International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 9, No. 10, 8 Oct. 2017
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RM2IC: Performance Analysis of Region based Mixed-mode Medical Image Compression
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Author(s)
Index Terms
Compressive Sensing, Image Compression, Lossless compression, Medical Image Processing, Region of Interest, Quality of Image
Abstract
The medical data science has been changing from conventional analog to more powerful digital imaging systems for some time. These imagining systems produced images in digital form. As digital technology evolves and exceeds the capability of analog imaging devices, so too does the expansion in the range of applications for image guided surgical and diagnostic systems. The optimization of bandwidth and storage are the major issues in image processing technology. The Compressive Sensing (CS) algorithm can become prominent tool for these issues because it can sample the signal with much lesser sample rate than twice of the maximum frequency of the signal and reconstruct the signal similar to the original signal. This paper, presents a novel scheme Region based Mixed-mode Medical Image Compression (RM2IC). Here, the region of interest is compressed with lossless hybrid compression methods and the non-region of interest is com-pressed with lossy hybrid CS algorithm. RM2IC is compared with different existing hybrid compression methods and it outperforms better visual perceptional quality of reconstructed image and reduces the compression rate. The performance analysis is done based on PSNR, MSE and compression ratio.
Cite This Paper
Lakshminarayana. M, Mrinal Sarvagya," RM2IC: Performance Analysis of Region based Mixed-mode Medical Image Compression", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.9, No.10, pp. 12-21, 2017. DOI: 10.5815/ijigsp.2017.10.02
Reference
[1]X. Xiao, Y. Zhuang, Z. Wang and X. Zhang, "A reconstruction algorithm based on 3D tree-structure Bayesian compressive sensing for underwater videos," 2015 IEEE International Conference on Information and Automation, Lijiang, 2015, pp. 886-891.
[2]F. Thakkar and V. K. Srivastava, "Improved compressive sensing for grayscale mages with zigzag scanning and block DCT," 2015 International Conference on Computer, Communication and Control (IC4), Indore, 2015, pp. 1-4.
[3]Jiang Yuan, ShenPei, ZhaoPing, DaiJiYang and ChenZhen, "An improved algorithm of search for compressive sensing image recovery based on lp norm," 2015 Chinese Automation Congress (CAC), Wuhan, 2015, pp. 1962-1968.
[4]F. C. Chang and H. C. Huang, "The Analysis of Reconstruction Efficiency with Compressive Sensing in Different K-Spaces," 2015 Third International Conference on Robot, Vision and Signal Processing (RVSP), Kaohsiung, 2015, pp. 67-70.
[5]N. Eslahi and A. Aghagolzadeh, "Compressive Sensing Image Restoration Using Adaptive Curvelet Thresholding and Nonlocal Sparse Regularization," in IEEE Transactions on Image Processing, vol. 25, no. 7, pp. 3126-3140, July 2016.
[6]Lakshminarayana. M and M. Sarvagya, “Scaling the Effectiveness of Existing Compressive Sensing in Multimedia Contents”, International Journal of Computer Applications, vol. no. 115, no. 9, 2015, pp.16-26.
[7]V. Pavithra and S. M. Renuka Devi, "An image representation scheme by hybrid compressive sensing," 2013 IEEE Asia Pacific Conference on Postgraduate Research in Microelectronics and Electronics (Prime Asia), Visakhapatnam, 2013, pp. 114-119.
[8]Ponuma R, Aarthi V and Amutha R, "Cosine Number Transform based hybrid image compression-encryption," 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, 2016, pp. 172-176.
[9]E. Atar, O. K. Ersoy and L. Özyılmaz, "Character/text data compression and encryption by compressive sensing and hybrid cryptography," 2016 24th Signal Processing and Communication Application Conference (SIU), Zonguldak, 2016, pp. 365-368.
[10]M. Deriche, M. A. Qureshi and A. Beghdadi, "An image compression algorithm using reordered wavelet coefficients with compressive sensing," 2015 International Conference on Image Processing Theory, Tools and Applications (IPTA), Orleans, 2015, pp. 498-503.
