Journal of Medical Systems

, 43:282 | Cite as

Brain Tumor Detection and Segmentation by Intensity Adjustment

  • P. G. RajanEmail author
  • C. Sundar
Patient Facing Systems
Part of the following topical collections:
  1. Wearable Computing Techniques for Smart Health


In recent years, Brain tumor detection and segmentation has created an interest on research areas. The process of identifying and segmenting brain tumor is a very tedious and time consuming task, since human physique has anatomical structure naturally. Magnetic Resonance Image (MRI) scan analysis is a powerful tool that makes effective detection of the abnormal tissues from the brain. Among different techniques, Magnetic Resonance Image (MRI) is a liable one which contains several modalities in scanning the images captured from interior structure of human brain. A novel hybrid energy-efficient method is proposed for automatic tumor detection and segmentation. The proposed system follows K-means clustering, integrated with Fuzzy C-Means (KMFCM) and active contour by level set for tumor segmentation. An effective segmentation, edge detection and intensity enhancement can detect brain tumor easily. For that, active contour with level set method has been utilized. The performance of the proposed approach has been evaluated in terms of white pixels, black pixels, tumor detected area, and the processing time. This technique can deal with a higher number of segmentation problem and minimum execution time by ensuring segmentation quality. Additionally, tumor area length in vertical and horizontal positions is determined to measure sensitivity, specificity, accuracy, and similarity index values. Further, tumor volume is computed. Knowledge of the information of tumor is helpful for the physicians for effective diagnosing in tumor for treatments. The entire experimentation was implemented in MATLAB environment and simulation results were compared with existing approaches.


MRI brain tumor K-means clustering Fuzzy C-means Active contour by level set Edge detection and intensity adjustment 


Compliance with Ethical Standards

Disclosure of Potential Conflicts of Interest

No conflicts of interest: Author 1 & 2 declares that they have no conflict of interest.

Research Involving Human Participants and/or Animals

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008″.

Informed Consent

Informed consent was obtained from all patients for being included in the study.


