Methods on Skull Stripping of MR Head Scan Images - A Review


Abstract:


The high resolution magnetic resonance (MR) brain images contain some non-brain tissues such as skin, fat, muscle, neck and eye balls compared to the functional images namely Positron Emission Tomography (PET), Single Photon Emission Computed Tomography (SPECT) and Functional Magnetic Resonance Imaging (fMRI) which usually contain relatively less non-brain tissues. The presence of these non-brain tissues is considered as a major obstacle for automatic brain image segmentation techniques. Therefore, quantitative morphometric studies of MR brain images often require a preliminary processing to isolate the brain from extra-cranial or non-brain tissues, commonly referred to as skull-stripping. This paper describes the available methods on skull stripping and an exploratory review of recent literature on the existing skull stripping methods.

Skull Stripping Literature Review (as of 2015)


Resource materials:

  1. Summary table

  2. References list (Updated on 30 October 2015)

References

  1. Haacke, E.M., Brown, R.W., Thompson, M.R. and Venkatesan, R., Magnetic Resonance Imaging, Physical Principles and Sequence Design, John Willey & Sons, New York, USA, 1999.

  2. Quencer, R.M. and Bradley, W.G., MR Imaging of the Brain: What Constitutes the Minimum Acceptable Capability?, American Journal of Neuroradiology, 22(8), 2001, 1449-1450.

  3. Cheour, M., Advantages of Brain MRI, 2010, Available at: RadiologyInfo.org.

  4. Schmid, P., Segmentation of Digitized Dermatoscopic Images by Two-Dimensional Colour Clustering, IEEE Transactions on Medical Imaging, 18(2), 1999, 164-171.

  5. NLM-National Library of Medicine, (Rockville Pike, Bethesda U.S., 2011), Available online at: http://www.nlm.nih.gov.

  6. Gonzalez, R.C. and Woods, R.E., Digital Image Processing, 3rd Edition, Prentice Hall of India (P) Ltd., New Delhi, 2008.

  7. Pham, D.L., Xu, C. and Prince, J.L., Current Methods in Medical Image Segmentation, Annual Review of Biomedical Engineering, 2(1), 2000, 315-338.

  8. Sharma, N. and Aggarwal, L.M., Automated Medical Image Segmentation Techniques, Journal of Medical Physics, 35(1), 2010, 3-14.

  9. Hizukuri, A., Nakayama, R., Nakako, N., Kawanaka, H., Takase, H., Yamamoto, K., Tsuruoka S., Computerized Segmentation Method for Individual Calcifications within Clustered Microcalcifications while Maintaining Their Shapes on Magnification Mammograms, Journal of Digital Imaging, 25, 2012, 377-386.

  10. Younis, A., Ibrahim, M., Kabuka, M., John N., An Artificial Immune-Activated Neural Network Applied to Brain 3D MRI Segmentation, Journal of Digital Imaging, 21(1) Supplement, 2008, 69-88.

  11. Erickson B. J., Avula R. T. V., An algorithm for automatic segmentation and classification of magnetic resonance brain images, Journal of Digital Imaging, 11(2), 1998, 74-82.

  12. Handels, H., Tolxdorff. T., A New Segmentation Algorithm For Knowledge Acquisition In Tissue-Characterizing Magnetic Resonance Imaging, Journal of Digital Imaging, 3(2), 1990, 89-94.

  13. HoganR. E., Mark, K. E., Choudhuri, I., Wang, L., Joshi, S., Miller, M. I., Bucholz R. D., Magnetic Resonance Imaging Deformation-Based Segmentation Of The Hippocampus In Patients With Mesial Temporal Sclerosis And Temporal Lobe Epilepsy, Journal of Digital Imaging, 13(1), 2000, 217-218.

  14. 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., Brain, M. and 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, 27(2), 2006, 99-113.

  15. Matsumoto, S., Asato, R., Konishi, J., A Fast Way To Visualize The Brain Surface With Volume Rendering of MRI Data, Journal of Digital Imaging, 12(4), 1999, 185-190.

