Brain Cancer Cell Detection Optimization Schemes Using Image Processing and Soft Computing

  • Chudapa Thammasakorn
  • Chakchai So-InEmail author
  • Wiyada Punjaruk
  • Urachart Kokaew
  • Boonsup Waikham
  • Songyut Permpol
  • Phet Aimtongkham
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 362)


This paper introduces a novel methodology to automatically measure a number of brain cancer cells using optimized image processing and soft-computing for classification. The former approach is used to prepare the cell image from the medical laboratory, such as background removal, image adjustment, and cell detection including noise reduction. Then, Gabor filter is applied to retrieve the key features before feeding into different soft-computing techniques to identify the actual cells. The results show that the performance of Fuzzy C-Mean with image processing optimization is outstanding compared to neural networks, genetic algorithms, and support vector machines, i.e., 96 % versus less than 90 % in precision, in addition to the superior computational time of around two seconds.


Brain cancer cell Cell counting Cell detection Image processing Machine learning Soft computing 


  1. 1.
    Lyer, V., Lee, S.: MRI, CT, and PET/CT for ovarian cancer detection and adnexal lesion characterization. Am. J. Roentgenol. 194, 311–321 (2010)CrossRefGoogle Scholar
  2. 2.
    Stephen, C.F.: Cross-linking of matrix polymers in the growing cell walls of angiosperms. Ann. Rev. Plant Physiol. 37, 165–186 (1986)CrossRefGoogle Scholar
  3. 3.
    Sarai, A., Siebers, J., Selvaraj, S., Gromiha, M.M., Kono, H.: Integration of bioinformatics and computational biology to understand protein-DNA recognition mechanism. J. Bioinform. Comput. Biol. 83–169 (2005)Google Scholar
  4. 4.
    Faggiano, E., Lorenzi, T., Perotto, S.: TV-H−1 variational inpainting applied to metal artifact reduction in CT images. Comput. Vis. Med. Image Process IV. 4, 277–282 (2013)CrossRefGoogle Scholar
  5. 5.
    Jang, H., Topal, E.: A review of soft computing technology applications in several mining problems. Appl. Soft Comput. 22, 638–651 (2014)CrossRefGoogle Scholar
  6. 6.
    Zhang, J., Zhan, Z., Lin, Y., Chen, N., Gong, Y., Zhong, J., Chung, H.S.H., Li, Y., Shi, Y.: Evolutionary computation meets machine learning: a survey. IEEE Comput. Intell. Mag. 6(4), 68–75 (2011)CrossRefGoogle Scholar
  7. 7.
    Phukpattaranon, P., Limsiroratana, S., Boonyaphiphat, P., Kayasut, K.: Automated breast cancer cell image segmentation. In: International Conference on Biomedical Engineering, pp. 241–244. Springer, Malaysia (2006)Google Scholar
  8. 8.
    Malek, J., Sebri, A., Mabrouk, S., Torki, K., Tourki, R.: Automated breast cancer diagnosis based on GVF-snake segmentation, wavelet features extraction and fuzzy classification. J. Sig. Process Syst. 55, 49–66 (2008)CrossRefGoogle Scholar
  9. 9.
    Han, J., Breckon, T.P., Randell, D.A., Landini, G.: The application of support vector machine classification to detect cell nuclei for automated microscopy. Mach. Vis. Appl. 23(1), 15–24 (2010)CrossRefGoogle Scholar
  10. 10.
    Arteta, C., Lempitsky, V., Noble, J.A., Zisserman, A.: Learning to detect cells using non-overlapping extremal regions. In: Medical Image Comput and Computer-Assisted Intervention. pp. 348–356, Springer, France (2012)Google Scholar
  11. 11.
    Al-tarawneh, M.S.: Lung cancer detection using image processing techniques. Leonardo Electron. J. Practices. Technol. 11, 147–158 (2012)Google Scholar
  12. 12.
    Bagley, J.D.: The behavior of adaptive systems which employ genetic and correlation algorithms. Doctoral dissertation (1967)Google Scholar
  13. 13.
    Hoppner, F., Klawonn, F., Kruse, R., Runkler, T.: Fuzzy Cluster Analysis, pp. 36–43. Wiley, New York (1999)zbMATHGoogle Scholar
  14. 14.
    Othman, A.: Generalised object detection and semantic analysis: casino example using matlab. Clin. Orthop. Relat. Res. (2011)Google Scholar
  15. 15.
    Corinna, C., Vladimir, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar
  16. 16.
    Prasad, N., Domke, J.: Filter Visualization. Technical Report. University of Maryland (2005)Google Scholar
  17. 17.
    Matlab R2014a (
  18. 18.
    Lihongyan.: Using genetic algorithms for image segmentation of the source. (2006)Google Scholar
  19. 19.
    Waleed, A., Siti, A., Shahnorbanun, H.: MRI brain segmentation via hybrid firefly search algorithm. J. Theor. Appl Inf. Technol. 61(1), 73–90 (2014)Google Scholar
  20. 20.
    Cristianini, N., Taylor, J.H.: An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, Cambridge (2000)CrossRefzbMATHGoogle Scholar
  21. 21.
    Omprakash, P., Yogendra, P.S., Maravi, S., Sanjeev, S.: A comparative study of histogram equalization based image enhancement techniques for brightness preservation and contrast enhancement. Sig. Image Process. 4, 11–25 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Chudapa Thammasakorn
    • 1
  • Chakchai So-In
    • 1
    Email author
  • Wiyada Punjaruk
    • 2
  • Urachart Kokaew
    • 1
  • Boonsup Waikham
    • 1
  • Songyut Permpol
    • 1
  • Phet Aimtongkham
    • 1
  1. 1.Applied Network Technology (ANT), Department of Computer Science, Faculty of ScienceKhon Kaen UniversityKhon KaenThailand
  2. 2.Department of Physiology, Faculty of MedicineKhon Kaen UniversityKhon KaenThailand

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