Fully automated segmentation of multiple sclerosis lesions in multispectral MRI
Wels M, Huber M, Hornegger J (2008)
Publication Type: Journal article, Original article
Publication year: 2008
Journal
Original Authors: Wels M., Huber M., Hornegger J.
Publisher: MAIK Nauka/Interperiodica (МАИК Наука/Интерпериодика)
Book Volume: 18
Pages Range: 347-350
Journal Issue: 2
DOI: 10.1134/S1054661808020235
Abstract
This paper addresses segmentation of multiple sclerosis lesions in multispectral 3-D brain MRI data. For this purpose, we propose a novel fully automated segmentation framework based on probabilistic boosting trees, which is a recently introduced strategy for supervised learning. By using the context of a voxel to be classified and its transformation to an overcomplete set of Haar-like features, it is possible to capture class specific characteristics despite the well-known drawbacks of MR imaging. By successively selecting and combining the most discriminative features during ensemble boosting within a tree structure, the overall procedure is able to learn a discriminative model for voxel classification in terms of posterior probabilities. The final segmentation is obtained after refining the preliminary result by stochastic relaxation and a standard level set approach. A quantitative evaluation within a leave-one-out validation shows the applicability of the proposed method. © 2008 Pleiades Publishing, Ltd.
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Germany (DE)How to cite
APA:
Wels, M., Huber, M., & Hornegger, J. (2008). Fully automated segmentation of multiple sclerosis lesions in multispectral MRI. Pattern Recognition and Image Analysis, 18(2), 347-350. https://dx.doi.org/10.1134/S1054661808020235
MLA:
Wels, Michael, M. Huber, and Joachim Hornegger. "Fully automated segmentation of multiple sclerosis lesions in multispectral MRI." Pattern Recognition and Image Analysis 18.2 (2008): 347-350.
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