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3D EM/MPM Image Segmentation Using an FPGA Embedded Design Implementation

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Abstract

This paper presents a Field Programmable Gate Array (FPGA) based embedded system which is used to achieve high speed segmentation of 3D images. Segmentation is performed using Expectation-Maximization (EM) with Maximization of Posterior Marginals (MPM) Bayesian algorithm. This algorithm segments the 3D image using neighboring pixels based on a Markov Random Field (MRF) model. In this system, the embedded processor controls a custom circuit which performs the MPM and portions of the EM algorithm. The embedded processor completes the EM algorithm and also controls image data transmission between host computer and on-board memory. The whole system has been implemented on Xilinx Virtex 6 FPGA and achieved over 100 times processing improvement compared to standard desktop computer. Three new techniques were the key to achieve this speed: Pipelined computational cores, sixteen parallel data paths and a novel memory interface for maximizing the external memory bandwidth.

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References

  1. Athanasiadis, T., Mylonas, P., Avrithis, Y., Kollias, S. (2007). Semantic image segmentation and object labeling. IEEE Transactions on Circuits and Systems for Video Technology.

  2. Bravo, I., Mazo, M., Lazaro, J., Jimenez, P., Gardel, A., Marron, M. (2008). Novel HW architecture based on FPGAs oriented to solve the eigen problem. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 16(12).

  3. Chang, M., Tekalp, A., Sezan, M. (1997). Simultaneous motion estimation and segmentation. IEEE Transactions on Image Processing.

  4. Chen, Z., Su, A.W., Sun, M. (2012). Resource-efficient FPGA architecture and implementation of hough transform. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 16(12).

  5. Christopher, L., Delp, E., Meyer, C., Carson, P. (2002). 3-D bayesian ultrasound breast image segmentation using the EM-MPM algorithm. Proceedings of the IEEE Symposium on Biomedical Imaging.

  6. Christopher, L.A., & Delp, E.J. (2014). Comparing three-dimensional Bayesian segmentations for images with low signal-to-noise ratio and strong attenuation. Journal of Electronic Imaging, 23(4), 043,018.

    Article  Google Scholar 

  7. Comer, M.L., & Delp, E.J. (2000). The EM/MPM algorithm for segmentation of textured image: Analysis and further experimental results. IEEE Transactions on Image Processing, 9(10).

  8. Compton, K., & Hauck, S. (2002). Reconfigurable computing: A survey of system and software. ACM Computing Survey.

  9. Delp, E., Christopher, L., Meyer, C., Carson, P. (2003). New approaches in 3D ultrasound segmentation. Proceedings SPIE and IST Electronic Imaging and Technology Conference.

  10. Diaz, J., Ros, E., Pelayo, F., Ortigosa, E., Mota, S. (2006). FPGA-based real-time optical-flow system. IEEE Transactions on Circuits and Systems for Video Technology.

  11. Dick, C. (1998). Computing multidimensional DFT using Xilinx FPGAs. Signal Processing Algorithm and Technology.

  12. Dillinger, P., Leinen, J., Suslov, J., Patzak, S., Winkler, R., Schwan, H. (2005). FPGA based real-time image segmentation for medical systems and data processing. Real Time Conference 14th IEEE-NPSS.

  13. Draper, B., Beveridge, R., Bohm, W., Ross, C., Chawathe, M. (2002). Implementing Image Applications on FPGAs. Pattern Recognition.

  14. Glide-Hurst, C.K., Duric, N., Littrup, P. (2008). Volumetric breast density evaluation from ultrasound tomography images. Medical Physics, 35, 3988–3997.

    Article  Google Scholar 

  15. Hernandez, O. (2006). A High-Performance VLSI Architecture for the Histogram Peak-Climbing Data Clustering Algorithm. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 14(2).

  16. Hong, J., & Wang, M. (2004). High speed processing of biomedical images using programmable GPU. International Conference on Image Processing(ICIP).

  17. Jin, S., Cho, J., Pham, X., Lee, K., Park, S., Kim, M., Jeon, J. (2010). FPGA design and implementation of a real-time stereo vision system. IEEE Transactions on Circuits and Systems for Video Technology.

  18. Li, J., Shekhar, R., Papachristou, C. (2004). A brick caching scheme for 3D medical imaging. Biomedical Imaging: Nano to Macro, 563–566.

  19. Liu, C., & Christopher, L. (2014). Three dimensional moving pictures with a single imager and microfluidic lens. IEEE Transactions on Consumer Electronics, 60(2), 258–266.

    Article  Google Scholar 

  20. Malarkhodi, S., Banu, R., Malarvizhi, M. (2010). VLSI implementation of uterus image segmentation using multi-feature EM algorithm based on Gabor filter: FPGA implementation of uterus image segmentation using multi-feature EM algorithm based on gabor filter. Computing Communication and Networking Technologies(ICCCNT).

  21. Maroulis, D., Iakovidis, D.K., Bariamis, D. (2008). FPGA-based system for real-time video texture analysis. Journal of Signal Processing Systems, 53, 419–433.

    Article  Google Scholar 

  22. Marroquin, J., Mitter, S., Poggio, T. (1987). Probabalistic solution of ill-posed problems in computational vision. Journal of the American Statistical Association, 82(89).

  23. Palero, R.J.C., Girones, R.G., Cortes, A.S. (2006). A novel FPGA architecture of a 2-d wavelet transform. Journal of VLSI Signal Processing Systems for Signal, Image and Video Technology, 42, 273–284.

    Article  MATH  Google Scholar 

  24. Salem, M., Appel, M., Winkler, M., Meffert, F. (2008). FPGA based Smart Camera for 3D wavelet-based image segmentation. Distributed Smart Cameras, ICDSC.

  25. Shanthi, K., Ashok, L., Anandu, A., Das, B. (2009). FPGA implementation of image segmentation processor. Emerging Trends in Engineering and Technology(ICETET), 364–367.

  26. Sherbondy, A., Houston, M., Napel, S. (2003). Fast volume segmentation with simultaneous visualization using programmable graphics hardware. IEEE Visualization.

  27. Sun, Y. (2011). 3D image segmentation implementation on FPGA using EM/MPM algorithm Phd Thesis.

  28. Sun, Y., & Christopher, L. (2010). 3D image segmentation implementation on FPGA using the EM/MPM algorithm. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

  29. Xilinx (2005). PLB IPIF. Xilinx Document DS448(v2.02a) Xilinx Inc.

  30. Xilinx, I. (2009). Memory interface solution. Xilinx Document User’s Guide 086(v3.6) Xilinx Inc.

  31. Xilinx, I. (2010). Microblaze processor reference guide. Xilinx Document UG081(v11.1) Xilinx Inc.

  32. Zhang, X., Li, Y., Wang, J., Chen, Y. (2009). Design of high-speed image processing system based on FPGA. Electronic Measurement and Instruments.

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Authors and Affiliations

  1. Indiana University Purdue University at Indianapolis, Indianapolis, Indiana, USA

    Chao Liu, Yan Sun & Lauren Christopher

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  1. Chao Liu
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  2. Yan Sun
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  3. Lauren Christopher
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Correspondence to Lauren Christopher.

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Liu, C., Sun, Y. & Christopher, L. 3D EM/MPM Image Segmentation Using an FPGA Embedded Design Implementation. J Sign Process Syst 81, 411–424 (2015). https://doi.org/10.1007/s11265-014-0965-1

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  • DOI: https://doi.org/10.1007/s11265-014-0965-1

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