Skip to main content
SpringerLink
Log in

Integrating Xilinx FPGA and intelligent techniques for improved precision in 3D brain tumor segmentation in medical imaging

  • Research
  • Published:
Journal of Real-Time Image Processing Aims and scope Submit manuscript

Abstract

Medical image processing plays an indispensable role in diagnosing and treating brain tumors. The objective of this study was to introduce an innovative hardware architecture designed to enhance the accuracy of segmenting brain tumors in 3D MRI images. The proposed approach combines intelligent algorithms, specifically particle swarm optimization and Darwin particle swarm optimization, to achieve high precision in tumor segmentation. We implemented this method on a global framework for Xilinx Virtex6 FPGA. The performance and robustness of the model were examined using two distinct datasets: BRATS 2021 and BRATS 2013. Experimental findings underscored the model's outstanding efficacy. The results indicated that our hardware architecture delivered robust and efficient performance in segmenting brain tumors. The introduced hardware architecture offers significant potential in aiding clinicians to diagnose patients and determine lesion extents. By employing advanced algorithms and hardware designs, this method promises to boost the speed and precision of brain tumor diagnosis and treatment. The study thus contributes an invaluable tool for healthcare professionals in the realm of brain tumor detection and management.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig.7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig.12
Fig. 13
Fig.14

Similar content being viewed by others

Data availability

Datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Khan, H., Shah, P.M., Shah, M.A., ul Islam, S., Rodrigues, J.J.P.C.: Cascading handcrafted features and convolutional neural network for IoT-enabled brain tumor segmentation. Comput. Commun. 153, 196–207 (2020)

    Article  Google Scholar 

  2. Ali, F., Riazul Islam, S.M., Kwak, D., Khan, P., Ullah, N., Yoo, S., Kwak, K.S.: Type-2 fuzzy ontology-aided recommendation systems for IoT-based healthcare. Comput. Commun. 119, 138–155 (2018)

    Article  Google Scholar 

  3. Mano, L.Y., Faiçal, B.S., Nakamura, L.H.V., Gomes, P.H., Libralon, G.L., Meneguete, G.L., Geraldo Filho, R.I., et al.: Exploiting IoT technologies for enhancing Health Smart Homes through patient identification and emotion recognition. Comput. Commun. 89, 178–190 (2016)

    Article  Google Scholar 

  4. Viergever, M.A., Maintz, J.A., Klein, S., Murphy, K., Staring, M., Pluim, J.P.: A survey of medical image registration–under review. Med. Image Anal. 33, 140–144 (2016)

    Article  Google Scholar 

  5. Din, I.U., Guizani, M., Rodrigues, J.J.P.C., Hassan, S., Korotaev, V.V.: Machine learning in the internet of things: designed techniques for smart cities. Future Gener. Comput. Syst. 100, 826–843 (2019)

    Article  Google Scholar 

  6. Younus, M.U., ul Islam, S., Ali, I., Khan, S., Khan, M.K.: A survey on software defined networking enabled smart buildings: architecture, challenges and use cases. J. Netw. Comput. Appl. 137, 6277 (2019)

    Article  Google Scholar 

  7. BABU, K. Rajesh, NAGAJANEYULU, P. V., et PRASAD, K. Satya. Brain tumor segmentation of T1w MRI images based on clustering using dimensionality reduction random projection technique. Current Medical Imaging, 2021, vol. 17, no 3, p. 331–341.

    Article  Google Scholar 

  8. Budati, A.K., Katta, R.B.: An automated brain tumor detection and classification from MRI images using machine learning technique s with IoT. Environ. Dev. Sustain. 24(9), 10570–10584 (2022)

    Article  Google Scholar 

  9. Babu, K.R., Nagajaneyulu, P.V., Prasad, K.S.: Performance analysis of CNN fusion-based brain tumour detection using Chan-Vese and level set segmentation algorithms. Int J Signal Imaging Syst Eng 12(1–2), 62–70 (2020)

    Article  Google Scholar 

  10. Neelima, K., Meruva, K.R, Subhas, C.: Image fusion using Xilinx system generator for MRI and CT medical image modalities. In: 2023 International Conference on Emerging Smart Computing and Informatics (ESCI), pp. 1–5. IEEE (2023)

  11. KOUSALYA, B., ASMAHASEEN, M., MANIMEGALAI, R., et al. FPGA Based Brain Tumor Extraction with Support Vector Machine Classifier from MRI Images using MATLAB. SSRG Int. J. VLSI Sig. Process., 2017, p. 6–11.

