IMPLEMENTATION METHOD ON MEDICAL IMAGE COMPRESSION SYSTEM: A REVIEW

Authors

  • Azlan Muharam Reconfigurable Computing for Analytic Acceleration Focus Group (ReCAA), Microelectronics and Nanotechnology – Shamsuddin Research Centre (MiNT-SRC), Universiti Tun Hussein Onn Malaysia (UTHM) Beg Berkunci 101 Parit Raja Batu Pahat Johor 86400, Malaysia
  • Afandi Ahmad Reconfigurable Computing for Analytic Acceleration Focus Group (ReCAA), Microelectronics and Nanotechnology – Shamsuddin Research Centre (MiNT-SRC), Department of Computer Engineering, Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia (UTHM) Beg Berkunci 101 Parit Raja Batu Pahat Johor 86400, Malaysia

DOI:

https://doi.org/10.11113/jt.v79.7873

Keywords:

Medical image processing, hardware, software, compression

Abstract

The rapid development of medical imaging and the invention of various medicines have benefited mankind and the whole community. Medical image processing is a niche area concerned with the operations and processes of generating images of the human body for clinical purposes.  Potential areas such as image acquisition, image enhancement, image compression and storage, and image based visualization also include in medical image processing analysis. Unfortunately, medical image compression dealing with three-dimensional (3-D) modalities still in the pre-matured stage. Along with that, very limited researchers take a challenge to apply hardware on their implementation. Referring to the previous work reviewed, most of the compression method used lossless rather than lossy. For implementation using software, MATLAB and Verilog are the famous candidates among researchers. In term of analysis, most of the previous works conducted objective test compared with subjective test. This paper thoroughly reviews the recent advances in medical image compression mainly in terms of types of compression, software and hardware implementations and performance evaluation. Furthermore, challenges and open research issues are discussed in order to provide perspectives for future potential research. In conclusion, the overall picture of the image processing landscape, where several researchers more focused on software implementations and various combinations of software and hardware implementation.  

Author Biography

  • Afandi Ahmad, Reconfigurable Computing for Analytic Acceleration Focus Group (ReCAA), Microelectronics and Nanotechnology – Shamsuddin Research Centre (MiNT-SRC), Department of Computer Engineering, Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia (UTHM) Beg Berkunci 101 Parit Raja Batu Pahat Johor 86400, Malaysia
    Computer Engineering

References

N. H. Ja’Afar, A. Ahmad, and A. Amira. 2013. Distributed Arithmetic Architecture of Discrete Wavelet Transform (DWT) with hybrid method. Proc. IEEE Int. Conf. Electron. Circuits Syst. 501-507.

C. Dhaarani, D. Venugopal, and A. Sivanantha Raja. 2014. Medical Image Compression using Ripplet Transform. Proc. - 2014 Int. Conf. Intell. Comput. Appl. ICICA 2014. 233-238.

R. Pizzolante, A. Castiglione, B. Carpentieri, and A. De Santis. 2014. Parallel Low-complexity Lossless Coding of Three-dimensional Medical Images. Proc.-2014 Int. Conf. Network-Based Inf. Syst. NBiS 2014. 91-98.

T. Li, Y. Wang, C. Chang, N. Hu, and Y. Zheng. 2014. Color-appearance-model Based Fusion of Gray and Pseudo-color Images for Medical Applications. Inf. Fusion. 19(1): 103-114.

J. Zhang, G. Lin, L. Wu, C. Wang, and Y. Cheng. 2015. Wavelet and Fast Bilateral Filter Based De-speckling Method for Medical Ultrasound Images. Biomed. Signal Process. Control. 18: 1-10.

T. Nadu and S. S. Lakshmi. 2014. Segmentation and Compression of Medical Image Using MSPIHT in Telemedicine Application. ICICES. 978.

M. Razaak and M. G. Martini. 2014. Rate-distortion and Rate-quality Performance Analysis of HEVC Compression of Medical Ultrasound Videos. Procedia Comput. Sci. 40: 230-236.

R. Sumalatha and M. V. Subramanyam. 2015. Hierarchical Lossless Image Compression for Telemedicine Applications. Procedia Comput. Sci. 54: 838-848.

A. Arthur and V. Saravanan. 2012. Efficient Medical Image Compression Technique for Telemedicine Considering Online and Offline Application. 2012 Int. Conf. Comput. Commun. Appl. ICCCA 2012.

