Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/77076
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Type: Conference paper
Title: Fast and robust 3-D MRI brain structure segmentation
Author: Wels, M.
Zheng, Y.
Carneiro, G.
Huber, M.
Hornegger, J.
Comaniciu, D.
Citation: 12th International Conference on Medical Image Computing and Computer-Assisted Intervention, held in London, 20-24 September, 2009 / G.-Z. Yang, D. Hawkes, D. Rueckert, A. Noble and C. Taylor (eds.), pp.575-583
Publisher: Springer-Verlag Berlin
Publisher Place: Heidelberg
Issue Date: 2009
Series/Report no.: Lecture Notes in Computer Science; 5762
ISBN: 3642042708
9783642042706
ISSN: 0302-9743
1611-3349
Conference Name: International Conference on Medical Image Computing and Computer-Assisted Intervention (12th : 2009 : London)
Statement of
Responsibility: 
Michael Wels, Yefeng Zheng, Gustavo Carneiro, Martin Huber, Joachim Hornegger and Dorin Comaniciu
Abstract: We present a novel method for the automatic detection and segmentation of (sub-)cortical gray matter structures in 3-D magnetic resonance images of the human brain. Essentially, the method is a topdown segmentation approach based on the recently introduced concept of Marginal Space Learning (MSL). We show that MSL naturally decomposes the parameter space of anatomy shapes along decreasing levels of geometrical abstraction into subspaces of increasing dimensionality by exploiting parameter invariance. At each level of abstraction, i.e., in each subspace, we build strong discriminative models from annotated training data, and use these models to narrow the range of possible solutions until a final shape can be inferred. Contextual information is introduced into the system by representing candidate shape parameters with high-dimensional vectors of 3-D generalized Haar features and steerable features derived from the observed volume intensities. Our system allows us to detect and segment 8 (sub-)cortical gray matter structures in T1-weighted 3-D MR brain scans from a variety of different scanners in on average 13.9 sec., which is faster than most of the approaches in the literature. In order to ensure comparability of the achieved results and to validate robustness, we evaluate our method on two publicly available gold standard databases consisting of several T1-weighted 3-D brain MR scans from different scanners and sites. The proposed method achieves an accuracy better than most state-of-the-art approaches using standardized distance and overlap metrics.
Keywords: Image processing and computer vision; simulation and modeling; user interfaces and human computer interaction; computer imaging; vision; pattern recognition and graphics; artificial intelligence; imaging; radiology
Rights: © Springer-Verlag Berlin Heidelberg 2009
RMID: 0020114353
DOI: 10.1007/978-3-642-04271-3_70
Appears in Collections:Computer Science publications

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