Abstract
Segmentation of brain images generated by magnetic resonance imaging (MRI) is an important part of clinical medicine as it enables three-dimensional reconstruction and downstream analysis of normal and pathological regions. Segmenting white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF) automatically are challenging tasks. In this paper, a clustering-based segmentation of MR images is performed using a modified quantum-inspired firefly algorithm with mutation operation. In the proposed method, a mutation operation based on the X-gate has overcome the restriction on initial centroids trapped in local optima. The objective function is chosen to be the minimum intra-cluster distance. The suggested approach has been tested on several sections of human brain images with differing cluster numbers. Correlation, SSIM, entropy, and PSNR have been used to evaluate the outputs of the method. The evaluation metrics indicate that the proposed clustering-based algorithm successfully segmented the MR images.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Jeena, R.S., Kumar, S.: A comparative analysis of MRI and CT brain images for stroke diagnosis. In: 2013 Annual International Conference on Emerging Research Areas and 2013 International Conference on Microelectronics, Communications and Renewable Energy, pp. 1–5 (2013)
Kloska, S.P., Wintermark, M., Engelhorn, T., Fiebach, J.B.: Acute stroke magnetic resonance imaging: current status and future perspective. Neuroradiology 52(3), 189–201 (2009)
Mamelak, A.N., Jacoby, D.B.: Targeted delivery of antitumoral therapy to glioma and other malignancies with synthetic chlorotoxin (TM-601). Expert Opin. Drug Deliv. 4(2), 175–186 (2007)
Ostrom, Q.T., et al.: The epidemiology of glioma in adults: a “state of the science’’ review. Neuro-Oncol. 16(7), 896–913 (2014)
Olivero, W.C., Lister, J.R., Elwood, P.W.: The natural history and growth rate of asymptomatic meningiomas: a review of 60 patients. J. Neurosurg. 83(2), 222–224 (1995)
Lee, W., et al.: MR imaging features of clear-cell meningioma with diffuse leptomeningeal seeding. AJNR Am. J. Neuroradiol. 21(1), 130–132 (2000)
Masangcap, M.L.G., Sison, A.M., Medina, R.P.: An improved initialization method using firefly movement and light intensity for better clustering performance. In: Proceedings of the 2nd International Conference on Software Engineering and Information Management, pp. 30–34 (2019)
Xie, H., et al.: Improving k-means clustering with enhanced firefly algorithms. Appl. Soft Comput. 84, 105763 (2019)
Khrissi, L., Akkad, N.E., Satori, H., Satori, K.: Simple and efficient clustering approach based on cuckoo search algorithm. In: 2020 Fourth International Conference on Intelligent Computing in Data Sciences (ICDS), pp. 1–6 (2020)
Pal, R., Yadav, S., Karnwal, R., Aarti: EEWC: energy-efficient weighted clustering method based on genetic algorithm for HWSNs. Complex Intell. Syst. 6(2), 391–400 (2020). https://doi.org/10.1007/s40747-020-00137-4
Zhao, J., Tang, J., Shi, A., Fan, T., Xu, L.: Improved density peaks clustering based on firefly algorithm. Int. J. Bio-Inspir. Comput. 15(1), 24 (2020)
Dey, A., Dey, S., Bhattacharyya, S., Platos, J., Snasel, V.: Novel quantum inspired approaches for automatic clustering of gray level images using particle swarm optimization, spider monkey optimization and ageist spider monkey optimization algorithms. Appl. Soft Comput. 88, 106040 (2020)
Das, S., De, S., Dey, S., Bhattacharyya, S.: Magnetic resonance image segmentation using a quantum-inspired modified genetic algorithm (QIANA) based on FRCM (2020)
Dhal, K.G., Das, A., Ray, S., Gálvez, J.: Randomly attracted rough firefly algorithm for histogram based fuzzy image clustering. Knowl.-Based Syst. 216, 106814 (2021)
Verma, H., Verma, D., Tiwari, P.K.: A population based hybrid FCM-PSO algorithm for clustering analysis and segmentation of brain image. Expert Syst. Appl. 167, 114121 (2021)
Dey, A., Dey, S., Bhattacharyya, S., Platos, J., Snasel, V.: Quantum inspired meta-heuristic approaches for automatic clustering of colour images. Int. J. Intell. Syst. 36(9), 4852–4901 (2021)
Choudhury, A., Samanta, S., Pratihar, S., Bandyopadhyay, O.: Multilevel segmentation of Hippocampus images using global steered quantum inspired firefly algorithm. Appl. Intell. 52, 7339–7372 (2021). https://doi.org/10.1007/s10489-021-02688-6
Deutsch, D.: Quantum theory, the church-turing principle and the universal quantum computer. Proc. R. Soc. London A Math. Phys. Sci. 400(1818), 97–117 (1985)
Han, K.H., Kim, J.H.: Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Trans. Evol. Comput. 6(6), 580–593 (2002)
Yang, X.-S.: Firefly algorithms for multimodal optimization. In: Watanabe, O., Zeugmann, T. (eds.) SAGA 2009. LNCS, vol. 5792, pp. 169–178. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04944-6_14
Yang, X.S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press (2010)
Dey, N. (ed.): Applications of Firefly Algorithm and its Variants. STNC, Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-0306-1
eHealth Lab, Department of Computer Science, U.O.C.: Dataset: http://www.medinfo.cs.ucy.ac.cy/index.php/facilities/32-software/218-datasets. Accessed 10 Mar 2022
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Choudhury, A., Samanta, S., Pratihar, S., Bandyopadhyay, O. (2022). Segmentation of Brain MR Images Using Quantum Inspired Firefly Algorithm with Mutation. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2022. Lecture Notes in Computer Science(), vol 13346. Springer, Cham. https://doi.org/10.1007/978-3-031-07704-3_30
Download citation
DOI: https://doi.org/10.1007/978-3-031-07704-3_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-07703-6
Online ISBN: 978-3-031-07704-3
eBook Packages: Computer ScienceComputer Science (R0)