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).
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.
|Title:||Multi-spectral probabilistic diffusion using Bayesian classification|
|Event:||1st International Conference on Scale-Space Theory in Computer Vision (Scale-Space 97)|
|Dates:||1997-07-02 - 1997-07-04|
|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
UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
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