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 |
| Volume | 2564 |
| Publication status | Published - 1995 Dec 1 |
| Event | Applications of Digital Image Processing XVIII - San Diego, CA, USA Duration: 1995 Jul 12 → 1995 Jul 14 |
Other
| Other | Applications of Digital Image Processing XVIII |
|---|---|
| City | San Diego, CA, USA |
| Period | 95/7/12 → 95/7/14 |
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All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
- Condensed Matter Physics
Cite this
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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. Vol. 2564 1995. p. 528-534.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Automatic recognition of corpus callosum from sagittal brain MR images
AU - Lee, Chulhee
AU - Unser, Michael A.
AU - Ketter, Terence A.
PY - 1995/12/1
Y1 - 1995/12/1
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.
UR - http://www.scopus.com/inward/record.url?scp=0029518130&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0029518130&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0029518130
SN - 0819419230
SN - 9780819419231
VL - 2564
SP - 528
EP - 534
BT - Proceedings of SPIE - The International Society for Optical Engineering
A2 - Tescher, Andrew G.
ER -