D. U. Shah and C. H. Vithlani. 2011. Efficient implementations of Discrete Wavelet Transforms using FPGAs. Int. J. Adv. Eng. Technol. 1(4): 100-111.

M. Firoozbakht, J. Dehmeshki, M. Martini, Y. Ebrahimdoost, H. Amin, M. Dehkordi, A. Youannic, and S. D. Qanadli. 2010. Compression of Digital Medical Images based on Multiple Regions of Interest. 4th Int. Conf. Digit. Soc. ICDS 2010, Incl. CYBERLAWS 2010 1st Int. Conf. Tech. Leg. Asp. e-Society. 260-263.

V. Sanchez, R. Abugharbieh, and P. Nasiopoulos. 2010. 3-D Scalable Medical Image Compression with Optimized Volume of Interest Coding. IEEE Trans. Med. Imaging. 29(10): 1808-1820.

P. Suapang, K. Dejhan, and S. Yimmun. 2010. Medical Image Archiving, Processing, Analysis and Communication System for Teleradiology. IEEE Reg. 10 Annu. Int. Conf. Proceedings/TENCON. 339-345.

M. Nagabushanam, 2011. Design and FPGA Implementation of Modified Distributive Arithmetic based Dwt-Idwt Processor for Image Compression. International Conference on Communications and Signal Processing (ICCSP). 1-4.

Q. Min and R. J. T. Sadleir. 2012. Medical Image Compression Using Region-based Prediction. IEEE EMBS Int. Conf. Biomed. Eng. Sci. 677-682.

G. Ukrit, Mferni. Suresh. 2013. Effective Lossless Compression for Medical Image Sequences Using Composite Algorithm. Int. Conf. Circuits, Power Comput. Technol. 1122-1126.

J. A. Sophia. P Eben. 2014. Implementation of Region Based Medical Image Compression for Telemedicine Application. IEEE Int. Conf. Comput. Intell. Comput. Res. 1-4.

S. A. Rein, F. H. P. Fitzek, and T. Sikora. 2015. Evaluation of the Wavelet Image Two-line Coder: A Low Complexity Scheme for Image Compression. Signal Process. Image Commun. 37: 58-74.

A. Ahmad, B. Krill, A. Amira, and H. Rabah. 2010. Efficient Architectures for 3D HWT using Dynamic Partial Reconfiguration. J. Syst. Archit. 56: 305-316.

N. Zhou, H. Li, D. Wang, S. Pan, and Z. Zhou. 2015. Image Compression and Encryption Scheme based on 2-D Compressive Sensing and Fractional Mellin Transform. Opt. Commun. 43: 10-21.

Q. Min and Robert J.T. Sadleir. 2012. An Edge-based Prediction Approach for Medical Image Compression. IEEE EMBS Int. Conf. Biomed. Eng. Sci. 717-722.

K. G. Thanushkodi and S. Bhavani. 2013. Comparison of Fractal Coding Methods for Medical Image Compression. IET Image Process. 7(7): 686-693.

S. Kim, H. Sohn, J. H. Chang, T. Song, and Y. Yoo. 2010. A PC-based Fully-programmable Medical Ultrasound Imaging System Using a Graphics Processing Unit. IEEE Int. Ultrason. Symp. 314-317.

I. Chiuchisan. 2013. A new FPGA-based Real-time Configurable System for Medical Image Processing. E-Health Bioeng. Conf. EHB 2013. 0-3.

S. Saha, K. H. Uddin, M. S. Islam, M. Jahiruzzaman, and A. B. M. A. Hossain. 2014. Implementation of Simplified Normalized Cut Graph Partitioning Algorithm on FPGA for image Segmentation. Ski. 2014 - 8th Int. Conf. Software, Knowledge, Inf. Manag. Appl. 3.

Y. Li, W. Jia, B. Luan, Z. H. Mao, H. Zhang, and M. Sun. 2015. A FPGA implementation of JPEG baseline Encoder for Wearable Devices. 2015 41st Annu. Northeast Biomed. Eng. Conf. NEBEC 2015. 3-4.

