Automatic glioma characterization from dynamic susceptibility contrast imaging: Brain tumor segmentation using knowledge-based fuzzy clustering

Journal of Magnetic Resonance Imaging

Volumn 30, Issue 1, 2009, Pages 1-10

  Emblem, Kyrre E ^a   Nedregaard, Baard ^a   Hald, John K ^a   Nome, Terje ^a   Due Tonnessen, Paulina ^a   Bjornerud, Atle ^a,b  

^a OSLO UNIVERSITY HOSPITAL   (Norway)

^b UNIVERSITY OF OSLO   (Norway)

Author keywords

DSC MR imaging; Fuzzy clustering; Histogram method; Presurgical glioma grading; Tumor segmentation

Indexed keywords

GADOBUTROL;

ADOLESCENT; ADULT; AGED; ARTICLE; CANCER SURVIVAL; CLINICAL ARTICLE; CLINICAL ASSESSMENT; COMPARATIVE STUDY; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DIAGNOSTIC VALUE; DISEASE SEVERITY; FEMALE; FUZZY SYSTEM; GLIOMA; HIGH RISK PATIENT; HISTOGRAM; HUMAN; KAPLAN MEIER METHOD; MALE; NUCLEAR MAGNETIC RESONANCE IMAGING; PATIENT IDENTIFICATION; PRIORITY JOURNAL; RADIODIAGNOSIS; RADIOLOGIST; RECEIVER OPERATING CHARACTERISTIC; TUMOR VOLUME;

ADOLESCENT; ADULT; AGED; AREA UNDER CURVE; BRAIN NEOPLASMS; CONTRAST MEDIA; ECHO-PLANAR IMAGING; FEMALE; FUZZY LOGIC; GLIOMA; HUMANS; IMAGE ENHANCEMENT; IMAGE PROCESSING, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MAGNETIC RESONANCE IMAGING; MALE; MIDDLE AGED; OBSERVER VARIATION; ORGANOMETALLIC COMPOUNDS; REPRODUCIBILITY OF RESULTS; RISK FACTORS; SENSITIVITY AND SPECIFICITY; SURVIVAL ANALYSIS; YOUNG ADULT;

EID: 67650035155     PISSN: 10531807     EISSN: 15222586     Source Type: Journal    
DOI: 10.1002/jmri.21815     Document Type: Article

References (40)

