MARGA: Multispectral Adaptive Region Growing Algorithm for brain extraction on axial MRI

Computer Methods and Programs in Biomedicine

Volumn 113, Issue 2, 2014, Pages 655-673

  Roura, Eloy ^a   Oliver, Arnau ^a   Cabezas, Mariano ^a   Vilanova, Joan C ^b   Rovira, Àlex ^c   Ramió Torrentà, Lluís ^d   Lladó, Xavier ^a  

^a UNIVERSITY OF GIRONA   (Spain)

^b Girona Magnetic Resonance Center   (Spain)

^c HOSPITAL UNIVERSITARI VALL D HEBRON   (Spain)

^d GIRONA BIOMEDICAL RESEARCH INSTITUTE IDIBGI   (Spain)

Author keywords

Biomedical engineering; Image analysis; Image segmentation; Magnetic resonance imaging; Skull stripping

Indexed keywords

BIOMEDICAL ENGINEERING; BRAIN; DATABASE SYSTEMS; EXTRACTION; IMAGE ANALYSIS; IMAGE SEGMENTATION; MAGNETIC RESONANCE IMAGING; SCANNING;

ADAPTIVE REGION GROWING; BRAIN EXTRACTION TOOLS; BRAIN SURFACE EXTRACTORS; MAGNETIC RESONANCE IMAGES (MRI); MEDICAL IMAGE COMPUTING; SCHIZOPHRENIC PATIENTS; SKULL STRIPPING; STATISTICAL PARAMETRIC MAPPING;

BRAIN MAPPING;

ALGORITHM; ARTICLE; BRAIN EXTRACTION; BRAIN EXTRACTION TOOL; BRAIN SLICE; BRAIN STEM; BRAIN SURFACE EXTRACTOR; CLINICAL ARTICLE; COMPUTER PROGRAM; CONTROLLED STUDY; DATA BASE; HUMAN; INFORMATION PROCESSING; MULTIPLE SCLEROSIS; MULTISPECTRAL ADAPTIVE REGION GROWING ALGORITHM; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; NUCLEAR MAGNETIC RESONANCE SCANNER; QUALITATIVE ANALYSIS; QUANTITATIVE ANALYSIS; SCHIZOPHRENIA; SKULL STRIPPING; STATISTICAL PARAMETRIC MAPPING;

BIOMEDICAL ENGINEERING; IMAGE ANALYSIS; IMAGE SEGMENTATION; MAGNETIC RESONANCE IMAGING; SKULL STRIPPING;

ALGORITHMS; BRAIN MAPPING; CASE-CONTROL STUDIES; DATABASES, FACTUAL; HUMANS; MAGNETIC RESONANCE IMAGING; SCHIZOPHRENIA;

EID: 84892819929     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2013.11.015     Document Type: Article

References (34)

