Skip to main content
SpringerLink
Log in

Brain Tumor Detection from Brain MRI Using Soft IP Core on FPGA

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

Field-programmable gate array (FPGA) attempts a proper solution for fulfilling the requirements of high-performance real-time DSP systems. Any IP core based on FPGA has the benefit that it merges flexibility, timing efficiency, and algorithm adaptations from programmable logic with the efficiency provided by the processor placed inside the system. This type of tectonics is a compatible approach for the implementation of real-time biomedical applications. In this work, we have designed and proposed a soft IP core on FPGA that can detect the presence of brain tumors from MRI images with noticeably great performance. This designed brain tumor detection system requires only 6.49 µs to give satisfactory output. It is a low-cost system that can perform more flexibly than the alternate CPU-based approaches for having dynamic reconfigurability. The XILINX VIVADO Integrated Design suite has been used as the software designing platform. This designed IP core can read several brain MRI images and process them in parallel. The average power consumption of this IP core is around 82 mW and the maximum memory space is 30.906 MB. Therefore, this design can be used effectively as a faster, small power and memory-consuming system for clinical usage.

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
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26

Similar content being viewed by others

Data Availability

Brain MRI images database used from GitHub [https://github.com/topics/brain-mri].

References

  1. A. Ahmad, A. Amira, H. Rabah, Y. Berviller, FPGA-based architectures of finite radon transform for medical image de-noising, in 2010 IEEE Asia Pacific Conference on Circuits and Systems (2010), pp. 20–23. https://doi.org/10.1109/APCCAS.2010.5774903

  2. A. Alkamil, D.G. Perera, Efficient FPGA-based reconfigurable accelerators for SIMON cryptographic algorithm on embedded platforms, in 2019 International Conference on ReConFigurable Computing and FPGAs (ReConFig) (2019), pp. 1–8. https://doi.org/10.1109/ReConFig48160.2019.8994803

  3. H.M. Amjad, J. Niu, K. Hu, N. Akram, L. Besnard, Verilog code generation scheme from signal language, in 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST) (2019), pp. 457–462. https://doi.org/10.1109/IBCAST.2019.8667266

  4. H. Bessalah, F. Alim-Ferhat, H. Salhi, S. Seddiki, M. Issad, O. Kerdjidj, On line wavelets transform on a xilinx FPGA circuit to medical images compression, in 2009 International Conference on Digital Image Processing (2009), pp. 8–12. https://doi.org/10.1109/ICDIP.2009.89

  5. M.F. Binothman, N. Abdullah, N.A.B.A. Rusli, An overview of MRI brain classification using FPGA implementation, in 2010 IEEE Symposium on Industrial Electronics and Applications (ISIEA) (2010), pp. 623–628. https://doi.org/10.1109/ISIEA.2010.5679389

  6. B. Bipin, J.J. Nair, Image convolution optimization using sparse matrix vector multiplication technique, in 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (2016), pp. 1453–1457. https://doi.org/10.1109/ICACCI.2016.7732252

  7. I. Bouganssa, M. Sbihi, M. Zaim, Implementation on a FPGA of edge detection algorithm in medical image and tumors characterization, in 2016 5th International Conference on Multimedia Computing and Systems (ICMCS) (2016), pp. 59–64. https://doi.org/10.1109/ICMCS.2016

  8. S.C. Chan, H.O. Ngai, K.L. Ho, A programmable image processing system using FPGA, in Proceedings of IEEE International Symposium on Circuits and Systems—ISCAS’94, vol. 2 (1994), pp. 125–1282. https://doi.org/10.1109/ISCAS.1994.408921

  9. B. Chandra, M. Sharma, Segmentation’s based feature extraction of MRI images using wavelet and implementation on FPGA, in 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT) (2016), pp. 135–141. https://doi.org/10.1109/ICACDOT.2016.7877566

  10. X. Chen, X. Wang, Y. Liu, Z. Liu, J. An, FPGA verification of radar signal processing based on SoC, in 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP) (2019), pp. 1–4. https://doi.org/10.1109/ICSIDP47821.2019.9172898

  11. I. Chiuchisan, Implementation of medical image processing algorithm on reconfigurable hardware, in 2013 E-Health and Bioengineering Conference (EHB) (2013), pp. 1–4. https://doi.org/10.1109/EHB.2013.6707298

  12. I. Chiuchisan, A new FPGA-based real-time configurable system for medical image processing, in 2013 E-Health and Bioengineering Conference (EHB) (2013), pp. 1–4. https://doi.org/10.1109/EHB.2013.6707301

  13. I. Chiuchisan, An approach to the verilog-based system for medical image enhancement. 2015 E-Health and Bioengineering Conference (EHB) (2015), pp 1–4. https://doi.org/10.1109/EHB.2015.7391461

  14. S. Cinar, M.N. Kurnaz, Segmentation of medical images by using kNN classifier on field programmable logic array (FPGA), in National Conference on Electrical, Electronics and Computer Engineering (2010), pp. 516–520

  15. Y. Cui, C. Wang, Y. Chen, Z. Wei, M. Chen, W. Liu, Dynamic reconfigurable pufs based on FPGA, in 2019 IEEE International Workshop on Signal Processing Systems (SiPS) (2019), pp. 79–84. https://doi.org/10.1109/SiPS47522.2019.902044

