The era of digital technology, bulky data transmitted over the network. The main challenge is to maintain the quality of the data packet delivered at the receiving end at a very high speed, failing the image quality would fall down and also everything would turn into slow motion. To maintain the speed and quality, lossless compression of the image is required. Medical images are a bigger challenge as they have different formats, especially MRI images. The thesis proposes compression of medical images using improvised DCT algorithm. Since finer details are important in medical images a special masking technique has been used, a mathematical formulation has also been derived to achieve the goal of maintaining the quality with speed. Finally, DCT and inverse DCT is applied on the images to compress the images. To check the robustness of the proposed algorithm MSE, PSNR and compression has been computed. The proposed algorithm had been compared with standard DCT algorithm and it is found that the proposed algorithm improves MSE, PSNR and compression ratio by 8% percent on an average. It can be concluded that the proposed algorithm performs better than standard DCT algorithm for medical images.

ImageCompression,PSNR,MSE,DCT,InverseDCT,MRI

[1]. Matsuda, H. Mori, and S. Itoh, “Lossless coding of still images using minimum-rate predictors,” in Image Processing, 2000, International Conference on, vol. 1, 2000.
[2] M. J. Weinberger, G. Seroussi, and G. Sapiro, "The loco-i lossless image compression algorithm: Principles and standardization into jpegs," IEEE Transactions on Image Processing, vol. 9, no. 8, pp. 1309–1324, Aug 2000.

[3] Xin Li and Michael T. Orchard, “Edge-Directed Prediction for Lossless Compression of Natural Images,” IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 10, NO. 6, JUNE 2001
[4] Victor Sanchez, RafeefAbugharbieh and PanosNasiopoulos, “3-D Scalable Medical Image Compression with Optimized Volume of Interest Coding,” IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 29, NO. 10, OCTOBER 2010
[5] BivayKumar, PankajRai "A Fuzzy & K-L based reduced reference image quality", IOSER journal of computer engineering (IOSR-JCE), e-ISSN: 2278-0661, p-ISSN: 22788727, volume 17, Issue 5, pp 118-128, 2015.

[6] Kegu, Guangtaozhi, Weisilin, “No- reference image sharpness assessment in high autoregressive parameter space” IEEE, volume: 24, Issue: 10, Oct. 2015.
[7] Z. Xu, J. Bartrina-Rapesta, I. Blanes, V. Sanchez, J. Serra-Sagrist, M. Garca-Bach, and J. F. Muoz, “Diagnostically lossless coding of x-ray angiography images based on background suppression,” Computers & Electrical Engineering, 2016.
[8] H. B. Kekrea, Prachi Natub, and Tanuja Sarode, "Color Image Compression using Vector Quantization and Hybrid Wavelet Transform ",Procedia Computer Science 89, 2016.
[9] ShabilaBeevi, Mariya Thomas, Madhu S. Nair, M. Wilscy,"Lossless color image compression using double level RCT in BBWCA", Procedia Computer Science, 2016.
[10] Maria Georgea, Manju Thomasa, Jayadas C.K,"A Methodology for Spatial Domain Image Compression Based on Hops Encoding”, Procedia Technology, 2016.
[11] Azza Ouled Zaid and Amira Houimli, “HDR Image Compression with Optimized JPEG Coding,” 25th European Signal Processing Conference (EUSIPCO), 2017.
[12] Himani, Pawan Kumar Mishra, "Medical image compression using block processing with DCT", international journal of advanced research in computer and Communication Engineering (IJARCCE), vol. 7, issue 3, March 2018.

[13] Akshay Kekre, Dr. Sanjay Pokle, "Improved Image Compression Using Wavelet Transform and Differential Pulse Code Modulation Technique", International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064, Issue.2, Vol.4, 2015
[14] Ganesh Vilas Madhikar; S. S. Lokhande, "Detection and Classification of Brain Tumour Using Modified Region Growing and Neural Network in MRI Images", International Journal of Science and Research (IJSR), Volume 3 Issue 12, December 2014, 2362 - 2365