[11]R. Obermeier, J. H. Juesas and J. A. Martinez-Lorenzo, "Imaging breast cancer in a hybrid DBT / NRI system using compressive sensing," 2015 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting, Vancouver, BC, 2015, pp. 392-393.
[12]E. A. Bernal and Q. Li, "Hybrid vectorial and tensorial Compressive Sensing for hyperspectral imaging," 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), South Brisbane, QLD, 2015, pp. 2454-2458.
[13]P. C, S. S. Gorthi and D. Mishra, "Compressive Sensing framework for simultaneous compression and despeckling of SAR images," 2015 Eighth International Conference on Advances in Pattern Recognition (ICAPR), Kolkata, 2015, pp. 1-6.
[14]J. Zhang, B. Sun and H. Xu, "Analysis of the effect of sparsity on the performance of SAR imaging based on CS theory," 2016 International Conference on Communication and Signal Processing (ICCSP), Melmaruvathur, Tamilnadu, India, 2016, pp. 0384-0388.
[15]A. Thapliyal and R. Kumar, "Temporal compression in wireless sensor networks using compressive sensing and ARMA modeling," 2016 2nd International Conference on Advances in Computing, Communication, & Automation (ICACCA) (Fall), Bareilly, India, 2016, pp. 1-4.
[16]B. Yang, H. Xu, Y. You and X. Xie, "The hybrid Cramér-Rao bounds on elevation in compressive sensing SAR tomography," 2016 4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing (CoSeRa), Aachen, 2016, pp. 128-132.
[17]J. Sevilla, G. Martín, J. Nascimento and J. Bioucas-Dias, "Hyperspectral image reconstruction from random projections on GPU," 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, 2016, pp. 280-283.
[18]Lakshminarayana. M and M. Sarvagya, 2015, “Lossless Compression of Medical Image to Overcome Network Congestion Constraints”, Springer, Proceedings of Third International Conference on Emerging Research in Computing, Information, Communication and Application (ERCICA-2015), Bangalore, vol. 01, pp.305-311.
[19]M. Lakshminarayana and M. Sarvagya, 2015, "Random sample measurement and reconstruction of medical image signal using Compressive Sensing," International Conference on Computing and Network Communications (CoCoNet), Trivandrum, pp. 255-262.
[20]Lakshminarayana. M and M. Sarvagya, 2016, “Algorithm to Balance Compression and Signal Quality using Novel Compressive Sensing in Medical Images”, Springer-Software Engineering Perspectives and Application in Intelligent Systems, pp.317-327.
[21]Lakshminarayana. M and Mrinal Sarvagya, "CARIC: A Novel Modeling of Combinatorial Approach for Radiological Image Compression", Springer, Cybernetics and Mathematics Applications in Intelligent Systems, CSOC-2017, Vol.2, pp.82-91, April-2017.
[22]M. V. R. Manimala, C. D. Naidu and M. N. Giriprasad, "Sparse recovery algorithms based on dictionary learning for MR image reconstruction," 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, 2016, pp. 1354-1360.
[23]A. S. Unde, R. Malla and Deepthi P. P., "Low complexity secure encoding and joint decoding for distributed compressive sensing WSNs," 2016 3rd International Conference on Recent Advances in Information Technology (RAIT), Dhanbad, 2016, pp. 89-94.
[24]Huang Bingchao, Wan JianWei, Xu Ke and Nian Yongjian, "Block compressive sensing of hyperspectral images based on prediction error," 2015 4th International Conference on Computer Science and Network Technology (ICCSNT), Harbin, 2015, pp. 1395-1399.
[25]N. Eslahi and A. Aghagolzadeh, "Compressive Sensing Image Restoration Using Adaptive Curvelet Thresholding and Nonlocal Sparse Regularization," in IEEE Transactions on Image Processing, vol. 25, no. 7, pp. 3126-3140, July 2016.
[26]"Finding Articles, Databases and Images". Cornell UniversityLibrary. https://www.library.cornell.edu/research/introduction/articles. (Refereed on 03th Dec-2016.