A. Ahmad, B. Krill, A. Amira, and H. Rabah. 2009. 3D Haar wavelet Transform with Dynamic Partial Reconfiguration for 3D Medical Image Compression. 2009 IEEE Biomedical Circuits and Systems Conference. 1: 137-140.

A. Ahmad and A. Amira. 2009. Efficient Reconfigurable Architectures for 3D Medical Image Compression. Int. Conf. Field-Programmable Technol. 472-474.

N. Kehtarnavaz and S. Mahotra. 2011. FPGA Implementation Made Easy for Applied Digital Signal Processing Courses. 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2892-2895.

K. Benkrid, A. Akoglu, C. Ling, Y. Song, Y. Liu, and X. Tian. 2012. High Performance Biological Pairwise Sequence Alignment: FPGA Versus GPU Versus Cell BE Versus GPP. Int. J. Reconfigurable Comput. 2012.

G. Grossi, R. Lanzarotti, and J. Lin. 2015. High-rate compression of ECG signals by an accuracy-driven sparsity model relying on natural basis. Digit. Signal Process. A Rev. Journal. vol. 45: 96–106.

A. H. Abouali. 2014. Object-based VQ for Image Compression. Ain Shams Eng. Journal. 6(1): 211-216.

Z. Liu, H. Yin, Y. Chai, and S. X. Yang. 2014. A Novel Approach for Multimodal Medical Image Fusion. Expert Syst. Appl. 41(16): 7425-7435.

T. Chen, Y. Wang, H. Zhang, and C. Xiao. 2010. An Embedded 3D Medical Image Processing and Visualization Platform Based on Dual-core Processor. Proc. World Congr. Intell. Control Autom. 2007. 2936-2941.

N. L. Bian, Jing, Yinghong Dong, Kejing Li. 2013. Research and Application of 3D Interactive Processing System for Medical Image based on MITK Algorithm. Int. Conf. Mechatron. Sci. Electr. Eng. Comput. 1951-1954.

M. Weeks and M. Bayoumi. 1998. 3D Discrete Wavelet Transform Architectures. ISCAS ’98. Proc. 1998 IEEE Int. Symp. Circuits Syst. 4.

B. Das and S. Banerjee. 2003. A Memory Efficient 3-D DWT Architecture. 16th International Conference on VLSI Design, Proceedings.

Y.-H. . B. Wu, L.-X. Jin, and H.-J. Tao. 2010. An Improved Fast Parallel SPIHT Algorithm and its FPGA Implementation. Proc. 2010 2nd Int. Conf. Futur. Comput. Commun. ICFCC 2010. 1(3): 1191-1195.

R. Gavvala, M. M. Gopal, S. S. Chandra, and S. S. Rao. 2012. Pass-parallel VLSI Architecture of BPC for Embedded Block Coder in JPEG2000. Asia Pacific Conf. Postgrad. Res. Microelectron. Electron. 111-117.

S. M. Ismail, A. E. Salama, and M. F. Abu-ElYazeed. 2007. FPGA Implementation of an Efficient 3D-WT Temporal Decomposition Algorithm for Video Compression. 2007 IEEE International Symposium on Signal Processing and Information Technology.

Z. Shi, L.-Y. Deng, and Q.-J. Chen. 2007. Numerical Solution of Differential Equations By Using Haar Wavelets. 2-4.

R. Singh and A. Khare. 2014. Fusion of Multimodal Medical Images using Daubechies Complex Wavelet Transform-A Multiresolution Approach. Inf. Fusion. 19(1): 49-60.

S. J. Pinto and Jayanand P.Gawande. 2012. Performance Analysis of Medical Image Compression Techniques. 3rd Asian Himalayas International Conference on Internet (AH-ICI). 5-8.

Z. Zuo, X. Lan, L. Deng, S. Yao, and X. Wang. 2015. An improved Medical Image Compression Technique with Lossless Region of Interest. Optik (Stuttg). 126(21): 2825-2831.

M. Sabarimalai Sur and S. Dandapat. 2014. Wavelet-based Electrocardiogram Signal Compression Methods and Their Performances: A Prospective Review. Biomed. Signal Process. Control. 14(1): 73-107.

P. Suapang, M. Thongyoun, and S. Chivapreecha. 2013. Medical Image Compression and Quality Assessment. Proc. SICE Annu. Conf. 841-846.

