Abstract
The storage and transmission of medical data such as CT/MR DICOM images are an essential part of the telemedicine application. In this paper, a prediction-based lossless compression algorithm using least square approach is proposed for the compression of CT images. Prior to compression, the preprocessing was performed by neutrosophic median filter. The gradient adjusted prediction scheme was employed for the determination of prediction coefficients, and polynomial least square fitting approach was used for optimal selection of prediction coefficients. The selected prediction coefficients are finally encoded by Huffman coder for transmission. The quality of the reconstructed image was validated by performance metrics and compared with other compression techniques like JPEG, contextual vector quantization and vector quantization using bat optimization (BAT-VQ). The proposed neutrosophic set-based least square compression algorithm was found to be efficient and tested on DICOM abdomen CT datasets. The hardware implementation was done by Raspberry Pi processor using Java platform for transferring the data through cloud network for telemedicine application.
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References
A. Al Muhit, M.S. Islam, M. Othman, VLSI implementation of discrete wavelet transform for image compression, in International Conference on Autonomous Robots and Agents, New Zealand (2004), pp. 391–395
A. Avramović, B. Reljin, Gradient edge detection predictor for image lossless compression, in Elmar, 2010 Proceedings 2010 Sept 15 (IEEE, 2010), pp. 131–134
A.K. Tiwari, R.R. Kumar, Least squares based optimal switched predictors for lossless compression of images, in 2008 IEEE International Conference on Multimedia and Expo (ICME) (IEEE, 2008), pp. 1129–1132
A.K. Tiwari, R.R. Kumar, Least-squares based switched adaptive predictors for lossless video coding, in Image Processing, 2007. ICIP 2007. IEEE International Conference on 2007 Sept. 16, vol. 6 (IEEE, 2007), pp. 6–69
A. Avramović, G. Banjac, On predictive-based lossless compression of images with higher bit depths. Telfor J. 4(2), 122–127 (2012)
M.U. Ayoobkhan, E. Chikkannan, K. Ramakrishnan, Lossy image compression based on prediction error and vector quantisation. EURASIP J. Image Video Process. 2017(1), 35 (2017)
M.U. Ayoobkhan, E. Chikkannan, K. Ramakrishnan, Feed-forward neural network-based predictive image coding for medical image compression. Arab. J. Sci. Eng. 43(8), 4239–4247 (2018)
J. Begaint, D. Thoreau, P. Guillotel, C. Guillemot, Region-based prediction for image compression in the cloud. IEEE Trans. Image Process. 27(4), 1835–1846 (2018)
J. Bégaint, D. Thoreau, P. Guillotel, C. Guillemot, Region-based prediction for image compression in the cloud. IEEE Trans. Image Process. 27(4), 1835–1846 (2018)
B. Carpentieri, M.J. Weinberger, G. Seroussi, Lossless compression of continuous tone images. Proc. IEEE 88(11), 1797–1809 (2000)
P. Corsonello, S. Perri, P. Zicari, G. Cocorullo, Microprocessor-based FPGA implementation of SPIHT image compression subsystems. Microprocess. Microsyst. 29(6), 299–305 (2005)
D. Novikov, N. Egorov, M. Gilmutdinov, Local-adaptive blocks-based predictor for lossless image compression, in Problems of Redundancy in Information and Control Systems, 2016 XV International Symposium 2016 Sep 26 (IEEE, 2016), pp. 92–99
J. Demsar, Statistical comparisons of classifiers over multiple datasets. J. Mach. Learn. Res. 7, 1–30 (2006)
T.W. Fry, S.A. Hauck, SPIHT image compression on FPGAs”. IEEE Trans. Circuits Syst. Video Technol. 15(9), 1138–1147 (2005)
Y. Guo, A. Şengür, A novel image segmentation algorithm based on neutrosophic filtering and level set. Neutrosophic Sets Syst. 1(unknown), 46–49 (2013)
Y. Guo, R. Xia, A. Şengür, K. Polat, A novel image segmentation approach based on neutrosophic c-means clustering and indeterminacy filtering. Neural Comput. Appl. 28(10), 3009–3019 (2017)
Y. Guo, Ü. Budak, A. Şengür, F. Smarandache, A retinal vessel detection approach based on shearlet transform and indeterminacy filtering on fundus images. Symmetry 9(10), 235 (2017)
S.M. Hosseini, A.R. Naghsh-Nilchi, Medical ultrasound image compression using contextual vector quantization. Comput. Biol. Med. 42(7), 743–750 (2012)
H.J. Hwang, S. Kim, H.J. Kim, Reversible data hiding using least square predictor via the LASSO. EURASIP J. Image Video Process. 2016(1), 42 (2016)
J. Knezovic, M. Kovac, H. Mlinaric, Classification and blending prediction for lossless image compression, in Electrotechnical Conference, 2006. MELECON 2006. IEEE Mediterranean 2006 May 16 (IEEE, 2006), pp. 486–489
