Skip to main content
SpringerLink
Log in

A New Hybrid Adaptive Cuckoo Search-Squirrel Search Algorithm for Brain MR Image Analysis

  • Chapter
  • First Online:
Hybrid Machine Intelligence for Medical Image Analysis

Part of the book series: Studies in Computational Intelligence ((SCI,volume 841))

Abstract

This chapter presents a new hybrid adaptive cuckoo search-squirrel search (ACS-SS) algorithm for brain magnetic resonance (MR) image analysis. Thresholding is one of the popular methods utilized for brain image segmentation. Thresholding-based methods are easily implemented. In this context, we present an optimal multilevel thresholding technique for brain MR images using edge magnitude information. The edge magnitude is computed using the gray-level co-occurrence matrix (GLCM) of the brain image slice. The optimum thresholds are found by maximizing the edge magnitude. A new hybrid evolutionary computing technique, namely ACS-SS, is investigated to maximize the edge magnitudes. The proposed scheme is tested with T2-w brain MR images from Harvard medical education database. The results are compared with cuckoo search (CS), squirrel search (SS), and adaptive cuckoo search (ACS) algorithms. It is witnessed that the findings, using the proposed ACS-SS technique, are superior to the other techniques in terms of qualitative and quantitative measures. The advantages of the proposed technique are as follows: (i) The ACS-SS shows improved fitness function values; (ii) the ACS technique gives speed improvement.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Sahoo, P.K., Soltani, S., Wong, A.K.C.: A survey of thresholding techniques. Comput. Vision Graph. Image Process. 41(2), 233–260 (1988)

    Article  Google Scholar 

  2. Sankur, B., Sezgin, M.: Image thresholding techniques: a survey over categories. Pattern Recogn. 34(2), 1573–1583 (2001)

    Google Scholar 

  3. Kapur, J.N., Sahoo, P.K., Wong, A.K.C.: A new method for gray-level picture thresholding using the entropy of the histogram. Comput. Vision Graph. Image Process. 29(3), 273–285 (1985)

    Article  Google Scholar 

  4. Maitra, M., Chatterjee, A.: A novel technique for multilevel optimal magnetic resonance brain image thresholding using bacterial foraging. Measurement 41, 1124–1134 (2008)

    Article  Google Scholar 

  5. Panda, R., Agrawal, S., Bhuyan, S.: Edge magnitude based multilevel thresholding using Cuckoo search technique. Expert Syst. Appl. 40(18), 7617–7628 (2013)

    Article  Google Scholar 

  6. Otsu, N.: A threshold selection method from gray level histograms. Automatica 11(285–296), 23–27 (1975)

    Google Scholar 

  7. Tobias, O.J., Seara, R.: Image segmentation by histogram thresholding using fuzzy sets. IEEE Trans. Image Process. 11, 1457–1465 (2002)

    Article  Google Scholar 

  8. Kittler, J., Illingworth, J.: Minimum error thresholding. Pattern Recogn. 19(1), 41–47 (1986)

    Article  Google Scholar 

  9. Mortazavi, D., Kouzani, A.Z., Soltanian-Zadeh, H.: Segmentation of multiple sclerosis lesions in MR images: a review. Neuroradiology 54(4), 299–320 (2012)

    Article  Google Scholar 

  10. Suzuki, H., Toriwaki, J.I.: Automatic segmentation of head MRI images by knowledge guided thresholding. Comput. Med. Imaging Graph. 15(4), 233–240 (1991)

    Article  Google Scholar 

  11. Zavaljevski, A., Dhawan, A.P., Gaskil, M., Ball, W., Johnson, J.D.: Multi-level adaptive segmentation of multi-parameter MR brain images. Comput. Med. Imaging Graph. 24(2), 87–98 (2000)

    Article  Google Scholar 

  12. Sandhya, G., Kande, G.B.: An efficient approach for the detection of White Matter, Gray Matter, and cerebrospinal fluid from MR images of the brain using an advanced multilevel thresholding. In: 2017 Third International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB), pp. 422–426. IEEE (2017)

    Google Scholar 

  13. Oliva, D., Hinojosa, S., Cuevas, E., Pajares, G., Avalos, O., Gálvez, J.: Cross entropy based thresholding for magnetic resonance brain images using Crow Search Algorithm. Expert Syst. Appl. 79, 164–180 (2017)

