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Unsupervised Tissue-type Segmentation of 3D Dual-echo MR Head Data
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Institution: |
Surgical Planning Laboratory, Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. |
Publisher: |
Image and Vision Computing |
Publication Date: |
Jun-1992 |
Volume Number: |
10 |
Issue Number: |
6 |
Pages: |
349-360 |
Citation: |
Image and Vision Computing 1992;10(6):349-360. |
Keywords: |
3D dual-echo MR, tissue-type segmentation, Medical image analysis, brain tissue assessment, clustering |
Appears in Collections: |
PNL, NCIGT, SPL |
Generated Citation: |
Gerig G., Martin J., Kikinis R., Kübler O., Shenton M.E., Jolesz F.A. Unsupervised Tissue-type Segmentation of 3D Dual-echo MR Head Data. Image and Vision Computing 1992;10(6):349-360. |
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| Paper: | Download, View online |
| Export citation: |
The visualization of 3D phenomena and the extraction of quantitative information from magnetic resonance (MR) image data require efficient semiautomated or automated segmentation techniques. The application of multivariate statistical classification to the segmentation of dual-echo volume data of the human head into tissue types is studied. Tests of the radiometric variability of tissue classes within the data volume demonstrate the improvement of the image acquisition technology and the suitability of statistical methods to perform brain tissue segmentation.
Additional Material
2 Files (3MB)
Gerig-IVC1992-fig10.jpg (121.227kB) Gerig-IVC1992.ps (3MB)