  16. Mahmood, Q., Chodorowski, A., Mehnert, A., Gellermann, J., Persson, M., Unsupervised Segmentation of Head Tissues from Multi-modal MR Images for EEG Source Localization, Journal of Digital Imaging, 2014.

  17. Hata, Y., Kobashi, S., Kondo, K., Kitamura, Y. T., Yanagida, T., Transcranial Ultrasonography System for Visualizing Skull and Brain Surface Aided by Fuzzy Expert System, IEEE Transactions on Systems, Man and Cybernetics, 2005, 35(6) 2005, 1360-1373.

  18. Klein, A., Ghosh, S.S., Avants, B., Yeo, B., Fischl, B., Ardekani, B., Gee, J.C., Mann, J. and Parsey, R.V., Evaluation of Volume-Based and Surface-Based Brain Image Registration Methods, NeuroImage, 51(1), 2010, 214-220.

  19. Kalkers, N.F., Ameziane, N., Bot, J.C., Minneboo, A., Polman, C.H. and Barkhof, F., Longitudinal Brain Volume Measurement in Multiple Sclerosis: Rate of Brain Atrophy is Independent of the Disease Subtype, Architectural Neurology, 58(10), 2002, 1572-1576.

  20. Wels, M., Zheng, Y., Huber, M., Hornegger, J. and Comaniciu, D., A Discriminative Model-Constrained EM Approach to 3D MRI Brain Tissue Classification and Intensity Non-Uniformity Correction, Physics in Medicine and Biology, 56(11), 2011, 3269-3300.

  21. Wang, L., Chen, Y., Pan, X., Hong, X. and Xia, D., Level Set Segmentation of Brain Magnetic Resonance Images Based on Local Gaussian Distribution Fitting Energy, Journal of Neuroscience Methods, 188(2), 2010, 316-325.

  22. Thompson, P.M., Mega, M.S., Woods, R.P., Zoumalan, C.I., Lindshield, C.J., Blanton, R.E., Moussai, J., Holmes, C.J., Cummings, J.L. and Toga, A.W., Cortical Change in Alzheimer’s Disease Detected with a Disease-Specific Population-Based Brain Atlas, Cerebral Cortex, 11(1), 2001, 1-16.

  23. Tosun, D., Rettmann, M.E., Naiman, D.Q., Resnick, S.M., Kraut, M.A. and Prince, J.L., Cortical Reconstruction using Implicit Surface Evolution: Accuracy and Precision Analysis, NeuroImage, 29(3), 2006, 838-852.

  24. MacDonald, D., Kabani, N., Avis, D. and Evans, A.C., Automated 3-D Extraction of Inner and Outer Surfaces of Cerebral Cortex from MRI, NeuroImage, 12(3), 2000, 340-356.

  25. Zhao, L., Ruotsalainen, U., Hirvonen, J., Hietala, J. and Tohka, J., Automatic Cerebral and Cerebellar Hemisphere Segmentation in 3D MRI: Adaptive Disconnection Algorithm, Medical Image Analysis, 14(3), 360-372, 2010.

  26. Zivadinov, R., Bagnato, F., Nasuelli, D., Bastianello, S., Bratina, A., Locatelli, L., Watts, K., Finamore, L., Grop, A., Dwyer, M., Catalan, M., Clemenzi, A., Millefiorini, E., Bakshi, R. and Zorzon, M., Short-Term Brain Atrophy Changes in Relapsing-Remitting Multiple Sclerosis, Neurological Sciences, 223(2), 185-193, 2004.

  27. Rusinek, H., de Leon, M.J., George, A.E., Stylopoulos, L.A., Chandra, R., Smith, G., Rand, T., Mourino, M. and Kowalski, H., Alzheimer Disease: Measuring Loss of Cerebral Gray Matter with MR Imaging, Radiology, 178(1), 109-114, 1991.

