CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

A Methodical Approach for Detection and 3-D Reconstruction of Brain Tumor in MRI

by Sayali Lopes, Deepak Jayaswal
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 118 - Number 17
Year of Publication: 2015
Authors: Sayali Lopes, Deepak Jayaswal
10.5120/20840-3580

Sayali Lopes, Deepak Jayaswal . A Methodical Approach for Detection and 3-D Reconstruction of Brain Tumor in MRI. International Journal of Computer Applications. 118, 17 ( May 2015), 37-43. DOI=10.5120/20840-3580

@article{ 10.5120/20840-3580,
author = { Sayali Lopes, Deepak Jayaswal },
title = { A Methodical Approach for Detection and 3-D Reconstruction of Brain Tumor in MRI },
journal = { International Journal of Computer Applications },
issue_date = { May 2015 },
volume = { 118 },
number = { 17 },
month = { May },
year = { 2015 },
issn = { 0975-8887 },
pages = { 37-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume118/number17/20840-3580/ },
doi = { 10.5120/20840-3580 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:02:45.092657+05:30
%A Sayali Lopes
%A Deepak Jayaswal
%T A Methodical Approach for Detection and 3-D Reconstruction of Brain Tumor in MRI
%J International Journal of Computer Applications
%@ 0975-8887
%V 118
%N 17
%P 37-43
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

It always takes a skilled neurologist to detect a tumor in the MRI scans, which the numerologist does with the naked eye. Doctors have had only 2D cross sectional images for viewing the tumor in the MRI scans. This research presents a method for automatic tumor detection with an added feature of reconstructing its 3D image. The research involves implementation of various steps of detecting and extracting the tumor from the 2D slices of MRI brain images by Seeded region growing technique along with automatic seed selection and designing software for reconstructing 3D image from a set of 2D tumor images. The seeded region growing method is very attractive method for semantic image segmentation which involves high level knowledge of image components during the seed selection procedure. The volume of the tumor is also estimated based on the computation of these images to assist the radiologist.

References
  1. Anantatamukala, A. Gole, A. and Karunakar, Y. A Systematic Algorithm for 3-D Reconstruction of MRI based Brain Tumors using Morphological operators and Bicubic Interpolation. Proceedings of 2nd International Conference on Computer Technology and development(ICCTD). (2010), 305-309.
  2. Arakeri, M. and Reddy, G. An Effective and Efficient Approach to 3D Reconstruction and quantification of brain Tumor on Magnetic Resonance Images. IEEE Trans. Signal Processing, Image Processing and Pattern Recognition. (June 2013), 111-128.
  3. Koompairojn, S. Petkova, A. Hua, K. and Metarugcheep, P. Semi-automatic Segmentation and Volume Determination of Brain Mass-like Lesion. 21st IEEE International Symposium on Computer Based Medical Systems. (2008), 35-40.
  4. Clark, M. Lawrence, L. Golgof, D. Velthuizen, R. Murtagh, F. and Silbiger, M. Automatic Tumor Segmentation Using Knowledge based technique. IEEE Trans. Medical Imaging. (April 1998), 158-167.
  5. Moon, N. Bullitt, E. Leemput, K. and Gerig, G. Model-based brain tumor segmentation. Proceedings of IEEE Int. Conf. Pattern Recognition. (2002), 526-531.
  6. Kharrat, A. Messaoud, M. Benamrane, N. Abid, M. Detection of Brain Tumor in Medical Images. Proceedings of IEEE Int. Conf. Signals, Citcuits and Systems. (2009), 1-6.
  7. Islam, R. Mamun, A. bhuiyan, M. Rahman, S. Segmentation and 3D visualization of volumetric Image for Detection of Tumor in Cancerous Brain. Proceedings of IEEE Int. Conf. on Electrical and Computer Engineering. (December 2012), 863-866.
  8. Natrajan, P. Krishnan, N. Kenkre, N. Nancy, S. Singh,B. Tumor Detection using threshold operation in MRI Brain Images. Proceedings of IEEE Int. Conf. on Computational Intelligence and Computing Research. (2012), 1-4.
  9. Kumar, P. Kumar, N. and Sumithra, M. Tumor Detection in Brain MRI Using Improved Segmentation Algorithm. Proceedings of IEEE Int. Conf. on Computing, Communications and Networking Technologies. (July 2013), 1-7.
  10. Xu, T. Mandal, M. Automatic Brain Tumor Extraction from T1- Weighted Coronal MRI Using Fast Bounding Box and Dynamic Snake. Proceedings of IEEE Int. Conf. on EMBS. (August- September 2012), 444-447.
  11. Hamamci, A. Kucuk, N. Karaman,K. Engin, K. Unal,G. Tumor-cut: Segmentation of Brain Tumors on Contrast Enhanced MR Images for Radiosurgery Applications. IEEE Trans. Medical Imaging. (March 2012), 790-804.
  12. Karimaghaloo, Z. Shah, M. Francis, S. Arnold, D. Collins, D and Arbel, T. Automatic Detection of Gadolinium-Enhancing Multiple Sclerosis Lesions in Brain MRI Using Conditional Random Fields. IEEE Trans. Medical Imaging. (June 2012), 1181 – 1194.
  13. Verma, N. Muralidhar, G. Model-driven, probabilistic level set based segmentation of magnetic resonance images of the brain. Proceedings of IEEE Int. Conf. on EMBS. (August- September2011), 2821-2824.
  14. Kazerooni, A. Ahmadian, A. Segmentation of Brain Tumors in MRI Images Using Multi-Scale Gradient Vector Flow. Proceedings of IEEE Int. Conf. on EMBS. (August- September 2011), 7973-27.
  15. Ghanavati, S. Junning, L. Ting, L. Babyn, P. Doda, W. Lampropoulos, G. Automatic brain tumor detection in Magnetic Resonance Image. 21st IEEE International Symposium on Computer Based Medical Systems. (May 2012), 574-577.
  16. Gordillo, N. Montseny, E and Sobrevilla, P. New fuzzy approach to brain tumor segmentation. Proceedings of IEEE Int. Conf. on Fuzzy Systems. (2010), 1-8.
  17. Corso, J. Sharon, E. Dube, S. El-Saden, S. Sinha, U. and Yuille, A. Efficient multilevel brain tumor segmentation with integrated bayesian model classification. IEEE Trans. Med. Imaging. (2008), 629-640.
  18. Ratana, R. Sharma, S. Sharma, S. Brain tumor detection based on multi-parameter MRI image analysis. ICGST Int. J. Graphics, Vision and Image Processing. (2009), 9-17.
  19. Wang, T. Cheng, I and Basu, A. Fully automatic brain tumor Segmentation using a normalized Gaussian Bayesian classifier and3D Fluid vector flow. Proceedings of IEEE Int. Conf. on Image Processing. (2010), 2553-2556.
  20. Soille, P. Morphological Image Analysis: Principles and Applications, Springer-Verlag, 1999, pp. 173-174
  21. Fan, J. Zeng, G. Body, M. Hacid, M. Seeded region growing: an extensive and comparative study. Pattern Recognition Letters. Elsevier. (2005), 1139-1156.
  22. Measurement of Tumor "Size" in Recurrent Malignant Glioma: 1D, 2D, or 3D? http://www. ajnr. org/content/26/4/770/T1. expansion. html 25/03/2015
  23. Brain Cancer In-Depth Report: http://www. nytimes. com/health/guides/disease/brain-tumor-adults/print. html 25/03/15
Index Terms

Computer Science
Information Sciences

Keywords

Brain Tumor Segmentation and 3D visualization.

Powered by PhDFocusTM