    Article  Google Scholar 

  14. Kamathe, R.S., Joshi, K.R.: A novel method based on independent component analysis for brain MR image tissue classification into CSF, WM and GM for atrophy detection in Alzheimer’s disease. Biomed. Signal Process. Control 40, 41–48 (2018)

    Article  Google Scholar 

  15. Akkus, Z., Kostandy, P.M., Philbrick, K.A., Erickson, B.J.: Extraction of brain tissue from CT head images using fully convolutional neural networks. In: Medical Imaging 2018: Image Processing, vol. 10574, pp. 1057420. International Society for Optics and Photonics (2018)

    Google Scholar 

  16. Sathya, P.D., Kayalvizhi, R.: Optimal segmentation of brain MRI based on adaptive bacterial foraging algorithm. Neurocomputing 74(14), 2299–2313 (2011)

    Article  Google Scholar 

  17. Sathya, P.D., Kayalvizhi, R.: Amended bacterial foraging algorithm for multilevel thresholding of magnetic resonance brain images. Measurement 44(10), 1828–1848 (2011)

    Article  Google Scholar 

  18. Shan, Y., Zu, H., Guang, Z.Y., Liu, J.: Automated histogram-based brain segmentation in T1-weighted three-dimensional magnetic resonance head images. NeuroImage 17, 1587–1598 (2002)

    Article  Google Scholar 

  19. Joliot, M., Mazoyer, B.M.: Three-dimensional segmentation and interpolation of magnetic resonance brain images. IEEE Trans. Med. Imaging 12(2), 269–277 (1993)

    Article  Google Scholar 

  20. Lai, C.C., Chang, C.Y.: A hierarchical evolutionary algorithm for automatic medical image segmentation. Expert Syst. Appl. 36(1), 248–259 (2009)

    Article  Google Scholar 

  21. Sharma, M., Purohit, G.N., Mukherjee, S.: Information retrieves from brain MRI images for tumor detection using hybrid technique K-means and artificial neural network (KMANN). In: Networking Communication and Data Knowledge Engineering, pp. 145–157. Springer, Singapore (2018)

    Google Scholar 

  22. Rao, C.R., Kumar, M.N.V.S.S., Rao, G.S.B.: A novel segmentation algorithm for feature extraction of brain MRI tumor. In: Information and Decision Sciences, pp. 455–463. Springer, Singapore (2018)

    Google Scholar 

  23. Samanta, A.K., Khan, A.A.: Computer aided diagnostic system for automatic detection of brain tumor through MRI using clustering based segmentation technique and SVM classifier. In: International Conference on Advanced Machine Learning Technologies and Applications, pp. 343–351. Springer, Cham (2018)

    Chapter  Google Scholar 

  24. Ouadfel, S., Taleb-Ahmed, A.: Social spiders optimization and flower pollination algorithm for multilevel image thresholding: a performance study. Expert Syst. Appl. 55, 566–584 (2016)

    Article  Google Scholar 

  25. Mlakar, U., Potočnik, B., Brest, J.: A hybrid differential evolution for optimal multilevel image thresholding. Expert Syst. Appl. 65, 221–232 (2016)

    Article  Google Scholar 

  26. Dehshibi, M.M., Sourizaei, M., Fazlali, M., Talaee, O., Samadyar, H., Shanbehzadeh, J.: A hybrid bio-inspired learning algorithm for image segmentation using multilevel thresholding. Multimedia Tools Appl. 76(14), 15951–15986 (2017)

    Article  Google Scholar 

  27. El Aziz, M.A., Ewees, A.A., Hassanien, A.E.: Whale optimization algorithm and moth-flame optimization for multilevel thresholding image segmentation. Expert Syst. Appl. 83, 242–256 (2017)

    Article  Google Scholar 

  28. Panda, R., Agrawal, S., Samantaray, L., Abraham, A.: An evolutionary gray gradient algorithm for multilevel thresholding of brain MR images using soft computing techniques. Appl. Soft Comput. 50, 94–108 (2017)

    Article  Google Scholar 

  29. Harvard Medical School. [Online]. Available: http://www.med.harvard.edu/AANLIB. Accessed Oct 2018

  30. Lim, K.O., Pfefferbaum, A.: Segmentation of MR brain images into cerebrospinal fluid spaces, white and gray matter. J. Comput. Assist. Tomogr. 13(4), 588–593 (1989)

