CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Comparative Analysis of Brain Tumor Detection using Different Segmentation Techniques

by A. Ramaswamy Reddy, E. V. Prasad, L. S. S. Reddy
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 82 - Number 14
Year of Publication: 2013
Authors: A. Ramaswamy Reddy, E. V. Prasad, L. S. S. Reddy
10.5120/14229-1229

A. Ramaswamy Reddy, E. V. Prasad, L. S. S. Reddy . Comparative Analysis of Brain Tumor Detection using Different Segmentation Techniques. International Journal of Computer Applications. 82, 14 ( November 2013), 14-28. DOI=10.5120/14229-1229

@article{ 10.5120/14229-1229,
author = { A. Ramaswamy Reddy, E. V. Prasad, L. S. S. Reddy },
title = { Comparative Analysis of Brain Tumor Detection using Different Segmentation Techniques },
journal = { International Journal of Computer Applications },
issue_date = { November 2013 },
volume = { 82 },
number = { 14 },
month = { November },
year = { 2013 },
issn = { 0975-8887 },
pages = { 14-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume82/number14/14229-1229/ },
doi = { 10.5120/14229-1229 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:57:42.785211+05:30
%A A. Ramaswamy Reddy
%A E. V. Prasad
%A L. S. S. Reddy
%T Comparative Analysis of Brain Tumor Detection using Different Segmentation Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 82
%N 14
%P 14-28
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this study, we would like to present brain tumor detection methods, based on the conventional K-means technique, Expectation Maximization (EM) algorithm and a new Spatial Fuzzy-technique analysis of brain MR images. Though, the K-means and EM algorithm were already used in Brain MR image segmentation, as well as image segmentation in general, it fails to utilize the strong spatial correlation between neighboring pixels. A spatial Fuzzy C-means (SFCM's) technique, which is utilize the spatial information properly and produce high quality segmentation of brain tumor images. Five ground truth images are taken to test the segmentation performance of K-means, EM algorithms and Spatial Fuzzy C-means technique. The segmentation results, which are proved more accurate segmentation by the SFCM's compared to that of K-means and EM algorithm, are presented statistically and graphically.

References
  1. Ahmed, S. and K. M. Iftekharuddin, 2011. Efficacy of texture, shape and intensity feature fusion for posterior-fossa tumor segmentation in MRI. IEEE Trans. Inform. Technol. Biomed. , 15: 206-213. DOI: 10. 1109/TITB. 2011. 2104376
  2. Yong yang, shuying huang, 2007. Image segmentation by fuzzy c-means clustering algorithm with a novel penalty term Computing and Informatics, Vol. 26, 2007, 17–31.
  3. André michael coleman, June 2008. Dissertation: An adaptive landscape classification procedure using Geoinformatics and artificial neural networks, faculty of earth and life sciences Vrije universiteit, Amsterdam The Netherlands.
  4. Vannier, M. W. , C. M. Speidel and D. L. Rickman, 1991. Validation of Magnetic Resonance Imaging (MRI) multispectral tissue classification. Comput. Med. Imaging Graph, 15: 217-223. PMID: 1913572
  5. Matthew Deighton and Maria Petrou, 2003. supervised segmentation of Volume Textures using 3D Probabilistic Relaxation. SCIA,LNCS 2749,pp 869-876. Springer-Verlag Berlin Heidelberg.
  6. Shweta Jain, Shubha Mishra, 2013. ANN Approach Based On Back Propagation Network and Probabilistic Neural Network to Classify Brain Cancer. (IJITEE) ISSN: 2278-3075, Volume-3, Issue-3,pp: 101-105.
  7. Shan Shen, William Sandham, Member, IEEE, Malcolm Granat, and Annette Ster. MRI Fuzzy Segmentation of Brain Tissue Using Neighborhood Attraction With Neural-Network Optimization. ieee transactions on information technology in biomedicine, vol. 9, no. 3, september 2005
  8. Chuang, K. S. , H. L. Tzeng, S. Chen, J. Wu and T. J. Chen, 2006. Fuzzy c-means clustering with spatial information for image segmentation. Comput. Med. Imaging Graph. , 30: 9-15. PMID: 16361080
  9. Indah, S. , S. Adhi, S. W. Thomas and T. Maesadji, 2011. MRI brain images segmentation based on optimized fuzzy logic and spatial information. Int. J. Video and Image Proc. Netw. Secu. , 11: 6-6.
  10. Boccignone G, Napoletano P, Caggiano V, Ferraro M. 2005 A multiresolution diffused expectation-maximization algorithm for medical image segmentation. Comput Biol Med. 2007 Jan;37(1):83-96.
  11. Saeed, M. , W. C. Karl, H. R. Rabiee and T. Q. Nguyen, 1998. A new multiresolution algorithm for image segmentation. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, May 12-15, IEEE Xplore Press, Seattle, WA,pp:2753-2756.
  12. Pham, D. L. , 2003. Unsupervised tissue classification in medical images using edge-adaptive clustering. Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Sep. 17-21, IEEE Xplore Press, pp: 634-637.
  13. Christ,M. C. J. Parvathi, R. M. S. 2011 Fuzzy c-means algorithm for medical image segmentation Electronics Computer Technology (ICECT), 3rd International Conference on (Volume:4,pp:33-36).
  14. Kannan, S. R. , 2005. Segmentation of MRI using new unsupervised fuzzy C mean algorithm. ICGST-GVIP J.
  15. Alizadeh, S. , M. Ghazanfari and M. Fathian, 2008. Using data mining for learning and clustering FCM. Int. J. Computat. Intell. , 4: 118-125.
Index Terms

Computer Science
Information Sciences

Keywords

K-Means Expectation Maximization Fuzzy MR images SFCM's

Powered by PhDFocusTM