BIOMEDICAL IMAGING GROUP (BIG)
Laboratoire d'imagerie biomédicale (LIB)

Automated Segmentation of the Corpus Callosum in Midsagittal Brain Magnetic Resonance Images

C. Lee, S. Huh, T.A. Ketter, M. Unser

Optical Engineering, vol. 39, no. 4, pp. 924–935, April 2000.

We propose a new algorithm to find the corpus callosum automatically from midsagittal brain MR (magnetic resonance) images using the statistical characteristics and shape information of the corpus callosum. We first extract regions satisfying the statistical characteristics (gray level distributions) of the corpus callosum that have relatively high intensity values. Then we try to find a region matching the shape information of the corpus callosum. In order to match the shape information, we propose a new directed window region growing algorithm instead of using conventional contour matching. An innovative feature of the algorithm is that we adaptively relax the statistical requirement until we find a region matching the shape information. After the initial segmentation, a directed border path pruning algorithm is proposed in order to remove some undesired artifacts, especially on the top of the corpus callosum. The proposed algorithm was applied to over 120 images and provided promising results. 

@ARTICLE(http://bigwww.epfl.ch/publications/lee0001.html,
AUTHOR="Lee, C. and Huh, S. and Ketter, T.A. and Unser, M.",
TITLE="Automated Segmentation of the {C}orpus {C}allosum in
	Midsagittal Brain Magnetic Resonance Images",
JOURNAL="Optical Engineering",
YEAR="2000",
volume="39",
number="4",
pages="924--935",
month="April",
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