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Multi-spectral probabilistic diffusion using Bayesian classification

Arridge, SR; 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
URI: http://discovery.ucl.ac.uk/id/eprint/35013
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