Automatic Brain and Tumor Segmentation-Reference-Cited by-同舟云学术

Automatic Brain and Tumor Segmentation

Published:2002 Issue: Volume: Page:372-379
ISSN:0302-9743
Container-title:Medical Image Computing and Computer-Assisted Intervention — MICCAI 2002
language:
Short-container-title:

Author:

Moon Nathan,Bullitt Elizabeth,van Leemput Koen,Gerig Guido

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/3-540-45786-0_46

Reference17 articles.

1. Just, M., Thelen, M.: Tissue characterization with T1, T2, and proton density values: Results in 160 patients with brain tumors. Radiology (1988) 779–785

2. Gerig, G., Martin, J., Kikinis, R., Kubler, O., Shenton, M., Jolesz, F.: Automating segmentation of dual-echo MR head data. In: IPMI. Volume 511. (1991) 175–185

3. Velthuizen, R., Clarke, L., Phuphianich, S., Hall, L., Bensaid, A., Arrington, J., Greenberg, H., Siblinger, M.: Unsupervised measurement of brain tumor volume on MR images. JMRI (1995) 594–605

4. Vinitski, S., Gonzales, C., Mohamed, F., Iwanaga, T., Knobler, R., Khalili, K., Mack, J.: Improved intracranial lesion characterization by tissue segmentation based on a 3D feature map. Mag Re Med (1997) 457–469

5. Kamber, M., Shingal, R., Collins, D., Francis, D., Evans, A.: Model-based, 3-D segmentation of multiple sclerosis lesions in magnetic resonance brain images. IEEE-TMI (1995) 442–453

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