Abstract
Technological advancement of medical imaging techniques are progressing constantly, dealing with images of increasing resolutions. In hospitals, medical imaging techniques like X-rays, magnetic resonance imaging and computed tomography etc. are of high resolution consuming large storage space. Such high resolution of medical images transmitted over the network utilizes large bandwidth that often results in degradation of image quality. So, compression of images is only a solution for efficient archival and communication of medical images. Predictive based coding technique is explored in this paper for medical image compression as it performs well for lossless compression. This paper presents a comparative investigation on 2D predictor’s coding efficiency and complexity on CT images. It was observed that among 2D predictors Gradient Edge Detection (GED) predictor gave better results than Median Edge Detector (MED) and DPCM. GED predictor at proper threshold value achieved approximately same results in terms of various performance metrics as Gradient Adaptive Predictor (GAP) though it is less complex.
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References
Gupta, M.: Low complexity near lossless image compression technique for telemedicine. Int. J. Comput. Appl. 29(7), 0975–8887 (2011)
Swathy, S., Jumana, N.: A study on medical image compression techniques. Int. J. Innovative Res. Comput. Commun. Eng. 5(4), 8105–8110 (2017)
Ghadah, K.: Fast lossless compression of medical images based on polynomial. Int. J. Comput. Appl. 70(15), 28–32 (2013)
Shridevi, S., Vijaykumar, V.R., Anuja, R.: A survey on various compression method for medical images. Int. J. Int. Syst. Appl. 3, 13–19 (2012)
Verma, P., Sahu, A., Sahu, S., Sahu, N.: Comparison between different compression and decompression techniques on MRI scan images. Int. J. Adv. Res. Comput. Eng. Technol 1(7), 109 (2012)
Gupta, M., Alam, S.: ROI based medical image compression for telemedicine using IWT and SPIHT. Int. J. Adv. Res. Comput. Sci. Manag. Stud. 2(11), 340–348 (2014)
Chandrika, V., Parvathi, C.S., Bhaskar, P.: Medical image compression using EZW coding. IJEE. 5(2), 87–91 (2013)
Urvashi, Sood, M., Bhardwaj, C.: Reconstruction methods in compressive sensing for biomedical images. J. Global Pharma Technol. 6(9), 134–143 (2017)
Shrutika, S., Dharwadkar, N.V., Subodh, S.I.: A review on various medical image compression methods. Int. J. Innov. Res. Electr., Electron., Instrum. Control Eng. 4(1), 27–29 (2016)
Avramovic, A., Banjac, G.: On predictive based lossless compression of images with higher bit depth. Telfor J 4(2), 122–127 (2012)
Suma, Vidya, V.: A review of the effective techniques of compression in medical image processing. Int. J. Comput. Appl. 97(6), 0975–8887 (2014)
Avramovic, A., Reljin, B.: Gradient edge detection predictor for image lossless compression. In: 52nd International Symposium ELMAR (2010)
Tomar, R., Jain, K.: Lossless image compression using differential pulse code modulation and its application. Int. J. Sig. Process., Image Process. Pattern Recogn. 9(1), 197–202 (2016)
Al-Mahmood, H., Al-Rubaye, Z.: Lossless image compression based on predictive coding and bit plane slicing. Int. J. Comput. Appl. 93(1) (2014)
Avramovicl, A., Savicl, S.: Lossless predictive compression of medical images. Serblan J. Electr. Eng. 8, 27–36 (2011)
CIPR. http://www.cipr.rpi.edu/resource/sequences/sequence01.html
OsiriX Dataset. http://pubimage.hcuge.ch:8080/
Taubman, D., Marcellin, M.: JPEG2000: Image Compression Fundamentals, Standard and Practice. Kluwer Academic Publisher Group, Dordrecht (2004)
Wienberger M., Seroussi G.: LOCO-I; a low complexity, context based, lossless image compression algorithm. In: Conference on Data Compression. 140–149 (1996)
Puthooran, E., Anand, R.S., Mukherjee, S.: Lossless compression of medical images using a dual level DPCM with context adaptive switching neural network predictor. Int. J. Comput. Intell. Syst. 6(6), 1082–1093 (2013)
Bhardwaj, C., Urvashi, Sood, M.: Implementation and performance assessment of compressed sensing for images and video signals. Int. J. Comput. Intell. Syst. 6, 123–133 (2017)
Weinberger, M.J., Seroussi, G., Sapiro, G.: The LOCO-I lossless image compression algorithm: Principles and standardization into JPEG-LS. IEEE Trans. Image Proces. 9(8), 1309–1324 (2000)
Gopi, K., Ramashri, T.: Medical image compression using wavelets. IOSR J. VLSI Sig. Process. 4(2), 0106 (2013)
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Urvashi, Puthooran, E., Sood, M. (2019). Implementation and Performance Assessment of Gradient Edge Detection Predictor for Reversible Compression of Biomedical Images. In: Luhach, A., Singh, D., Hsiung, PA., Hawari, K., Lingras, P., Singh, P. (eds) Advanced Informatics for Computing Research. ICAICR 2018. Communications in Computer and Information Science, vol 955. Springer, Singapore. https://doi.org/10.1007/978-981-13-3140-4_18
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