A classification-based glioma diffusion model using MRI data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4013 LNAI, Issue , 2006, Pages 98-109

  Morris, Marianne ^a   Greiner, Russell ^a   Sander, Jörg ^a   Murtha, Albert ^b   Schmidt, Mark ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

^b CROSS CANCER INSTITUTE   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); LEARNING ALGORITHMS; MATHEMATICAL MODELS; PATIENT MONITORING; PATTERN RECOGNITION; TISSUE; TUMORS;

CDM MODEL; DIFFUSION MODEL; GLIOMAS; GROWTH PATTERNS;

BRAIN MODELS;

EID: 33746086338     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11766247_9     Document Type: Conference Paper

References (21)

1. Brain Tumor Growth Prediction: http://wvw.cs.ualberta.ca/~btgp/ai06.html
2. Brown M., and Semelka R., MRI Basic Principles and Applications (2003) Wiley, Hoboken, NJ, USA
3. Clatz O., Bondiau P., Delingette H., et al., In Silico Tumor Growth: Application to Glioblastomas. MICCAI 2004, LNCS 3217 (2004) 337-345
4. Friston K., Ashburner J., Frith C., Poline J., Heather J., and Frackowiak R., Spatial Registration and Normalization of Images Human Brain Mapping 2 (1995) 165-189
5. Friston K., and Ashburner J., Multimodal Image Coregistration and Partitioning a Unified Framework 6 (1997) NeuroImage 209-217
6. Halperin E., Bentel G., and Heinz E., Radiation Therapy Treatment Planning in Supratentorial Glioblastoma Multiforme, Int. J. Radiat. Oncol. Biol. Phys. 17 (1989) 1347-1350
7. Hochberg F., and Pruitt A., Assumptions in the Radiotherapy of Glioblastoma, Neurology 30 (1980) 907-911
8. Holmes C., Hoge R., Collins L., et al., Enhancement of MR images using registration for signal averaging. J Comput Assist Tomogr, 22(2) (1998) 324-333
9. Kansal A., Torquato S., Harsh G., et al., Simulated brain tumor growth dynamics using a three-dimensional cellular automaton. J Theor Biol. 203 (2000) 367-382
10. Lee C.H., Greiner R., and Schmidt M., Support Vector Random Fields for Spatial Classification (2005) PKDD 2005
11. le Cessie S. and van Houwelingen J., Ridge Estimators in Logistic Regression. Applied Statistics, 41(1) (1992) 191-201
12. Morris M., Classification-based Glioma Diffusion Modeling. MSc Thesis, University of Alberta (2005)
13. Platt J., Fast Training of Support Vector Machines using Sequential Minimal Optimization. Advances in Kernel Methods -Support Vector Learning, eds., Schoelkopf B., Burges C., and Smola A. (1998) MIT Press
14. Price S., Burnet N., Donovan T., et al., Diffusion tensor imaging of brain tumorss at 3T: a potential tool for assessing white matter tract invasion? Clinical Radiology, 58 (2003) 455-462
15. Student, The Probable Error of a Mean. Biometrika, 6 (1908) 1-25
16. Swanson K., Alvord E., Murray J., A Quantitative Model for Differential Motility of Gliomas in grey and White Matter, Cell Prolif, 33 (2000) 317-329
17. Swanson K., Alvord E., Murray J., Quantifying efficacy of chemotherapy of brain tumors with homogeneous and heterogeneous drug delivery. Acta Biotheor, 50(4) (2002) 223-237
18. Swanson K., Alvord E., Murray J., Virtual brain tumors (gliomas) enhance the reality of medical imaging and highlight inadequacies of current therapy, British Journal of Cancer, 86 (2002) 14-18
19. Tabatabai M., Williams D. and Bursac Z., Hyperbolastic growth models: theory and application. Theor Biol Med Model, 2(1) (2005), 14
20. Van Rijsbergen C. J., Information Retrieval (1979) second edition, London, Butter worths.
21. Zizzari A., Methods on Tumor Recognition and Planning Target Prediction for the Radiotherapy of Cancer (2004) PhD Thesis, University of Magdeburg