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Review of brain MRI image segmentation methods

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Abstract

Brain image segmentation is one of the most important parts of clinical diagnostic tools. Brain images mostly contain noise, inhomogeneity and sometimes deviation. Therefore, accurate segmentation of brain images is a very difficult task. However, the process of accurate segmentation of these images is very important and crucial for a correct diagnosis by clinical tools. We presented a review of the methods used in brain segmentation. The review covers imaging modalities, magnetic resonance imaging and methods for noise reduction, inhomogeneity correction and segmentation. We conclude with a discussion on the trend of future research in brain segmentation.

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References

  • Acton ST, Mukherjee DP (2000) Scale space classification using area morphology. IEEE Trans Image Process 9(4): 623–635

    Article  Google Scholar 

  • Adalsteinsson D, Sethian JA (1995) A fast level set method for propagating interfaces. J Comput Phys 118: 269–277

    Article  MATH  MathSciNet  Google Scholar 

  • An S, An D (1984) Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell 6: 721–741

    Article  Google Scholar 

  • Andersen AH et al (2002) Automated segmentation of multispectral brain MR images. J Neurosci Methods 122(1): 13–23

    Article  Google Scholar 

  • Ardizzone E, Pirrone R, Gambino O (2005) Exponential entropy driven HUM on knee MR images. In: 27th annual international conference of the engineering in medicine and biology society, pp 1769–1772

  • Balafar MA (2008) Medical image segmentation using fuzzy C-mean (FCM) and dominant grey levels of image. In: Visual information engineering conference

  • Balafar M et al (2008a) Medical image segmentation using fuzzy C-mean (FCM), Bayesian method and user interaction. In: International conference on wavelet analysis and pattern recognition

  • Balafar MA et al (2008b) New multi-scale medical image segmentation based on fuzzy c-mean (FCM). In: IEEE conference on innovative technologies in intelligent systems and industrial applications, pp 66–70

  • Balafar MA et al (2008c) Medical image segmentation using anisotropic filter, user interaction and fuzzy C-mean (FCM). In: Advanced intelligent computing theories and applications with aspects of contemporary intelligent computing techniques: 4th international conference on intelligent computing, Springer, p 169

  • Balafar MA et al (2008d) Medical image segmentation using fuzzy C-mean (FCM), learning vector quantization (LVQ) and user interaction. In: Advanced intelligent computing theories and applications with aspects of contemporary intelligent computing techniques: 4th international conference on intelligent computing, Springer, p 177

  • Balafar MA et al (2008e) MRI segmentation of medical images using FCM with initialized class centers via genetic algorithm. In: International symposium on information technology

  • Buades A, Coll B, Morel J (2005) A non-local algorithm for image denoising. In: IEEE computer society conference on computer vision and pattern recognition, Citeseer, pp 60–65

  • Caselles V, Kimmel R, Sapiro G (1997) Geodesic active contours. Int J Comput Vis 22(1): 61–79

    Article  MATH  Google Scholar 

  • Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2): 266–277

    Article  MATH  Google Scholar 

  • Chang PL, Teng WG (2007) Exploiting the self-organizing map for medical image segmentation. In: Twentieth IEEE international symposium on computer-based medical systems, pp 281–288

  • Clark MC et al (1998) Automatic tumor segmentation using knowledge-based techniques. IEEE Trans Med Imaging 17(2): 187–201

    Article  Google Scholar 

  • Dave RN (1991) Characterization and detection of noise in clustering. Pattern Recogn Lett 12(11): 657–664

    Article  Google Scholar 

  • Diplaros A, Vlassis N, Gevers T (2007) A spatially constrained generative model and an EM algorithm for image segmentation. IEEE Trans Neural Netw 18(3): 798–808

    Article  Google Scholar 

  • Dokur Z (2008) A unified framework for image compression and segmentation by using an incremental neural network. Expert Syst Appl 34(1): 611–619

    Article  Google Scholar 

  • Edelstein WA et al (1986) The intrinsic signal-to-noise ratio in NMR imaging. Magn Reson Med 3(4): 604–618

    Article  Google Scholar 

  • Gallea R et al (2008) Noise filtering using edge-driven adaptive anisotropic diffusion. In: 21st IEEE international symposium on computer-based medical systems, pp 29–34

  • Hall LO, Bensaid AM, Clarke LP, Velthuizen RP, Silbiger MS, Bezdek J (1992) A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. IEEE Trans Neural Netw 3: 672–682

    Article  Google Scholar 

  • Han X, Fischl B (2007) Atlas renormalization for improved brain MR image segmentation across scanner platforms. IEEE Trans Med Imaging 26(4): 479–486

    Article  Google Scholar 

  • Kaus MR et al (1999) Segmentation of meningiomas and low grade gliomas in MRI. Lecture Notes in Computer Science, pp 1–10

  • Kim HS et al (2008) Speckle reducing anisotropic diffusion based on directions of gradient. In: Proceedings of the 2008 international conference on advanced language processing and web information technology, pp 198–203

  • Li SZ (1994) Markov random field models in computer vision. Lect Notes Comput Sci 801: 361–370

    Article  Google Scholar 

  • Li N, Liu M, Li Y (2007) Image segmentation algorithm using watershed transform and level set method. In: IEEE international conference on acoustics, speech and signal processing, pp 613–616

  • Likar B, Viergever MA, Pernus F (2001) Retrospective correction of MR intensity inhomogeneity by information minimization. IEEE Trans Med Imaging 20(12): 1398–1410

