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

Image Segmentation Algorithms on MR Brain Images

by G. Evelin Suji, Y. V. S. Lakshimi, G. Wiselin Jiji
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 67 - Number 16
Year of Publication: 2013
Authors: G. Evelin Suji, Y. V. S. Lakshimi, G. Wiselin Jiji
10.5120/11478-7031

G. Evelin Suji, Y. V. S. Lakshimi, G. Wiselin Jiji . Image Segmentation Algorithms on MR Brain Images. International Journal of Computer Applications. 67, 16 ( April 2013), 18-20. DOI=10.5120/11478-7031

@article{ 10.5120/11478-7031,
author = { G. Evelin Suji, Y. V. S. Lakshimi, G. Wiselin Jiji },
title = { Image Segmentation Algorithms on MR Brain Images },
journal = { International Journal of Computer Applications },
issue_date = { April 2013 },
volume = { 67 },
number = { 16 },
month = { April },
year = { 2013 },
issn = { 0975-8887 },
pages = { 18-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume67/number16/11478-7031/ },
doi = { 10.5120/11478-7031 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:24:59.133617+05:30
%A G. Evelin Suji
%A Y. V. S. Lakshimi
%A G. Wiselin Jiji
%T Image Segmentation Algorithms on MR Brain Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 67
%N 16
%P 18-20
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Magnetic Resonance Image plays a major role in medical diagnostics. Image segmentation is done to divide an image into meaningful structures. Image segmentation is the initial step in image analysis and pattern recognition. It becomes more important while dealing with medical images where pre-surgery and post-surgery decisions are required for the purpose of initiating and speeding up the recovery process. Manual segmentation of abnormal tissues cannot be compared with modern day's high speed computing machines. Segmentation is done to extract the features of the image that are used for analysis, interpretation, and understanding of images. Accuracy of the extracted features decides the accuracy of the algorithm. Selection of a suitable algorithm is highly based on the application. This paper highlights the various image segmentation algorithms, used in medical images.

References
  1. S. Wareld, J. Dengler, J. Zares, C. Guttmann, W. Gil, J. Ettinger, J. Hiller, and R. Kikinis, Automatic identication of grey matter structures from mri to improve the segmentation of white matter lesions, Journal of Image Guided Surgery, 1(6):326-338,1995.
  2. Mathew C. Clark, Segmentating MRI Volumes of the Brain With Knowledge- Based Clustering. MS Thesis, Department of Computer Science and Engineering, University of South Florida, 1994.
  3. M. Masroor Ahmed, Dzulkifli Bin Mohamad, Segmentation of Brain MR Images for Tumor Extraction by Combining Kmeans Clustering and Perona-Malik Anisotropic Diffusion Model, International Journal of Image Processing, vol. 2, issue (1), pp 27-34.
  4. Sameena Banu, The comparative study on color Image segmentation Algorithm, IJERA, vol 2, pp 1277-1281,2012.
  5. Krishna Kant Singh, Akansha Singh, A study of Image Segmentation Algorithms For Different Types of Images, International Journal of Computer Science, vol 7, 2010.
  6. Vipula Singh, Digital Image Processing with MATLAB and LabVIEW, Reed Elsevier India Private Limited.
  7. S. Jayaraman, S. Esakkirajan, T. Veerakumar, Digital Image Processing, Tata McGraw Hill Education Private Limited.
  8. R. C Gonzalez, R. E Woods and S. L Eddins, Digital Image Processing Using MATLAB, Pearson, Fifth Impression,2009.
  9. Issac N. Bankman, Hand book of Medical Image Processing and analysis, Second Edition, Accademic press,2008.
  10. Ravikanth Malladi, James A. Sethian and Baba C. Vemuri, Shape modeling with front propagation: a level set approach, IEEE Transactons on Pattern analysis and machine intelligence, vol 17,1995.
  11. D. Narain Ponraj, M. Evangelin Jenifer, P. Poongodi, J. Samuel Manoharan, A survey on the preprocessing Techniques of mammogram for the detection of Breast Cancer,JETCIS, vol2, pp 656-664.
  12. S. K. Pal et al. , A review on Image segmentation techniques, Pattern Recognition, 29, 1277,1294, 1993.
  13. J. M. Keller, and C. L. Carpenter, Image Segmentation in the presence of Uncertainty, International Journal of Intelligent Systems, Vol. SMC-15, 193-208, 1990.
  14. S. K. Pal and R. A. King, On Edge Detection of X-ray Images Using Fuzzy Sets, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. PAMI-5, No. 1, 69-77, 1983.
  15. I. Bloch, Fuzzy Connectivity and Mathematical Morphology, Pattern Recognition letters, Vol. 14, 483-488, 1993.
  16. T. L. Huntsberger, C. L. Jacobs and R. L. Canon, Iterative Fuzzy Image Segemntation, Pattern Recognition, Vol. 18, No. 2, 131-138, 1985.
  17. Zhenghao Shi, Lifeng He, Application of neural Networks in Medical Image Processing, Proceedings of the second International Symposium on Networking and Network Security. ISBN 978-952-5726-09-1, 2010, 023-026.
  18. K. Suzuki et al, Neural Edge Enhancement from Noisy Images, IEEE Trans. Pattern Ana. Mach. Intell. 25, 2003, 1582-1596.
Index Terms

Computer Science
Information Sciences

Keywords

Accuracy Algorithm Analysis Features Segmentation