    Article  Google Scholar 

  31. Mokji, M.M., Abu Bakar, S.A.R.: Adaptive thresholding based on co-occurrence matrix edge information. In: First Asia International Conference on Modelling & Simulation, pp. 444–450. IEEE (2007)

    Google Scholar 

  32. Chanda, B., Majumder, D.D.: A note on the use of the gray-level co-occurrence matrix in threshold selection. Sig. Process. 15, 149–167 (1988)

    Article  Google Scholar 

  33. Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 6, 610–621 (1973)

    Article  Google Scholar 

  34. Albregtsen, F.: Statistical Texture Measures Computed from Gray Level Co-occurrence Matrices. Image Processing Laboratory, Department of Informatics, University of Oslo (1995)

    Google Scholar 

  35. Yang, X.S., Deb, S.: Cuckoo search via Levy flights. In: World Congress on Nature and Biologically Inspired Computing, pp. 210–214. IEEE (2009)

    Google Scholar 

  36. Yang, X.S., Deb, S.: Engineering optimization by Cuckoo search. Int. J. Math. Modeling Numer. Optim. 1(4), 330–343 (2010)

    MATH  Google Scholar 

  37. Mantegna, R.N.: Fast, accurate algorithm for numerical simulation of Lévy stable stochastic processes. Phys. Rev. E 49(4), 4677–4683 (1994)

    Article  Google Scholar 

  38. Naik, M.K., Panda, R.: A novel adaptive cuckoo search algorithm for intrinsic discriminant analysis based face recognition. Appl. Soft Comput. 38, 661–675 (2016)

    Article  Google Scholar 

  39. Jain, M., Singh, V., Rani, A.: A novel nature-inspired algorithm for optimization: squirrel search algorithm. Swarm Evol. Comput. (2018)

    Google Scholar 

  40. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  41. Zhang, L., Zhang, L., Mou, X., Zhang, D.: FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process. 20(8), 2378–2386 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  42. Baraldi, A., Parmiggiani, F.: An investigation of the textural characteristics associated with gray level co-occurrence matrix statistical parameters. IEEE Trans. Geosci. Remote Sens. 33(2), 293–304 (1995)

    Article  Google Scholar 

  43. Das, R., Thepade, S., Ghosh, S.: Framework for content-based image identification with standardized multiview features. ETRI J. 38(1), 174–184 (2016)

    Article  Google Scholar 

  44. Thepade, S., Das, R., Ghosh, S.: Decision fusion-based approach for content-based image classification. Int. J. Intell. Comput. Cybern. 10(3), 310–331 (2017)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Telecommunication Engineering, VSS University of Technology, Burla, 768018, India

    Sanjay Agrawal & Rutuparna Panda

  2. Department of Electronics and Communication Engineering, ABIT, Cuttack, India

    Leena Samantaray

  3. Department of Electrical and Electronics Engineering, VSS University of Technology, Burla, 768018, India

    Lingraj Dora

Authors
  1. Sanjay Agrawal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Leena Samantaray
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rutuparna Panda
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lingraj Dora
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rutuparna Panda .

Editor information

Editors and Affiliations

  1. Department of Information Technology, RCC Institute of Information Technology, Beliaghata, West Bengal, India

    Siddhartha Bhattacharyya

  2. Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Gangtok, Sikkim, India

    Debanjan Konar

  3. Department of Computer Science, VSB-Technical University of Ostrava, Ostrava-Poruba, Czech Republic

    Jan Platos

  4. Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Gangtok, Sikkim, India

    Chinmoy Kar

  5. Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Gangtok, Sikkim, India

    Kalpana Sharma

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Agrawal, S., Samantaray, L., Panda, R., Dora, L. (2020). A New Hybrid Adaptive Cuckoo Search-Squirrel Search Algorithm for Brain MR Image Analysis. In: Bhattacharyya, S., Konar, D., Platos, J., Kar, C., Sharma, K. (eds) Hybrid Machine Intelligence for Medical Image Analysis. Studies in Computational Intelligence, vol 841. Springer, Singapore. https://doi.org/10.1007/978-981-13-8930-6_5

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-8930-6_5

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-8929-0

  • Online ISBN: 978-981-13-8930-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation