Automated detection of stable fracture points in computed tomography image sequences

A. S. Chowdhuty, S. M. Bhandarkar, G. Datta, Jack C Yu

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

6 Citations (Scopus)

Abstract

Automated detection of stable fracture points in a sequence of Computed Tomography (CT) images is a challenging task. In this paper, an innovative scheme for automatic fracture detection in CT images is presented. The input to the system is a sequence of CT image slices of a fractured human mandible. Techniques based on curvature scale-space theory and graph-based filtering (using prior anatomical knowledge) are used to first detect candidate fracture points in the individual CT slices. Subsequently, a Kalman filter incorporating a Bayesian perspective is employed for testing the consistency of the candidate fracture points across all the CT slices in a given sequence. For the purpose of checking statistical consistency, both 95% and 99% high posterior density (HPD) prediction intervals are constructed. A spatial consistency term is formulated for each candidate fracture point in terms of the number of slices in the CT image sequence, the number of times a fracture point detected in that sequence and the number of times it is found to be statistically consistent. Fracture points with spatial consistency terms close to unity are deemed to be stable fracture points for the CT image sequence under consideration.

Original languageEnglish (US)
Title of host publication2006 3rd IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro - Proceedings
Pages1320-1323
Number of pages4
Volume2006
StatePublished - Nov 17 2006
Event2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Arlington, VA, United States
Duration: Apr 6 2006Apr 9 2006

Other

Other2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro
CountryUnited States
CityArlington, VA
Period4/6/064/9/06

Fingerprint

Tomography
Kalman filters
Testing

Keywords

  • Bayesian statistics
  • Computed tomography
  • Curvature scale space
  • Graph-based filtering
  • Kalman filter
  • Spatial consistency

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Chowdhuty, A. S., Bhandarkar, S. M., Datta, G., & Yu, J. C. (2006). Automated detection of stable fracture points in computed tomography image sequences. In 2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings (Vol. 2006, pp. 1320-1323). [1625169]

Automated detection of stable fracture points in computed tomography image sequences. / Chowdhuty, A. S.; Bhandarkar, S. M.; Datta, G.; Yu, Jack C.

2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings. Vol. 2006 2006. p. 1320-1323 1625169.

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

Chowdhuty, AS, Bhandarkar, SM, Datta, G & Yu, JC 2006, Automated detection of stable fracture points in computed tomography image sequences. in 2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings. vol. 2006, 1625169, pp. 1320-1323, 2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Arlington, VA, United States, 4/6/06.
Chowdhuty AS, Bhandarkar SM, Datta G, Yu JC. Automated detection of stable fracture points in computed tomography image sequences. In 2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings. Vol. 2006. 2006. p. 1320-1323. 1625169
Chowdhuty, A. S. ; Bhandarkar, S. M. ; Datta, G. ; Yu, Jack C. / Automated detection of stable fracture points in computed tomography image sequences. 2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings. Vol. 2006 2006. pp. 1320-1323
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