Image Segmentation Using Excess Entropy

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...

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Bibliographic Details
Published in:Journal of signal processing systems 2009, Vol.54 (1-3), p.205-214
Main Authors: Bardera, A., Boada, I., Feixas, M., Sbert, M.
Format: Article
Language:English
Subjects:
Circuits and Systems
Computer Imaging
Electrical Engineering
Engineering
Image Processing and Computer Vision
Pattern Recognition
Pattern Recognition and Graphics
Signal,Image and Speech Processing
Vision
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Summary: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.
ISSN:1939-8018
1939-8115
DOI:10.1007/s11265-008-0194-6