  28. Tanskanen, P., Veijola, J.M., Piippo, U.K., Haapea, M., Miettunen, J.A., Pyhtinen, J., Bullmore, E.T., Jones, P.B. and Isohanni, M.K., Hippocampus and Amygdala Volumes in Schizophrenia and other Psychoses in the Northern Finland 1966 Birth Cohort, Schizophrenia Research , 75(2-3), 283-294, 2005.

  29. Blanton, R.E., Levitt, J.G., Peterson, J.R., Fadale, D., Sporty, M.L., Lee, M., To, D., Mormino, E.C., Thompson, P.M., McCracken, J.T. and Toga, A.W., Gender Differences in the Left Inferior Frontal Gyrus in Normal Children, NeuroImage, 22(2), pp. 626-636, 2004.

  30. Brummer, M.E., Mersereau, R.M., Eisner, R.L., Lewine, R.R.J., Caeslles, V., Kimmel, R. and Sapiro, G., Automatic Detection of Brain Contours in MRI Datasets, IEEE Transactions on Image Processing, 12(2), 1993, 153-166.

  31. Smith, S.M., Fast Robust Automated Brain Extraction, Human Brain Mapping, 17(3), 2002, 143.

  32. Zhuang, A.H., Valentino, D.J. and Toga, A.W., Skull Stripping Magnetic Resonance Images using a Model-Based Level Sets, NeuroImage, 32(1), 2006, 79-92.

  33. Tsai, C., Manjunath, B.S. and Jagadeesan, R., Automated Segmentation of Brain MR Images, Pattern Recognition, 28(12), 1995, 1825-1837.

  34. Sandor, S. and Leahy, R.M., Surface-Based Labeling of Cortical Anatomy using a Deformable Atlas, IEEE Transactions on Medical Imaging, 16(1), 1997, 41-54.

  35. Lemieux, G., Krakow, K.H. and Woermann, F.G., Fast, Automatic Segmentation of the Brain in T1-weighted Volume Magnetic Resonance Image Data, Proc. of SPIE Medical Imaging:Image Processing, 3661, 1999, 152-160.

  36. Shattuck, D.W., Sandor-Leahy, S.R., Schaper, K.A., Rottenberg, D.A. and Leahy, R.M., Magnetic Resonance Image Tissue Classification using a Partial Volume Model, NeuroImage, 13(5), 2001, 856-876.

  37. Shanthi, K.J. and Sasikumar, M., Skull Stripping and Automatic Segmentation of Brain MRI using Seed Growth and Threshold Techniques, Proc. International Conference on Intelligent and Advanced Systems, Kuala Lumpur, 1, 2007, 422-426.

  38. Mikheev, B., Nevsky, G., Govindan, S., Grossman, R. and Rusinek, H., Fully Automatic Segmentation of the Brain from T1-weighted MRI using Bridge Burner Algorithm, Journal of Magnetic Resonance Imaging, 27(6), 2008, 1235-1241.

  39. Park, G.J. and Lee, C., Skull Stripping Based on Region Growing for Magnetic Resonance Images, NeuroImage, 47(4), 2009, 1394-1407.

  40. Gao, J. and Xie, M., Skull Stripping MR Brain Images using Anisotropic Diffusion Filtering and Morphological Processing, Proc. of International Symposium on Computer Network and Multimedia Technology, Wuhan, 1, 2009, 1-4.

  41. Somasundaram, K. and Kalavathi. P., Automatic Skull Stripping of Magnetic Resonance Images (MRI) of Human Head Scans using Image Contour, Image Processing, Allied Publisher, New Delhi, 2010), 147-151.

  42. Somasundaram, K. and Kalavathi. P., A Hybrid Method for Automatic Skull Stripping of Magnetic Resonance Images (MRI) of Human Head Scans, IEEE Xplore Digital Library, 2010, 1-5.

  43. Somasundaram, K. and Kalaiselvi, T., Fully Automatic Brain Extraction Algorithm for Axial T2-Weighted Magnetic Resonance Images, Computers in Biology and Medicine, 40(10), 2010, 811-822.

  44. Somasundaram, K. and Kalaiselvi, T., Automatic Brain Extraction Methods for T1 Magnetic Resonance Images using Region Labeling and Morphological Operations, Computers in Biology and Medicine, 41(8), 2011.

