Authors: Rand B. Mohammed1 & Roelof van Silfhout2
1Mechatronics Engineering Department, Faculty of Engineering, Tishk International University, Erbil, Iraq
2School of Electrical and Electronic Engineering, University of Manchester, Manchester, United Kingdom
Abstract: This paper proposes and implements two lossless methods, to compress real-time greyscale medical images, which are Huffman coding and a new lossless method called Reduced Lossless Compression Method (RLCM), both of which were tested when applying a random sample of greyscale medical images with a size of 256×256 pixels. Different factors were measured to check the compression method performances such as the compression time, the compressed image size, and the compression ratio (CR). The system is fully implemented on a field programmable gate array (FPGA) using a fully hardware based (no software driven processor) system architecture. A Terasic DE4 board was used as the main platform for implementing and testing the system using Quartus-II software and tools for design and debugging. The impact of compressing the image and carrying the compressed data through parallel lines is like the impact of compressed the same image inside a single core with a higher compression ratio, in this system between 7.5 and 126.8.
Keywords: Embedded System, Greyscale Image, Real-Time System, Huffman Coding, Lossless Compression Method, Medical Images
Adiwijaya, P., Faoziyah, F., Permana., & Wirayuda, T. (2013). Tamper detection and recovery of medical image watermarking using modified LSB and Huffman compression. Lodz, IEEE, pp. 129 – 132.
Akhtar, N., Khan, S., & Siddiqui, G. (2014). A novel lossy image compression method. Bhopal, IEEE, pp. 866 – 870.
Arif, A. S., Mansor, S., Karim, H. A., & Logeswaran, R. (2012). Lossless compression of fluoroscopy medical images using correlation and the combination of Run-Length and Huffman Coding. Langkawi, IEEE, pp. 759 – 762.
Arthur, A., & Saravanan, V. (2012). Efficient medical image compression technique for telemedicine considering online and offline application. Dindigul, Tamilnadu, IEEE, pp. 1 – 5.
Ashraf, R., Akbar, M., & Jafri, N. (2006). Diagnostically lossless compression-2 of medical images. Arlington, VA, IEEE, pp. 28 – 32.
Bahmanyar, R., Datcu, M., & Rigoll, G. (2014). Comparing the information extracted by feature descriptors from EO image using Huffman Coding. Klagenfurt, IEEE, pp. 1 – 6.
Chen, G. (2010). Application of processing techniques from color image to grey image. San Juan, PR, IEEE, pp. V2-372 – V2-375.
Devaraj, K., Munukur, R. K., & Kesavamurthy, T. (2005). Lossless medical-image compression using multiple array technique. Hong Kong, IEEE, pp. 837 – 840.
Edmundson, D., & Schaefer, G. (2012). Fast JPEG image retrieval using optimised Huffman tables. Tsukuba, IEEE, pp. 3188 – 3191.
Firoozbakht, M. (2010). Compression of digital medical images based on multiple regions of interest. St. Maarten, IEEE, pp. 260 – 263.
Gonzalez, R., & Richard E. W. (2002). Digital image processing. Pearson Education International.
Javier Nunez-Garcia, Vassilios Mersinias, Kwang-Hyan Cho, Colin P. Smith and Olaf Wolkenhauer, 2003. A Study of the Statistical Distribution of the Intensity of Pixels within Spots of DNA Microarrays: what is the Appropriate Single-Valued Representative?
Jimenez-Rodriguez, L., Auli-Linas, F., Marcellin, M. W., & Serra-Sagrista, J. (2013). Visually lossless JPEG 2000 decoder. Snowbird, UT, IEEE, pp. 161 – 170.
Katharotiya, A., Patel, S., & Goyani, M. (2011). Comparative analysis between DCT & DWT techniques of image compression. Journal of Information Engineering and Applications.
Katti, R. S., & Ghosh, A. (2009). Security using shannon-fano-elias codes. Taipei, IEEE, pp. 2689 – 2692.
Kim, S., & Cho, N. I. (2012). A lossless color image compression method based on a new reversible color transform. San Diego, CA, IEEE, pp. 1 – 4.
Lee, L.K., & Liew, S.C. (2015). A survey of medical image processing tools. Kuantan, IEEE, pp. 171 – 176.
Lei, H. (2013). A new retrieval method based on YUV Clour information for digital libraries. Hong Kong, IEEE, pp. 128 – 130.
Mohammed, R. B. (2017). Novel scalable and real-time embedded transceiver system. PhD thesis, University of Manchester
Mohammed, R. B., & van Silfhout, R. (2018). Real-time transceiver system based on rapid-io protocol. Eurasian Journal of Science & Engineering, 4(2).
Raza, M., Adnan, A., Sharif, M., & Haider, S.W. (2012). Lossless compression method for medical image sequences using super-spatial structure prediction and inter-frame coding. Journal of Applied Research and Technology, 618 – 628.
Nelson, M. (1991). The Data Compression Book. s.l.: IDG Books Worldwide, Inc.
Ritter, F. (2011). Medical Image Analysis. IEEE Pulse, pp. 60 – 70.
Roy, S., Mitra, A., & Setua, S. K. (2015). Color & greyscale image representation using multivector. Hooghly, IEEE, pp. 1 – 6.
Ruan, X., & Katti, R. (2006). Reducing the Length of Shannon-Fano-Elias Codes and Shannon-Fano Codes. Washington, DC, IEEE, pp. 1 – 7.
Saboori, A., & Abolfazl Hosseini, S. (2015). Color image watermaking in YUV color space based on combination of DCT and PCA. Tehran, IEEE, pp. 308 – 313.
Burak, S., Carlo, G., Bernd, T., & Beaulieu, G.C. (2008). Region of interest based medical image compression. CiteSeerx.
Senturk, A., Senturk, Z. K., & Kara, R. (2015). Comparison of real time image transfer in wireless multimedia sensor networks. Bursa, IEEE, pp. 1226 – 1228.
Tang, J. (2010). A color image segmentation algorithm based on region growing. Chengdu, IEEE, pp. V6-634 – V6-637.
Telagarapu, P., Naveen, V. J., Lakshm, A., & Santhi, G. V. (2011). Image compression using DCT and wavelet transformations. International Journal of Signal Processing, Image Processing and Pattern Reconfiguration, 61 – 74.
Zhongshui, Q.U., & Jianwei, W. (2010). A color YUV image edge detection method based on histogram equalization transformation. Yantai, Shandong, IEEE, pp. 3546 – 3549.