  1. 1.
    Aslam, A., Khan, E., and Beg, M. M. S., Improved edge detection algorithm for brain tumor segmentation. Elsevier, second international symposium on computer vision and the internet (VisionNet’15), 2015.Google Scholar
  2. 2.
    Chanchlani, A., Chaudhari, M., Shewale, B. and Jha, A., Tumor detection in brain MRI using clustering and segmentation algorithm. IJARIIE-ISSN (O)-2395-4396, 3 Issue-3 2017.Google Scholar
  3. 3.
    Lakra, A., and Dubey, R. B., A comparative analysis of MRI brain tumor segmentation technique. Int. J. Comput. Appl. 125:5–14, 2015 (0975-8887).Google Scholar
  4. 4.
    Chadded, A., Automated feature extraction in brain tumor by magnetic resonance imaging using Gaussian mixture models. Int. J. Biomed. Imag. 2015:1–11, 2015 Hindawi Publishing Corporation.CrossRefGoogle Scholar
  5. 5.
    Devkota, B., Alsadoon, A., Prasad, P. W. C., Singh, A. K. and Elchouemi, A., Image segmentation for early stage brain tumor detection using mathematical morphological reconstruction. 6th International Conference on Smart Computing and Communications, ICSCC, Elsevier 2017.Google Scholar
  6. 6.
    Olenska, E. B., Thoene, M., Wlodarczyk, A. and Wojtkiewicz, J., Application of MRI for the diagnosis of neoplasms. Biomed Res. Int. Volume 2018. Hindawi.Google Scholar
  7. 7.
    Praveen, G. B., and Anitha, A., Hybrid approach for brain tumor detection and classification in magnetic resonance images. International Conference on Communication, Control and Intelligent Systems (CCIS) 2015.Google Scholar
  8. 8.
    Raj, J. A.and Kumar, S., An enhanced classifier for brain tumor classification. International Science Press, IJCTA, 2016.Google Scholar
  9. 9.
    Sudharani, K., Sarma, T. C., Prasad, K. S., Advanced morphological technique for automatic brain tumor detection and evaluation of statistical parameters. International Conference on Emerging Trends in Engineering, Science and Technology (ICETEST), 2015.Google Scholar
  10. 10.
    Zhao, L. and Jia, K., Multi scale CNNs for brain tumor segmentation and diagnosis”, Computational and Mathematical Methods in Medicine 8356294, 2016 Hindawi Publishing Corporation.Google Scholar
  11. 11.
    Soltaninejad, M., Yang, G., Lambrou, T., Allinson, N., Jones, T. L., Barrick, T. R., Howe, F. A. and Ye, Z., Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI. Int. J. Comput. Assist. Radiol. Surg . 2016.
  12. 12.
    N B Bahadure, A K Ray and H P Thethi, “Image analysis for MRI based brain tumor detection and feature extraction using biologically inspired BWT and SVM”, International Journal of Biomedical Imaging, 2017.Google Scholar
  13. 13.
    Varuna Shree, N., and Kumar, T. N. R., Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network. Springer Corporate Information, 2018.Google Scholar
  14. 14.
    Dhage, P., M. , Phegade M. R. and Shah, S. K., Watershed segmentation brain tumor detection. International Conference on Pervasive Computing (ICPC), 2015.Google Scholar
  15. 15.
    Cui, S., Mao, L., Jiang, J. and Xiong, S., Automatic semantic segmentation of brain gliomas from MRI images using a deep cascaded neural network. Hindawi J. Healthcare Eng., 2018.Google Scholar
  16. 16.
    Kumar, S., Dabas, C., and Godara, S., Classification of brain MRI tumor images: A hybrid approach. Proc. Comput. Sci. 122:510–517, 2017 Elsevier, information technology and quantitative management.CrossRefGoogle Scholar
  17. 17.
    Sheikh Abdullah, S. N. H., Bohani, F. A., Nayef, B. H., Sahran, S., Akash, O. A., Hussain, R. I. and Ismail, F., Round randomized learning vector quantization for brain tumor imaging. Hindawi Publishing Corporation Computational and Mathematical Methods in Medicine, 2016.Google Scholar
  18. 18.
    Roy, S., Bhattacharyya, D., Bandyopadhyay, S. K. and Kim, T. K., Heterogeneity of human brain tumor with lesion identification, localization and analysis from MRI. Elsevier, Information in medicine unlocked. 1–12, 2017.Google Scholar
  19. 19.
    Santosh, S., Raut, A. and Kulkarni, S., Implementation of image processing for detection of brain tumors. Proceedings of the IEEE international conference on computing methodologies and communication (ICCMC), 2017.Google Scholar
  20. 20.
    Swamy, S. and Kulkarni, P. K., Image processing for identifying brain tumor using intelligent system. Int. J. Innov. Res. Sci. Eng. Technol. 4(11), 2015.Google Scholar
  21. 21.
    Ilhan, U. and Ilhan, A., Brain tumor segmentation based on a new threshold approach. Elsevier, 9th international conference on theory and application of soft computing, ICSCCW 2017.Google Scholar
  22. 22.
    Vijay, V., Kavitha, A R. and Rebecca, S. R., Automated brain tumor segmentation and detection in MRI using enhanced Darwinian particle swarm optimization(EDPSO). Elsevier, 2nd international conference on intelligent computing, Communication & Convergence (ICCC), 2016.Google Scholar
  23. 23.
    Yang, Z., Shufan, Y., Li, G. and Weifeng, D., Segmentation of MRI brain images with an improved harmony searching algorithm. Hindawi Publishing Corporation Bio Medical Research International. 2016.Google Scholar
  24. 24.
    Bashir, H., Hussain, F. and Yousaf, M. H., Smart algorithm for 3D reconstruction and segmentation of brain tumor from MRI’s using slice selection mechanism. Smart Comput. Rev. 5(3), 2015.Google Scholar
  25. 25.
    Rajeev Ratan, A., Sanjay Sharma, B., and Sharma, S. K., Brain tumor detection based on multi-parameter MRI image analysis. IEEE 9, 2009.Google Scholar
  26. 26.
    Parveen, A. S., Detection of brain tumor in MRI images, using combination of fuzzy C-means and SVM. IEEE, 978-1-4799-5991-4/15, 2015.Google Scholar
  27. 27.
    Sharma, Y., and Meghrajani, Y. K., Brain tumor extraction from MRI image using mathematical morphological reconstruction. IEEE, 978-1-4799-6986-9/14, 2014.Google Scholar
  28. 28.
    Lavanyadevi, R., Machakowsalya, M., Nivethitha, J., and Kumar, A. N., Brain tumor classification and segmentation in MRI images using PNN. IEEE, 2017.Google Scholar
  29. 29.
    Akter, M. K., Khan, S. M., Azad, S. and Fattah, S. A., Automated brain tumor segmentation from MRI data based on exploration of histogram characteristics of the cancerous hemisphere. IEEE, 978-1-5386-2175-2/17, 2017.Google Scholar
  30. 30.
    Chato, L., Chow, E., and Latifi, S., Wavelet transform to improve accuracy of a prediction model for overall survival time of brain tumor patients based on MRI images. IEEE, 2575-2634/18, 2018.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Christian College of Engineering and TechnologyOddanchatramIndia

Personalised recommendations