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A Comparative Analysis of Various Image Segmentation Techniques

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Proceedings of 2nd International Conference on Communication, Computing and Networking

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 46))

Abstract

In this ascension era of technology, Magnetic resonance imaging (MRI) emerges as the utmost clinically acceptable imaging modality for detection and diagnosis of tumors. The Breast tumor is leading scrupulous diseases among women. In last two decades, image segmentation has got a high boost and attention from the researchers across the globe. To represent the image in such a way which is easy to analyze and more meaningful, the process of segmentation is used. It is the primal step in processing images of different types. Therefore, the image is sectioned into desirable building blocks. Basically, it provides the meaningful objects of the image. Literature provides a variety of image segmentation algorithms even though there is a requirement of an efficient segmentation technique which can work efficiently on all sorts of images. The key extract of an algorithm lies within the superiority of segmentation performed by a particular method. The availability of segmentation algorithms is quite large, so the analysis of these algorithms might be interesting to the researchers. This paper reviews segmentation techniques such as theory-based, region-based, thresholding, edge-based, Neural Network-based, Model-based, and Partial differential equation based on the basis of their functioning, utility, advantages, disadvantages, and applications.

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References

  1. S. Dey, Cancer cases in India likely to soar 25% by 2020, in Indian Council of Medical Research (May 2016) (http://timesofindia.indiatimes.com/india/Cancer-cases-in-India-likely-to-soar-25-by-2020-CMR/articleshow/52334632.cm8s)

  2. A. Qusay, Al-Faris et al., Computer-aided segmentation system for breast mri tumour using modified automatic seeded region growing (BMRI-MASRG). J. Digit. Imaging 27, 133–144 (2014)

    Article  Google Scholar 

  3. A.E. Hassanien, T. Kim, Breast cancer MRI diagnosis approach using support vector machine and pulse coupled neural networks. J. Appl. Logic 10(4), 277–284 (2012)

    Article  MathSciNet  Google Scholar 

  4. B.K. Gayathri, P. Raajan, in A survey of breast cancer detection based on image segmentation techniques, in International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE) (Jan 2016), pp. 1–5

    Google Scholar 

  5. A. Ella, Hassanien et al., MRI breast cancer diagnosis hybrid approach using adaptive ant-based segmentation and multilayer perceptron neural networks classifier. Appl. Soft. Comput. 14, 62–71 (2014)

    Article  Google Scholar 

  6. R.C. Gonzalez, R.E. Woods, S.L. Eddins, Digital Image Processing Using MATLAB, 2nd edn. (Tata McGraw Hill, Education Private Limited, 2010)

    Google Scholar 

  7. D. Banupriya, M. Sundaresan, in Enhanced Hybrid algorithms for compound image segmentation, in 2nd International Conference on Computing for Sustainable Global Development (INDIACom) (Mar 2015), pp. 672–676

    Google Scholar 

  8. R. Dass, Priyanka, S. Devi, in Image segmentation techniques. IJECT 3(1) (Jan–Mar 2012)

    Google Scholar 

  9. M.A. Aswathy, M. Jagannath, Detection of breast cancer on digital histopathology images: present status and future possibilities. Inf. Med. Unlocked 8, 74–79 (2017)

    Article  Google Scholar 

  10. J. Acharya, S. Gadhiya, K. Raviya, in Segmentation techniques for image analysis: a review. IJCSMR 2(1) (Jan 2013)

    Google Scholar 

  11. A. Javadpour, A. Mohammadi, Improving brain magnetic resonance image (MRI) segmentation via a novel algorithm based on genetic and regional growth. J. Biomed. Phys. Eng. 6(2) (2016)

    Google Scholar 

  12. M. Ahlem et. al., Comparison of automatic seed generation methods for breast tumor detection using region growing technique, in IFIP International Conference on Computer Science and its Applications, Springer International Publishing (2015), pp. 119–128

    Google Scholar 

  13. S.R. Shareef, Breast cancer detection based on watershed transformation. Int. J. Comput. Sci. 11(1) (Jan 2014)

    Google Scholar 

  14. A. Mustaqeem, A. Javed, T. Fatima, An efficient brain tumor detection algorithm using watershed and thresholding based segmentation. Image, Graph. Signal Process. 10, 34–39 (2012)

    Article  Google Scholar 

  15. N.M. Zaitoun, M.J. Aqel, Survey on image segmentation techniques, in International Conference on Communication, Management and Information Technology, (2015), pp. 797–806

    Google Scholar 

  16. N. Senthilkumaran, R. Rajesh, Edge detection techniques for image segmentation—a survey of soft computing approaches. Int. J. Recent Trends Eng. Inf. Pap. 1(2) (May 2009)

    Google Scholar 

  17. R. Muthukrishnan, M. Radha, Edge detection techniques for image segmentation. Int. J. Comput. Sci. Inf. Technol. (IJCSIT) 3(6) (Dec 2011)

    Google Scholar 

  18. http://www.google.co.in/breastmri

  19. S. Saini, K. Arora, A study analysis on the different image segmentation techniques. IJICT 4(14), 1445–1452 (2014)

    Google Scholar 

  20. N. Tirpude et. al., A study of brain magnetic resonance image segmentation techniques. Int. J. Adv. Res. Comput. Commun. Eng. 2(1) (Jan 2013)

    Google Scholar 

  21. A. Norouzi et al., Medical image segmentation methods, algorithms, and applications. IETE Tech. Rev. 31(3), 199–213 (2014)

