference on Image Processing, ICIP 2008
CountryUnited States
CitySan Diego, CA
Period10/12/0810/15/08

Keywords

  • Gaussian scale mixture
  • Image reconstruction
  • Muon tomography

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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  • Cite this

    Wang, G., & Qi, J. (2008). Statistical image reconstruction for muon tomography using gaussian scale mixture model. In Proceedings - International Conference on Image Processing, ICIP (pp. 2948-2951). [4712413] https://doi.org/10.1109/ICIP.2008.4712413