Abstract
A two tier system for MR image segmentation is proposed. The first stage involves probabilistically classifying the pixels of the input image based on second order grey level statistics obtained from the co-occurrence matrix. These probabilities then form the input to a multi-layer perceptron, allowing contextual constraints to be applied to local areas of the image thereby improving the spatial coherence of the classification.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
R.M. Haralick, K. Shanmugan, Its’Hak Dinstein Texture Features for Image Classification, 1973, IEEE Trans. Sys. Cyber., SMC-3, 610-621.
J.F. Haddon, J.F. Boyce Image Segmentation by Unifying Region and Boundary Information. PAMI-12 921-948. 1990.
S. Peleg A New Probabilistic Relaxation Scheme IEEE Trans. Patt. Anal. Mach. Int. PAMI-2, 362-369, 1980.
A.P. Dempster, N.M. Laird, D.B. Rubin. Maximum Likelihood Estimation From Incomplete Data via the EM Algorithm (with discussion). J.R. Statis. Soc. B 39, 1-38.
D.E. Rummelhart, G.E. Hinton, R.J.Williams Learning Internal Representations By Error Propagation. In Parallel Distributed Processing, Chapter 8. MIT Press, Cambridge, Mass., 1986
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1991 Springer-Verlag London Limited
About this paper
Cite this paper
Toulson, D.L., Boyce, J.F. (1991). Segmentation of MR Images Using Neural Nets. In: Mowforth, P. (eds) BMVC91. Springer, London. https://doi.org/10.1007/978-1-4471-1921-0_36
Download citation
DOI: https://doi.org/10.1007/978-1-4471-1921-0_36
Publisher Name: Springer, London
Print ISBN: 978-3-540-19715-7
Online ISBN: 978-1-4471-1921-0
eBook Packages: Springer Book Archive