Partial volume estimation and the fuzzy C-means algorithm

Dzung L. Pham, Jerry I. Prince

Research output: Contribution to conferencePaper

Abstract

Partial volume averaging (PVA) is present in nearly all practical imaging situations, medical imaging in particular. One method that has been used to account for the effects of PVA is the fuzzy c-means algorithm (FCM). We propose a new method for estimating the partial volume coefficient of each class at each voxel in a given image using a Bayesian statistical model. A prior probability on the partial volume coefficients is used to reflect how most voxels in the image are expected to be pure. We then show that the results obtained by this method are quite similar and in some cases equivalent to results obtained using FCM. Both algorithms are demonstrated on a magnetic resonance image of the brain.

Original languageEnglish (US)
Pages819-822
Number of pages4
StatePublished - Dec 1 1998
EventProceedings of the 1998 International Conference on Image Processing, ICIP. Part 2 (of 3) - Chicago, IL, USA
Duration: Oct 4 1998Oct 7 1998

Other

OtherProceedings of the 1998 International Conference on Image Processing, ICIP. Part 2 (of 3)
CityChicago, IL, USA
Period10/4/9810/7/98

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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  • Cite this

    Pham, D. L., & Prince, J. I. (1998). Partial volume estimation and the fuzzy C-means algorithm. 819-822. Paper presented at Proceedings of the 1998 International Conference on Image Processing, ICIP. Part 2 (of 3), Chicago, IL, USA, .