Segmentation and grading of brain tumors on apparent diffusion coefficient images using self-organizing maps

Computerized Medical Imaging and Graphics

Volumn 31, Issue 7, 2007, Pages 473-484

  Vijayakumar, C ^a   Damayanti, Gharpure ^b   Pant, R ^a   Sreedhar, C M ^a  

^a ARMED FORCES MEDICAL COLLEGE   (India)

^b UNIVERSITY OF PUNE   (India)

Author keywords

Apparent diffusion coefficients; Artificial neural networks; Brain tumors; Image segmentation; Magnetic resonance imaging; Multiresolution analysis; Self organizing maps; Tumor grading

Indexed keywords

ALGORITHMS; MAGNETIC RESONANCE IMAGING; MULTIRESOLUTION ANALYSIS; SELF ORGANIZING MAPS; TUMORS;

APPARENT DIFFUSION COEFFICIENTS; BRAIN TUMORS; TUMOR GRADING; WAVELET FILTERED IMAGES;

IMAGE SEGMENTATION;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BRAIN EDEMA; BRAIN MAPPING; BRAIN NECROSIS; BRAIN TISSUE; BRAIN TUMOR; CEREBROSPINAL FLUID; DIAGNOSTIC IMAGING; IMAGE ANALYSIS; IMAGE RECONSTRUCTION; MATHEMATICAL MODEL; NEUROIMAGING; PRIORITY JOURNAL; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; BRAIN NEOPLASMS; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; INDIA; NEURAL NETWORKS (COMPUTER);

EID: 34548034210     PISSN: 08956111     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compmedimag.2007.04.004     Document Type: Article

References (32)

1. Fletcher-Heath L.M., Hall L.O., Goldgof D.B., and Murtagh F.R. Automatic segmentation of non-enhancing brain tumors in magnetic resonance images. Artif Intell Med 21 (2001) 43-63
2. Clark M.C., Hall L.O., Goldgof D.B., Velthuizen R., Murtagh F.R., and Silbiger M.S. Automatic tumor segmentation using knowledge-based techniques. IEEE Trans Med Imag 17 (1998) 187-201
3. Clark M.C., Hall L.O., Goldgof D.B., Clark L.P., Silbiger M.S., and Li C. MRI segmentation using fuzzy clustering techniques: integrating knowledge. IEEE Eng Med Biol Mag 13 (1994) 730-742
4. Jianguo L., Jayaram K.U., Dewey O., David H., and Gul M. A system for brain tumor volume estimation via MR imaging and fuzzy connectedness. Comput Med Imaging Graphics 29 (2005) 21-34
5. Tim W.N., and Axel W. Tumor feature visualization with unsupervised learning. Med Image Anal 9 4 (2005) 344-351
6. Reddick W.E., Glass J.O., Cook E.N., Elkin T.D., and Deaton R.J. Automated segmentation and classi1cation of multispectral magnetic resonance images of brain using artificial neural networks. IEEE Trans. Med. Imaging 16 6 (1997) 911-918
7. Koh J., Suk M.S., and Bhandarkar S.M. A multilayer self organizing feature map for range image segmentation. Neural Netw 8 1 (1995) 67-86
8. Kung S.Y., and Taur J.S. Decision based neural networks with signal image classification applications. IEEE Trans Neural Netw 6 1 (1995) 170-181
9. Vesanto J., and Alhoniemi E. Clustering of the self-organizing map. IEEE Trans Neural Netw 11 5 (2000) 586-600
10. Stephan E.M., Peter B., Gabor B., Hatsuho M., Yoshiaki M., Imre R., et al. Normal brain and brain tumor: multicomponent apparent diffusion coefficient line scan imaging. Radiology 219 (2001) 842-849
11. Hasso A.N., Kortman K.E., and Bradley W.G. Supratentorial neoplasms. In: Stark D.D., and Bradley W.G. (Eds). Magnetic resonance imaging, vol. 1, 2nd ed. (1992), Mosby-Year Book, St. Louis, Mo 770-817
12. Hicks R.J. Supratentorial brain tumors. In: Edelman R.R., Hesselink J.R., and Zlatkin M.B. (Eds). Clinical magnetic resonance imaging. 2nd ed. vol. 1 (1996), Saunders, Philadelphia, PA 533-556
13. Nail B., Murat K., Fatih O., Cem T., and Taner U. Combination of single-voxel proton MR spectroscopy and apparent diffusion coefficient calculation in the evaluation of common brain tumors. AJNR 23 (2003) 225-233
14. Fumiyuki Y., Kaoru K., Kenichi S., Kazunori A., Kazuhiko S., Megu O., et al. Apparent diffusion coefficient of human brain tumors at MR imaging. Radiology 235 (2005) 985-991
15. Cem C., Omer K., Nilgun Y., Taskin Y., Sertac I., and Taner A. Perfusion and diffusion MR imaging in enhancing malignant cerebral tumors. Eur J Radiol 58 3 (2006) 394-403
16. Chaplot S., Patnaik L.M., and Jagannathan N.R. Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomed Signal Process Contr 1 1 (2006) 86-92
17. Ginneken B.v., Stegmann M.B., and Loog Ma. Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database. Med Image Anal 10 1 (2006) 19-40
18. Zhoua Z., and Ruana Z. Multicontext wavelet-based thresholding segmentation of brain tissues in magnetic resonance images. Magn Reson Imag 25 3 (2007) 381-385
19. Du-Ming T., and Cheng-Huei C. Automatic band selection for wavelet reconstruction in the application of defect detection. Image Vision Comput 21 5 (2003) 413-431
20. Du-Ming T., and Bo H. Automatic surface inspection using wavelet reconstruction. Pattern Recogn 3 6 (2001) 1285-1305
21. Nyul L.G., and Udupa J.K. On standardizing the MR image intensity scale. Magn Reson Med 42 (1999) 1072-1081
22. Daubechies I. Ten lectures on wavelets (1992), SIAM, Philadelphia, PA
23. Daubechies I. A wavelet tour of signal processing (1998), Academic Press, New York
24. Choi H, Baraniuk RG. Image segmentation using wavelet-domain classification. Rice-Preprint; 1999.
25. Kohonen T., Kaski S., Lagus K., Salojärvi J., Paatero V., and Saarela A. Self-organization of a massive document collection. IEEE Trans Neural Netw 11 (2000) 574-585
26. Kohonen T. Self-organizing maps. 3rd ed. (2001), Springer, Berlin
27. Cottrell M., Fort J.C., and Page G. Theoretical aspects of the SOM algorithm. Neurocomputing 21 (1998) 119-138
28. Ultsch A. Knowledge extraction from self-organizing neural networks. Information and Classification (1993), Springer
29. Ultsch A. Maps for the visualization of high-dimensional data spaces. Procedings of the workshop on self organizing maps. Kyushu, Japan (2003) 225-230
30. Obuchowski N.A. Receiver operating characteristic curves and their use in radiology. Radiology 229 (2003) 3-8
31. Mills S.J., Patankar T.A., Haroon H.A., Baleriaux D., Swindell R., and Jackson A. Do cerebral blood volume and contrast transfer coefficient predict prognosis in human glioma. Am J Neuroradiol 27 4 (2006) 853-858
32. Wintermark M., Sesay M., Barbier E., Borbely K., Dillon W.P., Eastwood J.D., et al. Comparative overview of brain perfusion imaging techniques. Stroke 36 9 (2005) e83-e99