Image Segmentation Using Excess Entropy
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We present a novel information-theoretic approach for thresholding-based segmentation that uses the excess entropy to measure the structural information of a 2D or 3D image and to locate the optimal thresholds. This approach is based on the conjecture that the optimal thresholding corresponds to the segmentation with maximum structure, i.e., maximum excess entropy. The contributions of this paper are several fold. First, we introduce the excess entropy as a measure of the spatial structure of an image. Second, we present an adaptive thresholding method based on the maximization of excess entropy. Third, we propose the use of uniformly distributed random lines to overcome the main drawbacks of the excess entropy computation. To show the good performance of the proposed segmentation approach different experiments on synthetic and real brain models are carried out.
KeywordsImage segmentation Feature extraction Image structure Thresholding Information theory Excess entropy Neuroimage segmentation
Our project is funded in part by Spanish Government grants number TIN2007-68066-C04-01 and TIN2007-67982-C02.
- 1.ITK (2008). ITK Insight Toolkit. http://www.itk.org.
- 2.VTK (2008). VTK Visualization Toolkit. http://www.vtk.org.
- 3.Bardera, A., Feixas, M., Boada, I., & Sbert, M. (2005). Medical image registration based on random line sampling. In IEEE International conference on image processing (ICIP’05). Genova, Italy (September).Google Scholar
- 5.Castro, F., & Sbert, M. (1998). Application of quasi-monte carlo sampling to the multipath method for radiosity. In Proceedings of 3rd International conference on Monte Carlo and Quasi-Monte Carlo methods in scientific computing, Claremont (CA), USA (June).Google Scholar
- 6.Cocosco, C., Kollokian, V., Kwan, R.-S., & Evans, A. (1997). Brainweb: Online interface to a 3D MRI simulated brain database. NeuroImage, 5(4), S424.Google Scholar
- 12.Feldman, D. P. (2002). A brief introduction to: Information theory, excess entropy and computational mechanics. Technical report, Department of Physics, University of California, Berkeley (CA), USA (October).Google Scholar
- 13.Gonzalez, R. C., & Woods, R. E. (2002). Digital image processing. Upper Saddle River: Prentice Hall.Google Scholar
- 16.Rigau, J., Feixas, M., Sbert, M., Bardera, A., & Boada, I. (2004). Medical image segmentation based on mutual information maximization. In Lecture Notes in computer science (MICCAI 2004) (pp. 135–142). Rennes-Saint Malo, France (September).Google Scholar