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

An Experimental Analysis of Fuzzy C-Means and K-Means Segmentation Algorithm for Iron Detection in Brain SWI using Matlab

by Beshiba Wilson, Julia Punitha Malar Dhas
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 104 - Number 15
Year of Publication: 2014
Authors: Beshiba Wilson, Julia Punitha Malar Dhas
10.5120/18281-9250

Beshiba Wilson, Julia Punitha Malar Dhas . An Experimental Analysis of Fuzzy C-Means and K-Means Segmentation Algorithm for Iron Detection in Brain SWI using Matlab. International Journal of Computer Applications. 104, 15 ( October 2014), 36-38. DOI=10.5120/18281-9250

@article{ 10.5120/18281-9250,
author = { Beshiba Wilson, Julia Punitha Malar Dhas },
title = { An Experimental Analysis of Fuzzy C-Means and K-Means Segmentation Algorithm for Iron Detection in Brain SWI using Matlab },
journal = { International Journal of Computer Applications },
issue_date = { October 2014 },
volume = { 104 },
number = { 15 },
month = { October },
year = { 2014 },
issn = { 0975-8887 },
pages = { 36-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume104/number15/18281-9250/ },
doi = { 10.5120/18281-9250 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:36:16.665114+05:30
%A Beshiba Wilson
%A Julia Punitha Malar Dhas
%T An Experimental Analysis of Fuzzy C-Means and K-Means Segmentation Algorithm for Iron Detection in Brain SWI using Matlab
%J International Journal of Computer Applications
%@ 0975-8887
%V 104
%N 15
%P 36-38
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

An accurate assessment of iron accumulation is required for diagnosis and therapy of iron overload in various neurodegenerative diseases. Susceptibility Weighted Imaging (SWI) offers information about any tissue that has a different susceptibility than its surrounding structures. Reliable methods to precisely quantify brain iron are essential. Image segmentation refers to partition of an image into different regions that differ in some characteristics. Accurate segmentation of medical images is a very difficult task. However, the process of accurate segmentation of these images is very important for a correct diagnosis by clinical tools. In this paper, an experimental analysis is done using fuzzy c-means and k-means segmentation algorithm for detection of iron content in SWI brain images.

References
  1. E. M. Haacke, S. Mittal, Z. Wu, J. Neelavalli, Y. -C. N. Cheng, Susceptibility-Weighted Imaging: Technical Aspects and Clinical Applications, Part 1, American Journal of Neuroradiology, 30:19 –30, Jan 2009.
  2. E. M. Haacke , Cheng N Y, House M J, et al. , Imaging Iron Stores In The Brain Using Magnetic Resonance Imaging, Magn Reson Imaging 2005,23:1–25.
  3. Harder S L, Hopp K M, Ward H, et al. , Mineralization of the deep gray matter with age: a retrospective review with susceptibility-weighted MR imaging, American Journal of Neuroradiology ,2008, 29:176 –83.
  4. Gelman B B, Iron in CNS disease, J Neuropathol Exp Neurol, 1995, 54:477– 86.
  5. R. C. Dubes and A. K. Jain. Algorithms for Clustering Data, Prentice Hall, 1988.
  6. L. Kaufman and P. J. Rousseeuw. Finding Groups in Data: an Introduction to Cluster Analysis. John Wiley & Sons, 1990.
  7. K. Mehrotra, C. Mohan, and S. Ranka. Elements of Artificial Neural Networks. MIT Press, 1996.
  8. L. O. Hall, A. M. Bensaid, L. Clarke, R. P. Velthuizen, M. Silbiger, and J. C. Bezdek, A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain, IEEE Trans. Neural Networks, 3 (1992), 672 - 681.
  9. R. M. Haralick, K. Shanmugam and I. Dinstein, Textural features for image classification, IEEE Trans. Syst. Man and Cyber. ,3 (1973), 610 - 621.
  10. W. Chumsamrong, P. Thitimajshima, and Y. Rangsanseri, Synthetic aperture radar (SAR) image segmentation using a new modified fuzzy c-means algorithm, Proceedings of Geoscience and Remote Sensing Symposium, 2 (2000), 624 - 626.
  11. J. C. Bezdek, Pattern recognition with fuzzy objective function algorithms, Plenum, New York, 1981.
Index Terms

Computer Science
Information Sciences

Keywords

Susceptibility Weighted Imaging Fuzzy c-means.

Powered by PhDFocusTM