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 language | English (US) |
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| Pages | 819-822 |
| Number of pages | 4 |
| State | Published - Dec 1 1998 |
| Event | Proceedings of the 1998 International Conference on Image Processing, ICIP. Part 2 (of 3) - Chicago, IL, USA Duration: Oct 4 1998 → Oct 7 1998 |
Other
| Other | Proceedings of the 1998 International Conference on Image Processing, ICIP. Part 2 (of 3) |
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| City | Chicago, IL, USA |
| Period | 10/4/98 → 10/7/98 |
ASJC Scopus subject areas
- Hardware and Architecture
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering