Unveiling the Secrets of Brain Tumors: A Fuzzy C-Means and U-Net Convolution Approach for Enhanced Segmentation
DOI:
https://doi.org/10.15837/ijccc.2024.2.5732Keywords:
Tumor, U-Net Convolution, Fuzzy C-Means Clutering, Anatomical Segmentation, Magnetic Resonance ImagingAbstract
The urge to unveil the secrets of digital visual enhancement has always been a dream for mankind. It has always been an expanding realm of research that has never failed to surprise humanity. In this paper, we have proposed a modified Clustering technique in Fuzzy C-Means named Narrow Fuzzy C-Means Clustering. This clustering method is implemented and fused with U-Net Convolution. The proposed segmentation algorithm uses this unique technique which assists in providing elevated and enhanced outcomes. The suggested approach helps to precisely segment the area of interest from the provided input images. The novel proposal provides an accuracy of 96.5% with a Dice Similarity Co-Efficient (DSC) of 0.94 which tends to determine the exact segmentation of the area of interest with a low false positive rate.
References
Soomro, T.A.; Zheng, L., Afifi, A.J., Ali, A., Soomro, S., Yin, M., Gao, J. (2023). Image Segmentation for MR Brain Tumor Detection Using Machine Learning: A Review, IEEE Rev Biomed Eng. 70-90, 2023.
https://doi.org/10.1109/RBME.2022.3185292
Ramya, D.; Lakshmi, C. (2023). Brain Tumor Segmentation using MRI Images by Optimized U-Net, In 2023 International Conference on Networking and Communications (ICNWC), 1-6, IEEE, 2023).
https://doi.org/10.1109/ICNWC57852.2023.10127271
Lingling Fang; Xin Wang (2023). Multi-input U-net model based on the integrated block and the aggregation connection for MRI brain tumor segmentation,Biomedical Signal Processing and Control, 79(1), 104027, 2023, ISSN: 1746-8094, https://doi.org/10.1016/j.bspc.2022.104027.
https://doi.org/10.1016/j.bspc.2022.104027
Jyothsna, P. et al. (2023). Brain Tumor Segmentation Using U-Net,. In Ogudo, K.A., Saha, S.K., Bhattacharyya, D. (eds) Smart Technologies in Data Science and Communication, Lecture Notes in Networks and Systems, Springer, Singapore, 558, 2023. https://doi.org/10.1007/978-981-19- 6880-8_16
Allah, A.M.G.; Sarhan, A.M., Elshennawy, N.M. (2023). Edge U-Net: Brain tumor segmentation using MRI based on deep U-Net model with boundary information, Expert Systems with Applications, 213, 118833, 2023.
https://doi.org/10.1016/j.eswa.2022.118833
Kumar, G.M.; Parthasarathy, E. (2023). Development of an enhanced U-Net model for brain tumor segmentation with optimized architecture, Biomedical Signal Processing and Control, 81, 104427, 2023.
https://doi.org/10.1016/j.bspc.2022.104427
Sangui, S.; Iqbal, T., Chandra, P.C., Ghosh, S.K., Ghosh, A. (2023). 3D MRI Segmentation using U-Net Architecture for the detection of Brain Tumor, Procedia Computer Science, 218, 542-553, 2023.
https://doi.org/10.1016/j.procs.2023.01.036
Raza, R.; Bajwa, U.I., Mehmood, Y., Anwar, M.W., Jamal, M.H. (2023). dResU-Net: 3D deep residual U-Net-based brain tumor segmentation from multimodal MRI, Biomedical Signal Processing and Control, 79, 103861, 2023.
https://doi.org/10.1016/j.bspc.2022.103861
Poonguzhali, R.; Ahmad, S., Sivasankar, P.T., Anantha Babu, S., Joshi, P., Joshi, G.P., Kim, S.W. (2023). Automated brain tumor diagnosis using deep residual u-net segmentation model, Computers, Materials & Continua, 74(1), 2179-2194, 2023.
https://doi.org/10.32604/cmc.2023.032816
Shiny, K.V. (2023). Brain tumor segmentation and classification using optimized U-Net, The Imaging Science Journal, 1-16, 2023.
https://doi.org/10.1142/S0219519423500501
Swathi, P.; Narayana Ramakrishna, M., Vilasini, K., Sai Hemanth, M., Nirmal Sumanth, J. (2022). 3D U-Net for Brain Tumor Detection and Segmentation, International Journal of Scientific Research in Science and Technology (IJSRST), Print ISSN: 2395-6011, Online ISSN: 2395- 602X, 9(3), 415-421, 2022.
