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Reseach Article

Application of Edge Detection for Brain Tumor Detection

by Pratibha Sharma, Manoj Diwakar, Sangam Choudhary
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 58 - Number 16
Year of Publication: 2012
Authors: Pratibha Sharma, Manoj Diwakar, Sangam Choudhary
10.5120/9366-3820

Pratibha Sharma, Manoj Diwakar, Sangam Choudhary . Application of Edge Detection for Brain Tumor Detection. International Journal of Computer Applications. 58, 16 ( November 2012), 21-25. DOI=10.5120/9366-3820

@article{ 10.5120/9366-3820,
author = { Pratibha Sharma, Manoj Diwakar, Sangam Choudhary },
title = { Application of Edge Detection for Brain Tumor Detection },
journal = { International Journal of Computer Applications },
issue_date = { November 2012 },
volume = { 58 },
number = { 16 },
month = { November },
year = { 2012 },
issn = { 0975-8887 },
pages = { 21-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume58/number16/9366-3820/ },
doi = { 10.5120/9366-3820 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:02:41.074649+05:30
%A Pratibha Sharma
%A Manoj Diwakar
%A Sangam Choudhary
%T Application of Edge Detection for Brain Tumor Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 58
%N 16
%P 21-25
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Brain tumors are created by abnormal and uncontrolled cell division in brain itself. If the growth becomes more than 50%, then the patient is not able to recover. So the detection of brain tumor needs to be fast and accurate. The objective of this paper is to provide an efficient algorithm for detecting the edges of brain tumor. The first step starts with the acquisition of MRI scan of brain and then digital imaging techniques are applied for getting the exact location and size of tumor. MRI images consist of gray and white matter and the region containing tumor has more intensity. So, first noise filters are used for noise removal and then enhancement techniques are applied to the given MRI scan of brain. After that the basic morphological operations are applied for extracting the region suffering from tumor. And then verification of region detected is done by using watershed segmentation.

References
  1. Oelze, M. L,Zachary, J. F. , O'Brien, W. D. , Jr. , Differentiation of tumor types in vivo by scatterer property estimates and parametric images using ultrasound backscatter , on page(s) :1014 - 1017 Vol. 1, 5-8 Oct. 2003.
  2. T. Logeswari and M. Karnan, An improved implementation of brain tumor detection using segmentation based on soft computing, Second International Conference on Communication Software and Networks, 2010. ICCSN'10. Page(s): 147-151.
  3. Devos, A, Lukas, L. ,Does the combination of magnetic resonance imaging and spectroscopic imaging improve the classification of brain tumours?? On Page(s): 407 – 410, Engineering in Medicine and Biology Society, 2004. IEMBS '04. 26th Annual International Conference of the IEEE, 1-5 Sept. 2004.
  4. Chunyan J, Xinhua Z, Wanjun H, Christoph M (2000). Segmentation and Quantification of Brain Tumor,"IEEE International conference on Virtual Environment, Human-Computer interfaces and Measurement Systems, USA pp. 12 14.
  5. Gopal, N. N. Karnan, M. , Diagnose brain tumor through MRI using image processing clustering algorithms such as Fuzzy C Means along with intelligent optimization techniques, Page(s): 1 – 4, Computational Intelligence and Computing Research (ICCIC), 2010 IEEE International Conference, 28-29 Dec. 2010.
  6. Farmer, M. E, Jain, A. K. , A wrapper-based approach to image segmentation and classification, Page(s): 2060 - 2072 , Image Processing, IEEE Transactions on journals and magazines, Dec. 2005
  7. P. Vasuda, S. Satheesh, Improved Fuzzy C-Means Algorithm for MR Brain Image Segmentation, Page(s): 1713-1715, (IJCSE) International Journal on Computer Science and Engineering,Vol. 02, 05, 2010.
  8. T. Logeswari, M. Karnan, An improved implementation of brain tumor detection using segmentation based on soft computing, Page(s): 006-014, Journal of Cancer Research and Experimental Oncology Vol. 2(1), March 2010.
  9. Ming niwu,chia-chen Lin and chin-chenchang, Brain Tumor Detection Using Color-Based K-Means Clustering Segmentation , Page(s): 245 – 250 , Intelligent Information Hiding and Multimedia Signal Processing, 2007. IIHMSP 2007. Third International Conference,26-28 Nov. 2007
  10. Gang Li , Improved watershed segmentation with optimal scale based on ordered dither halftone and mutual information, Page(s) 296 - 300, Computer Science and Information Technology (ICCSIT), 2010, 3rd IEEE International Conference ,9-11 July 2011
Index Terms

Computer Science
Information Sciences

Keywords

Morphological Operations Edge detection Noise filters