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.


























Similar content being viewed by others
Data Availability
Brain MRI images database used from GitHub [https://github.com/topics/brain-mri].
References
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
IEEE draft standard for VHDL language reference manual. IEEE P1076/D13, July 2019, pp. 1–796 (2019)
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
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
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
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
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
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
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
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
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
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
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
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
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
Corresponding author
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.
About this article
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
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00034-022-02233-x