Open Access Open Access  Restricted Access Subscription or Fee Access

A Survey on Image Segmentation Techniques for Medical Images

A. Rajendran, Dr. R. Dhanasekaran


Image segmentation is one of the primary steps in image analysis for object identification. The main aim is to recognise homogeneous regions within an image as distinct and belonging to different objects Image segmentation plays a crucial role in many medical imaging applications by automating or facilitating the delineation of anatomical structures and other regions of interest. We present herein a critical appraisal of the current status of semi-automated and automated methods for the segmentation of anatomical medical images. We conclude with a discussion on the future of image segmentation methods in biomedical research.


Medical Imaging, Classification, Deformable Models, Magnetic Resonance Imaging

Full Text:



Zhigeng Pan And Jianfeng Lu, A Bayes-Based Region-Growing Algorithm for Medical Image Segmentation,Computimg in science and technology,2007.

Simon Haykin “Neural Networks”, Prentice Hall,Second edition,2005

Hojjatoleslami .S.A. and Kittler.J “Region growing ; A new approach” CVVSSP Technical report,1995.

Ping-Lin Chang and Wei-Guang Teng, Exploiting the Self-Organizing Map for Medical Image Segmentation, Twentieth IEEE International Symposium on Computer-Based Medical Systems (CBMS'07).,2007

Dzung L. Pham_y, Chenyang Xu_, Jerry L. Prince. a survey of current methods in medical image segmentation, Technical Report JHU/ECE 99-01.

H.P. Ng, S.H. Ong, K.W.C. Foong, P.S. Goh, W.L. Nowinski, Medical image segmentation using k-means clustering and improved watershed algorithm, IEEE T. Image Process,2006.

Mohammad ali balafar, Abd.rahman ramli, M.Iqbal saripan, Syamsiah mashohor, medical image segmentation using fuzzy c-mean (fcm),bayesian method and user interaction, Proceedings of the 2008 International Conference on Wavelet Analysis and Pattern Recognition, Hong Kong, 30-31 Aug. 2008.

Grau1, J.C. Downs1,2, C.F. Burgoyne1,2 segmentation of connective tissue in the optic nerve head using an anisotropic markov random field,IEEE,2004.

Zhentai Lul, Qianjin Feng, Pengcheng Shi and Wufan Chen, Unsupervised Segmentation of Medical Image Based on FCM and Mutual Information, 2007 IEEE/ICME International Conference on Complex Medical Engineering.

Yong-li LiPP, Li-yan Dong,PP Wei-zhou GuanP P Zhen LiP, P Ling-yan ZhouP, The Application of Bayesian Method in Image Segmentation, IEEE, T. Med. Imag,2007.

Joshua V. Stough, Robert E. Broadhurst, Stephen M. Pizer and Edward L. Chaney, Custering on local appearance for deformable model segmentation,Medical Image Display & Analysis Group (MIDAG),2007

Val´erie Duay, Nawal Houhou and Jean-Philippe Thiran, Atlas-based segmentation of medical images locally constrained by level sets,IEEE,2005.

Yan Li and Zheru Chi , MR Brain Image Segmentation Based on Self-Organizing Map Network, International Journal of Information Technology Vol. 11, No. 8, 2005.

Naeem Shareef, Deliang l.Wang “Segmentation of medical images using LEGION”, IEEE transaction on medical imaging, vol.18, no.1, 1999.

Fernandes,Philippeo Liviera Lexandrnea Vaux,Pau Fernando Papaleo Fichtner, Segmentation of TEM Images Using Oscillatory Neural Networks, , IEEE T. Image Process,2001.

Zhao Wencang, Wei Hongli , Study of Medical Image Segmentation Algorithm Based Multi-wavelet Analysis,Proceedings of the IEEE International Conference on Automation and Logistics Qingdao, China September 2008.

Zheng Ying, LI Guangyao,Medical image segmentation based on wavelet transformation and IGGVF, IEEE T. Image Process,2008.

Huang Wenming, An Interactive Algorithm for Blurred Medical Image Segmentation Based on Curve Fitting, Second International Conference on Genetic and Evolutionary Computing,2008

N. Ayache, P. Cinquin, I. Cohen, L. Cohen, F. Leitner, and O. Monga. Segmentation of complex threedimensional medical objects: a challenge and a requirement for computer-assisted surgery planning and performance. In R.H. Taylor, S. Lavallee, G.C. Burdea, and R. Mosges, editors, Computerintegrated surgery: technology and clinical applications, pages 59–74. MIT Press, 1996.

G.E. Christensen, S.C. Joshi, and M.I. Miller. Volumetric transformation of brain anatomy. IEEE T Med. Imag., 16:864–877, 1997.

L.D. Cohen. On active contour models and balloons. CVGIP: Image Understand., 53:211–218, 1991.

D.L. Collins, C.J. Holmes, T.M. Peters, and A.C. Evans. Automatic 3-D model-based neuroanatomical segmentation. Human Brain Mapping, 3:190–208, 1995.

J.W. Davenport, J.C. Bezdek, and R.J. Hathaway. Parameter estimation for finite mixture distributions. Comput. Math. Applic., 15:810–828, 1988.