Ottom, M.A.; Rahman, H.A., Dinov, I.D. (2022). Znet: Deep Learning Approach for 2D MRI Brain Tumor Segmentation, IEEE Journal of Translational Engineering in Health and Medicine, 10, 2022, doi: 10.1109/JTEHM.2022.3176737. PMID: 35774412; PMCID: PMC9236306.
https://doi.org/10.1109/JTEHM.2022.3176737
Grace, J.P.S.; Ezhilarasi, P. (2022). Enhanced Marker-Controlled Watershed Segmentation Algorithm for Brain Tumor Segmentation. In: Mukhopadhyay, S., Sarkar, S., Dutta, P., Mandal, J.K., Roy, S. (eds) Computational Intelligence in Communications and Business Analytics (CICBA 2022), Communications in Computer and Information Science, Springer, Cham, 1579, 2022. https://doi.org/10.1007/978-3-031-10766-5_12.
https://doi.org/10.1007/978-3-031-10766-5_12
Jason Walsh; Alice Othmani, Mayank Jain, Soumyabrata Dev. (2022). Using U-Net network for efficient brain tumor segmentation in MRI images, Healthcare Analytics, 2, 100098, 2022, ISSN: 2772-4425, https://doi.org/10.1016/j.health.2022.100098.
https://doi.org/10.1016/j.health.2022.100098
Nasim, M.A.; Munem, A.A., Islam, M., Palash, M.A., Haque, M.M., Shah, F.M. (2022). Brain Tumor Segmentation using Enhanced U-Net Model with Empirical Analysis, 2022, ArXiv./abs/2210.13336.
Asthana, P.; Hanmandlu, M., Vashisth, S. (2022). Brain tumor detection and patient survival prediction using U-Net and regression model, International Journal of Imaging Systems and Technology, 32(5), 1801-1814, 2022.
https://doi.org/10.1002/ima.22735
Chinnam, S.K.R.; Sistla, V., Kolli, V.K.K. (2022). Multimodal attention-gated cascaded U-Net model for automatic brain tumor detection and segmentation, Biomedical Signal Processing and Control, 78, 103907, 2022.
https://doi.org/10.1016/j.bspc.2022.103907
Ilhan, A.; Sekeroglu, B., Abiyev, R. (2022). Brain tumor segmentation in MRI images using nonparametric localization and enhancement methods with U-net, International Journal of computerassisted radiology and Surgery, 17(3), 589-600, 2022.
https://doi.org/10.1007/s11548-022-02566-7
Li, S.; Liu, J., Song, Z. (2022). Brain tumor segmentation based on region of interest-aided localization and segmentation U-Net, International Journal of Machine Learning and Cybernetics, 13(9), 2435-2445, 2022.
https://doi.org/10.1007/s13042-022-01536-4
Mridha, K.; Simanta, S., Limbu, M. (2022). U-Net for Medical Imaging: A Novel Approach for Brain Tumor Segmentation, Global Journal of Innovation and Emerging Technology, 1(1), 20-28, 2022.
https://doi.org/10.58260/j.iet.2202.0104
Chattopadhyay, A.; Maitra, M. (2022). MRI-based brain tumor image detection using CNN-based deep learning method, Neuroscience informatics, 2(4), 100060, 2022.
https://doi.org/10.1016/j.neuri.2022.100060
Gan, X.; Wang, L., Chen, Q., Ge, Y., Duan, S. (2021) GAU-Net: U-Net based on global attention mechanism for brain tumor segmentation, In Journal of Physics: Conference Series, 1861(1), 012041, 2021. IOP Publishing. DOI 10.1088/1742-6596/1861/1/012041.
https://doi.org/10.1088/1742-6596/1861/1/012041
Siddique, N.; Paheding, S., Elkin, C.P., Devabhaktuni, V. (2021). U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications, in IEEE Access, 9, 82031- 82057, 2021, doi: 10.1109/ACCESS.2021.3086020.
https://doi.org/10.1109/ACCESS.2021.3086020
Tarkhan, A.; Simon, N., Bengtsson, T., Nguyen, K., Dai, J. (2021). Survival Prediction Using Deep Learning, In Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications, 2021, in Proceedings of Machine Learning Research, 146, 207-214, 2021, Available from https://proceedings.mlr.press/v146/tarkhan21a.html.
Futrega, M.; Milesi, A., Marcinkiewicz, M., Ribalta, P. (2021). Optimized U-Net for brain tumor segmentation, In International MICCAI Brainlesion Workshop, Cham: Springer International Publishing, 15-29, 2021.
https://doi.org/10.1007/978-3-031-09002-8_2
Maqsood, S.; Damasevicius, R., Shah, F.M. (2021). An efficient approach for the detection of brain tumor using fuzzy logic and U-NET CNN classification, In Computational Science and Its Applications - ICCSA 2021: 21st International Conference, Cagliari, Italy, September 13-16, 2021, Proceedings, Part V, Springer International Publishing, 21, 105-118, 2021.
https://doi.org/10.1007/978-3-030-86976-2_8
Jwaid, W.M.; Matar Al-Husseini, Z.S., Sabry, A.H. (2021). Development of Brain Tumor Segmentation of Magnetic Resonance Imaging (MRI) using U-Net Deep Learning, Eastern-European Journal of Enterprise Technologies, 112, 2021.
https://doi.org/10.15587/1729-4061.2021.238957
Trivedi, H.; Thumar, K., Ghelani, K., Gandhi, D. (2021). An Analysis of Brain Tumor Segmentation Using Modified U-Net Architecture, In Innovations in Information and Communication Technologies (IICT-2020), Proceedings of International Conference on ICRIHE-2020, Delhi, India: IICT-2020, Springer International Publishing, 141-147, 2021.
