Breast MRI Tumour Segmentation Using Modified Automatic Seeded Region Growing Based on Particle Swarm Optimization Image Clustering

  • Ali Qusay Al-Faris
  • Umi Kalthum Ngah
  • Nor Ashidi Mat Isa
  • Ibrahim Lutfi Shuaib
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 223)


In this paper, a segmentation system with a modified automatic Seeded Region Growing (SRG) based on Particle Swarm Optimization (PSO) image clustering will be presented. The paper is focused on Magnetic Resonance Imaging (MRI) breast tumour segmentation. The PSO clusters’ intensities are involved in the proposed algorithms of the automated SRG initial seed and threshold value selection. Prior to that, some pre-processing methodologies are involved. And breast skin is detected and deleted using the integration of two algorithms, i.e. Level Set Active Contour and Morphological Thinning. The system is applied and tested on the RIDER breast MRI dataset, and the results are evaluated and presented in comparison to the Ground Truths of the dataset. The results show higher performance compared to the previous segmentation approaches that have been tested on the same dataset.


Breast MRI PSO image clustering Tumour segmentation Seeded region growing Level set active contour Morphological thinning 


  1. 1.
    Jemal, A., et al.: Global cancer statistics. CA A Cancer J. Clin. 61(2):69–90 (2011)Google Scholar
  2. 2.
    Gardiner, I.: CAD improves breast MRI workflow: increasing throughput while maintaining accuracy in breast MRI reads requires powerful workflow tools. In: Imaging Technology News (2010)Google Scholar
  3. 3.
    Ibrahim, S., Khalid, N.E.A., Manaf, M.: Empirical study of brain segmentation using particle swarm optimization. In: International Conference on Information Retrieval and Knowledge Management, CAMP10, p. 235–239. Shah Alam, Selangor (2010)Google Scholar
  4. 4.
    Ganesan, R., Radhakrishnan, S.: Segmentation of computed tomography brain images using genetic algorithm. Int. J. Soft Comput. 4(4), 157–161 (2009)Google Scholar
  5. 5.
    Hussain, R., et al.: Fuzzy clustering based malignant areas detection in noisy breast magnetic resonant (MR) images. Int. J. Acad. Res. 3(2) (2011)Google Scholar
  6. 6.
    Kannan, S., Sathya, A., Ramathilagam, S.: Effective fuzzy clustering techniques for segmentation of breast MRI. Soft Comput. Fusion Found. Methodol. Appl. 15(3), 483–491 (2011)Google Scholar
  7. 7.
    Noor, N.M., et al.: Adaptive neuro-fuzzy inference system for brain abnormality segmentation. In: 2010 IEEE Control and System Graduate Research Colloquium, ICSGRC 2010 (2010)Google Scholar
  8. 8.
    Azmi, R., et al.: IMPST: a new interactive self-training approach to segmentation suspicious lesions in breast MRI. J. Med. Signals Sens. 1(2), 138–148 (2011)Google Scholar
  9. 9.
    Adams, R., Bischof, L.: Seeded region growing. IEEE Trans. Pattern Anal. Machine Intell. 16, 641–647 (1994)CrossRefGoogle Scholar
  10. 10.
    Khalid, N.E.A., et al.: Seed-based region growing study for brain abnormalities segmentation. In: International Symposium on Information Technology 2010 (ITSim 2010), p. 856–860. Kuala Lumpur, (2010)Google Scholar
  11. 11.
    Meinel, L.A.: Development of computer-aided diagnostic system for breast MRI lesion classification. Dissertation, in Biomedical Engineering, University of Iowa: Iowa (2005)Google Scholar
  12. 12.
    Mat-Isa, N.A., Mashor, M.Y., Othman, N.H.: Seeded region growing features extraction algorithm; its potential use in improving screening for cervical cancer. Int. J. Comput. Internet Manag. 13(1) (2005)Google Scholar
  13. 13.
    Wu, J., et al.: Texture feature based automated seeded region growing in abdominal MRI segmentation. In: BioMedical Engineering and Informatics. BMEI 2008. Sanya (2008)Google Scholar
  14. 14.
    Shan, J., Cheng, H.D., Wang, Y.: A novel automatic seed point selection algorithm for breast ultrasound images. In: 19th International Conference on Pattern Recognition, ICPR (2008)Google Scholar
  15. 15.
    Chun-yu, N., Shu-fen, L., Ming, Q.: Research on removing noise in medical image based on median filter method. In: IEEE International Symposium in IT in Medicine & Education, ITIME ’09. I.C. Publications, Editor 2009, 384–388 (2009)Google Scholar
  16. 16.
    Li, C., et al.: Level set evolution without re-initialization: a new variational formulation. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), San Diego (2005)Google Scholar
  17. 17.
    Lam, L., Lee, S.W., Suen, C.Y.: Thinning methodologies-a comprehensive survey. IEEE Trans. Pattern Anal. Mach. Intell. 14(9), 879 (1992)CrossRefGoogle Scholar
  18. 18.
    Ibrahim, S., et al.: Particle swarm optimization vs seed-based region growing: brain abnormalities segmentation. Int. J. Artif. Intell. 7(1), 174–188 (2011)Google Scholar
  19. 19.
    Omran, M.G.H.: A PSO-based clustering algorithm with application to unsupervised image classification. University of Pretoria etd (2005)Google Scholar
  20. 20.
    Ouadfel, S., Batouche, M., Taleb-Ahmed, A.: A modified particle swarm optimization algorithm for automatic image clustering. In: International Symposium on Modelling and Implementation of, Complex Systems, MISC’2010, (2010)Google Scholar
  21. 21.
    Wong, M.T., He, X., Yeh, W.-C.: Image clustering using particle swarm, optimization. (2011)Google Scholar
  22. 22.
    "RIDER Breast MRI", National Biomedical Imaging Archive (NBIA), U.o. Michigan, Editor 2007, U.S. National Cancer Institute.Google Scholar
  23. 23.
    Chalana, V., Kim, Y.: A methodology for evaluation of boundary detection algorithms on medical images. IEEE Trans. Med. Imag. 16, 642–652 (1997)CrossRefGoogle Scholar
  24. 24.
    Fenster, A., Chiu, B.: Evaluation of segmentation algorithms for medical imaging. In: 27th Annual Conference on IEEE Engineering in Medicine and Biology. Shanghai, China (2005)Google Scholar
  25. 25.
    Metz, C.E.: ROC methodology in radiologic imaging. Invest. Radiol. 21, 720–733 (1986)CrossRefGoogle Scholar
  26. 26.
    McNeil, B.J., Hanley, J.A.: Statistical approaches to analysis of receiver operating characteristic ROC curves. Med. Decis. Making 14, 137–150 (1984)CrossRefGoogle Scholar
  27. 27.
    Song, T., et al.: A hybrid tissue segmentation approach for brain MR images. Med. Biol. Eng. Comput. 44, 242 (2006)CrossRefGoogle Scholar
  28. 28.
    Ertas, Gökhan, et al.: Breast MR segmentation and lesion detection with cellular neural networks and 3D template matching. Comput. Biol. Med. 38, 116–126 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ali Qusay Al-Faris
    • 1
  • Umi Kalthum Ngah
    • 1
  • Nor Ashidi Mat Isa
    • 1
  • Ibrahim Lutfi Shuaib
    • 2
  1. 1.Imaging and Computational Intelligence Research Group (ICI)Universiti Sains MalaysiaPenangMalaysia
  2. 2.Advanced Medical and Dental Institute (AMDI)Universiti Sains MalaysiaPenangMalaysia

Personalised recommendations