Classification of brain tumors using MRI and MRS data

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Volumn 6514, Issue PART 1, 2007, Pages

  Wang, Qiang ^a   Liacouras, Eirini Karamani ^a   Miranda, Erickson ^a   Kanamalla, Uday S ^b   Megalooikonomou, Vasileios ^a  

^a Temple University   (United States)

^b TEMPLE UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BRAIN TUMORS; CLASSIFICATION MODEL; FUZZY CONNECTEDNESS; REGIONS OF INTEREST (ROIS).;

BRAIN; DIAGNOSIS; FEATURE EXTRACTION; IMAGE SEGMENTATION; MAGNETIC RESONANCE IMAGING; MAGNETIC RESONANCE SPECTROSCOPY;

TUMORS;

EID: 35248823722     PISSN: 16057422     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.713544     Document Type: Conference Paper

References (16)

1. R. Silipo; G. Deco, and H. Bartsch. "Brain tumor classification based on EEG hidden dynamics". Intelligent Data Analysis, Volume 3, Number 4, pp. 287-306(20), October 1999.
2. C. Majós, M. Julià-Sapé, J. Alonso, M. Serrallonga, C. Aguilera, J. Acebes, C. Arús and J. Gili. "Brain Tumor Classification by Proton MR Spectroscopy: Comparison of Diagnostic Accuracy at Short and Long TE". American Journal of Neuroradiology 25:1696-1704, November-December 2004.
3. L. Lukas, A. Devos, J.A.K. Suykens, L. Vanhamme, F.A. Howe,C. Majo's, A. Moreno-Torres, M. Van Der Graaf, A.R. Tate, C. Arús, and S. Van Huffel. "Brain tumor classification based on long echo proton MRS signals". Artificial Intelligence in Medicine: 31, 73-89, 2004.
4. N. Pal and S. Pal, "A review on image segmentation techniques," Pattern Recognition, vol. 26, pp. 1277-1294, 1993.
5. Y. Zhang, "A survey on evaluation methods for image segmentation," Pattern Recognition, vol. 29, pp. 1335-1346, 1996.
6. A. Worth, N. Makris, V. Caviness, and D. Kennedy, "Neuroanatomical segmentation in MRI: Technological objectives," International Journal of Pattern Recognition and Artificial Intelligence, vol. 11, pp. 1161-1187, 1997.
7. T. McInerney and D. Terzopoulos, "Deformable Models in Medical Image Analysis: A Survey", Medical Image Analysis, 1(2):91-108, 1996.
8. M. Prastawa, E. Bullitt, N. Moon, L. Van Leemput, and G. Gerig. "Automatic brain tumor segmentation by subject specific modification of atlas priors". Acad. Radiology. 10:1341-1348, 2003.
9. M. Schmidt, I. Levner, R. Greiner, A. Murtha, and A. Bistritz. "Segmenting Brain Tumors using Alignment-Based Features". International Conference on Machine Learning and Applications, Los Angeles, December 2005.
10. D. Kontos, Q. Wang, V. Megalooikonomou, A. H. Maurer, L. C. Knight, S. Kantor, R. S. Fisher, H. P. Simonian, H. P. Parkman, "A 3D Image Analysis Tool for SPECT Imaging", Proceedings of the SPIE Conference on Medical Imaging: Visualization, Image-Guided Procedures and Display, Vol. 5744, pp. 839-847, San Diego, CA, Feb. 2005.
11. A. Rosenfeld, "Fuzzy Digital Topology", Information and Control, vol. 40, pp. 76-87, 1979.
12. V. Megalooikonomou, H. Dutta, D. Kontos, "Fast and Effective Characterization of 3D Region Data", Proceedings of the IEEE International Conference on Image Processing (ICIP), Rochester, NY, pp. 421-424, Sep. 2002.
13. D. Kontos and V. Megalooikonomou, "Fast and effective characterization for classification and similarity searches of 2D and 3D spatial region data", Pattern Recognition, Vol. 38, No. 11, pp. 1831-1846, 2005.
14. D. Kontos and V. Megalooikonomou, "Fast and Effective Characterization of 3D Region of Interest in Medical Image Data", Proceedings of the SPIE International Symposium on Medical Imaging 2004, San Diego, CA, Feb. 2004, Volume 5370 Medical Imaging 2004, pp. 1324-1331.
15. T. M. Cover and J. A. Thomas, Elements of Information Theory, John Wiley & Sons, New York, NY, 1991.
16. Rulequest Research, "C5.0: An Informal Tutorial", http://www.rulequest.com/see5-unix.html.