Medical Image Segmentation by Combining Adaptive Artificial Bee Colony and Wavelet Packet Decomposition

  • Muhammad Arif
  • Guojun WangEmail author
  • Oana Geman
  • Jianer Chen
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1123)


Segmentation of MRI images plays a significant and helpful job in anticipation and treatment preliminaries. Be that as it may, the power in homogeneity, sporadic fringes and one of the most exceedingly terrible pieces of the difference may cause incredible challenges in the pieces of the seeping from brain MRI images. Heaps of specialists have made in therapeutic imaging. We proposed the novel technique for image segmentation. Our technique depends on the discrete wavelet packet decomposition and ant colony optimization to reduce the disadvantage of the conventional computations in handling of the surprising shapes in restorative images preparing. To improve the exhibition of our proposed procedure we utilize the artificial bee colony to optimize and classify the feature selected or extracted by the WPD. Results shows that our method perform better to segment the curvy shapes and haemorrhagic areas in MRI images.


Segmentation MRI Brain Classification Optimization 



This work was supported in part by the National Natural Science Foundation of China under Grant 61632009, in part by the Guangdong Provincial Natural Science Foundation under Grant 2017A030308006, and in part by the High-Level Talents Program of Higher Education in Guangdong Province under Grant 2016ZJ01, and in part by National Natural Science Foundation of China under Grant 61872097.


