Premature-Multiple Stage Brain Tumour Detection and Localization using a Fusion of K-Means Clustering and Patch-based Processing

Main Article Content

Sethuram Rao G
Vydeki.D
Pavithra K
Boomikha E
Rahul M
Padmanaban V
Dhanush Shobin G

Keywords

Magnetic Resonance Image, K-means clustering, Patch based algorithm, segmentation, Tumor

Abstract

Brain Tumour is a significant global health issue that results in high mortality rates. Early detection and treatment are crucial for a successful patient recovery. Brain MR images are used to obtain critical tumour characteristics such as location, size, and type to accurately diagnose the disease. This study proposes an efficient approach to detect and locate brain tumours in MR images using a fusion of k-means clustering, patch-based image processing, and object counting. The experimental results conducted on 20 MR images with ground truth show that the proposed technique is capable of detecting multiple tumours despite differences in intensity level, size, and location. The simulated results of the proposed method outperform other existing techniques, with average values of precision, accuracy, specificity, and dice score of 98.48%, 99.89%, 99.99%, and 95.88%, respectively.

Abstract 68 | PDF Downloads 125

References

1. Dixit, Abhishek, and Pooja Singh. "Brain Tumor Detection Using Fine-Tuned YOLO Model with Transfer Learning." In Artificial Intelligence on Medical Data, pp. 363-371. Springer, Singapore, 2023.
2. Lotlikar, Venkatesh S., Nitin Satpute, and Aditya Gupta. "Brain Tumor Detection Using Machine Learning and Deep Learning: A Review." Current Medical Imaging 18.6 (2022): 604-622.
3. Bruno, Federico, VincenzaGranata, Flavia Cobianchi Bellisari, Ferruccio Sgalambro, Emanuele Tommasino, Pierpaolo Palumbo, Francesco Arrigoni et al. "Advanced Magnetic Resonance Imaging (MRI) Techniques: Technical Principles and Applications in Nanomedicine." Cancers 14, no. 7 (2022): 1626.
4. Chattopadhyay, Arkapravo, and Mausumi Maitra. "MRI-based Brain Tumor Image Detection Using CNN based Deep Learning Method." Neuroscience Informatics (2022): 100060.
5. Aaraji, ZahraaSh, and Hawraa H. Abbas. "Automatic Classification of Alzheimer's Disease using brain MRI data and deep Convolutional Neural Networks." arXiv preprint arXiv:2204.00068 (2022).
6. Rahman, Jawwad Sami Ur, and Sathish Kumar Selvaperumal. "Integrated approach of brain segmentation using neuro fuzzy k-means." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 1 (2023): 270-276.
7. Pei, Linmin, Murat Ak, NourelHoda M. Tahon, SerafettinZenkin, SafaAlkarawi, Abdallah Kamal, Mahir Yilmaz et al. "A general skull stripping of multiparametric brain MRIs using 3D convolutional neural network." Scientific Reports 12, no. 1 (2022): 1-11.
8. Pei, Linmin, A. K. Murat, NourelTahon, SerafettinZenkin, SafaKarawi, Abdallah Kamal, Mahir Yilmaz, Lingling Chen, Mehmet Er, and RivkaColen. "A Generative Skull Stripping of
Multiparametric Brain MRIs Using 3D Convolutional Neural Network." (2022).
9. Duarte, KauêTartarotti Nepomuceno, Marinara Andrade Nascimento Moura, Paulo Sergio Martins, and Marco Antonio Garcia de Carvalho. "Brain Extraction in Multiple T1-weighted Magnetic Resonance Imaging slices using Digital Image Processing techniques." IEEE Latin America Transactions 20, no. 5 (2022): 831-838.
10. D. L. Pham, C. Xu, and J. L. Princem, “Current methods in medical image segmentation,” Annual Review of Biomedical Engineering, vol. 2, no. 1, pp. 315–337, 2000.
11. S. A. Karkanis, D. K. Iakovidis, D. E. Maroulis, D. A. Karras, and M.Tzivras, “Computer-aided tumor detection in endoscopic video using color wavelet features,” IEEE Transaction on Information Technology in Biomedicine, vol. 7, no. 3, pp. 141–152, 2003.
12. Grande-Barreto, Jonas, and Pilar Gómez-Gil. "Pseudo-label-assisted self-organizing maps for brain tissue segmentation in magnetic resonance imaging." Journal of Digital Imaging 35, no. 2 (2022): 180-192.
13. K. Sinha and G. R. Sinha, “Efficient segmentation methods for tumor detection in MRI images,” in Proceedings of the IEEE International Journal of Biomedical Imaging Students’ Conference on Electrical, Electronics and Computer Science, pp. 1–6, IEEE, Piscataway, NJ, USA, 2014.
14. Y. Megersa and G. Alemu, “Brain tumor detection and segmentation using hybrid intelligent algorithms,” in Proceedings of the AFRICON, pp. 1–8, IEEE, Piscataway, NJ, USA, 2015.
15. N. B. Bahadure, A. K. Ray, and H. P.Theethi, “Image analysis for MRI based brain tumor detection and feature extraction using biologically inspired BWT and SVM,” International Journal of Biomedical Imaging, vol. 2017, Article ID 9749108, 12 pages, 2017.
16. Hanuman and K. Sooknanan, “Brain tumor segmentation and volume estimation from T1-contrasted and T2 MRIs,” International Journal of Image Processing (IJIP), vol. 12, no. 2,pp. 48–62, 2018.
17. Hazra, A. Dey, and S. K. Gupta, M. A. Ansari, “Brain tumor detection based on segmentation using MATLAB,” in Proceedings of the International Conference on Energy, Communication, Data Analytics and So_ Computing (ICECDS), pp. 425–430, IEEE, Piscataway, NJ, USA, 2017.
18. Kharrat, N. Benamrane, M. B. Messaoud, and M. Abid, “Detection of brain tumor in medicalimages,” in Proceedings of the 3rd International Conference on Signals, Circuits and Systems (SCS), pp. 1–6, IEEE, Piscataway, NJ, USA, 2009.
19. Gujar and C. M. Meshram, “Brain tumor extraction using genetic algorithm,” International Journal on Future Revolution in Computer Science and Communication engineering (IJFRSCE), vol. 4, no. 6, pp. 33–39, 2018.
20. S. Pereira, A. Pinto, V. Alves, and C. A. Silva, “Brain tumor segmentation using convolutional neural networks in MRI images,” IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1240–1251, 2016.
21. Kullayamma and A. Praveen Kumar, “Brain tumor segmentation by using ant colony optimization,” International Journal of Scientific Research in Science and Technology, vol. 4,no. 8, pp. 62–69, 2018.
22. Bousselham, O. Bouattane, M. Youssfi, and A. Raihani, “Towards reinforced brain tumor segmentation on MRI images based on temperature changes on pathologic area,” International Journal of Biomedical Imaging, vol. 2019, Article ID 1758948, 18 pages, 2019.
23. C. Zhang, X. Shen, H. Cheng, and Q. Qian, “Brain tumor segmentation based on hybrid clustering and morphological operations,” International Journal of Biomedical Imaging, vol. 2019, Article ID 7305832, 11 pages, 2019.
24. S. K. Sahu, M. B. N. V. Prasad, and B. K. Tripathy, “A support vector machine binary classification and image segmentation of remote sensing data of chilikalagloon,” International Journal of Research in Information Technology, vol. 3, no. 5, pp. 191–204, 2015.