1. Talos IF, Zou KH, Ohno-Machado L, et al. Supratentorial low-grade glioma resectability: statistical predictive analysis based on anatomic MR features and tumor characteristics. Radiology 2006;239:506-513.
2. Dean BL, Drayer BP, Bird CR, et al. Gliomas: classification with MR imaging. Radiology 1990;174:411-415.
3. Johnson PC, Hunt SJ, Drayer BP. Human cerebral gliomas: correlation of postmortem MR imaging and neuropathologic findings. Radiology 1989;170:211-217. (Pubitemid 19016878)
4. Covarrubias DJ, Rosen BR, Lev MH. Dynamic magnetic resonance perfusion imaging of brain tumors. Oncologist 2004;9:528-537. (Pubitemid 39332316)
5. Knopp EA, Cha S, Johnson G, et al. Glial neoplasms: dynamic contrast-enhanced T2*-weighted MR imaging. Radiology 1999;211:791-798. (Pubitemid 29249482)
6. Lev MH, Ozsunar Y, Henson JW, et al. Glial tumor grading and outcome prediction using dynamic spin-echo MR susceptibility mapping compared with conventional contrast-enhanced MR: confounding effect of elevated rCBV of oligodendrogliomas [corrected]. AJNR Am J Neuroradiol 2004;25:214-221. (Pubitemid 38235298)
7. Law M, Oh S, Babb JS, et al. Low-grade gliomas: dynamic susceptibility- weighted contrast-enhanced perfusion MR imaging-prediction of patient clinical response. Radiology 2006;238:658-667. (Pubitemid 43134379)
8. Law M, Oh S, Johnson G, et al. Perfusion magnetic resonance imaging predicts patient outcome as an adjunct to histopathology: a second reference standard in the surgical and nonsurgical treatment of low-grade gliomas. Neurosurgery 2006;58:1099-1107. (Pubitemid 43879544)
9. Wetzel SG, Cha S, Johnson G, et al. Relative cerebral blood volume measurements in intracranial mass lesions: interobserver and intraobserver reproducibility study. Radiology 2002;224:797-803. (Pubitemid 34919816)
10. Emblem KE, Nedregaard B, Nome T, et al. Glioma grading by using histogram analysis of blood volume heterogeneity from MR-derived cerebral blood volume maps. Radiology 2008;247:808-817.
11. Young R, Babb J, Law M, Pollack E, Johnson G. Comparison of region-of-interest analysis with three different histogram analysis methods in the determination of perfusion metrics in patients with brain gliomas. J Magn Reson Imaging 2007;26:1053-1063. (Pubitemid 47548167)
12. Vannier MW, Butterfield RL, Jordan D, Murphy WA, Levitt RG, Gado M. Multispectral analysis of magnetic resonance images. Radiology 1985;154:221-224. (Pubitemid 15196317)
13. Kvinnsland Y, Brekke N, Taxt TM, Grüner R. Multispectral analysis of multimodal images. Acta Oncol 2009;48:277-284.
14. Dickson S, Thomas BT, Goddard P. Using neural networks to automatically detect brain tumours in MR images. Int J Neural Syst 1997;8:91-99.
15. Abdolmaleki P, Mihara F, Masuda K, Buadu LD. Neural networks analysis of astrocytic gliomas from MRI appearances. Cancer Lett 1997;118:69-78. (Pubitemid 27397226)
16. Zhou J, Chan K, Chong H, Krishnan S. Extraction of brain tumor from MR images using one-class support vector machine. Conf Proc IEEE Eng Med Biol Soc 2005;6:6411-6414.
17. Glotsos D, Spyridonos P, Cavouras D, Ravazoula P, Dadioti PA, Nikiforidis G. An image-analysis system based on support vector machines for automatic grade diagnosis of brain-tumour astrocytomas in clinical routine. Med Inform Internet Med 2005;30:179-193. (Pubitemid 43110287)
18. Clark MC, Hall LO, Goldgof DB, Velthuizen R, Murtagh FR, Silbiger MS. Automatic tumor segmentation using knowledge-based techniques. IEEE Trans Med Imaging 1998;17:187-201. (Pubitemid 128747672)
19. Bezdek JC, Hall LO, Clark MC, Goldgof DB, Clarke LP. Medical image analysis with fuzzy models. Stat Methods Med Res 1997;6:191-214. (Pubitemid 127576066)
20. Fletcher-Heath LM, Hall LO, Goldgof DB, Murtagh FR. Automatic segmentation of non-enhancing brain tumors in magnetic resonance images. Artif Intell Med 2001;21:43-63. (Pubitemid 32013920)
21. Kaus MR, Warfield SK, Nabavi A, Black PM, Jolesz FA, Kikinis R. Automated segmentation of MR images of brain tumors. Radiology 2001;218:586-591. (Pubitemid 32121080)
22. Itskovich VV, Samber DD, Mani V, et al. Quantification of human atherosclerotic plaques using spatially enhanced cluster analysis of multicontrast-weighted magnetic resonance images. Magn Reson Med 2004;52:515-523. (Pubitemid 39108962)
23. Kleihues P, Cavenee W. Astrocytic tumors & oligodendroglial tumors and mixed gliomas. The WHO classification of tumors of the nervous system, 2nd edition. Lyon: International Agency for Research on Cancer; 2000. p 9-70.
24. Rosen BR, Belliveau JW, Vevea JM, Brady TJ. Perfusion imaging with NMR contrast agents. Magn Reson Med 1990;14:249-265. (Pubitemid 20248610)
25. Ostergaard L, Weisskoff RM, Chesler DA, Gyldensted C, Rosen BR. High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part I. Mathematical approach and statistical analysis. Magn Reson Med 1996;36:715-725.
26. Boxerman JL, Schmainda KM, Weisskoff RM. Relative cerebral blood volume maps corrected for contrast agent extravasation significantly correlate with glioma tumor grade, whereas uncorrected maps do not. AJNR Am J Neuroradiol 2006;27:859-867. (Pubitemid 44897737)
27. Maes F, Collignon A, Vandermeulen D, Marchal G, Suetens P. Multimodality image registration by maximization of mutual information. IEEE Trans Med Imaging 1997;16:187-198. (Pubitemid 127762930)
28. Schmainda KM, Rand SD, Joseph AM, et al. Characterization of a first-pass gradient-echo spin-echo method to predict brain tumor grade and angiogenesis. AJNR Am J Neuroradiol 2004;25:1524-1532. (Pubitemid 44383603)
29. Grier JT, Batchelor T. Low-grade gliomas in adults. Oncologist 2006;11:681-693.
30. Pirzkall A, Nelson SJ, McKnight TR, et al. Metabolic imaging of low-grade gliomas with three-dimensional magnetic resonance spectroscopy. Int J Radiat Oncol Biol Phys 2002;53:1254-1264. (Pubitemid 34787862)
31. Zuiderveld K. Contrast limited adaptive histogram equalization. In: Heckbert PS, editor. Graphics gems IV. Boston: AP Professional; 1994. p 474-485
32. Ashburner J, Friston K. Multimodal image coregistration and partitioning - a unified framework. Neuroimage 1997;6:209-217. (Pubitemid 27488452)
33. Otsu NA. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 1979;9:62-66. (Pubitemid 9413341)
34. Mazzara GP, Velthuizen RP, Pearlman JL, Greenberg HM, Wagner H. Brain tumor target volume determination for radiation treatment planning through automated MRI segmentation. Int J Radiat Oncol Biol Phys 2004;59:300-312. (Pubitemid 38501535)
35. Law M, Yang S, Wang H, et al. Glioma grading: sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging. AJNR Am J Neuroradiol 2003;24:1989-1998. (Pubitemid 37444849)
36. Fryback DG, Thornbury JR. The efficacy of diagnostic imaging. Med Decis Making 1991;11:88-94.
37. Joe BN, Fukui MB, Meltzer CC, et al. Brain tumor volume measurement: comparison of manual and semiautomated methods. Radiology 1999;212:811-816. (Pubitemid 29410987)
38. Law M, Young R, Babb J, Pollack E, Johnson G. Histogram analysis versus region of interest analysis of dynamic susceptibility contrast perfusion MR imaging data in the grading of cerebral gliomas. AJNR Am J Neuroradiol 2007;28:761-766. (Pubitemid 46608621)
39. Price SJ, Jena R, Burnet NG, et al. Improved delineation of glioma margins and regions of infiltration with the use of diffusion tensor imaging: an image-guided biopsy study. AJNR Am J Neuroradiol 2006;27:1969-1974. (Pubitemid 44556519)
40. Mouridsen K, Christensen S, Gyldensted L, Ostergaard L. Automatic selection of arterial input function using cluster analysis. Magn Reson Med 2006;55:524-531. (Pubitemid 43352550)