Automatic segmentation of meningioma from non-contrasted brain MRI integrating fuzzy clustering and region growing

BMC Medical Informatics and Decision Making

Volumn 11, Issue 1, 2011, Pages

  Hsieh, Thomas M ^a,b   Liu, Yi Min ^a   Liao, Chun Chih ^a,b   Xiao, Furen ^a,b   Chiang, I Jen ^a   Wong, Jau Min ^a  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

^b NATIONAL TAIWAN UNIVERSITY HOSPITAL   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; BRAIN; BRAIN TUMOR; CLUSTER ANALYSIS; FUZZY LOGIC; HUMAN; MENINGIOMA; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY;

ALGORITHMS; BRAIN; BRAIN NEOPLASMS; CLUSTER ANALYSIS; FUZZY LOGIC; HUMANS; MAGNETIC RESONANCE IMAGING; MENINGIOMA; PATTERN RECOGNITION, AUTOMATED;

EID: 80052029970     PISSN: None     EISSN: 14726947     Source Type: Journal    
DOI: 10.1186/1472-6947-11-54     Document Type: Article

References (32)

1. MRI segmentation: Methods and applications. Clarke LP, Velthuizen RP, Camacho MA, Heine JJ, Vaidyanathan M, Hall LO, Thatcher RW, Silbiger ML, Magn Reson Imaging 1995 13 343 68 10.1016/0730-725X(94)00124-L 7791545
2. Adaptive segmentation of MRI data. Wells WM, Grimson WL, Kikinis R, Jolesz FA, IEEE Trans Med Imaging 1996 15 429 42 10.1109/42.511747 18215925 (Pubitemid 126780333)
3. Review of brain MRI image segmentation methods. Balafar MA, Ramli AR, Saripan MI, Mashohor S, Artif Intell Rev 2010 33 261 274 10.1007/s10462-010- 9155-0
4. Semi-automated brain tumor and edema segmentation using MRI. Xiea K, Yanga J, Zhanga ZG, Zhub YM, Eur J Radiol 2005 56 12 19 10.1016/j.ejrad.2005.03. 028 16168259 (Pubitemid 41316480)
5. Extraction of brain tumor from MR images using one-class support vector machine. Zhou J, Chan KL, Chong VFH, Krishnan SM, Conf Proc IEEE Eng Med Biol Soc 2005 6 6411 4 17281735 (Pubitemid 46230437)
6. Brain Tumor Segmentation Using Support Vector Machines. Ayachi R, Amor NB, Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 1-3 July 2009; Verona, Italy Berlin Heidelberg: Springer-Verlag, Sossai C, Chemello G, 2009 736 747
7. Brain tumor target volume determination for radiation treatment planning through automated MRI segmentation. Mazzara GP, Velthuizen RP, Pearlman JL, Greenberg HM, Wagner H, Int J Radia Oncol Biol Phy 2004 59 300 312 10.1016/j.ijrobp.2004.01.026 (Pubitemid 38501535)
8. Segmentation of tumors in magnetic resonance brain images using an interactive multiscale watershed algorithm. Letteboera MMJ, Olsenb OF, Damb EB, Willemsc PWA, Viergevera MA, Niessena WJ, ACAD Radiol 2004 11 1125 1138 10.1016/j.acra.2004.05.020 15530805 (Pubitemid 39458086)
9. Model-based brain and tumor segmentation. Moon N, Bullitt E, Leemput KV, Gerig G, Proceedings of the 16th International Conference on Pattern Recognition 2002 1 528 531 (Pubitemid 44845054)
10. A brain tumor segmentation framework based on outlier detection. Prastawa M, Bullitt E, Ho S, Gerig G, Med Image Anal 2004 8 275 283 10.1016/j.media. 2004.06.007 15450222 (Pubitemid 39185911)
11. A framework of fuzzy information fusion for the segmentation of brain tumor tissues on MR images. Dou W, Ruan S, Chen Y, Bloyet D, Constans JM, Image Vision Comput 2007 25 164 171 10.1016/j.imavis.2006.01.025 (Pubitemid 44873142)
12. A system for brain tumor volume estimation via MR imaging and fuzzy connectedness. Liu J, Udupa JK, Odhner D, Hackney D, Moonis G, Comput Med Imaging Graph 2005 29 21 34 10.1016/j.compmedimag.2004.07.008 15710538 (Pubitemid 40248631)
13. 3D brain tumor segmentation. Khotanlou H, Colliot O, Atif J, Bloch I, Fuzzy Set Syst 2009 160 1457 1473 10.1016/j.fss.2008.11.016
14. 3D brain tumor segmentation using fuzzy classification and deformable models. Khotanlou H, Atif J, Colliot O, Bloch I, WILF 2005, LANI 3849 Berlin Heidelberg: Springer-Verlag, Bloch I, Petrosino A, Tettamanzi AGB, 2006 312 318 (Pubitemid 43900874)