  12. William Thomas, H.M., Prasanna Kumar, S.C., Jayadevappa, D.: Automatic brain tumor segmentation using FPGA platform. Int. J. Pure Appl. Math. 118(18), 3483–3497 (2018)

    Google Scholar 

  13. LI, Yuhong, JIA, Fucang, et QIN, Jing. Brain tumor segmentation from multimodal magnetic resonance images via sparse representation. Artificial intelligence in medicine, 2016, vol. 73, p. 1–13.

    Article  Google Scholar 

  14. MAHAPATRA, Dwarikanath. Combining multiple expert annotations using semi-supervised learning and graph cuts for medical image segmentation. Computer Vision and Image Understanding, 2016, vol. 151, p. 114–123.

    Article  Google Scholar 

  15. William Thomas, H.M., Prasanna Kumar, S.C.: Detection of a brain tumor using segmentation and morphological operators from MRI scan with FPGA. In: International Conference on Theoretical Computing and Communication Technology (iCATccT). (2015)

  16. Preethi, S.: VLSI implementation of brain tumor segmentation using fuzzy C-mean clustering. J. Netw. Commun. Eng. 9(3), 56–58 (2017)

    Google Scholar 

  17. “DSP System Generator User Guide” 12.1 (2012)

  18. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948. (1995)

  19. Ghamisi, P., Couceiro, M.S., Benediktsson, J.A., Ferreira, N.M.F.: An efficient method for segmentation of images based on fractional calculus and natural selection. Expert Syst. Appl. 39, 12407–12417 (2012)

    Article  Google Scholar 

  20. Gtifa, W., Hamdaoui, F., Sakly, A.: Automated brain tumor segmentation from multi-modality MRI data based on new PSO Segmentation Method. Int. J. Med. Robot. Comput. Assist. Surg. 1168, e2487 (2022)

    Google Scholar 

  21. Huang, M., Yu, W., Zhu, D.: An improved image segmentation algorithm based on the Otsu method. In: 2012 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, pp. 135–139. IEEE (2012)

  22. Guo, X., Schwartz, L., Zhao, B.: Semi-automatic segmentation of multimodal brain tumor using active contours. In: Proceedings MICCAI BRATS, (2013).

  23. Sternberg, M.R., Hadgu, A.: A GEE approach to estimating sensitivity and specificity and coverage properties of the confidence intervals. Stat. Med. 20, 1529–1539 (2001)

    Article  Google Scholar 

  24. Baid, U., Ghodasara, S., Bilello, M., Mohan, S., Calabrese, E., Colak, E., Farahani, K., Kalpathy-Cramer, J., Kitamura, F.C., Pati, S., et al.: The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification (2021). arXiv:2107.02314.

Download references

Author information

Authors and Affiliations

  1. Laboratory of Automation and Electrical Systems and Environment, Monastir National School of Engineers (ENIM), University of Monastir, Monastir, Tunisia

    Wafa Gtifa & Anis Sakly

Authors
  1. Wafa Gtifa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anis Sakly
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors collaborated to create a cohesive and comprehensive study, and all have read and approved the final manuscript.

Corresponding author

Correspondence to Wafa Gtifa.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gtifa, W., Sakly, A. Integrating Xilinx FPGA and intelligent techniques for improved precision in 3D brain tumor segmentation in medical imaging. J Real-Time Image Proc 20, 115 (2023). https://doi.org/10.1007/s11554-023-01372-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11554-023-01372-x

Keywords

Search

Navigation