RELEVANT ALGORITHMS AND TECHNIQUES FOR BRAIN TUMOR SEGMENTATION USING MAGNETIC RESONACE IMAGING
DOI:
https://doi.org/10.61841/z8q3hj83Keywords:
-Brain Tumour,, Magnetic Resonance Imaging, Segmentation techniquesAbstract
Due to faster technological evolution, medical field too requires algorithms and techniques for carrying out diagnosis and treatment with better accuracy. Tumour segmentation plays a prominent role in medical image processes sing field. It aims to separate diseased tumour tissue from normal one with least error. Among various imaging modalities, Magnetic Resonance Imaging(MRI) is most predominantly used. MRI contains multiple noises, affecting the segmentation process. Hence the image has to be pre-processed to remove noises and improve data quality. This paper describes various segmentation techniques. Finally, evaluation metrics for analysing the segmentation techniques and some standard datasets are discussed.
Downloads
References
1. Murthy T. S. D and Sadashivappa G, “Brain tumor segmentation using thresholding, morphological operations and extraction of features of tumor,” , 2014 International Conference on Advances in Electronics Computers and Communications, pp. 1–6, 2014.
2. W. El Hajj Chehade, R. A. Kader, and A. El-Zaart, “Segmentation of MRI images for brain cancer detection,” in 2018 International Conference on Information and Communications Technology (ICOIACT), 2018, pp. 929–934.
3. Adams, R. And Bischof, L. “Seeded region growing”, IEEE Transactions on Patter Analysis and Machine Intelligence, Vol. 16, pp. 641-647, 1994.
4. Kaus, M., Warfield, S., Nabavi, A., Black, P., Jolesz, F. And Kikinis, R. “Automated segmentation of MRI of brain tumors”, Radiology, Vol.218, pp. 586-591, 2001.
5. Sato, M., Lakare, S., Wan, M. and Kaufman, A. “A gradient magnitude based region growing algorithm for accurate segmentation”, IEEE Proceedings of the International Conference on Image Processing, Vol.3, pp. 448-451, 2000.
6. Dou, W., Ruan, S., Chen, Y., Bloyet, D. And Constants, J.M. “A frame work of fuzzy information fusion for the segmentation of brain tumor tissues on MR images”, Image and Vision Computing, Vol. 25, pp. 164-171,2007.
7. D. Liu, X. Yu, Q. Feng, W. Chen, and G. Manogaran, “Brain Image Segmentation Based on Multi-Weight Probability Map,” IEEE Access, vol. 7, pp. 14736–14746, 2019.
8. E. Dam, M. Loog, and M. Letteboer, “Integrating automatic and interactive brain tumor segmentation,” in Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2004, vol. 3, pp. 790-793 Vol.3.
9. K. S. Angel Viji and J. Jayakumari, “Automatic detection of brain tumor based on magnetic resonance image using CAD System with watershed segmentation,” in 2011 International Conference on Signal Processing, Communication, Computing and Networking Technologies, 2011, pp. 145–150.
10. Clark, M., Hall, L., Goldgof, D., Velthuizen, R., Murtagh, R. and Silbiger, M. “Automatic tumor segmentation using knowledge based techniques”, IEEE Transactions on Medical Imaging, Vol. 17, No.2,
pp. 187-201, 1998.
11. M. H. Hesamian, W. Jia, X. He, and P. Kennedy, “Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges,” J. Digit. Imaging, vol. 32, no. 4, pp. 582–596, Aug. 2019.
12. E.-S. A. El-Dahshan, T. Hosny, and A.-B. M. Salem, “Hybrid intelligent techniques for MRI brain images classification,” Digit. Signal Process., vol. 20, no. 2, pp. 433–441, Mar. 2010.
13. M. Schmidt, I. Levner, R. Greiner, A. Murtha, and A. Bistritz, “Segmenting brain tumors using alignment- based features,” in Fourth International Conference on Machine Learning and Applications (ICMLA’05), 2005, pp. 6-11.
14. D. L. Pham and J. L. Prince, “Adaptive fuzzy segmentation of magnetic resonance images,” IEEE Trans. Med. Imaging, vol. 18, no. 9, pp. 737–752, Sep. 1999.
15. Abhishek, Avijit Kar & Debasis Bhattacharyya 2014, “Early Detection of Cervical Cancer using novel segmentation algorithms”, Invertis Journal of Science & Technology, vol. 7, no.2, pp.91-95
16. N. Dhanachandra, K. Manglem, and Y. J. Chanu, “Image Segmentation Using K -means Clustering Algorithm and Subtractive Clustering Algorithm,” Procedia Comput. Sci., vol. 54, pp. 764–771, Jan. 2015.
17. Evangelia I. Zacharaki and Anastasios Bezerianos, “Abnormality Segmentation in Brain Images via Distributed estimation”, IEEE Transaction on Information Technology in Biomedicine, vol.16, no. 3, pp. 330-338, 2012.
18. Dzung L.Pham and Jerry L. Prince, “Adaptive Fuzzy Segmentation of Magnetic Resonance Images”, IEEE Transactions on Medical Imaging, Vol.18, no. 9, pp. 737-752, 1999.
19. B. Karayiannis and Pin I.Pai, “Segmentation of Magnetic Resonance Images using Fuzzy algorithm for Learning Vector Quantization”, IEEE Transactions on Medical Imaging, Vol. 10, no. 2, 1999, pp. 172- 180
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.