  45. Carass, A., Cuzzocreo, J., Wheeler, M.B., Bazin, P.L., Resnick, S.M. and Prince, J.L., Simple Paradigm for Extra-Cerebral Tissue Removal: Algorithm and Analysis, NeuroImage, 56(4), 2011, 1982-1992.

  46. Cox, R.W., AFNI: Software for Analysis and Visualization of Functional Magnetic Resonance Neuroimages, Computers and Biomedical Research, 29(3), 1996,162-173.

  47. Ward, B.D., 3dIntracranial: Automatic Segmentation of Intracranial Region, Technical Report, Biophysics Research Institute, Medical College of Wisconsin, UK, 1999.

  48. Huh, S., Ketter, T.A., John, K.H. and Lee, C., Automated Cerebrum Segmentation from Three-Dimensional Sagittal Brain MR Images, Computers in Biology and Medicine, 32(5), 2002, 311-328.

  49. Dawant, B.M., Hartmann, S.L., Thirion, J.P., Maes, F., Vandermeulen, D. and Demaerel, P., Automatic 3-D Segmentation of Internal Structures of the Head in MR Images using a Combination of Similarity and Free-Form Transformations: Part I. Methodology and Validation on Normal Subjects, IEEE Transactions on Medical Imaging, 18(10), 1999, 909-916.

  50. Pham, D.L. and Prince, J.L., Adaptive Fuzzy Segmentation of Magnetic Resonance Images, IEEE Transactions on Medical Imaging, 18(9), 1999, 737-752.

  51. Hahn, H.K. and Peitgen, H.O., The Skull Stripping Problem in MRI Solved by Single 3D Watershed Transform, Proc. of Medical Image Computing and Computer Assisted Intervention (MICCAI), LNCS, 1935, 2000, 134-143.

  52. Grau, V., Mewes, A.U.J., Alcaiz, M., Kikinis, R. and Warfield, S.K., Improved Watershed Transform for Medical Image Segmentation Using Prior Information, IEEE Transactions on Medical Imaging, 23(4), 2004, 447-458.

  53. Ashburner, J. and Friston, K.J., Voxel Based Morphometry: The Methods, NeuroImage, 11(6), 2000, 805-821.

  54. Ashburner, J. and Friston, K.J., Unified Segmentation, NeuroImage, 26(3), 2005, 839-851.

  55. Zu, Y. S., Guang, H. Y. and Jing, Z. L., Automated Histogram-Based Brain Segmentation in T1- Weighted Three-Dimensional Magnetic Resonance Head Images, NeuroImage, 17(3), 2002, 1587-1598.

  56. Sadananthan, S., Zheng, W., Chee, M. and Zagorodnov, V., Skull Stripping using Graph Cuts, NeuroImage, 49(1), 2010, 225-239.

  57. Somasundaram, K. and Kalavathi. P., Skull Stripping of MRI Head Scans Based on 2D Region Growing, Proc. of ICOM11, Anna University of Technology, Tiruchirappalli, Tamil Nadu, India, 2011, 18-23.

  58. Somasundaram, K. and Kalavathi. P., Brain Segmentation in Magnetic Resonance Human Head Scans using Multi-Seeded Region Growing, Imaging Science Journal, 62(5), 2014, 273-284.

  59. Kalavathi. P., Computation of Brain Asymmetry in 2D Brain Images, International Journal of Scientific Engineering and Research, 5(7), 2014, 1167-1171.

  60. Aboutanos, G.B., Nikanne, J., Watkins, N. and Dawant, B.M., Model Creation and Deformation for the Automatic Segmentation of the Brain in MR Images, IEEE Transactions on Biomedical Engineering, 46(11), 1999, 1346-1356.

  61. Zeng, X., Staib, L.H., Schultz, R.T. and Duncan, J.S., Segmentation and Measurement of the Cortex from 3-D MR Images Using Coupled-Surfaces Propagation, IEEE Transactions on Medical Imaging, 18(10), 1999, 927-937.

  62. Suri, J.S., Two-Dimensional Fast Magnetic Resonance Brain Segmentation, IEEE Engineering in Medicine and Biology,. 20(4), 2001, 84-95.

  63. Baillard, C., Hellier, P. and Barillot, C., Segmentation of Brain 3D MR Images using Level Sets and Dense Registration, Medical Image Analysis, 5(3), 2001, 185-194.

  64. Atkins, M.S., Siu, K., Law, B., Orchard, J.J. and Rosenbaum, W.L., Difficulties of T1 Brain MRI Segmentation Techniques, Medical Imaging, Proc. of SPIE, 4684(1), 2001, 1837-1844.

  65. Jenkinson, M., Pechaud, M. and Smith, S., BET2 - MR-Based Estimation of Brain, Skull and Scalp Surfaces, (Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Oxford, UK, 2005).

  66. 3dSkullStrip, a part of the AFNI (Analysis of Functional Neuro Images) package. Available at http://afni.nimh.nih.gov

  67. Lao, Z., Shen, D. and Davatzikas, C., Statistical Shape Model for Automatic Skull-Stripping of Brain Images, Proc. of IEEE International Symposium on Biomedical Imaging, Washington, D.C., 2002, 855-858.

  68. John, C., Kevin, W., Emma, L., Chao, C., Barbara, P. and Declan, J., Statistical Morphological Skull Stripping of Adult and Infant MRI Data, Computers in Biology and Medicine, 37(3), 2007, 342-357.

  69. Yunjie, C., Jianwei, Z. and Shunfeng, W., A New Fast Brain Skull Stripping Method, Biomedical Engineering and Informatics, Proc. 2nd International Conference on Biomedical Engineering and Informatics, BMEI09, Tianjin, Chinna, 2009.

  70. Liu, J.X., Chen, Y.S. and Chen, L.F., Accurate and Robust Extraction of Brain Regions using Deformable Model Based on Radial Basis Functions, Journal of Neuroscience Methods, 183(2), 2009, 255-266.

  71. Merisaari, H., Parkkola, R., Alhoniemia, E., Teras, M., Lehtonend, L., Haataja, L., Lapinleimu, H. and Nevalainen, O.S., Gaussian Mixture Model-Based Segmentation of MR Images Taken from Premature Infant Brains, Journal of Neuroscience Methods, 182(1), 2009, 110-122.

  72. Tao, X. and Chang, M.C., A Skull Stripping Method using Deformable Surface and Tissue Classification, Medical Imaging, Proc. of SPIE, 7, 2010, 623-630.

  73. Somasundaram, K. and Kalavathi. P., Skull Stripping of MRI Head Scans based on Chan-Vese Active Contour Model, International Journal of Knowledge Management & e-learning, 3(1), 2011, 7-14.

  74. Hwang, J., Han, Y. and Park, H., Skull-Stripping Method for Brain MRI using a 3D Level Set with a Speedup Operator, Journal of Magnetic Resonance Imaging, 34(2), 2011, 445-456.

  75. Zhang, H., Liu, J., Zhu, Z. and Li, H., An Automated and Simple Method for Brain MR Image Extraction, BioMedical Engineering OnLine, 10(81), 2011.

  76. Somasundaram, K. and Kalavathi. P., A Novel Skull Stripping Technique for T1-weighted MRI Human Head Scans, ACM Digital Library, 2012, 1-8.

  77. Galdames, F.J., Jaillet, F. and Perez, C.A., An Accurate Skull Stripping Method Based on Simplex Meshes and Histogram Analysis in Magnetic Resonance Images, Journal of Neuroscience Methods, 206(2), 2012, 109-113.

  78. Somasundaram, K. and Kalavathi. P., Contour-Based Brain Segmentation Method for Magnetic Resonance Imaging Human Head Scans, Journal of Computer Assisted Tomography, 37(3), 2013, 353-368.

