Automatic recognition of corpus callosum from sagittal brain MR images

Chulhee Lee; Michael A. Unser; Terence A. Ketter

Automatic recognition of corpus callosum from sagittal brain MR images

Chulhee Lee, Michael A. Unser, Terence A. Ketter

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We propose a new method to find the corpus callosum from sagittal brain MR images automatically. First, we calculate the statistical characteristics of the corpus callosum and obtain shape information. The recognition algorithm consists of two stages: extracting regions satisfying the statistical characteristics (gray level distributions) of the corpus callosum, and finding a region matching the shape information. An innovative feature of the algorithm is that we adaptively relax the statistical requirement until we find a region matching the shape information. In order to match the shape information, we propose a new directed window region growing algorithm instead of using conventional contour matching. Experiments show promising results.

Original language	English
Title of host publication	Proceedings of SPIE - The International Society for Optical Engineering
Editors	Andrew G. Tescher
Pages	528-534
Number of pages	7
Publication status	Published - 1995
Event	Applications of Digital Image Processing XVIII - San Diego, CA, USA Duration: 1995 Jul 12 → 1995 Jul 14

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	2564
ISSN (Print)	0277-786X

Other

Other	Applications of Digital Image Processing XVIII
City	San Diego, CA, USA
Period	95/7/12 → 95/7/14

All Science Journal Classification (ASJC) codes

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

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Cite this

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title = "Automatic recognition of corpus callosum from sagittal brain MR images",

abstract = "We propose a new method to find the corpus callosum from sagittal brain MR images automatically. First, we calculate the statistical characteristics of the corpus callosum and obtain shape information. The recognition algorithm consists of two stages: extracting regions satisfying the statistical characteristics (gray level distributions) of the corpus callosum, and finding a region matching the shape information. An innovative feature of the algorithm is that we adaptively relax the statistical requirement until we find a region matching the shape information. In order to match the shape information, we propose a new directed window region growing algorithm instead of using conventional contour matching. Experiments show promising results.",

author = "Chulhee Lee and Unser, {Michael A.} and Ketter, {Terence A.}",

year = "1995",

language = "English",

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series = "Proceedings of SPIE - The International Society for Optical Engineering",

pages = "528--534",

editor = "Tescher, {Andrew G.}",

booktitle = "Proceedings of SPIE - The International Society for Optical Engineering",

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Lee, C, Unser, MA & Ketter, TA 1995, Automatic recognition of corpus callosum from sagittal brain MR images. in AG Tescher (ed.), Proceedings of SPIE - The International Society for Optical Engineering. Proceedings of SPIE - The International Society for Optical Engineering, vol. 2564, pp. 528-534, Applications of Digital Image Processing XVIII, San Diego, CA, USA, 95/7/12.

Automatic recognition of corpus callosum from sagittal brain MR images. / Lee, Chulhee; Unser, Michael A.; Ketter, Terence A.

Proceedings of SPIE - The International Society for Optical Engineering. ed. / Andrew G. Tescher. 1995. p. 528-534 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 2564).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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PY - 1995

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N2 - We propose a new method to find the corpus callosum from sagittal brain MR images automatically. First, we calculate the statistical characteristics of the corpus callosum and obtain shape information. The recognition algorithm consists of two stages: extracting regions satisfying the statistical characteristics (gray level distributions) of the corpus callosum, and finding a region matching the shape information. An innovative feature of the algorithm is that we adaptively relax the statistical requirement until we find a region matching the shape information. In order to match the shape information, we propose a new directed window region growing algorithm instead of using conventional contour matching. Experiments show promising results.

AB - We propose a new method to find the corpus callosum from sagittal brain MR images automatically. First, we calculate the statistical characteristics of the corpus callosum and obtain shape information. The recognition algorithm consists of two stages: extracting regions satisfying the statistical characteristics (gray level distributions) of the corpus callosum, and finding a region matching the shape information. An innovative feature of the algorithm is that we adaptively relax the statistical requirement until we find a region matching the shape information. In order to match the shape information, we propose a new directed window region growing algorithm instead of using conventional contour matching. Experiments show promising results.

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BT - Proceedings of SPIE - The International Society for Optical Engineering

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