Medical Image Compression Using Hybrid Compression Techniques
Keywords:
Medical image, lossless compression methods, lossy compression methods, hybrid compression methodsAbstract
Medical images are crucial in today's healthcare system for diagnosis, but managing the large volume of images generated by different imaging methods is a challenge for Hospital Management Systems (HMS). Image compression, the process of reducing redundancies in image data, helps to store and transmit images efficiently by reducing the file size. This not only saves storage space, but also makes it easier to send images over limited bandwidth channels. Image compression is therefore an important factor in managing medical images for storage and transmission. In this research many loss and lossless compression methods are tested. Then hybrid combinations of these method are used to design an accurate hybrid compression system suitable for medical image. The best compression result is acquired when the Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are combined, their hybrid compression ratio is 9.348.
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G. Al-Khafaji. and H. Al-Mahmood (2013). Lossless Compression of Medical Images using Multiresolution Polynomial Approximation Model. International Journal of Computer Applications, 76(3),38-42.
M. Mohammed and G. Al-Khafaji. (2013). Applied Minimized Matrix Size algorithm on the Transformed Images by DCT and DWT used for Image Compression. International Journal of Computer Applications, 70(15), 33-40.
Ogiela MR, Tadeusiewicz R. Compressing medical images and storing them in medical databases. Studies in Computational Intelligence (SCI).2007;84:47-63.
Bhavani, S., and K. Thanushkodi.(2010) "A survey on coding algorithms in medical image compression." International Journal on Computer Science and Engineering 2(5): 1429-1434.
M. D. Manigandan and S. Deepa. Comprehensive study on the effect of entropy encoding algorithms on medical image compression. International Research Journal of Engineering and Technology, 5(4):3460–3468, 2018.
Mohamed Uvaze Ahamed Ayoobkhan, Eswaran Chikkannan, Kannan Ramakrishnan, and Saravana Balaji Balasubramanian. Prediction-based Lossless Image Compression. In International Conference on ISMAC in Computational Vision and Bio-Engineering, pages 1749–1761. Springer, 2018.
Kenta Kurihara, Shoko Imaizumi, Sayaka Shiota, and Hitoshi Kiya. Anencryption-then-compression system for lossless image compression standards. IEICE transactions on information and systems, 100(1):52–56.
S. Cabeen and P. Gent, Image Compression and the Discrete Cosine Transform, College of the Redwoods, (2011).
S. W. Chiang and L. M. Po. Adaptive lossy LZW algorithm for palettized image compression. Electronics Letters, 33(10):852–854, 1997. Publisher: IET.
Tanzeem Muzaffar and Tae Sun Choi “Linked significant tree waveletbased image compression” Signal Processing, Vol. 88, Issue 10, (October 2008), pp 2554–2563.
Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, and Luc Van Gool. Practical full resolution learned lossless image compression. In proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 10629–10638, 2019.
A. Buades, B. Coll and J.M. Morel, A review of image denoising algorithms, with a new one, SIAM - Multiscale Modeling & Simulation, 4 No.2 49.
Tian M E I, Luo S, and Liao L 2005 An investigation into using singular value decomposition as a method of image compression 18–21.
Rachit Patel, Virendra Kumar, Vaibhav Tyagi, and Vishal Asthana. A fast and improved Image Compression technique using Huffman coding. In 2016.
Oriani, Emanuele. "qpsnr: A quick PSNR/SSIM analyzer for Linux". Retrieved 6 April 2011.
Singh S K and Kumar S 2009 A Framework to Design Novel SVD Based Color Image Compression 1–6 .
Anitha T G and S. Ramachandran, “Novel Algorithms for 2-D FFT and their Inverses for Image Compression,” Signal Processing, Image Processing & Pattern Recognition (ICSIPR), IEEE Publishers, pp 62-65. Feb 2013 .
Stephen A. Martucci and Iraj Sodagar, 1996. Zerotree Entropy Coding of Wavelet Coefficients For Very Low Bite Rate Vide, IEE, PP 533-536.
Meenakshi Garg Amit Singla. Cbir approach based on combined hsv, auto correlogram, color moments and gabor wavelet. volume 3, pages 9007– 9012. International Journal Of Engineering And Computer Science, ISSN:2319-7242, 10 October 2014.
Prasantha, Dr HS. "NOVEL APPROACH FOR IMAGE COMPRESSION USING MODIFIED SVD." International Journal of Creative Research Thoughts (IJCRT) 8, no. 8 (2020): 2234-2243.
Magar, Satyawati S., and Bhavani Sridharan. "Innovative approach to biomedical image compression using oscillation concept." In 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT), pp. 124-128. IEEE, 2016.
Kundlik, Miss Nilam V., and Manoj Kumar Singh. "Oscillation Concept for Image Compression.". In 2017 IEEE international conference on systems, man, and cybernetics (SMC), pp. 216-221. IEEE, 2017.
Li, Zhongqiang, Alexandra Ramos, Zheng Li, Michelle L. Osborn, Xin Li, Yanping Li, Shaomian Yao, and Jian Xu. "An optimized JPEG-XT-based algorithm for the lossy and lossless compression of 16-bit depth medical image." Biomedical Signal Processing and Control 64 (2021).
Sran, Paramveer Kaur, Savita Gupta, and Sukhwinder Singh. "Segmentation based image compression of brain magnetic resonance images using visual saliency." Biomedical Signal Processing and Control 62 (2020).
B.H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, Y. Burren, N. Porz, J. Slotboom, R. Wiest, L. Lanczi, E. Gerstner, M. Weber, T. Arbel, B.B. Avants, N. Ayache, P. Buendia, D.L. Collins, N. Cordier, J.J. Corso, A. Criminisi, T. Das, H. Delingette, Demiralp, C.R. Durst, M. Dojat, S. Doyle, J. Festa, F. Forbes, E. Geremia, B. Glocker, P. Golland, X. Guo, A. Hamamci, K.M. Iftekharuddin, R. Jena, N.M. John, E. Konukoglu, D. Lashkari, J.A. Mariz, R. Meier, S. Pereira, D. Precup, S.J. Price, T.R. Raviv, S.M.S. Reza, M. Ryan, D. Sarikaya, L. Schwartz, H. Shin, J. Shotton, C.A. Silva, N. Sousa, N.K. Subbanna, G. Szekely, T.J. Taylor, O.M. Thomas, N.J. Tustison, G. Unal, F. Vasseur, M. Wintermark, D.H. Ye, L. Zhao, B. Zhao, D. Zikic, M. Prastawa, M. Reyes, K. Van Leemput, The multimodal brain tumor image segmentation benchmark (BRATS), IEEE Trans. Med. Imaging 34 (10) (2015) 1993–2024, Available: https://pubmed.ncbi.nlm.nih.gov/25494501 (accessed in April 2021)
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