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Machine Learning for Brain Image Segmentation

Machine Learning for Brain Image Segmentation

Jonathan Morra, Zhuowen Tu, Arthur Toga, Paul Thompson
Copyright: © 2010 |Pages: 25
ISBN13: 9781605669564|ISBN10: 1605669563|ISBN13 Softcover: 9781616922177|EISBN13: 9781605669571
DOI: 10.4018/978-1-60566-956-4.ch005
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MLA

Morra, Jonathan, et al. "Machine Learning for Brain Image Segmentation." Biomedical Image Analysis and Machine Learning Technologies: Applications and Techniques, edited by Fabio A. Gonzalez and Eduardo Romero, IGI Global, 2010, pp. 102-126. https://doi.org/10.4018/978-1-60566-956-4.ch005

APA

Morra, J., Tu, Z., Toga, A., & Thompson, P. (2010). Machine Learning for Brain Image Segmentation. In F. Gonzalez & E. Romero (Eds.), Biomedical Image Analysis and Machine Learning Technologies: Applications and Techniques (pp. 102-126). IGI Global. https://doi.org/10.4018/978-1-60566-956-4.ch005

Chicago

Morra, Jonathan, et al. "Machine Learning for Brain Image Segmentation." In Biomedical Image Analysis and Machine Learning Technologies: Applications and Techniques, edited by Fabio A. Gonzalez and Eduardo Romero, 102-126. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-956-4.ch005

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Abstract

In this chapter, the authors review a variety of algorithms developed by different groups for automatically segmenting structures in medical images, such as brain MRI scans. Some of the simpler methods, based on active contours, deformable image registration, and anisotropic Markov random fields, have known weaknesses, which can be largely overcome by learning methods that better encode knowledge on anatomical variability. The authors show how the anatomical segmentation problem may be re-cast in a Bayesian framework. They then present several different learning techniques increasing in complexity until they derive two algorithms recently proposed by the authors. The authors show how these automated algorithms are validated empirically, by comparison with segmentations by experts, which serve as independent ground truth, and in terms of their power to detect disease effects in Alzheimer’s disease. They show how these methods can be used to investigate factors that influence disease progression in databases of thousands of images. Finally the authors indicate some promising directions for future work.

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