1. Park J.G., Lee C. Skull stripping based on region growing for magnetic resonance brain images. NeuroImage 2009, 47(4):1394-1407.
2. Ashburner J., Friston K.J. Unified segmentation. NeuroImage 2005, 26(3):839-851.
3. Cabezas M., Oliver A., Lladó X., Freixenet J., Bach Cuadra M. A review of atlas-based segmentation for magnetic resonance brain images. Computer Methods and Programs in Biomedicine 2011, 104(3):e158-e177.
4. Škerl D., Likar B., Pernuš F. A protocol for evaluation of similarity measures for non-rigid registration. Medical Image Analysis 2008, 12(1):42-54.
5. Modat M., Ridgway G.R., Taylor Z.A., Lehmann M., Barnes J., Hawkes D.J., Fox N.C., Ourselin S. Fast free-form deformation using graphics processing units. Computer Methods and Programs in Biomedicine 2010, 98(3):278-284.
6. Klein S., Staring M., Murphy K., Viergever M., Pluim J. elastix: a toolbox for intensity-based medical image registration. IEEE Transactions on Medical Imaging 2010, 29(1):196-205.
7. Roura E., Schneider T., Modat M., Daga P., Muhlert N., Chard D., Ourselin S., Lladó X., Wheeler-Kingshott C.A.M. Evaluating a multi-channel registration approach of FA and T1w on MS patients with simulated atrophy. International Society for Magnetic Resonance in Medicine 2013.
8. Saha S., Bandyopadhyay S. Automatic MR brain image segmentation using a multiseed based multiobjective clustering approach. Applied Intelligence 2011, 35(3):411-427.
9. Lladó X., Oliver A., Cabezas M., Freixenet J., Vilanova J.C., Quiles A., Valls L., Ramió-Torrentà L., Rovira A. Segmentation of multiple sclerosis lesions in brain MRI: a review of automated approaches. Information Sciences 2012, 186(1):164-185.
10. Lladó X., Ganiler O., Oliver A., Martí R., Freixenet J., Valls L., Vilanova J.C., Ramió-Torrentà L., Rovira A. Automated detection of multiple sclerosis lesions in serial brain MRI. Neuroradiology 2012, 54(8):787-807.
11. Li K., Guo L., Faraco C., Zhu D., Chen H., Yuan Y., Lv J., Deng F., Jiang X., Zhang T., Hu X., Zhang D., Miller L.S., Liu T. Visual analytics of brain networks. NeuroImage 2012, 61(1):82-97.
12. Bassett D.S., Nelson B.G., Mueller B.A., Camchong J., Lim K.O. Altered resting state complexity in schizophrenia. NeuroImage 2012, 59(3):2196-2207.
13. Hodneland E., Ystad M., Haasz J., Munthe-Kaas A., Lundervold A. Automated approaches for analysis of multimodal MRI acquisitions in a study of cognitive aging. Computer Methods and Programs in Biomedicine 2012, 106(3):328-341.
14. Smith S.M. Fast robust automated brain extraction. Human Brain Mapping 2002, 17(3):143-155.
15. Shattuck D.W., Sandor-Leahy S.R., Schaper K.A., Rottenberg D.A., Leahy R.M. Magnetic resonance image tissue classification using a partial volume model. NeuroImage 2001, 13(5):856-876.
16. Somasundaram K., Kalaiselvi T. Fully automatic brain extraction algorithm for axial T2-weighted magnetic resonance images. Computers in Biology and Medicine 2010, 40(10):811-822.
17. Balan A.G.R., Traina A.J.M., Ribeiro M.X., Marques P.M.A., Traina C. Smart histogram analysis applied to the skull-stripping problem in T1-weighted MRI. Computers in Biology and Medicine 2012, 42(5):509-522.
18. Marr D., Hildreth E. Theory of edge detection. Proceedings of the Royal Society of London. Series B, Biological Sciences 1980, 207(1167):187-217.
19. Kale E.H., Mumcuoglu E.U., Hamcan S. Automatic segmentation of human facial tissue by MRI-CT fusion: a feasibility study. Computer Methods and Programs in Biomedicine 2012, 108(3):1106-1120.
20. Köse C., Sevik U., Ikibaş C., Erdöl H. Simple methods for segmentation and measurement of diabetic retinopathy lesions in retinal fundus images. Computer Methods and Programs in Biomedicine 2012, 107(2):274-293.
21. Berber T., Alpkocak A., Balci P., Dicle O. Breast mass contour segmentation algorithm in digital mammograms. Computer Methods and Programs in Biomedicine 2013, 110(2):150-159.
22. Fennema-Notestine C., Ozyurt I.B., Clark C.P., Morris S., Bischoff-Grethe A., Bondi M.W., Jernigan T.L., Fischl B., Segonne F., Shattuck D.W., Leahy R.M., Rex D.E., Toga A.W., Zou K.H., Brown G.G. Quantitative evaluation of automated skull-stripping methods applied to contemporary and legacy images: effects of diagnosis, bias correction, and slice location. Human Brain Mapping 2006, 27(2):99-113.
23. Zhuang A.H., Valentino D.J., Toga A.W. Skull-stripping magnetic resonance brain images using a model-based level set. NeuroImage 2006, 32(1):79-92.
24. Hartley S., Scher A., Korf E., White L., Launer L. Analysis and validation of automated skull stripping tools: a validation study based on 296 MR images from the Honolulu Asia aging study. NeuroImage 2006, 30(4):1179-1186.
25. Lee J.-M., Yoon U., Nam S.H., Kim J.-H., Kim I.-Y., Kim S.I. Evaluation of automated and semi-automated skull-stripping algorithms using similarity index and segmentation error. Computers in Biology and Medicine 2003, 33(6):495-507.
26. Hu Q., Hou Z., Nowinski W. Supervised range-constrained thresholding. IEEE Transactions on Image Processing 2006, 15(1):228-240.
27. Beare R. Histogram-based thresholding - some missing methods. The Insight Journal 2011.
28. Vincent L. Morphological transformations of binary images with arbitrary structuring elements. Signal Processing 1991, 22(1):3-23.
29. Nikopoulos N., Pitas I. An efficient algorithm for 3D binary morphological transformations with 3D structuring elements of arbitrary size and shape. IEEE Workshop on Nonlinear Signal and Image Processing 1997.
30. Boesen K., Rehm K., Schaper K., Stoltzner S., Woods R., Lüders E., Rottenberg D. Quantitative comparison of four brain extraction algorithms. NeuroImage 2004, 22(3):1255-1261.
31. Holm S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 1979, 6(2):65-70.
32. Sarén-Koivuniemi T.J., Yli-Hankala A.M., van Gils M.J. Increased variation of the response index of nociception during noxious stimulation in patients during general anaesthesia. Computer Methods and Programs in Biomedicine 2011, 104(2):154-160.
33. Chen S., Epps J. Automatic classification of eye activity for cognitive load measurement with emotion interference. Computer Methods and Programs in Biomedicine 2013, 110(2):111-124.
34. Khushaba R.N., Kodagoda S., Liu D., Dissanayake G. Muscle computer interfaces for driver distraction reduction. Computer Methods and Programs in Biomedicine 2013, 110(2):137-149.