Multi-spectral image data fusion techniques for tissue classification of magnetic resonance (MR) images are presented. Using MR it is possible to obtain imagesof proton density the spin-lattice relaxation time constant ( T1) and the spin-spin relaxation time constant (T2) of the same anatomical section of the human body. In this paper we adopt a sensor fusion approach to tissue classification and segmentation in which each of the three images are treated as the output of different sensors. Regions of the images are modeled as noncausal Gaussian Markov random fields (GMRFs) and the underlying tissue label image is also assumed to follow a Gibbs distribution. Two different multi-spectral tissue labeling algorithms maximum a posteriori (MAP) estimation and the Dempster-Shafer evidential reasoning technique are presented. In the Bayesian MAP approach we use an independent opinion pool for data fusion and a deterministic relaxation to obtain the MAP solution. In practice the Bayesian approach may be too restrictive and a likelihood represented by a point probability value is usually an overstatement of what is actually known. In the Dempster-Shafer approach we adopt Dempster''s rule of combination for data fusion using belief intervals and ignorance to represent our confidence in a particular labeling and we present a new deterministic relaxation scheme that updates the belief intervals. Results obtained from real MR images are presented. 1.

Date Published: 1 July 1990
PDF: 9 pages
Proc. SPIE 1233, Medical Imaging IV: Image Processing, (1 July 1990); doi: 10.1117/12.18900

Published in SPIE Proceedings Vol. 1233:
Medical Imaging IV: Image Processing
Murray H. Loew, Editor(s)