  16. P. Dillinger, J.F. Vogelbruch, J. Leinen, S. Suslov, R. Patzak, H. Winkler, K. Schwan, FPGA based real-time image segmentation for medical systems and data processing, in 14th IEEE-NPSS Real Time Conference, 2005 (2005), p. 5. https://doi.org/10.1109/RTC.2005.1547401

  17. E. Giordano, F.D. Marco, G. Pravadelli, A model-based design flow for dynamic partial reconfigurable FPGAs, in 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) (2019), pp. 3099–3103. https://doi.org/10.1109/SMC.2019.8914616

  18. M.C. Herbordt, T. VanCourt, Y. Gu, B. Sukhwani, A. Conti, J. Model, D. DiSabello, Achieving high performance with FPGA-based computing. Computer 40(3), 50–57 (2007). https://doi.org/10.1109/MC.2007.79

    Article  Google Scholar 

  19. IEEE draft standard for VHDL language reference manual. IEEE P1076/D13, July 2019, pp. 1–796 (2019)

  20. K. Khandagle, S.S. Rathod, Implementation of MRI gradient generation system and controller on field programmable gate array (FPGA), in 2018 International Conference on Communication Information and Computing Technology (ICCICT) (2018), pp. 1–4. https://doi.org/10.1109/ICCICT.2018.8325876

  21. P. Li, T. Page, G. Luo, W. Zhang, P. Wang, P. Zhang, P. Maass, M. Jiang, J. Cong, FPGA acceleration for simultaneous medical image reconstruction and segmentation, in 2014 IEEE 22nd Annual International Symposium on Field-Programmable Custom Computing Machines (2014), pp. 172–172. https://doi.org/10.1109/FCCM.2014.54

  22. H. Lin, C. Hsueh, COS: a configurable os for embedded SoC systems, in 12th IEEE International Conference on Embedded and RealTime Computing Systems and Applications (RTCSA’06) (2006), pp. 242–245. https://doi.org/10.1109/RTCSA.2006.24

  23. S. Mahmood, J. Rettkowski, A. Shallufa, M. H¨ubner, D. Göhringer, IP core identification in FPGA configuration files using machine learning techniques, in 2019 IEEE 9th International Conference on Consumer Electronics (ICCE-Berlin) (2019), pp. 103–108. https://doi.org/10.1109/ICCE-Berlin47944.2019.8966236

  24. U.R. Nelakuditi, T.S.R. Bharadwaja, N. Bhagiradh, Novel VLSI architecture for real time medical image segmentation, in 2015 2nd International Conference on Electronics and Communication Systems (ICECS) (2015), pp. 1084–1088. https://doi.org/10.1109/ECS.2015.7124748

  25. X. Pang, D. Yu, J. Li, Y. Guo, Design and application of IP core in SoC technology, in 2010 Third International Symposium on Information Science and Engineering (2010), pp. 71–74. https://doi.org/10.1109/ISISE.2010.94

  26. R. Ranjbarzadeh, A.B. Kasgari, S.J. Ghoushchi, S. Anari, M. Naseri, M. Bendechache, Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images. Sci. Rep. 11(1), 10930 (2021). https://doi.org/10.1038/s41598-021-90428-8

    Article  Google Scholar 

  27. R. Rekha, K.P. Menon, FPGA implementation of exponential function using cordic IP core for extended input range, in 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information Communication Technology (RTEICT) (2018), pp. 597–600. https://doi.org/10.1109/RTEICT42901.2018.9012611

  28. K. Smelyakov, M. Shupyliuk, V. Martovytskyi, D. Tovchyrechko, O. Pono marenko, Efficiency of image convolution, in 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) (2019), pp. 578–583. https://doi.org/10.1109/CAOL46282.2019.9019450

  29. H.M.W. Thomas, S.C. Prasanna Kumar, Detection of a brain tumor using segmentation and morphological operatorsfrom MRI scan with FPGA, in 2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT) (2015), pp. 728–731. https://doi.org/10.1109/ICATCCT.2015.7456979

  30. C. Xuesen, H. Liguo, D. Jinbo, Design and implementation of FPGA base diagnosis of medical image data acquisition equipment, in IEEE 2011 10th International Conference on Electronic Measurement Instruments, vol. 3 (2011), pp. 51–55. https://doi.org/10.1109/ICEMI.2011.6037853

  31. S.S. Yadav, V. Mittal, FPGA implementation of segmented feature fusion in MRI images using wavelet. 2019 International Conference on Communication and Electronics Systems ICCES) (2019), pp. 1946–1951. https://doi.org/10.1109/ICCES45898.2019.9002032

Download references

Acknowledgements

The authors wish to thank Department of Electronics and Communication Engineering, Khulna University of Engineering and Technology, Bangladesh staffs and all professors of this department for supporting equipment and other facilities. This work was not supported in part by a grant.

Funding

No third party or organization provided funds for this research so far.

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Khulna University of Engineering and Technology, Khulna, 9203, Bangladesh

    Nazifa Tabassum, Sheikh Md. Rabiul Islam & Farzana Bulbul

Authors
  1. Nazifa Tabassum
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sheikh Md. Rabiul Islam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Farzana Bulbul
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nazifa Tabassum.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest regarding this research work.

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

Tabassum, N., Islam, S.M.R. & Bulbul, F. Brain Tumor Detection from Brain MRI Using Soft IP Core on FPGA. Circuits Syst Signal Process 42, 724–747 (2023). https://doi.org/10.1007/s00034-022-02233-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-022-02233-x

Keywords

Search

Navigation