We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Reseach Article

Automated Detection and Extraction of Brain Tumor from MRI Images

by Neha Tirpude, Rashmi Welekar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 77 - Number 4
Year of Publication: 2013
Authors: Neha Tirpude, Rashmi Welekar
10.5120/13383-1007

Neha Tirpude, Rashmi Welekar . Automated Detection and Extraction of Brain Tumor from MRI Images. International Journal of Computer Applications. 77, 4 ( September 2013), 26-30. DOI=10.5120/13383-1007

@article{ 10.5120/13383-1007,
author = { Neha Tirpude, Rashmi Welekar },
title = { Automated Detection and Extraction of Brain Tumor from MRI Images },
journal = { International Journal of Computer Applications },
issue_date = { September 2013 },
volume = { 77 },
number = { 4 },
month = { September },
year = { 2013 },
issn = { 0975-8887 },
pages = { 26-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume77/number4/13383-1007/ },
doi = { 10.5120/13383-1007 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:49:23.210278+05:30
%A Neha Tirpude
%A Rashmi Welekar
%T Automated Detection and Extraction of Brain Tumor from MRI Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 77
%N 4
%P 26-30
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image segmentation algorithms and techniques find its applications in a wide number of domains. Segmentation of brain tumor and overall internal structure of the brain is one of the main applications in the field of medical imaging. Magnetic resonance imaging (MRI) technique is one of the many imaging modalities that are available to scan and capture the internal soft tissue structures of the body. In this paper, proposed technique has been given to extract the tumor portion, successfully demarcate the tumor boundary, locate the tumor with a bounding circle and to diagnose whether the tumor is present or absent. A fuzzy clustering-based technique is proposed which helps to study & analyze the intricate structure of the brain, hence can be used as a visual analysis and a study tool.

References
  1. Gonzalez, R. , Woods, R. , 2009. Digital Image Processing, third ed. Pearson Education.
  2. Viner J. Brain Tumors. University of California, San Francisco Department of Neurosurgery. Available at http://nursing. ucsf medicalcenter. org/education/classMaterial/34_2. pdf. Accessed 17 May, 2013
  3. Dahab, D. A. , Ghoniemy, S. A. , Selim, G. M. , 2012. Automated Brain Tumor Detection and Identification using Image Processing and Probabilistic Neural Network Techniques. International Journal of Image Processing and Visual Communication, 1-8
  4. Sinha, M. , Mathur, K. , 2012. Improved Brain Tumor Detection with Ontology. International Journal of Computational Engineering Research. 584-588
  5. Rajendran, A. , Dhanasekaran, R. 2012. Brain Tumor Segmentation on MRI Brain Images with Fuzzy Clustering and GVF Snake Model. International Journal of Computation Communication. 530-539
  6. Noreen, N. , Hayat, K. , Madani, S. , 2011. MRI Segmentation through Wavelets and Fuzzy C-Means. World Applied Sciences Journal 13 Special Issue of Applied Math). 34-39
  7. Sajjad Mohsin, S. , Sajjad, S. , Malik, Z. , Abdullah,A. H. , 2012. Efficient Way of Skull Stripping in MRI to Detect Brain Tumor by Applying Morphological Operations, after Detection of False Background. International Journal of Information and Education Technology. 335-337
  8. Mustaqeem, A. , Javed, A. , Fatima, T. , 2012. An Efficient Brain Tumor Detection Algorithm using Watershed and Thresholding Based Segmentation. International Journal of Image, Graphics and Signal Processing. 34-39
  9. Vasuda, P. , Satheesh S. , 2010. Improved Fuzzy C-Means algorithm for MR brain image segmentation. International Journal on Computer Science and Engineering, 1713-1715
  10. Dasgupta, A. , 2012. Demarcation of Brain Tumor using Modified Fuzzy C-Means. International Journal of Engineering Research and Applications. 529-533,
  11. Rakesh, M. , Ravi, T. , 2012. Image Segmentation and Detection of Tumor Objects in MR Brain Images Using Fuzzy C-Means(FCM) Algorithm. International Journal of Engineering Research and Applications. 2088-2094
  12. Soesanti, I. , Susanto, A. , Widodo, T. S. , Tjokronagoro M. , 2011. Optimized Fuzzy Logic Application for MRI Brain Images Segmentation. International Journal of Computer Science and Information Technology. 137-146
  13. Patil, R. C. , Bhalchandra, A. S. Brain tumor extraction from MRI images using MATLAB. International Journal of Electronics, Communication & Soft Computing Science and Engineering. 1-4
  14. Tonarelli, L. , "Magnetic Resonance Imaging of Brain Tumor", Available at http://www. cewebsource. com/coursePDFs/MRIBrainTumor
  15. Westbrook, C. ,2010 MRI at a Glance, ISBN: 978-1-4051-4747-7, Wiley-Blackwell
  16. Tirpude, N. N. , Welekar, R. R. , 2013. Effect of Global Thresholding on Tumor-Bearing Brain MRI Images. International Journal of Engineering and Computer Science. 728-731
  17. Gonzalez, R. , Woods R. , 2008. Digital Image Processing, third ed. Pearson Education, Prentice Hall, pp. 741-742
  18. Gonzalez, R. , Woods R. , 2008. Digital Image Processing, third ed. Pearson Education, Prentice Hall, pp. 742-747
  19. Mohd Fauzi Othman, Mohd Ariffanan and Mohd Basri, "Probabilistic Neural Network for Brain Tumor Classification," 2nd International Conference on Intelligent Systems, Modelling and Simulation, 2011.
Index Terms

Computer Science
Information Sciences

Keywords

MRI Magnetic resonance imaging image segmentation fuzzy clustering thresholding