H. Zaineldin, M. A. Elhosseini, and H. A. Ali. 2015. Image Compression Algorithms In Wireless Multimedia Sensor Networks: A Survey. Ain Shams Eng. Journal. 6(2): 481-490.

N. Karimi, S. Samavi, S. Shirani, A. Banaei, and E. Nasr-Esfahani. 2015. Real-time Lossless Compression of Microarray Images by Separate Compaction of Foreground and Background. Comput. Stand. Interfaces. 39: 34-43.

X. Cheng, H. Long, W. Chen, J. Xu, Y. Huang, and F. Li. 2015. Three-dimensional Alteration of Cervical Anterior Spinal Artery and Anterior Radicular Artery in Rat Model of Chronic Spinal Cord Compression by micro-CT. Comput. Methods Programs Biomed. 37(2): 838-848.

B. Koc, Z. Arnavut, and H. Koçak. 2015. The Pseudo-distance Technique for Parallel Lossless Compression of Color-Mapped Images. Comput. Electr. Eng. 46: 456-470.

A. M. Rufai, G. Anbarjafari, and H. Demirel. 2013. Lossy Medical Image Compression Using Huffman Coding and Singular Value Decomposition. 21st Signal Process. Commun. Appl. Conf.

Y. Nian, M. He, and J. Wan. 2014. Distributed Near Lossless Compression Algorithm for Hyperspectral Images. Comput. Electr. Eng. 40(3): 1006-1014.

M. Sundaresan and E. Devika. 2012. Image Compression Using H.264 and Deflate Algorithm. Int. Conf. Pattern Recognition, Informatics Med. Eng. 242-245.

T. G. Shirsat and V. K. Bairagi. 2013. Lossless Medical Image Compression by IWT and Predictive Coding. 2013 International Conference on Energy Efficient Technologies for Sustainability. 1279-1283.

Y. Nian, M. He, and J. Wan. 2015. Lossless and Near-lossless Compression of Hyperspectral Images Based on Distributed Source Coding. Journal. Vis. Commun. Image Represent. 28: 113-119.

F. Sepehrband, M. Mortazavi, S. Ghorshi, and J. Choupan. 2011. Simple Lossless and Near-lossless Medical Image Compression based on Enhanced DPCM Transformation. IEEE Pacific RIM Conf. Commun. Comput. Signal Process. 66-72.

R. Pizzolante, B. Carpentieri, and A. Castiglione. 2013. A Secure Low Complexity Approach for Compression and Transmission of 3-D Medical Images. Proc.-2013 8th Int. Conf. Broadband, Wirel. Comput. Commun. Appl. BWCCA 2013. 387-392.

S. Amraee, N. Karimi, S. Samavi, and S. Shirani. 2011. Compression of 3D MRI Images based on Symmetry in prediction-error Field. Proc.-IEEE Int. Conf. Multimed. Expo.

K. T. Kumari, P Vasanthi. 2013. A Secure Fast 2D-Discrete Fractional Fourier Transform Based Medical Image Compression Using Hybrid Encoding Technique. Int. Conf. Curr. Trends Eng. Technol.

M. Satti and S. Kak. 2009. Multilevel Indexed Quasigroup Encryption for Data and Speech. IEEE Trans. Broadcast. 55: 270-281.

T. Phanprasit. 2013. Compression of Medical Image Using Vector Quantization. 0-3.

S. L. Pinjare, K. Mudnaf, and S. Kumar. 2012. Distributed Arithmetic Multiplier based Artificial Neural Network Architecture for Image Compression. 2nd IEEE Int. Conf. Parallel, Distrib. Grid Comput. 135-140.

A. N. Sazish and A. Amira. 2008. An Efficient Architecture for HWT using Sparse Matrix Factorisation and DA Principles. APCCAS 2008-2008 IEEE Asia Pacific Conf. Circuits Syst.1308-131.

M. Vucha. 2011. Design and FPGA Implementation of Systolic Array Architecture for Matrix Multiplication. 26(3): 18-22.

G. Saldana and M. Arias-Estrada. 2007. Compact FPGA-based Systolic Array Architecture Suitable for Vision Systems. J. High Perform. Syst. Archit. 3-8.

Y. Zhou and P. Shi. 2011. Distributed Arithmetic for FIR Filter Implementation on FPGA. 2011 Int. Conf. Multimed. Technol. ICMT 2011. 1(4): 294-297.

