CFP last date
20 December 2024
Reseach Article

Improvement of Brain Tumor Feature based Segmentation using Decision based Alpha Trimmed Global Mean Filter

by Pratibha Sharma, Harjit Singh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 121 - Number 21
Year of Publication: 2015
Authors: Pratibha Sharma, Harjit Singh
10.5120/21823-5074

Pratibha Sharma, Harjit Singh . Improvement of Brain Tumor Feature based Segmentation using Decision based Alpha Trimmed Global Mean Filter. International Journal of Computer Applications. 121, 21 ( July 2015), 13-20. DOI=10.5120/21823-5074

@article{ 10.5120/21823-5074,
author = { Pratibha Sharma, Harjit Singh },
title = { Improvement of Brain Tumor Feature based Segmentation using Decision based Alpha Trimmed Global Mean Filter },
journal = { International Journal of Computer Applications },
issue_date = { July 2015 },
volume = { 121 },
number = { 21 },
month = { July },
year = { 2015 },
issn = { 0975-8887 },
pages = { 13-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume121/number21/21823-5074/ },
doi = { 10.5120/21823-5074 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:09:00.659866+05:30
%A Pratibha Sharma
%A Harjit Singh
%T Improvement of Brain Tumor Feature based Segmentation using Decision based Alpha Trimmed Global Mean Filter
%J International Journal of Computer Applications
%@ 0975-8887
%V 121
%N 21
%P 13-20
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Detection of the brain tumor is an important application of medical image processing. The literature survey in this paper has shown that the many of the existing methods has unobserved the deprived quality images like images with amount of noise or poor brightness. Moreover the much of the existing work on tumor detection has abandoned the use of object based segmentation. The overall goal of this research work is to propose an efficient brain tumor detection using the feature detection androundness metric. To enhance the tumor detection rate further we have integrated the proposed object based tumor detection with the Decision based alpha trimmed global mean. The proposed technique has the ability to produce effective results even in case of high density of the noise.

References
  1. Prastawa, Marcel, Elizabeth Bullitt, Sean Ho, and Guido Gerig. "A brain tumor segmentation framework based on outlier detection. " Medical image analysis 8, no. 3 (2004): 275-283.
  2. Zook, Justin M. , and Khan M. Iftekharuddin. "Statistical analysis of fractal-based brain tumor detection algorithms. " Magnetic Resonance Imaging 23, no. 5 (2005): 671-678.
  3. Murugesan, M. , and R. Sukanesh. "Automated detection of brain tumor in EEG signals using artificial neural networks. " In Advances in Computing, Control, & Telecommunication Technologies, 2009. ACT'09. International Conference on, pp. 284-288. IEEE, 2009.
  4. Harati, Vida, Rasoul Khayati, and Abdolreza Farzan. "Fully automated tumor segmentation based on improved fuzzy connectedness algorithm in brain MR images. " Computers in biology and medicine 41, no. 7 (2011): 483-492.
  5. Parisot, Sarah, Hugues Duffau, Stéphane Chemouny, and Nikos Paragios. "Graph-based detection, segmentation & characterization of brain tumors. " InComputer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pp. 988-995. IEEE, 2012.
  6. Subashini, M. Monica, and Sarat Kumar Sahoo. "Brain Tumour Detection Using Pulse Coupled Neural Network (PCNN) and Back Propagation Network. " (2012): 10-15.
  7. Maiti, Ishita, and M. Chakraborty. "A new method for brain tumor segmentation based on watershed and edge detection algorithms in HSV colour model. " InNational Conference on Computing and Communication Systems, pp. 177-188. 2012.
  8. Ghanavati, Sahar, Junning Li, Ting Liu, Paul S. Babyn, Wendy Doda, and George Lampropoulos. "Automatic brain tumor detection in magnetic resonance images. " In Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on, pp. 574-577. IEEE, 2012.
  9. Vijay, J. , and J. Subhashini. "An efficient brain tumor detection methodology using K-means clustering algoriftnn. " In Communications and Signal Processing (ICCSP), 2013 International Conference on, pp. 653-657. IEEE, 2013.
  10. Ulku, Eyup Emre, and Ali Yilmaz Camurcu. "Computer aided brain tumor detection with histogram equalization and morphological image processing techniques. " In Electronics, Computer and Computation (ICECCO), 2013 International Conference on, pp. 48-51. IEEE, 2013.
  11. Halder, Amitava, Chandan Giri, and Amiya Halder. "Brain tumor detection using segmentation based Object labeling algorithm. " In Electronics, Communication and Instrumentation (ICECI), 2014 International Conference on, pp. 1-4. IEEE, 2014.
  12. Aswathy, S. U. , Deva Dhas, G. Glan, and S. S. Kumar. "A survey on detection of brain tumor from MRI brain images. " In Control, Instrumentation, Communication and Computational Technologies (ICCICCT), 2014 International Conference on, pp. 871-877. IEEE, 2014.
  13. Preetha, R. , and G. R. Suresh. "Performance Analysis of Fuzzy C Means Algorithm in Automated Detection of Brain Tumor. " In Computing and Communication Technologies (WCCCT), 2014 World Congress on, pp. 30-33. IEEE, 2014.
  14. Njeh, Ines, Lamia Sallemi, Ismail Ben Ayed, Khalil Chtourou, Stephane Lehericy, Damien Galanaud, and Ahmed Ben Hamida. "3D multimodal MRI brain glioma tumor and edema segmentation: A graph cut distribution matching approach. " Computerized Medical Imaging and Graphics (2014).
  15. Dhage, Padmakant, M. R. Phegade, and S. K. Shah. "Watershed segmentation brain tumor detection. " In Pervasive Computing (ICPC), 2015 International Conference on, pp. 1-5. IEEE, 2015.
  16. Nabizadeh, Nooshin, and Miroslav Kubat. "Brain tumors detection and segmentation in MR images: Gabor wavelet vs. statistical features. " Computers & Electrical Engineering (2015).
  17. Abdel-Maksoud, Eman, Mohammed Elmogy, and Rashid Al-Awadi. "Brain tumor segmentation based on a hybrid clustering technique. " Egyptian Informatics Journal 16, no. 1 (2015): 71-81.
Index Terms

Computer Science
Information Sciences

Keywords

Image Segmentation Brain Tumor MRI