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Automatic seed initialization for the expectation-maximization algorithm and its application in 3D medical imaging

Lynch, Michael and Ilea, Dana E. and Robinson, Kevin and Ghita, Ovidiu and Whelan, Paul F. (2007) Automatic seed initialization for the expectation-maximization algorithm and its application in 3D medical imaging. Journal of Medical engineering & technology , 31 (5). pp. 332-340. ISSN 0309-1902

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Abstract

Statistical partitioning of images into meaningful areas is the goal of all region-based segmentation algorithms. The clustering or creation of these meaningful partitions can be achieved in number of ways but in most cases it is achieved through the minimization or maximization of some function of the image intensity properties. Commonly these optimization schemes are locally convergent, therefore initialization of the parameters of the function plays a very important role in the final solution. In this paper we perform an automatically initialized expectation-maximization algorithm to partition the data in medical MRI images. We present analysis and illustrate results against manual initialization and apply the algorithm to some common medical image processing tasks

Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:computer vision; image analysis; Image segmentation; Medical imaging; Statistical pattern analysis; Expectation-maximization
Subjects:UNSPECIFIED
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Publisher:Informa Healthcare
Official URL:http://dx.doi.org/10.1080/03091900600647643
Copyright Information:© 2007 Informa Healthcare
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:18668
Deposited On:14 Aug 2013 14:07 by Mark Sweeney. Last Modified 27 Oct 2017 10:06

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