Skip to main content
SpringerLink
Log in

Morphological edge detection and brain tumor segmentation in Magnetic Resonance (MR) images based on region growing and performance evaluation of modified Fuzzy C-Means (FCM) algorithm

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The medical image processing has become indispensable with an increased demand for systematic and efficient detection of brain tumor in a short period of time. There are various techniques for medical image segmentation. Detecting a wide variety of brain images in terms of shape and intensity is a challenging and difficult task to bring out a reliable and authentic data for diagnosing brain tumor diseases. This paper presents an algorithm which combines Region of Interest (ROI), Region Growing and Morphological Operation (Dilation and Erosion). This method initially identifies the approximate Region Growing (RG). Region growing is a procedure that groups pixels into larger regions, which starts from the seed points. Region growing based techniques are better than the edge-based techniques in noisy images where edges are difficult to detect. The Morphological Edge Detection of the input image is done and the input image is reconstructed on the basis of dilation and erosion for the enhancement of the image. The proposed work is divided into preprocessing to reduce the noise, Fuzzy C-Means is used to Region growing, Morphological edge detection is to enhance the image. Then the morphological edge detection can be classified into two categories, one is dilation and another is Erosion. Finally apply Gaussian filter to get output. After that, Fuzzy C-Means clustering (FCM), followed by seeded region growing is applied to detect and segment the tumor from the brain MRI image.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Abdel-Maksoud E et al (2015) Brain tumor segmentation based on a hybrid clustering technique. Egyptian Informatics Journal

  2. Bahadure NB et al (2018) Comparative approach of MRI-based brain tumor segmentation and classification using genetic algorithm. Journal of Digital Imaging

  3. Bal A et al (2018) MRI brain tumor segmentation and analysis using rough-fuzzy C-means and shape based properties Journal of King Saud University - Computer and Information Sciences

  4. Bilenia, Aniket et al (2019) Brain tumor segmentation with skull stripping and modified fuzzy C-means, Springer.

  5. Srikanth Busa, Vangala NS, Grandhe P, Balaji V (2019) Automatic Brain Tumor Detection Using Fast Fuzzy C-Means Algorithm, Springer

  6. Chouhan SS et al (2018) Image segmentation using computational intelligence techniques:review ,Archives of Computational Methods in Engineering

  7. Hooda H et al (2014) Brain tumor segmentation: A performance analysis using k-means, fuzzy C-means and region growing algorithm, IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT)

  8. Kaur T et al (2017) A novel fully automatic multilevel thresholding technique based on optimized intuitionistic fuzzy sets and tsallis entropy for MR brain tumor image segmentation”, Australasian Physical & Engineering Sciences in Medicine

  9. Kauret H et al (2016) Segmentation of tumor region from brain mri images using fuzzy C-Means clustering And seeded region growing. IOSR Journal of Computer Engineering 18, 5

  10. Khalifa I et al (2012) MRI brain image segmentation based on wavelet and FCM algorithm international. Journal of Computer Applications 47(16)

  11. Lei T et al (2017) Significantly fast and robust fuzzy C-means clustering algorithm based on morphological reconstruction and membership filtering”, IEEE Transactions On Fuzzy Systems

  12. Li BN et al (2010) Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation. Computers in Biology and Medicine

  13. Moumen T Melegy and Hashim M Mokhtar et al (2014) Tumor segmentation in brain MRI using a fuzzy approach with class center priors” Journal on Image and Video Processing.

  14. Mohamed NA et al (1998) Modified Fuzzy C-Mean in medical image segmentation. International Conference of the IEEE Engineering in Medicine and Biology Society 20(3)

  15. Patil SS et al (2017) Brain tumor detection using segmentation based on fuzzy transform. International Journal of Engineering Science and Computing

  16. Priya SS, Valarmathi A (2019) Efficient fuzzy c-means based multilevel image segmentation for brain tumor detection in MR images, Spinger.

  17. Rajendran A, Dhanasekaran R (2011) Enhanced possibilistic Fuzzy C-Means algorithm for normal and pathological brain tissue segmentation on magnetic resonance brain image, Research Article - Computer Engineering and Computer Science

  18. Rajendran A, Dhanasekaran R et al (2012) Brain tumor segmentation on MRI brain images with fuzzy clustering and GVF snake model. Int J Comput Commun, ISSN 1841–9836 7

  19. Saha M, Panda C (2018) A review on various image segmentation techniques for brain tumor detection. International Journal of Scientific Research in Computer Science, Engineering and Information Technology IJSRCSEIT 3(1):2456–3307

    Google Scholar 

  20. Shanker Ravi, Bhattacharya M (2018) Brain Tumor Segmentation of Normal and Pathological Tissues Using K-mean Clustering with Fuzzy C-mean Clustering, Springer.

  21. Sharma M, Mukharjee S (2013) “Brain tumor segmentation using genetic algorithm and artificial neural network fuzzy inference system (ANFIS), Advances in Computing & Information Technology.

  22. Sharma AV et al (2014) Brain tumor detection based on segmentation using object labeling algorithm. International Journal of Engineering Research & Technology (IJERT) 3(5)

  23. Shen S et al (2005) MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization. IEEE Transactions on Information Technology in Biomedicine 9(3)

  24. Sompong C (2016) Brain tumor segmentation using cellular automata-based fuzzy C-means, International Joint Conference on Computer Science and Software Engineering (JCSSE)

  25. Srinivas B, Rao GS (2019) Performance Evaluation of Fuzzy C Means Segmentation and Support Vector Machine Classification for MRI Brain Tumor”, Springer Nature Singapore, 2019.

  26. Sonali Wadgure et al (2014) Detection of brain tumor from mri of brain using Fuzzy C-Mean (FCM)”, International Journal of Science, Engineering and Technology Research (IJSETR), 3 8

Download references

Author information

Authors and Affiliations

  1. St. Xavier’s Autonomous College, Palayamkottai affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, Tamil Nadu, 627012, India

    C. Jaspin Jeba Sheela

  2. Department of Computer Science, Women’s Christian College, Nagercoil affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, Tamil Nadu, 627012, India

    G. Suganthi

Authors
  1. C. Jaspin Jeba Sheela
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. G. Suganthi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to C. Jaspin Jeba Sheela.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sheela, C.J.J., Suganthi, G. Morphological edge detection and brain tumor segmentation in Magnetic Resonance (MR) images based on region growing and performance evaluation of modified Fuzzy C-Means (FCM) algorithm. Multimed Tools Appl 79, 17483–17496 (2020). https://doi.org/10.1007/s11042-020-08636-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-08636-9

Keywords

Search

Navigation