i-manager Publications

Comparison on Automated Brain Tumor Detection and Segmentation Approaches for MRI Brain Images

P. G. K. Sirisha*, D. Haritha**

*-** Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Kakinada, Andhra Pradesh, India.

Periodicity:July - September'2019
DOI : https://doi.org/10.26634/jip.6.3.16322

Abstract

Magnetic Resonance Imaging (MRI) technology is used to study the internal structure of brain in the form of digital images. The accurate detection of tumor region in the brain images is a challenging task. Brain tumor detection is an important task for doctors to give better treatment for the patients. Brain tumor regions can be effectively identified and located by segmentation of MRI brain images. This paper discuss and compares the efficiency of two novel optimization methods for Detection and Segmentation of MRI brain images namely “Shuffled Frog Leaping Algorithm (SFLA)- Expected Maximization (EM) frame work” and “Shuffled Frog Leaping Algorithm (SFLA) –Tabu Search (TS) frame work” for brain tumor detection in 2D MRI brain images. The results obtained had been compared with Particle Swarm Optimization Incorporating Fuzzy C Means Clusrering (PSO-FCM) method and EM methods. Finally, the results show that SFLA-TS method gives better results when compared to SFLA-EM method in identifying tumor regions in 2D MRI brain images.

Keywords

Brain Tumor detection, Segmentation, SFLA,EM,TS

How to Cite this Article?

Sirisha, P. G. K., and Haritha, D. (2019). Comparison on Automated Brain Tumor Detection and Segmentation Approaches for MRI Brain Images. i-manager's Journal on Image Processing, 6(3), 24-32. https://doi.org/10.26634/jip.6.3.16322

References

[1]. Balafar, M. A., Ramli, A. R., Saripan, M. I., & Mashohor, S. (2010). Review of brain MRI image segmentation methods. Artificial Intelligence Review, 33(3), 261-274. https://doi.org/10.1016/j.mri.2019.06.010

[2]. Bazi, Y., Bruzzone, L., & Melgani, F. (2007). Image thresholding based on the EM algorithm and the generalized Gaussian distribution. Pattern Recognition, 40(2), 619-634. https://doi.org/10.1016/j.patcog. 2006.05.006

[3]. Despotović, I., Goossens, B., & Philips, W. (2015). MRI segmentation of the human brain: Challenges, methods, and applications. Computational and Mathematical Methods in Medicine. https://doi.org/10.1155/ 2015/450341

[4]. Gordillo, N., Montseny, E., & Sobrevilla, P. (2013). State of the art survey on MRI brain tumor segmentation. Magnetic Resonance Imaging, 31(8), 1426-1438. https://doi.org/10.1016/j.mri.2013.05.002

[5]. Hamdaoui, F., Mtibaa, A., & Sakly, A. (2014, December). Comparison between MPSO and MSFLA metaheuristics for MR brain image segmentation. In 2014 15^th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA) (pp. 164-168). IEEE. https://doi.org/ 10.1109/STA.2014.7086725

[6]. Kwon, G. R., Basukala, D., Lee, S. W., Lee, K. H., & Kang, M. (2016). Brain image segmentation using a combination of expectation‐maximization algorithm and watershed transform. International Journal of Imaging Systems and Technology, 26(3), 225-232. https://doi.org/10.1002/ima.22181

[7]. Ladgham, A., Hamdaoui, F., Sakly, A., & Mtibaa, A. (2015). Fast MR brain image segmentation based on modified Shuffled Frog Leaping Algorithm. Signal, Image and Video Processing, 9(5), 1113-1120. https://doi.org/ 10.1007/s11760-013-0546-y

[8]. Ladgham, A., Sakly, A., & Mtibaa, A. (2014, December). MRI brain tumor recognition using modified shuffled frog leaping algorithm. In 2014 15^th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA) (pp. 504-507). IEEE. https://doi.org/ 10.1109/STA.2014.7086694

[9]. Lama, R. K., Choi, M. R., & Kwon, G. R. (2016). Image interpolation for high-resolution display based on the complex dual-tree wavelet transform and hidden Markov model. Multimedia Tools and Applications, 75(23), 16487- 16498. https://doi.org/10.1007/s11042-016-3245-1

[10]. Shah, S. A., & Chauhan, N. C. (2015). An automated approach for segmentation of brain MR images using gaussian mixture model based hidden markov random field with expectation maximization. Journal of Biomedical Engineering and Medical Imaging, 2(4), 57- 70. http://doi.org/10.14738/jbemi. 24.1411

[11]. Shen, Q., Shi, W. M., & Kong, W. (2008). Hybrid particle swarm optimization and tabu search approach for selecting genes for tumor classification using gene expression data. Computational Biology and Chemistry, 32(1), 53-60. https://doi.org/10.1016/j.compbiolchem. 2007.10.001

[12]. Sirisha, P. G. K., & Haritha, D. (2016). Optimized segmentation of brain images using shuffled frog leaping algorithm-expectation–maximization framework. In International Conference on Emerging Multidisciplinary Research and Computational Intelligence-ICEMRCI (pp. 151-157). Retrieved from https://www.worldresearch journal.com/specialissue/22.pdf

[13]. Sirisha, P. G. K., & Haritha, D. (2018). Optimized segmentation of brain images using shuffled frog leaping algorithm-Tabu Search framework. International Journal of Pharmaceutical Research, 10(4), 197-206. https://doi.org/10.31838/ijpr/2018.10.04.047

[14]. Yahya, A. A., Tan, J., & Hu, M. (2013). A novel model of image segmentation based on watershed algorithm. Advances in Multimedia. https://doi.org/10.1155/2013/ 120798

[15]. Yousefi, S., Azmi, R., & Zahedi, M. (2012). Brain tissue segmentation in MR images based on a hybrid of MRF and social algorithms. Medical Image Analysis, 16(4), 840-848. https://doi.org/10.1016/j.media.2012.01.001

	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Pdf	35	35	200	20
Online	35	35	200	15
Pdf & Online	35	35	400	25

Comparison on Automated Brain Tumor Detection and Segmentation Approaches for MRI Brain Images

Abstract

Keywords

How to Cite this Article?

References

If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Options for accessing this content: