A Review on Segmentation Techniques in Medical Images


  • Aayushi Priya Assistant Professor,Department of CSE, Barkatullah Vishwavidyalaya, M.P, India
  • Rajeev Tiwari Barkatullah Vishwavidyalaya,Bhopal, India




Image segmentation is an essential but critical component in low level vision image analysis, pattern recognition, and in robotic systems. It is one of the most difficult and challenging tasks in image processing which determines the quality of the final result of the image analysis. Image segmentation is the process of dividing an image into different regions such that each region is homogeneous. A precise segmentation of medical image is an important stage in contouring throughout radiotherapy preparation. Medical images are mostly used as radiographic techniques in diagnosis, clinical studies and treatment planning. This review paper defines the limitation and strength of each methods currently existing for the segmentation of medical images.


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Liew, S.C., Liew, S.W., Zain, J.M., “Tamper localization and lossless recovery watermarking scheme with ROI segmentation and multilevel authentication”, J. Digit. Imaging, Springer, vol. 26, issue 2, pp. 316– 325,2012.

Beveridge, J.R., Griffith, J., Kohler, R.R., Hanson, A.R., Rise-man, E.M., “Segmenting images using localized histograms and region merging”, Int. J Comput. Vision, vol. 2, issue 3, pp. 311–347,1989.

Ostu, N., “A threshold selection method from gray-level histogram”, IEEE Trans. Syst. Man Cybern. SMC-8, pp. 62–66,1978.

Nicola, Greggio, et al., “Fast estimation of Gaussian mixture models for image segmentation”, Mach. Vis. Appl., vol. 23, issue 4, pp. 773–789, 2012.

Cootes, T.F., Taylor, C.J. “On representing edge structure for model matching”, In: Proceedings of the IEEE CVPR, vol. 1,2001.

Bosch, J., Mitchell, S., Lelieveldt, B., Nijland, F., Kamp, O., Sonka, M., Reiber, J, “Automatic segmentation of echocardiographic sequences by active appearance motion models”, IEEE Trans. Med. Imaging vol. 21, issue 11, pp. 1374–1383,2002.

Scott, I.M., Cootes, T.F., Taylor, C.J., “Improving appearance model matching using local image structure”, In: Proceedings of the IPMI.LNCS, Springer,2003.

H.S. Prasantha, Dr.Shashidhara.H.L, Dr. K.N.B. Murthy, Madhavi Lata.G, “Medical Image Segmentation”, International Journal on Computer Science and Engineering Vol. 02, issue. 04, pp. 1209-1218,2010.

Yu-Qian, Z., Wei-hua, G., Zhen-cheng, C., Jing-tian, T., Ling-Yun, L., “Medical images edge detection based on mathematical morphology”, In: Engineering in Medicine and Biology Society, pp.6492–6495,2006.

Prewitt, J.M.: Object Enhancement and Extraction, vol. 75. Academic Press, New York(1970)

Albovik: Handbook of Image and Video Processing. Academic Press, New York(2000)

Haralick, R.M., Shapiro, L.G.: Computer and Robot Vision. vol. 1, Addition-Wesley Publishing Company Inc., Boston(1992).

Beucher, S., Meyer, F., “The morphological approach to segmentation: The watershed transform”, In: Dougherty, E.R. (ed.) Mathematical Morphology in Image Processing, vol. 12, pp. 433–481.1993.

Krinidis, S., Chatzis, V., “A robust fuzzy local information c-means clustering algorithm”, IEEE Trans. Image Process., vol. 19, pp. 1328– 1337,2010.

Kaus, M.R., von Berg, J., Weese, J., Niessen, W., Pekar, V., “Automated segmentation of the left ventricle in cardiac MRI”, Med. Image Anal. Vol. 8, issue 3, pp. 245–254,2004.

Cordes, D., Haughton, V., Carew, J.D., Arfanakis, K., Maravilla, K., “Hierarchical clustering to measure connectivity in fMRI resting-state data”, Magn. Reson. Imaging vol. 20, issue 4, pp. 305–317,2002.

Comaniciu, D., Meer, P., “Mean shift: a robust approach toward feature space analysis”, IEEE Trans. Pattern Anal. Mach. Intell. Vol. 24, issue 5, pp. 603–619,2002.

Mageshwari, Dr. I. Laurence Aroquiaraj and T. Dharani, “A Realistic Medical Image Segmentation of Abnormal HIV in Blood Cells Using Morphological Operations”, IEEE,2016.

Ahmed Fakhry, Tao Zeng, and Shuiwang Ji, “Residual Deconvolutional Networks for Brain Electron Microscopy Image Segmentation”, IEEE Transactions on Medical Imaging,2016.

Krupali D. Mistry and Bijal J. Talati, “Integrated Approach for Bone Tumor Detection from MRI Scan Imagery”, International Conference on Signal and Information Processing, IEEE,2017.




How to Cite

Priya, A., & Tiwari, R. (2017). A Review on Segmentation Techniques in Medical Images. SMART MOVES JOURNAL IJOSCIENCE, 3(2), 1–5. https://doi.org/10.24113/ijoscience.v3i2.190