L. Wenna, G. Yang, Y. Yufeng, and G. Liqun. 2011. Medical Image Coding based on Wavelet Transform and Distributed Arithmetic Coding. 2011 Chinese Control Decis. Conf. 4159-4162.

M. Martina, G. Masera, M. R. Roch, and G. Piccinini. 2015. Result-biased distributed-arithmetic-based filter Architectures for Approximately Computing the DWT. IEEE Trans. Circuits Syst. I Regul. Pap. 62(8): 2103-2113.

A. M. Al-Haj. 2003. Fast Discrete Wavelet Transformation Using FPGAs and Distributed Arithmetic. Int. J. Appl. Sci. Eng. 1(2): 160-171.

A. Otero, Y. E. Krasteva, E. De La Torre, and T. Riesgo. 2010. Generic systolic Array for Run-time Scalable Cores. Lect. Notes Comput. Sci. 5992: 4-16.

P. C. Chandrasekhar and S. N. Reddy. 2012. FPGA Implementation of Systolic Array Architecture for 3D- DWT Optimizing Speed and Power. 2(10): 39-50.

M. M. Azadfar. 2008. Implementation of A Optimized Systolic Array Architecture for FSBMA using FPGA for Real-time Applications. IJCSNS. 8(3): 46.

M. E. Haque, A. Al Kaisan, M. R. Saniat, and A. Rahman. 2014. GPU Accelerated Fractal Image Compression for Medical Imaging in Parallel Computing Platform. 1-7.

N. Zhang, J. Wang, and Y. Chen.2010. Image parallel Processing based on GPU. 2nd International Conference on Advanced Computer Control. 367-370.

V. Akkala, P. Rajalakshmi, P. Kumar, and U. B. Desai. 2014. FPGA based ultrasound Backend System with Image Enhancement Technique. ISSNIP Biosignals Biorobotics Conf.

L. M. Russo, E. C. Pedrino, E. Kato, and V. O. Roda. 2012. Image Convolution Processing: A GPU versus FPGA Comparison. SPL 2012 - 8th Southern Programmable Logic Conference.

R. R. Osorio and J. D. Bruguera. 2006. High-throughput Architecture for H.264/AVC CABAC Compression System. IEEE Trans. Circuits Syst. Video Technol. 16(11): 1376-1384.

W. Wang, B. Guo, S. Zhang, and Q. Ye. 2009. A CABAC Accelerating Algorithm based on Adaptive Probability Estimation Update. 2nd Int. Congr. Image Signal Process. CISP’09.

P. Meng, G. R. C. Jr, R. Kastner, and D. A. Demer. 2013. GPU Accelerated Post-processing for Multifrequency Biplanar Interferometric Imaging.

H. Liu, T. Ma, S. Chen, Y. Liu, S. Wang, and Y. Jin. 2012. Development of GPU based Image Reconstruction Method For Clinical SPECT. IEEE Nucl. Sci. Symp. Med. Imaging Conf. Rec. 3415-3418.

S. Kinouchi, T. Yamaya, E. Yoshida, H. Tashima, H. Kudo, H. Haneishi, and M. Suga. 2012. GPU-based PET Image Reconstruction Using an Accurate Geometrical System Model. Ieee Trans. Nucl. Sci. 59(5): 1977-1983.

J. S. Lee and T. Ebrahimi. 2012. Perceptual Video Compression: A Survey. IEEE J. Sel. Top. Signal Process. 6(6): 684-697.

A. Amselem, T. Hatsui, and M. Yamaga. 2011. Real-time Embedded Lossless Compression for Sparse Signal Data Optimized for X-Ray Free- Electron Laser Experiments. IEEE Nuclear Science Symposium Conference Record. 2180-2182.

I. Chiuchisan. 2013. Implementation of Medical Image Processing Algorithm on Reconfigurable Hardware. IEEE Int. Conf. E-Health Bioeng. 4-7.

Downloads

Published

2017-10-22

Issue

Vol. 79 No. 7: November 2017

Section

Science and Engineering

License

Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.

How to Cite

IMPLEMENTATION METHOD ON MEDICAL IMAGE COMPRESSION SYSTEM: A REVIEW. (2017). Jurnal Teknologi, 79(7). https://doi.org/10.11113/jt.v79.7873