    Article  Google Scholar 

  • Lions PL, Morel JM, Coll T (1992) Image selective smoothing and edge detection by nonlinear diffusion. SIAM J Numer Anal 29(1): 182–193

    Article  MATH  MathSciNet  Google Scholar 

  • Liu S, Li J (2006) Automatic medical image segmentation using gradient and intensity combined level set method. In: The 28th IEEE EMBS annual international conference, pp 3118–3121

  • Mäkelä T et al (2002) A review of cardiac image registration methods. IEEE Trans Med Imaging 21(9): 1011–1021

    Article  Google Scholar 

  • Matsuzawa J et al (2001) Age-related volumetric changes of brain gray and white matter in healthy infants and children. Cereb Cortex 11(4): 335

    Article  Google Scholar 

  • Nowak RD (1999) Wavelet-based Rician noise removal for magnetic resonance imaging. IEEE Trans Image Process 8(10): 1408–1419

    Article  Google Scholar 

  • Paragios N, Deriche R (1999) Coupled geodesic active regions for image segmentation. Rapport De Recherche-Institut National De Recherche En Informatique Et En Automatique

  • Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7): 629–639

    Article  Google Scholar 

  • Pohle R, Toennies KD (2001) Segmentation of medical images using adaptive region growing. In: Proceedings of SPIE medical imaging, pp 1337–1346

  • Ren J, He M (2007) A level set method for image segmentation by integrating channel anisotropic diffusion information. In: 2nd IEEE conference on industrial electronics and applications, pp 2554–2557

  • Robb RA (2000) Biomedical imaging, visualization, and analysis, edited by Wiley-Liss, USA

  • Rohlfing T et al (2004) Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains. NeuroImage 21(4): 1428–1442

    Article  Google Scholar 

  • Rousson M, Brox T, Deriche R (2003) Active unsupervised texture segmentation on a diffusion based feature space. In: IEEE computer society conference on computer vision and pattern recognition, pp 699–704

  • Sethian JA (1996) A fast marching level set method for monotonically advancing fronts. Proc Natl Acad Sci 93(4): 1591–1595

    Article  MATH  MathSciNet  Google Scholar 

  • Sijbers J et al (1998) Estimation of the noise in magnitude MR images. Magn Reson Imaging 16(1): 87–90

    Article  Google Scholar 

  • Smolka B (2008) Modified biased anisotropic diffusion processing of noisy color images. In: 9th international conference on signal processing, pp 777–780

  • Song T et al (2007) A modified probabilistic neural network for partial volume segmentation in brain MR image. IEEE Trans Neural Netw 18(5): 1424–1432

    Article  Google Scholar 

  • Suzuki H, Toriwaki J (1991) Automatic segmentation of head MRI images by knowledge guided thresholding. Comput Med Imaging Graph 15(4): 233

    Article  Google Scholar 

  • Tian D, Fan L (2007) A brain MR images segmentation method based on SOM neural network. In: The 1st international conference on bioinformatics and biomedical engineering, pp 686–689

  • Tisdall D, Atkins MS (2005) MRI denoising via phase error estimation. In: Proceedings of SPIE pp 646–654

  • Tolias YA, Panas SM (1998) On applying spatial constraints in fuzzy image clustering using a fuzzy rule-based system. IEEE Signal Process Lett 5(10): 245–247

    Article  Google Scholar 

  • Vincent L, Soille P (1991) Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans Pattern Anal Mach Intell 13(6): 583–598

    Article  Google Scholar 

  • Vovk U, Pernus F, Likar B (2007) A review of methods for correction of intensity inhomogeneity in MRI. IEEE Trans Med Imaging 26(3): 405

    Article  Google Scholar 

  • Wang J (2007) Discriminative Gaussian mixtures for interactive image segmentation. In: IEEE international conference on acoustics, speech and signal processing, pp 386–396

  • Warfield SK et al (2000) Adaptive, template moderated, spatially varying statistical classification. Med Image Anal 4(1): 43–55

    Article  Google Scholar 

  • Yoon SW et al (2004) Medical endoscopic image segmentation using snakes. IEICE Trans Inf Syst 87(3): 785–789

    Google Scholar 

  • You YL et al (1996) Behavioral analysis of anisotropic diffusion in image processing. IEEE Trans Image Process 5(11): 1539–1553

    Article  Google Scholar 

  • Zhang DQ, Chen SC (2004) A novel kernelized fuzzy c-means algorithm with application in medical image segmentation. Artif Intell Med 32(1): 37–50

    Article  Google Scholar 

  • Zhang Y et al (2007) A novel medical image segmentation method using dynamic programming. In: International conference on medical information visualisation-bioMedical visualisation, pp 69–74

  • Zhao HK et al (1996) A variational level set approach to multiphase motion. J Comput Phys 127(1): 179–195

    Article  MATH  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Department of Computer & Communication Systems, Faculty of Engineering, University Putra Malaysia, 43400, Serdang, Selangor, Malaysia

    M. A. Balafar, A. R. Ramli, M. I. Saripan & S. Mashohor

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  1. M. A. Balafar
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  2. A. R. Ramli
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  3. M. I. Saripan
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  4. S. Mashohor
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Correspondence to M. A. Balafar.

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Balafar, M.A., Ramli, A.R., Saripan, M.I. et al. Review of brain MRI image segmentation methods. Artif Intell Rev 33, 261–274 (2010). https://doi.org/10.1007/s10462-010-9155-0

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