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Brain Tumor Segmentation using Genetic Algorithm and FCM Clustering Approach

by Garima Garg, Sonia Juneja
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 49 - Number 2
Year of Publication: 2012
Authors: Garima Garg, Sonia Juneja
10.5120/7601-0331

Garima Garg, Sonia Juneja . Brain Tumor Segmentation using Genetic Algorithm and FCM Clustering Approach. International Journal of Computer Applications. 49, 2 ( July 2012), 24-27. DOI=10.5120/7601-0331

@article{ 10.5120/7601-0331,
author = { Garima Garg, Sonia Juneja },
title = { Brain Tumor Segmentation using Genetic Algorithm and FCM Clustering Approach },
journal = { International Journal of Computer Applications },
issue_date = { July 2012 },
volume = { 49 },
number = { 2 },
month = { July },
year = { 2012 },
issn = { 0975-8887 },
pages = { 24-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume49/number2/7601-0331/ },
doi = { 10.5120/7601-0331 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:47:03.867507+05:30
%A Garima Garg
%A Sonia Juneja
%T Brain Tumor Segmentation using Genetic Algorithm and FCM Clustering Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 49
%N 2
%P 24-27
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image processing is any type of signal processing in which we take any abnormal image of brain tumor and then produce an output which is extracted portion of tumor by applying genetic algorithm with fuzzy clustering means method. FCM is superior over different clustering approaches. This combined approach is used to improve segmentation efficiency and obtain higher value of true positive pixels belong to tumorous region. Genetic algorithm is a stochastic global optimization algorithm, their combination can prevent FCM being trapped in local optimum and give more better results in comparison to neural networks and CAD approaches.

References
  1. "The essential guide to brain tumors", National brain tumor society (NBTS), 2007.
  2. K. Selvanayaki "CAD System for Automatic Detection of Brain Tumor through Magnetic Resonance Image " et. al. / International Journal of Engineering Science and Technology Vol. 2(10), 2010, 5890-5901
  3. 3 Ehab F. Badran, Esraa Galal Mahmoud, and Nadder Hamdy "An Algorithm for Detecting Brain Tumors in MRI Images" Department of Electronics and Communications Engineering 978-1-4244-7042-6/10/$26. 00 ©2010 IEEE
  4. T. Logeswari, M. KARNAN, "An Enhanced Implementation of Brain Tumor Detection Using Segmentation Based on Soft Computing", 2010 International Conference on Signal Acquisition and Processing 978-0-7695-3960-7/10 © 2010 IEEE
  5. Andac Hamamci*, Nadir Kucuk, Kutlay Karaman, Kayihan Engin and Gozde Unal"Tumor-Cut: Segmentation of Brain Tumors on Contrast Enhanced MR Images for Radiosurgery Applications", IEEE Transactions on medical imaging, vol 31, no 3, March 2012
  6. M. Usman Akram', Anam Usman "Computer Aided System for Brain Tumor Detection and Segmentation" 978-1-61284-941-6/11/$26. 00 ©2011 IEEE
  7. Introduction to genetic algorithm by S. N. Shivanandnam and S. N. Deepa
  8. http://en. wikipedia. org/wiki/MATLAB/ Math Works. com.
  9. Huynh Van Lung and Jong-Myon Kim, Member, IEEE "A Generalized Spatial Fuzzy C-Means Algorithm for Medical Image Segmentation. " FUZZ-IEEE 2009, Korea, August 20-24, 2009.
Index Terms

Computer Science
Information Sciences

Keywords

Clustering Brain tumor segmentation fuzzy c means Genetic Algorithm Digital Imaging and Communications in Medicine

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