CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

An Approach to Detect Brain Tumor

by Prashengit Dhar, Md. Burhan Uddin Chowdhury
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 8
Year of Publication: 2018
Authors: Prashengit Dhar, Md. Burhan Uddin Chowdhury
10.5120/ijca2018917654

Prashengit Dhar, Md. Burhan Uddin Chowdhury . An Approach to Detect Brain Tumor. International Journal of Computer Applications. 182, 8 ( Aug 2018), 41-45. DOI=10.5120/ijca2018917654

@article{ 10.5120/ijca2018917654,
author = { Prashengit Dhar, Md. Burhan Uddin Chowdhury },
title = { An Approach to Detect Brain Tumor },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2018 },
volume = { 182 },
number = { 8 },
month = { Aug },
year = { 2018 },
issn = { 0975-8887 },
pages = { 41-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number8/29844-2018917654/ },
doi = { 10.5120/ijca2018917654 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:10:51.030803+05:30
%A Prashengit Dhar
%A Md. Burhan Uddin Chowdhury
%T An Approach to Detect Brain Tumor
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 8
%P 41-45
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image processing has covered a significant area in medical science. Medical image processing is currently very popular for the effectiveness of image process in medical imaging especially on MR image based task. But it is not so easy. Medical image processing is a challenging task. Wrong decision may cause to great harm to the people. MR images are often used to diagnose and analyze brain tumor. This paper represents a new way for detecting tumor in the brain. This proposed methodology is supported by color information. YCbCr color model is employed for this purpose. Input image is transformed into YCbCr. The segmentation is mainly done by gathering information of Y component. Color based thresholding is performed to segment the image. A morphological action is employed to make the image fine. Then the image is filtered with area. Finally calculated metric value for each object. Highest metric value refers to the tumor object.

References
  1. S.U. Aswathy, G. Glan Deva Dhas, S.S. Kumar, “A Survey on Detection of Brain Tumor From MRI Brain Images”, 2014 international conference on control, Instrumentation, Communication and Computational Technologies(ICCICCT), pp. 871-877, 2014.
  2. Manoj K Kowear and Sourabh Yadev, “Brain tumor detection and segmentation using histogram thresholding”, International Journal of engineering and Advanced Technology, April 2012.
  3. Rajesh C. patil, A.S. Bhalchandra, “Brain tumor extraction from MRI images Using MATLab”, IJECSCSE, ISSN: 2277-9477,Volume 2, issue 1.
  4. M.Karuna, Ankita Joshi, “Automatic detection and severity analysis of brain tumors using gui in matlab” IJRET: International Journal of Research in Engineering and Technology, ISSN: 2319-1163, Volume: 02 Issue:10,Oct-2013
  5. R. Preetha, G. R. Suresh, “Performance Analysis of Fuzzy C Means Algorithm in Automated Detection of Brain Tumor”,IEEE CPS, WCCCT, 2014
  6. Luxit Kapoor, Sanjeev Thakur,”A Survey on Brain Tumor Detection Using Image Processing Techniques”, 7th International Conference on Cloud Computing, Data Science & Engineering, 2017
  7. C.Hemasundara Rao, Dr. P.V. Naganjaneyulu,Dr.K.Satya Prasad, “Brain tumor detection and segmentation using conditional random field”, IEEE 7th International Advance Computing Conference, 2017
  8. Mohammad Havaeia,Axel Davy, David Warde-Farley, Antoine Biard, Aaron Courville, Yoshua Bengio, Chris Pal, “Brain Tumor Segmentation with Deep Neural Networks”, arXiv:1505.03540v3 [cs.CV] 20 May 2016
  9. E. Grosso, M.Lopez, C. Salvatore, I. Castiglioni, “ A decision support system for the assisted diagnosis of brain tumors: A feasiblity study for(18)F-FDG PET preclinical studies”, 34th Annual international conference of the IEEE EMBS, 2012, pp.6255-6258.
  10. D. Sridhar and Murali Krishna, “Brain tumor classification using discrete cosine transform and probabilistic neural network”, IEEE International conference on Signal Processing , Image Processing and Pattern Recognition, 2013, pp. 1-5.
  11. V. SalaiSelvam and S. Shenbagadevi, “Brain Tumor Detection using Scalp EEG with modified Wavelet-ICA and Multi Layer Feed Forward Neural Network”, 33rd annual international conference of the IEEE EMBS, IEEE, 2011, pp. 6104-6109.
  12. J. Zhou, K. L. Chan, V. F. H. Chong and S. M. Krishnan, “Extraction of Brain Tumor from MR images using one class support vector machine”, 27th annual conference on engineering in medicine and biology, IEEE, pp.6411-6414.
  13. Yao tienchen, “Brain tumor detection using three dimensional Bayesian level set method with volume rendering”, international conference on Wavelet analysis and pattern recognition, IEEE, 2012, pp.158-163.
  14. Mohd Fauzi Othman, Mohd Ariffanan, Mohd Basri, “ Probabilistic Neural network for brain tumor classification”, second international conference on intelligent systems, modeling and simulation, IEEE, 2011, 1366-138.
  15. Sahar Ghanavati, Junning Li, Ting Liu, Paul S. Babyn, Wendy Doda and George Lompropoulos, “Automatic brain tumor detection in magnetic resonance images”, 9th international conference on Biomedical Imaging, IEEE, 2012, pp. 574-577.
  16. Hongming Li and Yong Fan, “Label propagation with robust initialization for brain tumor segmentation”, 9th international symposium on Bio medical imaging, IEEE, 2012, pp.1715-1718.
Index Terms

Computer Science
Information Sciences

Keywords

Metric value YCbCr morphological closing

Powered by PhDFocusTM