J. Mohan, A.T. Chandra, V. Krishnaveni, Y. Guo, Image denoising based on neutrosophic wiener filtering, in Advances in Computing and Information Technology 2013. (Springer, Berlin, 2013), pp. 861–869
J. Mohan, V. Krishnaveni, Guo Y, Validating the neutrosophic approach of MRI denoising based on structural similarity, in IET Conference on Image Processing (IPR 2012), p. 111
M.A. Kabir, M. Mondal, Edge-based and prediction-based transformations for lossless image compression. J. Imaging 4(5), 64 (2018)
C. Karri, U. Jena, Fast vector quantization using a bat algorithm for image compression. Eng. Sci. Technol. Int. J. 19(2), 769–781 (2016)
L.J. Kau, Y.P. Lin, Adaptive lossless image coding using least squares optimization with edge-look-ahead. IEEE Trans. Circuits Syst. II Express Briefs 52(11), 751–755 (2005)
L.F. Lucas, N.M. Rodrigues, L.A. da Silva Cruz, S.M. de Faria, Lossless compression of medical images using 3-D predictors. IEEE Trans. Med. Imaging 36(11), 2250–2260 (2017)
M. Kazemi, M.B. Menhaj, A non-local means approach for Gaussian noise removal from images using a modified weighting kernel. arXiv preprint arXiv:1612.01006. 2016 Dec 3
M. Klimesh, V. Stanton, D. Watola, Hardware implementation of a lossless image compression algorithm using a field programmable gate array, in TMO Progress Report (2001), pp. 42–144
J. Mohan, A.T. Chandra, V. Krishnaveni, Y. Guo, Evaluation of neutrosophic set approach filtering technique for image denoising. Int. J. Multimed. Appl. 4(4), 73 (2012)
J. Mohan, V. Krishnaveni, Y. Guo, Performance analysis of neutrosophic set approach of median filtering for MRI denoising. Int. J Electron. Commun. Eng. Technol. 3, 148–163 (2012)
J. Mohan, V. Krishnaveni, Y. Guo, MRI denoising using nonlocal neutrosophic set approach of Wiener filtering. Biomed. Signal Process. Control 8(6), 779–791 (2013)
J. Mohan, V. Krishnaveni, Y. Guo, A new neutrosophic approach of Wiener filtering for MRI denoising. Meas. Sci. Rev. 13(4), 177–186 (2013)
J. Mohan, V. Krishnaveni, Y. Guo, MRI denoising using nonlocal neutrosophic set approach of Wiener filtering. Biomed. Signal Process. Control 8(6), 779–791 (2013)
J. Park, J. Yoo, Preprocessing techniques for high-efficiency data compression in wireless multimedia sensor networks. Adv. Multimed. 1(2015), 1 (2015)
R. Mosqueron, J. Dubois, M. Paindavoine, Embedded image processing/compression for high-speed CMOS sensor, in 14th IEEE European Signal Processing Conference, Italy (2006), pp. 1–5
H. Shen, W.D. Pan, D. Wu, Predictive lossless compression of regions of interest in hyperspectral images with no-data regions. IEEE Trans. Geosci. Remote Sens. 55(1), 173–182 (2017)
A. Skodras, C. Christopoulos, T. Ebrahimi, The JPEG 2000 still image compression standard. IEEE Signal Process. Mag. 18(5), 36–58 (2001)
S.W. Fu, J.J. Ding, Y.W. Huang, C.W. Hsiao, H.H. Chen, Collagen image compression using the JPEG-based predictive lossless coding scheme, in Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2017 (IEEE, 2017), pp. 524–533
K. Vlcek, J. Vlcek, R. Kucera, DSP implementation of image compression by multiresolutional analysis. Radioengineering 7(1), 7–9 (1998)
X. Li, M.T. Orchard, Edge directed prediction for lossless compression of natural images, in Image Processing, 1999. ICIP 99. Proceedings. 1999 International Conference in 1999, vol. 4 (IEEE, 1999), pp. 58-62
X. Wu, N. Memon, CALIC-a context-based adaptive lossless image codec, in Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings. 1996 IEEE International Conference on 1996 May 7, vol. 4 (IEEE, 1996), pp. 1890–1893
A. Sahoo, P. Das, Dictionary based intra prediction for image compression, in Proceedings of the Second International Conference on Research in Intelligent and Computing in Engineering pp. 73–76
X. Song, Q. Huang, S. Chang, J. He, H. Wang, Lossless medical image compression using geometry-adaptive partitioning and least square-based prediction. Med. Biol. Eng. Comput. 1–10
Acknowledgements
The authors would like to acknowledge the support provided by DST under IDP scheme (No: IDP/MED/03/2015). We thank Dr. P. Sebastian Varghese (consultant radiologist, Metro Scans & Laboratory, Trivandrum) for providing the medical CT images and supporting us in the preparation of the manuscript.
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Kumar, S.N., Fred, A.L., Kumar, H.A. et al. Lossless Compression of CT Images by an Improved Prediction Scheme Using Least Square Algorithm. Circuits Syst Signal Process 39, 522–542 (2020). https://doi.org/10.1007/s00034-019-01152-8
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DOI: https://doi.org/10.1007/s00034-019-01152-8