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Fast Segmentation Methods for Medical Images

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Akshay Upadhyay, Ramgopal Kashyap

Akshay Upadhyay and Ramgopal Kashyap. Fast Segmentation Methods for Medical Images. International Journal of Computer Applications 156(3):18-23, December 2016. BibTeX

	author = {Akshay Upadhyay and Ramgopal Kashyap},
	title = {Fast Segmentation Methods for Medical Images},
	journal = {International Journal of Computer Applications},
	issue_date = {December 2016},
	volume = {156},
	number = {3},
	month = {Dec},
	year = {2016},
	issn = {0975-8887},
	pages = {18-23},
	numpages = {6},
	url = {},
	doi = {10.5120/ijca2016912399},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Image segmentation is about splitting the whole image into segments. In case of image analysis, image processing one of the crucial steps is segmentation of the image. Segmentation of image concern about dividing the entire image in sub parts that may be similar or dissimilar with respect to features. Output of image segmentation has consequence on analysis of image, further processing of images. Analysis of image comprises depiction of object and object representation, measurement of features. Therefore characterization, area of interest’s visualization in the image, description have crucial job in the segmentation of the image. This survey explains some methods of image segmentation. Segmenting an image into meaningful parts is a fundamental operation in image processing. Image segmentation is the process of partitioning a digital image into multiple segments. In this paper, various image segmentation methods are explained like edge detection, region based segmentation, neural network techniques ,energy based and hybrid methods, etc. In this paper review of image segmentation is explained by using different techniques. The efficiency of the segmentation process improved with the help of several algorithms, namely, active contour, level set, fuzzy clustering and K-means clustering. This paper analyses the performance of algorithms for image segmentation in detail. Intensity and texture based image segmentation is the two levels of the level set method. The combination of both intensity and texture based image segmentation provides better results than the traditional methods. The detailed survey of segmentation techniques provides the requirement of the suitable enhancement method that supports both intensity and texture based segmentation for better results.