15. Review of MR image segmentation techniques using pattern recognition. Bezdek JC, Hall LO, Clarke LP, Med Phys 1993 20 1033 48 10.1118/1.597000 8413011 (Pubitemid 23249849)
16. Brain State Recognition Using Fuzzy C-Means (FCM) Clustering with Near Infrared Spectroscopy (NIRS). Kobashi S, Hata Y, Kitamura YT, Hayakata T, Yanagida T, Fuzzy Days 2001 LNCS 2206 Berlin Heidelberg: Springer-Verlag, Reusch B, 2001 124 136 (Pubitemid 33359492)
17. New Robust Fuzzy C-Means Based Gaussian Function in Classifying Brain Tissue Regions. Kannan SR, Sathya A, Ramathilagam S, Pandiyarajan R, Contemporary Computing, Communications in Computer and Information Science Berlin Heidelberg: Springer, Ranka S et al, 2009 40 158 169 10.1007/978-3-642- 03547-0-16
18. MRI measurement of brain tumor response: comparison of visual metric and automatic segmentation. Clarke LP, Velthuizen RP, Clark M, Gaviria J, Hall L, Goldgof D, Murtagh R, Phuphanich S, Brem S, Magn Reson Imaging 1998 16 271 279 10.1016/S0730-725X(97)00302-0 9621968 (Pubitemid 28264523)
19. Automatic glioma characterization from dynamic susceptibility contrast imaging: Brain tumor segmentation using knowledge-based fuzzy clustering. Emblem KE, Nedregaard B, Hald JK, Nome T, Due-Tonnessen P, Bjornerud A, J Magn Reson Imaging 2009 30 1 10 10.1002/jmri.21815 19557840
20. Tumor extraction from multimodal MRI. Wafa M, Zagrouba E, Computer Recognition Sys. 3, AISC 57 Berlin Heidelberg: Springer-Verlag, Kurzynski M, Wozniak M, 2009 415 422
21. Automatic segmentation of non-enhancing brain tumors in magnetic resonance images. Fletcher-Heatha LM, Halla LO, Goldgofa DB, Murtaghb FR, Artif Intell Med 2001 21 43 63 10.1016/S0933-3657(00)00073-7 11154873 (Pubitemid 32013920)
22. Improved fuzzy-c-means for noisy image segmentation. Wafa M, Zagrouba E, SIGMAP 2009 74 78
23. Fuzzy spatial growing for glioblastoma multiforme segmentation on brain magnetic resonance imaging. Veloz A, Chabert S, Salas R, Orellana A, Vielma J, CIARP 2007, LNCS 4756 Berlin Heidelberg: Springer-Verlag, Rueda L, Mery D, Kittler J, 2007 861 870
24. Fuzzy c-means clustering with spatial information for image segmentation. Chuang KS, Tzeng HL, Chen S, Wu J, Chen TJ, Comput Med Imag Grap 2006 30 9 15 10.1016/j.compmedimag.2005.10.001 (Pubitemid 43162367)
25. Fuzzy c-means clustering based on spatial neighborhood information for image segmentation. Li Y, Shen Y, J Syst Eng Electron 2010 21 323 328
26. Magnetic resonance imaging of Tuberculum Sellae meningiomas. Taylor SL, Barakos JA, Harsh GR, Wilson CB, Neurosurgery 1992 31 621 627 10.1227/00006123-199210000-00002 1407446
27. Extended bifrontal craniotomy for midline anterior fossa meningiomas: minimization of retraction-related edema and surgical outcomes. Chi JH, Parsa AT, Berger MS, Kunwar S, McDermott MW, Neurosurgery 2006 59 426 434 10.1227/01.NEU.0000223497.06588.4A (Pubitemid 44325832)
28. Automatic tumor segmentation using knowledge-based techniques. Clark MC, Hall LO, Goldgof DB, Velthuizen R, Reed F, Silbiger MS, IEEE T Med Imaging 1998 17 187 201 10.1109/42.700731 (Pubitemid 128747672)
29. Segmentation and visualization of nasopharyngeal carcinoma using MRI. Zhou J, Lim T, Chong V, Huang J, Comput Biol Med 2003 33 407 424 10.1016/S0010-4825(03)00018-0 12860465 (Pubitemid 36829356)
30. Engineering and Algorithm Design for an Image Processing API: A Technical Report on ITK-The Insight Toolkit. Yoo TS, Ackerman MJ, Lorensen WE, Schroeder W, Chalana V, Aylward S, Metaxes D, Whitaker R, Proc of Medicine Meets Virtual Reality Amsterdam: IOS Press, Westwood J, 2002 586 592
31. Rapid and automatic detection of brain tumors in MR images. Wang Z, Hu Q, Loe KF, Aziz A, Nowinski WL, Proceedings of the SPIE 2004, Amini AA, Manduca A, 2004 5369 602 612
32. Duda RO, Hart PE, Stork DG, Pattern Classification New York: Wiley 2 2000