J.C. Dunn. A fuzzy relative of the ISODATA process and its use in detecting compact well-sparated clusters. Journal of Cybernetics, 3:32–57, 1973.

S. Geman and D. Geman. Stochastic relaxation, Gibbs distrutions, and the Bayesian restoration of images. IEEE T. Patt. Anal. Mach. Intel., PAMI-6:721–741, 1984.

A.F. Goldszal, C. Davatzikos, D.L. Pham, M.X.H. Yan, R.N. Bryan, and S.M. Resnick. An image processing system for qualitative and quantitative volumetric analysis of brain images. Journal of Computer Assisted Tomography, 22:827–837, 1998.

W.E.L. Grimson, G.J. Ettinger, T. Kapur, M.E. Leventon, W.M.Wells, et al. Utilizing segmented MRI data in image-guided surgery. Int. J. Patt. Rec. Art. Intel., 11:1367–1397, 1997.

K. Held, E.R. Kops, B.J. Krause, W.M. Wells, R. Kikinis, et al. Markov random field segmentation of brain MR images. IEEE T. Med. Imag., 16(6), 1997.

A.K. Jain and R.C. Dubes. Algorithms for clustering data. Prentice Hall, 1988.

V.S. Khoo, D.P. Dearnaley, D.J. Finnigan, A. Padhani, S.F. Tanner, and M.O. Leach. Magnetic resonance imaging (MRI): considerations and applications in radiotheraphy treatment planning. Radiother. Oncol., 42:1–15, 1997.

S.M. Larie and S.S. Abukmeil. Brain abnormality in schizophrenia: a systematic and quantitative review of volumetric magnetic resonance imaging studies. J. Psych., 172:110–120, 1998.

Tianhu Lei and Wilfred Sewchand. Statistical approach to X-Ray CT imaging and its applications in image analysis – part II: A new stochastic model-based image segmentation technique for X-Ray CT image. IEEE T. Med. Imag., 11(1):62–69, 1992.

S.Z. Li. Markov random field modeling in computer vision. Springer, 1995.

I.N. Manousakas, P.E. Undrill, G.G. Cameron, and T.W. Redpath. Split-and-merge segmentation of magnetic resonance medical images: performance evaluation and extension to three dimensions.Comput. Biomed. Res., 31:393–412, 1998.

H.W. Muller-Gartner, J.M. Links, et al. Measurement of radiotracer concentration in brain gray matter using positron emission tomography: MRI-based correction for partial volume effects. J. Cereb. Blood Flow Metab., 12:571–583, 1992.

S.D. Pathak, P.D. Grimm, V. Chalana, and Y. Kim. Pubic arch detection in transrectal ultrasound guided prostate cancer therapy. IEEE T. Med. Imag., 17:762–771, 1998.

D.L. Pham and J.L. Prince. An adaptive fuzzy c-means algorithm for image segmentation in the presence of intensity inhomogeneities. Patt. Rec. Let., pages 57–68, 1999.

T.R. Reed and J.M. Hans Du Buf. A review of recent texture segmentation and feature extraction tecniques. CVGIP: Im. Understand., 57:359–372, 1993..

S. Sandor and R. Leahy. Surface-based labeling of cortical anatomy using a deformable atlas. IEEE T. Med. Imag., 16:41–54, 1997.

Sijbers, P. Scheunders, M. Verhoye, A. Van Der Linden, D. Van Dyck, et al. Watershed-based segmentation of 3D MR data for volume quantization. Mag. Res. Imag., 15:679–688, 1997.

Talairach and P. Tournoux. Co-Planar Stereotaxic Atlas of the Human Brain. 3-Dimensional Proportional System: An Approach to Cerebral Imaging. Thieme Medical Publisher, Inc., Stuttgart, NY, 1988.

P. Taylor. Invited review: computer aids for decision-making in diagnostic radiology— a literature review. Brit. J. Radiol.., 68:945–957, 1995.

P. Thompson and A.W. Toga. Detection, visualization and animation of abnormal anatomic structure with a probabilistic brain atlas based on random vector field transformations. Med. Im. Anal., 1:271–294, 1997.

K. Udupa and S. Samarasekera. Fuzzy connectedness and object definition: Theory, algorithms and applications in image segmentation. Graph. Mod. Im. Proc., 58(3):246–261, 1996..

D.L. Vilarino, V.M. Brea, D. Cabello, and J.M. Pardo. Discrete-time CNN for image segmentation byactive contours. Patt. Rec. Let., 19:721–734, 1998.

Vincent and P. Soille. Watersheds in digital spaces: an efficient algorithm based on immersion simulation. IEEE T. Patt. Anal. Mach. Intel., 13:583–598, 1991.

A.J. Worth, N. Makris, V.S. Caviness, and D.N. Kennedy. Neuroanatomical segmentation in MRI: technological objectives. Int. J. Patt. Rec. Art. Intel., 11:1161–1187, 1997.

Xu and J.L. Prince. Snakes, shapes, and gradient vector flow. IEEE T. Im. Process., 7:359–369,1998.

L.A. Zadeh. Fuzzy sets. Information and Control, 8:338–353, 1965.

A.P. Zijdenbos and B.M. Dawant. Brain segmentation and white matter lesion detection in MR images.Critical Reviews in Biomedical Engineering, 22:401–465, 1994.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.