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Paper

ACCELERATION OF THE EXPECTATION-MAXIMIZATION ALGORITHM FOR A TWOFOLD GAUSSIAN MIXTURE MODEL BY USING THE HISTOGRAM OF THE OBSERVATIONS INSTEAD OF THE OBSERVATIONS - Evaluation of its Accuracy by Generated HistogramsTopics: Segmentation and Grouping; Statistical Approach

Keyword(s):Computer-assisted image analysis, Computer-assisted image interpretation, Statistical data interpretation.

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Subjects/Areas/Topics:Computer Vision, Visualization and Computer Graphics
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Image and Video Analysis
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Segmentation and Grouping
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Statistical Approach

Abstract: Volume representations of blood vessels acquired by 3D rotational angiography are very suitable for diagnosing a stenosis or an aneurysm. For optimal treatment, physicians need to know the shape of the diseased vessel parts. Binary segmentation by thresholding is the first step in our shape extraction procedure. Assuming a twofold Gaussian mixture model, the model parameters (and thus the threshold for binary segmentation) can be extracted from the observations (i.e. the gray values) by the Expectation-Maximization (EM) algorithm. Since the EM algorithm requires a number of iterations through the observations, and because of the large number of observations, the EM algorithm is very time-consuming. Therefore, we developed a method to apply the EM algorithm to the histogram of the observations, requiring a single pass through the observations and a number of iterations through the much smaller histogram. This variant gives almost the same results as the original EM algorithm, at least for our clinical volumes. We have used this variant for an evaluation of the accuracy of the EM algorithm: the maximum relative error in the mixing coefficients was less than 7%, the maximum relative error in the parameters of the two Gaussian components was less than 2.5%.(More)

Volume representations of blood vessels acquired by 3D rotational angiography are very suitable for diagnosing a stenosis or an aneurysm. For optimal treatment, physicians need to know the shape of the diseased vessel parts. Binary segmentation by thresholding is the first step in our shape extraction procedure. Assuming a twofold Gaussian mixture model, the model parameters (and thus the threshold for binary segmentation) can be extracted from the observations (i.e. the gray values) by the Expectation-Maximization (EM) algorithm. Since the EM algorithm requires a number of iterations through the observations, and because of the large number of observations, the EM algorithm is very time-consuming. Therefore, we developed a method to apply the EM algorithm to the histogram of the observations, requiring a single pass through the observations and a number of iterations through the much smaller histogram. This variant gives almost the same results as the original EM algorithm, at least for our clinical volumes. We have used this variant for an evaluation of the accuracy of the EM algorithm: the maximum relative error in the mixing coefficients was less than 7%, the maximum relative error in the parameters of the two Gaussian components was less than 2.5%.

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Bruijns J. and (2009). ACCELERATION OF THE EXPECTATION-MAXIMIZATION ALGORITHM FOR A TWOFOLD GAUSSIAN MIXTURE MODEL BY USING THE HISTOGRAM OF THE OBSERVATIONS INSTEAD OF THE OBSERVATIONS - Evaluation of its Accuracy by Generated Histograms.In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009) ISBN 978-989-8111-69-2, pages 229-236. DOI: 10.5220/0001652902290236

@conference{visapp09, author={J. Bruijns}, title={ACCELERATION OF THE EXPECTATION-MAXIMIZATION ALGORITHM FOR A TWOFOLD GAUSSIAN MIXTURE MODEL BY USING THE HISTOGRAM OF THE OBSERVATIONS INSTEAD OF THE OBSERVATIONS - Evaluation of its Accuracy by Generated Histograms}, booktitle={Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)}, year={2009}, pages={229-236}, publisher={SciTePress}, organization={INSTICC}, doi={10.5220/0001652902290236}, isbn={978-989-8111-69-2}, }

TY - CONF

JO - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009) TI - ACCELERATION OF THE EXPECTATION-MAXIMIZATION ALGORITHM FOR A TWOFOLD GAUSSIAN MIXTURE MODEL BY USING THE HISTOGRAM OF THE OBSERVATIONS INSTEAD OF THE OBSERVATIONS - Evaluation of its Accuracy by Generated Histograms SN - 978-989-8111-69-2 AU - Bruijns, J. PY - 2009 SP - 229 EP - 236 DO - 10.5220/0001652902290236