  1. R.Yogamangalam,,B.Karthikeyan,(2013),“Segmentation Techniques Comparison in Image Processing” , International Journal of Engineering and Technology (IJET).
  2. Muhammad Waseem Khan (2014) “ A Survey: Image Segmentation Techniques” International Journal of Future Computer and Communication, Vol. 3.
  3. A.Manikannan,,J.SenthilMurugan(2015.)“A comparative study about region based and model based using segmentation techniques” International Journal of Innovative Research in Computer and Communication Engineering
  4. S.Aksoy,“Image Segmentation”, Department of Computer Engineering, Bilkent Univ.
  5. K. G. Gunturk, “EE 7730- Image Analysis I”, Louisiana state university.
  6. Niket Amoda, Ramesh K Kulkarni (2013)” Image Segmentation and Detection using Watershed Transform and Region Based Image Retrieval” International Journal of Emerging Trends & Technology in Computer Science.
  7. Y. Chang, X. Li, (1994) “Adaptive Image Region Growing”, IEEE Trans. On Image Processing, Vol. 3, No. 6, .
  8. N. Waoo, R. Kashyap and A. Jaiswal (2010),”,DNA Nano array analysis using hierarchical quality threshold clustering,” The 2nd IEEE International Conference onInformation Management and Engineering (ICIME), Chengdu,, pp. 81-85.doi: 10.1109/ICIME.2010.5477579
  9. Lahouaoui.Lalaou,Tayeb.Mohamadi (2013) ”A comparative study of Image Region-Based Segmentation Algorithms” International Journal of Advanced Computer Science and Applications, Vol. 4, No. 6.
  10. A. Mayer et H. Green span(2006)”Segmentation of brain MRI by adaptive mean shift. International Symposium on Biomedical Imaging” Macro to Nano, pages 319–322.
  11. K. K. Singh, A. Singh,“A Study of Image Segmentation Algorithms for Different Types of Images”, Segmentation. AMR, 756-759, 3430-3434.
  12. Karthikeyan, B.Vaithiyanathan, V.Venkatraman, B.Menaka (2011),’ Analysis of image segmentation for radiographic images’ in Indian Journal of Science and Technology 5, pp. 3660-3664
  13. Baradez, M.O., McGuckin, C.P., Forraz, N., Pettengell, R., Hoppe, A. (2004) ‘Robust and automated unimodal histogram thresholding and potential applications’, Pattern Recognitation, 37, (6), pp. 1131–1148
  14. Mushrif, M.M., Ray, A.K. (2008 ) ”Color image segmentation: Rough set theoretic approach’, Pattern Recognit. Lett., 29, (4), pp. 483–493
  15. Rafael C. Gonzalez, Richard E. Woods, (2007) “Digital Image Processing”, 2nd ed., Beijing: Publishing House of Electronics Industry,.
  16. N. R. Pal, S. K. Pal (, 1993) ,“A Review on Image Segmentation Techniques”, Pattern Recognition, Vol. 26, No. 9, pp. 1277- 1294.
  17. W. X. Kang, Q. Q. Yang, R. R. Liang (2009),“The Comparative Research on Image Segmentation Algorithms”, IEEE Conference on ETCS, pp. 703-707.
  18. Ravi C.Shinde, Jibu Mathew C and Prof. C.Y. Pati (2015) ”Segmentation Technique for Soybean Leaves Disease Detection” International Journal of Advanced Research.
  19. Rafael C. Gonzalez, Richard E. Woods and Steven L. Eddins (2010) “Digital Image Processing Using MATLAB” Tata McGraw Hill Education,
  20. Majumdar, Diptesh, Dipak Kumar Kole, Aruna Chakraborty, and Dwijesh Dutta.(2014) "Review Detection & Diagnosis of Plant Leaf Disease Using Integrated Image Processing Approach". International Journal of Computer Engineering and Applications, Volume VI, Issue-III.
  21. Muthukrishnan.R and M.Radha (2011) ” Edge detection techniques for image segmentation” International Journal of Computer Science & Information Technology (IJCSIT) Vol 3, No 6, Dec.
  22. Wang, L., He, L., Mishra, A., & Li, C. (2009). Active contours driven by local Gaussian distribution fitting energy. Signal Processing, 89(12), 2435-2447.
  23. Juneja, P. & Kashyap, R. (2016). Energy based Methods for Medical Image Segmentation.International Journal Of Computer Applications, 146(6), 22-27.
  24. Lankton,S. & Tannenbaum, A. (2008). Localizing Region-Based Active Contours. IEEE Transactions On Image Processing, 17(11), 2029-2039.
  25. Kashyap, R. & Gautam, P. (2016). Modified region based segmentation of medical images. In 2015 International Conference on Communication Networks (ICCN). IEEE.
  26. Liu, T., Xu, H., Jin, W., Liu, Z., Zhao, Y., & Tian, W. (2014).” Medical Image Segmentation Based on a Hybrid Region-Based Active Contour Mode” Computational And Mathematical Methods In Medicine
  27. Kashyap, R. & Gautam, P. (2016). Fast Level Set Method for Segmentation of Medical Images. Proceedings Of The International Conference On Informatics And Analytics - ICIA-16.
  28. H. Zhang, J. E. Fritts, S. A. Goldman (2008).,“Image Segmentation Evaluation: A Survey of unsupervised methods”, computer vision and image understanding, pp. 260-280,
  29. P.Daniel Ratna Raju G.Neelima (2012 “Image Segmentation by using Histogram Thresholding” IJCSET ,January.
  30. Senthi lkumaran N and Vaithegi S (2016)”Image segmentation by using thresholding techniques for medical imaging” computer science and engineering:a international journal.
  31. Dilpreet Kaur , Yadwinder Kaur (2014)” Various Image Segmentation Techniques: A Review” , International Journal of Computer Science and Mobile Computing, Vol.3 Issue.5, May-, pg. 809-814


Segmentation, Image analysis, Active contour model, fuzzy C-mean(FCM), Gaussian mixture model(GMM), Level set method.