Arridge, SR and Simmons, A (1997) Multi-spectral probabilistic diffusion using Bayesian classification. In: terHaarRomeny, B and Florack, L and Koenderink, J and Viergever, M, (eds.) SCALE-SPACE THEORY IN COMPUTER VISION. (pp. 224 - 235). SPRINGER-VERLAG BERLIN
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Abstract
This paper proposes a diffusion scheme for multi-spectral images which incorporates both spatial derivatives and feature-space classification. A variety of conductance terms are suggested that use the posterior probability maps and their spatial derivatives to create resistive boundaries that reflect objectness rather than intensity differences alone. A theoretical test case is discussed as well as simulated and real magnetic resonance dual echo images. We compare the method for both supervised and unsupervised classification.
| Type: | Proceedings paper |
|---|---|
| Title: | Multi-spectral probabilistic diffusion using Bayesian classification |
| Event: | 1st International Conference on Scale-Space Theory in Computer Vision (Scale-Space 97) |
| Location: | UTRECHT, NETHERLANDS |
| Dates: | 1997-07-02 - 1997-07-04 |
| ISBN: | 3-540-63167-4 |
| Keywords: | scale space, anisotropic diffusion, feature-space classification, Magnetic Resonance Imaging, IMAGE SEGMENTATION, EDGE-DETECTION |
| UCL classification: | UCL > School of BEAMS > Faculty of Engineering Science > Computer Science |
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