    Article  Google Scholar 

  22. N. Senthilkumaran et al., Efficient implementation of niblack thresholding for MRI brain image segmentation. Int. J. Comput. Sci. Inf. Technol. 5(2), 2173–2176 (2014)

    Google Scholar 

  23. R. Yogamangalam, B. Karthikeyan, Segmentation techniques comparison in image processing. IJET 5(1) (Mar 2013)

    Google Scholar 

  24. H.M. Moftah et al., Adaptive k-means clustering algorithm for MR breast image segmentation. Neural Comput. Appl. 24, 1917–1928 (2013)

    Article  Google Scholar 

  25. S. Chebbout, H.F. Merouani, Comparative study of clustering based colour image segmentation techniques, in Eighth International Conference on Signal Image Technology and Internet Based Systems (2012), pp. 839–844

    Google Scholar 

  26. P. Singh, R.S. Chadha, A novel approach to image segmentation. IJARCSSE 3(4) (Apr 2013)

    Google Scholar 

  27. N. Kaur, J. Singh, V. Sharma, Analysis and comprehensive study: image segmentation techniques. IJRASET 3(I) (Jan 2015)

    Google Scholar 

  28. A.S. Manraj, Current image segmentation techniques-a review. IJCSIT 6(2), 1940–1942 (2015)

    Google Scholar 

  29. A.E. Hassanien et. al., A Novel hybrid perceptron neural network algorithm for classifying breast MRI tumors, in AMLTA 2014, CCIS 488, Springer International Publishing Switzerland (2014), pp. 357–366

    Google Scholar 

  30. A. Raad et.al., Breast cancer classification using neural network approach: MLP and RBF, in The 13th international Arab conference on Information Technology (Dec 2012), pp. 15–19

    Google Scholar 

  31. S. Hallad, H. Roopa, Comparing three neural network techniques in the classification of breast cancer. Int. J. Adv. Res., Ideas Innov. Technol. 3(4), 198–203 (2017)

    Google Scholar 

  32. S. Matta, Review: various image segmentation techniques. IJCSIT 5(6), 7536–7539 (2014)

    Google Scholar 

  33. P. Sudharsan, C.L.Y. sivakumari, A comparitive analysis of segmentation techniques in mammogram images. IJERT 2(12) (Dec 2013)

    Google Scholar 

  34. N.R. Pal, S.K. Pal, A review on image segmentation techniques. Pattern Recogn. Elsevier 26(9), 1277–1294 (1993)

    Article  Google Scholar 

  35. R. Azmi, N. Norozi, A new markov random field segmentation method for breast lesion segmentation in MR images. Med. Signals Sens. 1(3), 156–164 (2011)

    Google Scholar 

  36. X. Jiang, R. Zhang, S. Nie, Image segmentation based on PDEs Model: a Survey, in 3rd International Conference on Bioinformatics and Biomedical Engineering (2009), pp. 1–4

    Google Scholar 

  37. P. Karch, I. Zolotova, An experimental comparisons of modern methods of segmentation, in IEEE 8th International Symposium on SAMI (2010), pp. 247–252

    Google Scholar 

  38. D. Baswaraj et.al., Active contours and image segmentation: the current state of the art. Glob. J. Comput. Sci. Technol. Graph. Vis. 12(11) (2012)

    Google Scholar 

  39. R. Dass, Vikash, Comparative analysis of threshold based, K-means and level set segmentation algorithms. IJCST 4(1) (Mar 2013)

    Google Scholar 

  40. J. Patra et al., Segmentation techniques used image recognization and SAR image processing. IOSR J. Comput. Eng. (IOSR-JCE) 16(2), 37–43 (2014)

    Article  Google Scholar 

  41. A. Taneja et. al., A performance study of image segmentation techniques, in 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions) (2015), pp. 1–6

    Google Scholar 

  42. P. Getreuer, Chan vese segmentation. Image Process. On Line (IPOL) 2, 214–224 (2012)

    Article  Google Scholar 

  43. R. Dass, S. Devi, Priyanka, Effect of wiener-helstrom filtering cascaded with bacterial foraging optimization to despeckle the ultrasound images. Int. J. Comput. Sci. Issues 9(4), 372–380 (2012)

    Google Scholar 

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Authors and Affiliations

  1. Deenbandhu Chhottu Ram University of Science and Technology, Sonipat, Murthal, India

    Poonam Jaglan, Rajeshwar Dass & Manoj Duhan

Authors
  1. Poonam Jaglan
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  2. Rajeshwar Dass
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  3. Manoj Duhan
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Corresponding author

Correspondence to Poonam Jaglan .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technical Teachers Training and Research, Chandigarh, India

    C. Rama Krishna

  2. Department of Educational Television Centre, National Institute of Technical Teachers Training and Research, Chandigarh, India

    Maitreyee Dutta

  3. Department of Computer Science and Engineering, National Institute of Technical Teachers Training and Research, Chandigarh, India

    Rakesh Kumar

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Jaglan, P., Dass, R., Duhan, M. (2019). A Comparative Analysis of Various Image Segmentation Techniques. In: Krishna, C., Dutta, M., Kumar, R. (eds) Proceedings of 2nd International Conference on Communication, Computing and Networking. Lecture Notes in Networks and Systems, vol 46. Springer, Singapore. https://doi.org/10.1007/978-981-13-1217-5_36

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  • DOI: https://doi.org/10.1007/978-981-13-1217-5_36

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1216-8

  • Online ISBN: 978-981-13-1217-5

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