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International Conference on Advanced Computing Networking and Informatics pp 459–467Cite as

A Comparative Analysis of Medical Image Segmentation

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 870))

Abstract

Image segmentation is the technique of dividing an image into one of kind regions (segments) following some homogeneous criteria. It is an important technique in any image analysis process. Segmentation of medical images like magnetic resonance images, mammogram, cardiac Magnetic resonance (MRIs) images helps in detection and diagnosis of breast tumor, brain tumor, etc. We need a strong and efficient image segmentation method, as most segmentation methods are computationally high priced, and the amount of medical imaging information is growing and very sensitive. In this paper, we delve into different methods available for medical image segmentation with their standpoints. We also compare the two authors’ results based on the parameters True Positive Factor (TPF), True Negative Factor (TNF), and Sum of True Volume Factor (SVTF).

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References

  1. A. Melouah, S. Layachi, A novel automatic seed placement approach for region growing segmentation in mammograms, in IPAC’15, pp. 23–25 Nov 2015 (ACM, Batna Algeria © 2015). ISBN 978-1-4503-3458-7/15/11. https://doi.org/10.1145/2816839.2816892

  2. A. Afifi, S. Ghoniemy, E.A. Zanaty, S.F. EI-Zoghdy, New region growing based on thresholding technique applied to MRI data, I. J. Comput. Netw. Inf. Secur. 61–67 (2015), Published Online June 2015 in MECS (www.mecs-press.org/). https://doi.org/10.5815/ijcnis.2015.07.08

    Article  Google Scholar 

  3. A.Q. Al Faris, U.K. Ngah, N.A.M. Isa, I.L. Shuaib, Computer-aided segmentation system for breast MRI tumour using modified automatic seeded region growing (BMRI) (MASRG). J. Digit Imaging 27, 133–144 (2014). https://doi.org/10.1007/s10278-013-9640-5. Springer

    Article  Google Scholar 

  4. A.A. Malek, W.E.Z.W.A. Rahman, A. Ibrahim, R. Mahmud, S.S. Yasiran, A.K. Jumaat, Region and boundary segmentation of microclassifications using seed-based region growing and mathematical morphology, in International Conference on Mathematics Education Research 2010 (ICMER 2010), pp. 634–639, www.sciencedirect.com. Elsevier

  5. A.A. Malek, W.E.Z.W.A. Rahman, S.S. Yasiran, A.K. Jumaat, U.M.A. Jalil, Seed point selection for seed-based region growing in segmenting microclassifications, in International Conference on Statistics in Science, Business and Engineering (ICSSBE) IEEE Conference 2012

    Google Scholar 

  6. A. Eklund, P. Dufort, D. Forsberg, S.M. LaConte, Medical image processing on GPU—past, present and future. Med. Image Anal. 17, 1073–1094 (2013)

    Article  Google Scholar 

  7. C. Petitjean, J.-N. Dacher, A review of segmentation methods in short axis cardiac MR image. Med. Image Anal. 15, 169–184 (2011)

    Article  Google Scholar 

  8. E. Smistad, T.L. Falch, M. Bozorgi, A.C. Elster, F. Lindseth, Medical image segmentation on GPUs—a comprehensive review. Med. Image Anal. 20, 1–18 (2015)

    Article  Google Scholar 

  9. J. Liu, M. Li, J. Wang, F. Wu, T. Liu, Y. Pan, A survey of MRI-based brain tumor segmentation methods. Tsinghua Sci. Technol. 19(6), 578–595, ISSN: 1007-0214 04/10, Dec 2014

    Google Scholar 

  10. K. Usman, K. Rajpoot, Brain tumor classification from multi-modality MRIs using wavelets and machine learning. Pattern Anal. and Appl. 20, 871–881 (2017)

    Article  MathSciNet  Google Scholar 

  11. N. Shrivastava, J. Bharti, Empirical analysis of image segmentation techniques, in SmartCom 2016 © Springer Nature Singapore Pte Ltd, CCIS 628 (2016), pp. 143–150. https://doi.org/10.1007/978-981-10-3433-6_18

    Google Scholar 

  12. R. Karim, P. Bhagirath, P. Claus, R.J. Housden, Z. Chen, Z. Karimaghaloo, H.M. Sohn, L.L. Rodriguez, S. Vera, X. Alba, A. Hennemuth, H.O. Peittgen, T. Arbel, M.A. Gonzalez Ballester, A.F. FRangi, M. Gotte, R. Razavi, T. Schaeffeter, K. Rhode, Evaluation of state-of-the-art segmentation algorithms for left ventricle infract from late Gadolinium enhancement MR images. Med. Image Anal. 30, 95–107 (2016)

    Article  Google Scholar 

  13. N.M. Zaitoun, M.J. Aqel, Survey on image segmentation techiniques. Proc. Comput. Sci. 65, 797–806 (2015). Elsevier

    Article  Google Scholar 

  14. V. Tavakoli, A.A. Amini, A survey of shaped-based registration and segmentation techniques for cardiac images. Comput. Vis. Image Underst. 117, 966–989 (2013)

    Article  Google Scholar 

  15. M. Polak, H. Zhang, M. Pi, An evaluation metric for image segmentation of multiple objects. Image Vis. Comput. 27, 1223–1227 (2009). Elsevier

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science & Engineering, MANIT, Bhopal, Madhya Pradesh, India

    Neeraj Shrivastava & Jyoti Bharti

  2. Department of Computer Science & Engineering, IES IPS Academy, Indore, Madhya Pradesh, India

    Neeraj Shrivastava

Authors
  1. Neeraj Shrivastava
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  2. Jyoti Bharti
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Corresponding author

Correspondence to Neeraj Shrivastava .

Editor information

Editors and Affiliations

  1. Medi-Caps University, Indore, Madhya Pradesh, India

    Raj Kamal

  2. Loughborough University, Loughborough, UK

    Michael Henshaw

  3. Medi-Caps Group of Institutions, Indore, Madhya Pradesh, India

    Pramod S. Nair

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© 2019 Springer Nature Singapore Pte Ltd.

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Cite this paper

Shrivastava, N., Bharti, J. (2019). A Comparative Analysis of Medical Image Segmentation. In: Kamal, R., Henshaw, M., Nair, P. (eds) International Conference on Advanced Computing Networking and Informatics. Advances in Intelligent Systems and Computing, vol 870. Springer, Singapore. https://doi.org/10.1007/978-981-13-2673-8_48

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  • DOI: https://doi.org/10.1007/978-981-13-2673-8_48

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2672-1

  • Online ISBN: 978-981-13-2673-8

  • eBook Packages: EngineeringEngineering (R0)

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