https://doi.org/10.1007/978-3-030-66218-9_16
Andreas S. Panayides et al. (2020). AI in Medical Imaging Informatics: Current Challenges and Future Directions, in IEEE Journal of Biomedical and Health Informatics, 24(7), 1837-1857, 2020. doi: 10.1109/JBHI.2020.2991043.
https://doi.org/10.1109/JBHI.2020.2991043
Rehman, M.U.; Cho, S. Kim, J.H. Chong, K.T. (2020). BU-Net: Brain Tumor Segmentation Using Modified U-Net Architecture, Electronics, 9, 2203, 2020. https://doi.org/10.3390/electronics9122203.
https://doi.org/10.3390/electronics9122203
Sunita M. Kulkarni; Sundari, G. (2020). A Framework for Brain Tumor Segmentation and Classification using Deep Learning Algorithm, International Journal of Advanced Computer Science and Applications (IJACSA), 11(8), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110848.
https://doi.org/10.14569/IJACSA.2020.0110848
Eric Ke Wang; Chien-Ming Chen, Mohammad Mehedi Hassan, Ahmad Almogren. (2020). A deep-learning based medical image segmentation technique in Internet-of-Medical-Things domain, Future Generation Computer Systems, 108, 135-144, 2020. ISSN 0167-739X, https://doi.org/10.1016/j.future.2020.02.054.
https://doi.org/10.1016/j.future.2020.02.054
Hazra, D.; Byun, Y. (2020). Brain tumor detection using skull stripping and U-net architecture, International Journal of Machine Learning and Computing, 10(2), 400-405, 2020.
Ahmed, S.F., Rahman, F.S., Tabassum, T., Bhuiyan, M.T.I. (2019). 3D U-Net: fully convolutional neural network for automatic brain tumor segmentation, In 2019 22nd International Conference on Computer and Information Technology (ICCIT), IEEE, 1-6, 2019.
https://doi.org/10.1109/ICCIT48885.2019.9038237
Agrawal Ritu; Sharma Manisha, Singh Bikesh Kumar. (2019). Segmentation of Brain Tumour Based on Clustering Technique: Performance Analysis, Journal of Intelligent Systems, 28(2), 291-306, 2019. https://doi.org/10.1515/jisys-2017-0027.
https://doi.org/10.1515/jisys-2017-0027
Kermi, A.; Mahmoudi, I., Khadir, M.T. (2019). Deep convolutional neural networks using U-Net for automatic brain tumor segmentation in multimodal MRI volumes, In Brainlesion: Glioma, Multiple Sclerosis, Stroke, and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part II 4 (pp. 37-48). Springer International Publishing, 2019.
https://doi.org/10.1007/978-3-030-11726-9_4
Zhou, Z.; Siddiquee, M.M.R., Tajbakhsh, N., Liang, J. (2019). Unet++: Redesigning skip connections to exploit multiscale features in image segmentation, IEEE Transactions on medical imaging, 39(6), 1856-1867, 2019.
https://doi.org/10.1109/TMI.2019.2959609
Bahadure, N.B.; Ray, A.K., Thethi, H.P. (2018). Comparative Approach of MRI-Based Brain Tumor Segmentation and Classification Using Genetic Algorithm, J Digit Imaging, 477-489, 2018. doi 10.1007/s10278-018-0050-6. PMID: 29344753; PMCID: PMC6113145.
https://doi.org/10.1007/s10278-018-0050-6
Mehta, R., Arbel, T. (2018). 3D U-Net for brain tumor segmentation, In Brainlesion: Glioma, Multiple Sclerosis, Stroke, and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part II 4, 254-266. Springer International Publishing, 2018.
https://doi.org/10.1007/978-3-030-11726-9_23
Dong, H.; Yang, G., Liu, F., Mo, Y., Guo, Y. (2017). Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks, In: Valdés Hernández, M., González-Castro, V. (eds) Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science, Springer, Cham, 723, 2017. https://doi.org/10.1007/978-3-319-60964-5_44.
https://doi.org/10.1007/978-3-319-60964-5_44
Pereira, S.; Pinto, A., Alves, V., Silva, C.A. (2016). Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images, in IEEE Transactions on Medical Imaging, 35(5), 1240-1251, 2016. doi: 10.1109/TMI.2016.2538465.
https://doi.org/10.1109/TMI.2016.2538465
Işın, A.; Direkoğlu, C., Şah, M. (2016). Review of MRI-based brain tumor image segmentation using deep learning methods, Procedia Computer Science, 102, 317-324, 2016.
https://doi.org/10.1016/j.procs.2016.09.407
Pan, M.S.; Tang, J.T., Yang, X.L. (2010). A modified adaptive median filter method and its applications in medical images, Biomedical Engineering: Applications, Basis and Communications, 22(06), 489-496, 2010.
Additional Files
Published
Issue
Section
License
Copyright (c) 2024 J. Pearline Sheba, P. Ezhilarasi, S. Rajesh Kannan
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.