  79. Dale, A.M., Fischl, B. and Sereno, M.I., Cortical Surface-Based Analysis I: Segmentation and Surface Reconstruction, NeuroImage, 9(2), 1999, 179-194.

  80. Wang, Y., Nie, J., Yap, P.-T., Shi, F., Guo, L., Shen, D., Robust Deformable-Surface-Based Skull- Stripping for Large-Scale Studies, Proc. of Medical Image Computing and Computer Assisted Inter- vention (MICCAI), LNCS, 6893, 2011, 635-642.

  81. Kobashi, S., Moto, F.Y., Ogawa, M.D., Ando, K., Ishikura, R., Kando, S.H. and Katy, Y., Fuzzy-ASM Based Automated Skull Stripping Method from Infantile Brain MR Images, IEEE International Conference on Granular Computing, San Jose, California, 1, 2007, 632-635.

  82. Mahapatra, D., Skull Stripping of Neonatal Brain MRI: Using Prior Shape Information with Graph Cuts, Journal of Digital Imaging, 25(6), 2012, 802-814.

  83. Leung, K.K., Barnes, J., Modat, M., Ridgway, G.R., Bartlett, J.W., Fox, N.C. and Ourselin, S., Brain MAPS: An Automated, Accurate and Robust Brain Extraction Technique using a Template Library, NeuroImage, 55(3), 2011, 1091-1108.

  84. Eskildsen, S.F., Coupe, P., Fonov, V., Manjon, J.V., Leung, K.K., Guizard, N., Wassef, S.N., Ostergaard, L.R. and Collins, D.L., BEaST: Brain Extraction Based on Non-Local Segmentation Technique, NeuroImage, 59(3), 2012, 2362-2373.

  85. Kapur, T., Grimson, W.E.L., Wells III, W.M. and Kikinis, R., Segmentation of Brain Tissue from Magnetic Resonance Images, Medical Image Analysis, 1(2), 1996, 109-127.

  86. Atkins, M.S. and Mackiewich, B., Fully Automatic Segmentation of the Brain in MRI, IEEE Transactions on Medical Imaging, 17(1), 1998, 98-107.

  87. Abramoff, M.D., Magelhaes, P.J. and Ram, S.J., Image Processing with ImageJ, Biophotonics International, 11(7), 2004, 36-42.

  88. Bauer, S., Fejes, T., Reyes, M., A Skull-Stripping Filter for ITK, Insight Journal, 2012. http://hdl.handle.net/10380/3353.

  89. Rehm, K., Schaper, K., Anderson, J., Woods, R., Stoltzner, S. and Rottenberg, D., Putting Our Heads Together: A Consensus Approach to Brain/Non-brain Segmentation in T1-weighted MR Volumes, NeuroImage, 22(3), 2004, 1262-1270.

  90. Woods, R.P., Grafton, S.T., Watson, J.D.G., Sicotte, N.L. and Mazziotta, J.C., Automated Image Registration: II Intersubject Validation of Linear and Nonlinear Models, Journal of Computer Assisted Tomography, 22(1), 1998, 153-165.

  91. Segonne, F., Dale, A.M., Busa, E., Glessner, M., Salat, D., Hahn, H.K. and Fischl, B., A Hybrid Approach to the Skull Stripping Problem in MRI, NeuroImage, 22(3), 2004, 1060-1075.

  92. Rex, D.E., Shattuck, D.W., Woods, R.P., Narr, K.L., Luders, E., Rehm, K., Stolzner, S.E., Rottenberg, D.A. and Toga, A.W., A Meta-Algorithm for Brain Extraction in MRI, NeuroImage, 23(2), 2004, 625-637.

  93. Shi, F., Wang, L., Gilmore, J. H., Lin, W., Shen, D., Learning-Based Meta-Algorithm for MRI Brain Extraction, Proc. of Medical Image Computing and Computer Assisted Intervention (MICCAI), LNCS, 6893, 2011, 313-321.

  94. Huang, A., Abugharbieh, R., Tam, R. and Traboulsee, A., MRI Brain Extraction with Combined Expectation Maximization and Geodesic Active Contours, IEEE International Symposium on Signal Processing and Information Technology, 107(1), 2006, 107-111.

