Generic placeholder image

Current Medical Imaging


ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Review Article

Review on 2D and 3D MRI Image Segmentation Techniques

Author(s): S. Shirly * and K. Ramesh

Volume 15, Issue 2, 2019

Page: [150 - 160] Pages: 11

DOI: 10.2174/1573405613666171123160609

Price: $65


Background: Magnetic Resonance Imaging is most widely used for early diagnosis of abnormalities in human organs. Due to the technical advancement in digital image processing, automatic computer aided medical image segmentation has been widely used in medical diagnostics.

Discussion: Image segmentation is an image processing technique which is used for extracting image features, searching and mining the medical image records for better and accurate medical diagnostics. Commonly used segmentation techniques are threshold based image segmentation, clustering based image segmentation, edge based image segmentation, region based image segmentation, atlas based image segmentation, and artificial neural network based image segmentation.

Conclusion: This survey aims at providing an insight about different 2-Dimensional and 3- Dimensional MRI image segmentation techniques and to facilitate better understanding to the people who are new in this field. This comparative study summarizes the benefits and limitations of various segmentation techniques.

Keywords: Magnetic resonance imaging, image segmentation, image processing, 2-dimensional, image segmentation, 3- dimensional image segmentation.

Graphical Abstract
Sonka M, Hlavac V, Boyle R. Image processing, analysis, and machine vision. Cengage Learning 2014. Available from:
Al-Amri SS, Kalyankar NV. Image segmentation by using threshold techniques. J Comput 2010; 2(5): arXiv:1005.4020 [cs.CV].
Pal NR, Pal SK. A review on image segmentation techniques. Patt Recogn 1993; 26(9): 1277-94.
Haralick RM, Shapiro LG. Image segmentation techniques. Comp Vis Graph Image Process 1985; 2 9(1): 100-32.
Varshney SS, Rajpal N, Purwar R. Comparative study of image segmentation techniques and object matching using segmentation. In: Proceeding of International Conference on Methods and Models in Computer Science (ICM2CS) 2009. IEEE; Delhi, India; pp. 1-6.
Sharma N, Aggarwal LM. Automated medical image segmentation techniques. J Med Phys 2010; 35(1): 3.
Edelman RR, Warach S. Magnetic resonance imaging. N Engl J Med 1993; 328(10): 708-16.
Iglesias JE, Sabuncu MR. Multi-atlas segmentation of biomedical images: A survey. Med Image Anal 2015; 24(1): 205-19.
Hossam MM, Hassanien AE, Shoman M. 3D brain tumor segmentation scheme using K-mean clustering and connected component labeling algorithms. 10th international conference on intelligent systems design and applications 2010: IEEE; pp. 320-4.
El-Melegy MT, Mokhtar HM. Tumor segmentation in brain MRI using a fuzzy approach with class center priors. EURASIP J Image Video Process 2014; 2014(1): 1-14.
Li C, Huang R, Ding Z, Gatenby JC, Metaxas DN, Gore JC. A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Trans Image Process 2011; 20(7): 2007-16.
Suzuki H, Toriwaki J-i. Automatic segmentation of head MRI images by knowledge guided thresholding. Comput Med Imaging Graph 1991; 15(4): 233-40.
Banerjee S, Mukherjee DP, Majumdar DD. Fuzzy c-means approach to tissue classification in multimodal medical imaging. Inf Sci 1999; 115(1): 261-79.
Michopoulou SK, Costaridou L, Panagiotopoulos E, Speller R, Panayiotakis G, Todd-Pokropek A. Atlas-based segmentation of degenerated lumbar intervertebral discs from MR images of the spine. Biomed Eng IEEE Trans 2009; 56(9): 2225-31.