  1. 1.
    Greenberg, S.M., et al.: Cerebral microbleeds: a guide to detection and interpretation. Lancet Neurol. 8(2), 165–174 (2009)CrossRefGoogle Scholar
  2. 2.
    Javaid, Q., Arif, M., Talpur, S.: Segmentation and classification of calcification and hemorrhage in the brain using fuzzy C-mean and adaptive neuro-fuzzy inference system. Quaid-e-Awam Univ. Res. J. Eng. Sci. Technol. 15(1), 50–63 (2016)Google Scholar
  3. 3.
    Arif, M., Wang, G.: Fast curvelet transform through genetic algorithm for multimodal medical image fusion. Soft Comput. 1–22 (2019).
  4. 4.
    Spaite, D.W., et al.: Mortality and prehospital blood pressure in patients with major traumatic brain injury: implications for the hypotension threshold. JAMA Surg. 152(4), 360 (2016)CrossRefGoogle Scholar
  5. 5.
    Arif, M., Alam, K.A., Hussain, M.: Application of data mining using artificial neural network: survey. Int. J. Database Theory Appl. 8(1), 245–270 (2015)CrossRefGoogle Scholar
  6. 6.
    Arif, M., Abdullah, N.A., Phalianakote, S.K., Ramli, N., Elahi, M.: Maximizing information of multimodality brain image fusion using curvelet transform with genetic algorithm. In: 2014 International Conference on Computer Assisted System in Health, pp. 45–51. IEEE (2014)Google Scholar
  7. 7.
    Arif, M., Shakeel, H.: Virtualization security: analysis and open challenges. Int. J. Hybrid Inf. Technol. 8(2), 237–246 (2015)CrossRefGoogle Scholar
  8. 8.
    Gorog, D.A., Fayad, Z.A., Fuster, V.: Arterial thrombus stability: does it matter and can we detect it? J. Am. Coll. Cardiol. 70(16), 2036–2047 (2017)CrossRefGoogle Scholar
  9. 9.
    Geman, O., Chiuchisan, I., Ungurean, I., Hagan, M., Arif, M.: Ubiquitous healthcare system based on the sensors network and android internet of things gateway. In: 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), pp. 1390–1395. IEEE (2018)Google Scholar
  10. 10.
    Rizvi, S.Q.A., Wang, G., Chen, J.: A service oriented healthcare architecture (SOHA-CC) based on cloud computing. In: Wang, G., Chen, J., Yang, L.T. (eds.) SpaCCS 2018. LNCS, vol. 11342, pp. 84–97. Springer, Cham (2018). Scholar
  11. 11.
    Javaid, Q., Arif, M., Shah, M.A., Nadeem, M., et al.: A hybrid technique for De-Noising multi-modality medical images by employing cuckoo’s search with curvelet transform. Mehran Univ. Res. J. Eng. Technol. 37(1), 29 (2018)CrossRefGoogle Scholar
  12. 12.
    Muhammad, A., Guojun, W.: Segmentation of calcification and brain hemorrhage with midline detection. In: 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC), pp. 1082–1090. IEEE (2017)Google Scholar
  13. 13.
    Javaid, Q., Arif, M., Awan, D., Shah, M.: Efficient facial expression detection by using the Adaptive-Neuro-Fuzzy-Inference-System and the Bezier curve. Sindh Univ. Res. J. SURJ (Sci. Ser.) 48(3), 595–600 (2016)Google Scholar
  14. 14.
    Aja-Fernández, S., Curiale, A.H., Vegas-Sánchez-Ferrero, G.: A local fuzzy thresholding methodology for multiregion image segmentation. Knowl. Based Syst. 83(1), 1–12 (2015)CrossRefGoogle Scholar
  15. 15.
    Zhou, S., Wang, J., Zhang, S., Liang, Y., Gong, Y.: Active contour model based on local and global intensity information for medical image segmentation. Neurocomputing 186, 107–118 (2016)CrossRefGoogle Scholar
  16. 16.
    Saha, S., Bandyopadhyay, S.: MRI brain image segmentation by fuzzy symmetry based genetic clustering technique. In: 2007 IEEE Congress on Evolutionary Computation, pp. 4417–4424. IEEE (2007)Google Scholar
  17. 17.
    Grannan, B.L., Yanamadala, V., Walcott, B.P., Stapleton, C.J., Ogilvy, C.S.: Repeated neurovascular imaging in subarachnoid hemorrhage when initial studies are negative. J. Clin. Neurosci. 21(6), 993–996 (2014)CrossRefGoogle Scholar
  18. 18.
    Weishaupt, D., Köchli, V.D., Marincek, B.: How does MRI Work?: An Introduction to the Physics and Function of Magnetic Resonance Imaging, 2nd edn, p. 170. Springer, Heidelberg (2008). Scholar
  19. 19.
    Masulli, F., Schenone, A.: A fuzzy clustering based segmentation system as support to diagnosis in medical imaging. Artif. Intell. Med. 16(2), 129–147 (1999)CrossRefGoogle Scholar
  20. 20.
    Gilligan, J., Reilly, P., Pearce, A., Taylor, D.: Management of acute traumatic intracranial haematoma in rural and remote areas of Australia. ANZ J. Surg. 87(1–2), 80–85 (2017)CrossRefGoogle Scholar
  21. 21.
    Cocosco, C.A., Zijdenbos, A.P., Evans, A.C.: A fully automatic and robust brain MRI tissue classification method. Med. Image Anal. 7(4), 513–527 (2003)CrossRefGoogle Scholar
  22. 22.
    Steed, T., et al.: Iterative probabilistic voxel labeling: automated segmentation for analysis of The Cancer Imaging Archive glioblastoma images. Am. J. Neuroradiol. 36(4), 678–685 (2015)CrossRefGoogle Scholar
  23. 23.
    Vishnuvarthanan, G., Rajasekaran, M.P., Subbaraj, P., Vishnuvarthanan, A.: An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images. Appl. Soft Comput. 38, 190–212 (2016)CrossRefGoogle Scholar
  24. 24.
    El-Dahshan, E., Salem, A.-B.M., Younis, T.H.: A hybrid technique for automatic MRI brain images classification. Studia Univ. Babes-Bolyai Informatica 54(1), 55–67 (2009)Google Scholar
  25. 25.
    Arif, M., Wang, G., Wang, T., Peng, T.: SDN-based secure VANETs communication with fog computing. In: Wang, G., Chen, J., Yang, L.T. (eds.) SpaCCS 2018. LNCS, vol. 11342, pp. 46–59. Springer, Cham (2018). Scholar
  26. 26.
    Arif, M., Wang, G., Peng, T.: Track me if you can? Query based dual location privacy in VANETs for V2V and V2I. In: 2018 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/12th IEEE International Conference on Big Data Science and Engineering (TrustCom/BigDataSE), pp. 1091–1096. IEEE (2018)Google Scholar
  27. 27.
    Arif, M., Wang, G., Chen, S.: Deep learning with non-parametric regression model for traffic flow prediction. In: 2018 IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, 16th International Conference on Pervasive Intelligence and Computing, 4th International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), pp. 681–688. IEEE (2018)Google Scholar
  28. 28.
    Arif, M., Wang, G., Bhuiyan, M.Z.A., Wang, T., Chen, J.: A survey on security attacks in VANETs: communication, applications and challenges. Veh. Commun. 19, 100179 (2019)Google Scholar
  29. 29.
    Arif, M., Alam, K.A., Hussain, M.: Crime mining: a comprehensive survey. Int. J. u-and e-Serv. Sci. Technol. 8(2), 357–364 (2015)CrossRefGoogle Scholar
  30. 30.
    Arif, M., Dar, A.R.: Survey on fraud detection techniques using data mining. Int. J. u-and e-Serv. Sci. Technol. 8(3), 165–170 (2015)Google Scholar
  31. 31.
    Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imaging 20(1), 45–57 (2001)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Computer ScienceGuangzhou UniversityGuangzhouChina
  2. 2.Department of Health and Human DevelopmentStefan cel Mare University SuceavaSuceavaRomania

Personalised recommendations