  95. Carass, A., Cuzzocreo, J., Wheeler, M.B., Bazin, P.L., Bassett, S.S. and Prince, J.L., A Joint Registration and Segmentation Approach to Skull Stripping, IEEE Symposium on Biomedical Imaging, 2007, 655-659.

  96. Iglesias, J.E., Liu, C.Y., Thompson, P.M. and Tu, Z., Robust Brain Extraction Across Datasets and Comparison with Publicly Available Methods, IEEE Transactions on Medical Imaging, 30(9), 2011,1617-1634.

  97. Lee, J.M., Yoon, U., Nam, S.M., Kim, J.H., Kim, I.Y. and Kim, S.I., Evaluation of Automated and Semi-Automated Skull Stripping Algorithms using Similarity Index and Segmentation Error, Computers in Biology and Medicine, 33(6), 2003, 495-507.

  98. Boesen, K., Rehm, L., Schaper, K., Stoltzner, S., Woods, R., Luders, E. and Rottenberg, D., Quantitative Comparison of Four Brain Extraction Algorithms, NeuroImage, 22(3), 2004, 1255-1261.

  99. Hartley, S.W, Scher, A.I., Korf, E.S.C., White, L.R. and Launer, L.J., Analysis and Validation of Automated Skull Stripping Tools: A Validation Study Based on 296 MR Images from Honolulu Asia Aging Study, NeuroImage, 30(4), 2006, 1179-1186.

  100. Shattuck, D.W., Prasad, G., Mirza, M., Narr, K.L. and Toga, A.W., Online Resource for Validation of Brain Segmentation Methods, NeuroImage, 45(2), 2009, 431-439.

  101. Richard, A., Biomedical Imaging, Visualization and Analysis, John Wiley & Sons Inc, New York, USA, 2000.

  102. Yoon, U.C., Kim, J.S., Kim, I.Y. and Kim, S.I., Adaptive Fuzzy C-Means for Improved Classification as a Preprocessing Procedure of Brain Parcellation, Journal of Digital Imaging, 14(2), 2001, 238-240.

  103. Sled, J.G., Zijdenbos, A.P. and Evans, A.C., A Nonparametric Method for Automatic Correction of Intensity Nonuniformity in MRI Data, IEEE Transactions on Medical Imaging, 17(1), 1998, 87-97.

  104. Somasundaram, K. and Kalavathi. P., Medical Image Denoising using Non-Linear Spatial Mean Filters for Edge Detection, (Signal and Image Processing, New Delhi, 2012) 149-154.

  105. Somasundaram, K. and Kalavathi. P., Analysis of Imaging Artifacts in MR Brain Images, Oriental Journal of Computer Science & Technology, 5(1), 2012, 135-141.

  106. Somasundaram, K. and Kalavathi. P., Medical Image Contrast Enhancement based on Gamma Correction, International Journal of Knowledge Management & e-learning, 3(1), 2011, 15-18.

  107. Speier, W., Iglesias, J. E., El-Kara, L., Tu, Z., Arnold, C., Robust Skull Stripping of Clinical Glioblastoma Multiforme Data, Proc. of Medical Image Computing and Computer Assisted Inter- vention (MICCAI), LNCS, 6893, 2011, 659-666.

  108. Bauer, S., Nolte, L.-P., Reyes, M., Skull-stripping for Tumor-bearing Brain Images. In Philippe Buchler and Stephen Ferguson, editors, Annual Meeting of the Swiss Society for Biomedical Engineering (SSBE), page 2, Bern, April 2011.

  109. ANALYZE 12.0 – Visualization and analysis software for medical imaging. Available at : http://analyzedirect.com/analyze-12-0/

If you use these resources please kindly cite the following reference.

Reference:

P. Kalavathi, V. B. S. Prasath. Methods on skull stripping of MRI head scan images - A review. Journal of Digital Imaging, 29(3), 365-379, June 2016. doi:10.1007/s10278-015-9847-8


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