Zhang D-Q, Chen S-C. A novel kernelized fuzzy c-means algorithm with application in medical image segmentation. Artif Intell Med 2004; 32(1): 37-50.
Hsiao Y-T, Chuang C-L, Jiang J-A, Chien C-C. A contour based image segmentation algorithm using morphological edge detection. IEEE international conference on systems, man and cybernetics; 2005. IEEE: Waikoloa, HI, USA; pp. 2962-67.
Hao L. Registration-based segmentation of medical images. School of Computing National University of Singapore 2006.
Khan W. Image segmentation techniques: A survey. J Image Graph 2013; 1(4): 166-70.
Sujji GE, Lakshmi Y, Jiji GW. MRI brain image segmentation based on thresholding. Inter J Adv Comp Res 2013; 3(1): 97-101.
Dogdas B, Shattuck DW, Leahy RM. Segmentation of skull and scalp in 3‐D human MRI using mathematical morphology. Hum Brain Map 2005; 26(4): 273-85.
Qu X, Zhang W, Guo D, Cai C, Cai S, Chen Z. Iterative thresholding compressed sensing MRI based on contourlet transform. Inverse Probl Sci Eng 2010; 18(6): 737-58.
Gibbs P, Buckley DL, Blackband SJ, Horsman A. Tumour volume determination from MR images by morphological segmentation. Phys Med Biol 1996; 41(11): 2437.
Kole DK, Halder A. Automatic brain tumor detection and isolation of tumor cells from MRI images. Int J Comput Appl 2012; 39(16): 26-30.
Chevrefils C, Chériet F, Grimard G, Aubin C-E. Watershed segmentation of intervertebral disk and spinal canal from MRI images. In: Kamel M, Campilho A. (eds) Image analysis and recognition. ICIAR 2007. Lecture notes in computer science, Springer: Berlin, Heidelberg; pp. 1017-27.
Kaus MR, Warfield SK, Nabavi A, Black PM, Jolesz FA, Kikinis R. Automated segmentation of MR images of brain tumors. Radiology 2001; 218(2): 586-91.
Salman YM. Modified technique for volumetric brain tumor measurements. J Biomed Sci Eng 2009; 2(01): 16.
Pham DL, Xu C, Prince JL. Current methods in medical image segmentation 1. Annu Rev Biomed Eng 2000; 2(1): 315-37.
Sumengen B, Manjunath B. Multi-scale edge detection and image segmentation. 13th European signal processing conference 2005. IEEE: Antalya, Turkey; pp. 1-4.
Xiaohan Y, Yla-Jaaski J. A new algorithm for image segmentation based on region growing and edge detection.In: IEEE international symposium on circuits & systems. 516-9.
Naz S, Majeed H, Irshad H, Eds. editors. Image segmentation using fuzzy clustering: A survey. In: 6th International Conference on Emerging Technologies (ICET) 2010. IEEE: Islamabad, Pakistan; pp. 181-6.
Pednekar AS, Kakadiaris IA. Image segmentation based on fuzzy connectedness using dynamic weights. IEEE Trans Image Process 2006; 15(6): 1555-62.
Kannan S, Ramathilagam S, Pandiyarajan R. Modified bias field fuzzy C-means for effective segmentation of brain MRI. In: Gavrilova ML, Tan CJK (eds). Transactions on Computational Science VIII 2010. Lecture Notes in Computer Science: Springer, Berlin, Heidelberg; pp. 127-45.
Amza C. A review on neural network–based image segmentation techniques. De Montfort University, mechanical and manufacturing engg, the gateway leicester, LE1 9BH, United Kingdom 2012; pp. 1-23.
Suganthi D, Purushothaman S. FMRI segmentation using echo state neural network. Comput Secur 2009; 2(1): 1-9.
Si T, De A, Bhattacharjee AK. Artificial neural network based lesion segmentation of brain MRI. Communications on Applied Electronics (CAE) 2016; 4(5).
Reddick WE, Glass JO, Cook EN, Elkin TD, Deaton RJ. Automated segmentation and classification of multispectral magnetic resonance images of brain using artificial neural networks. IEEE Trans Med Imaging 1997; 16(6): 911-8.
Magnotta VA, Heckel D, Andreasen NC, et al. Measurement of brain structures with artificial neural networks: Two-and three-dimensional applications 1. Radiology 1999; 211(3): 781-90.
Kalinić H. Atlas-based image segmentation. Survey 2009.
Michopoulou SK, Costaridou L, Panagiotopoulos E, Speller R, Panayiotakis G, Todd-Pokropek A. Atlas-based segmentation of degenerated lumbar intervertebral discs from MR images of the spine. IEEE Trans Biomed Eng 2009; 56(9): 2225-31.
Bezdek JC, Ehrlich R, Full W. FCM: The fuzzy c-means clustering algorithm. Comp Geosci 1984; 10(2): 191-203.
Wu D, Ceritoglu C, Miller MI, Mori S. Direct estimation of patient attributes from anatomical MRI based on multi-atlas voting. Neuroimage Clin 2016; 12: 570-81.
Dowling JA, Lambert J, Parker J, et al. An atlas-based electron density mapping method for magnetic resonance imaging (MRI)-alone treatment planning and adaptive MRI-based prostate radiation therapy. Int J Radiat Oncol Biol Phys 2012; 83(1): e5-e11.
Lorenzo-Valdés M, Sanchez-Ortiz GI, Mohiaddin R, Rueckert D, Eds. editors. Atlas-based segmentation and tracking of 3D cardiac MR images using non-rigid registration. In: Dohi T, Kikinis R (eds). Medical Image Computing and Computer-Assisted Intervention- MICCAI 2002. MICCAI 2002. Lecture Notes in Computer Science, vol 2488. Springer, Berlin, Heidelberg.
Pham DL, Prince JL. Adaptive fuzzy segmentation of magnetic resonance images. IEEE Trans Med Imaging 1999; 18(9): 737-52.
Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging 2001; 20(1): 45-57.
Ahmed MN, Yamany SM, Mohamed N, Farag AA, Moriarty T. A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans Med Imaging 2002; 21(3): 193-9.
Yang M-S, Tsai H-S. A Gaussian kernel-based fuzzy c-means algorithm with a spatial bias correction. Pattern Recognit Lett 2008; 29(12): 1713-25.
Liao L, Lin T, Li B. MRI brain image segmentation and bias field correction based on fast spatially constrained kernel clustering approach. Pattern Recognit Lett 2008; 29(10): 1580-8.
Greenspan H, Ruf A, Goldberger J. Constrained Gaussian mixture model framework for automatic segmentation of MR brain images. IEEE Trans Med Imaging 2006; 25(9): 1233-45.
Zeng J, Xie L, Liu Z-Q. Type-2 fuzzy Gaussian mixture models. Patt Recogn 2008; 41(12): 3636-43.
Li C, Xu C, Anderson AW, Gore JC. MRI tissue classification and bias field estimation based on coherent local intensity clustering: A unified energy minimization framework. Inf Process Med Imaging 2009; 21:2 88-99.
Ji Z-X, Sun Q-S, Xia D-S. A modified possibilistic fuzzy c-means clustering algorithm for bias field estimation and segmentation of brain MR image. Comput Med Imaging Graph 2011; 35(5): 383-97.
Ji Z, Xia Y, Sun Q, Chen Q, Xia D, Feng DD. Fuzzy local Gaussian mixture model for brain MR image segmentation. IEEE Trans Inf Technol Biomed 2012; 16(3): 339-47.
Tang H, Wu E, Ma Q, Gallagher D, Perera G, Zhuang T. MRI brain image segmentation by multi-resolution edge detection and region selection. Comput Med Imaging Graph 2000; 24(6): 349-57.
Chevrefils C, Chériet F, Grimard G, Aubin C-E. Watershed segmentation of intervertebral disk and spinal canal from MRI images. 4th International Conference on Image Analysis And Recognition, ICIAR 2007, Montreal, Canada; pp.1017-27.
Wang J, Kong J, Lu Y, Qi M, Zhang B. A modified FCM algorithm for MRI brain image segmentation using both local and non-local spatial constraints. Comput Med Imaging Graph 2008; 32(8): 685-98.
Kapur T, Grimson WEL, Wells WM, Kikinis R. Segmentation of brain tissue from magnetic resonance images. Med Image Anal 1996; 1(2): 109-27.

Rights & Permissions Print Export Cite as
© 2022 Bentham Science Publishers | Privacy Policy