Semi-lossless Fractal MRI Image Compression Based on Fixed length Technique
DOI:
https://doi.org/10.29304/jqcm.2021.13.3.854Keywords:
Medical, Image, Compression, CT, MRI, LosslessAbstract
Medical image compression plays an essential role to handle large amounts of data for communication and storage purposes. Fractal image compression is a potential lossy compression models with a resulting image that loses some of its information. However, health data communication usually cannot afford any lose for patients visual information. This paper proposes a new high efficiency semi-lossless fractal image compression method (SLFIC) based on fractal theory and fixed length technique. Technically, the resultant lossy fractals compressed image is analyzed and error in comparison with the original image is detected. Then, Fixed-length is developed to compress the detected errors and attached to the compressed image. In practice, a potential performance by the new developed model has been obtained in comparison with two other lossless models: ( Lion optimization algorithm (LOA) and Lempel Ziv Markov chain Algorithm (LZMA) with Linde–Buzo–Gray (LBG) (L2-LBG)) and(Neural Network Radial Basis Function (NNRBF)). Moreover, a high quality that has been obtained by the proposed system based on Peak Signal to Noise Ratio (PSNR) and Compression Ratio (CR).
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References
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[2] Zhongqiang Li, Alexandra Ramos, Zheng Li, Michelle L. Osborn, Xin Li, Yanping Li, Shaomian Yao, 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,Volume 64,2021.
[3] Lucas LFR, Rodrigues NMM, da Silva Cruz LA, de Faria SMM. Lossless Compression of Medical Images Using 3-D Predictors. IEEE Trans Med Imaging. 2017.
[4] M. Hernández-Cabronero, V. Sanchez, I. Blanes, F. Aulí-Llinàs, M. W. Marcellin and J. Serra-Sagristà, "Mosaic-Based Color-Transform Optimization for Lossy and Lossy-to-Lossless Compression of Pathology Whole-Slide Images," in IEEE Transactions on Medical Imaging, vol. 38, no. 1, pp. 21-32, Jan. 2019.
[5] Hasanujjaman, A. Banerjee, U. Biswas and M. K. Naskar, "Fractal Image Compression of an Atomic Image using Quadtree Decomposition," 2019 Devices for Integrated Circuit (DevIC), 2019.
[6] R. Suresh Kumar, P. Manimegalai, Near lossless image compression using parallel fractal texture identification, Biomedical Signal Processing and Control, Volume 58, 2020.
[7] Khaitan, Sspriya & Agarwal, Rashi. (2019). Multi-Fractal Image Compression. 340-345. 10.1109/COMITCon.2019.8862190.
[8] Runwen Hu, Shijun Xiang, Lossless robust image watermarking by using polar harmonic transform, Signal Processing, Volume 179,2021.
[9] Chaurasia, Vijayshri & Gumasta, Rakesh & Kurmi, Yashwant. (2017). Fractal image compression with optimized domain pool size.
[10] Raghavendra, C., Sivasubramanian, S. & Kumaravel, A. Improved image compression using effective lossless compression technique. Cluster Comput 22, 3911–3916 (2019).
[11] W. Li, Q. Pan, J. Lu and S. Li, "Research on Image Fractal Compression Coding Algorithm Based on Gene Expression Programming," 2018 17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES), 2018.
[12] Sujitha, B, Parvathy, VS, Lydia, EL, Rani, P, Polkowski, Z, Shankar, K. Optimal deep learning based image compression technique for data transmission on industrial Internet of things applications. Trans Emerging Tel Tech. 2021.
[13] Ilango, S. & Seenivasagam, V. & Ramamurthy, Madhumitha. (2019). Hybrid two-dimensional dual tree—biorthogonal wavelet transform and discrete wavelet transform with fuzzy inference filter for robust remote sensing image compression. Cluster Computing.
[14] Fan, Chunxiao & Hu, Zhou & Jia, Lu & Min, Hai. (2021). A novel lossless compression encoding framework for SAR remote sensing images. Signal, Image and Video Processing.
[15] Dhiah Al-Shammary, Ibrahim Khalil, Zahir Tari, Albert Y. Zomaya, Fractal self-similarity measurements based clustering technique for SOAP Web messages, Journal of Parallel and Distributed Computing,Volume 73, Issue 5, 2013.
[16] D. Al-Shammary and I. Khalil, "Dynamic Fractal Clustering Technique for SOAP Web Messages," 2011 IEEE International Conference on Services Computing, 2011.
[17] Geetha, K, Anitha, V, Elhoseny, M, Kathiresan, S, Shamsolmoali, P, Selim, MM. An evolutionary lion optimization algorithm‐based image compression technique for biomedical applications. Expert Systems. 2020.
[18] B, Perumal & Rajasekaran, M.. (2015). Efficient image compression techniques for compressing multimodal medical images using neural network radial basis function approach. International Journal of Imaging Systems and Technology.
[19] W. Alawsi, Z. Oleiwi, A. Alwan, M. Fadhil, H. Hadi, and N. Hadi, “Performance Analysis of Noise Removal Techniques For Digital Images”, JQCM, vol. 12, no. 1, pp. Comp Page 15 -, Feb. 2020.
[20] A. Abdulelah, S. Abed Hamed, M. RASHEED, S. SHIHAB, T. RASHID, and M. Kamil Alkhazraji, “The Application of Color Image Compression Based on Discrete Wavelet Transform”, JQCM, vol. 13, no. 1, pp. Comp Page 18 -, Feb. 2021.
[21] H. Z. Neima, “Brain MRI Enhancement using Brightness Preserving Dynamic Fuzzy Histogram Equalization”, JQCM, vol. 6, no. 1, pp. 38-51, Aug. 2017.
[22] A. Ali, M. RASHEED, S. SHIHAB, T. RASHID, and S. Abed Hamed, “A Modified Heat Diffusion Based Method for Enhancing Physical Images”, JQCM, vol. 13, no. 1, pp. Comp Page 77 -, Mar. 2021.
[23] T. A. Al-Assadi and Z. Hussain Khalil, “Image Compression By Using Enhanced Fractal Methods”, JQCM, vol. 3, no. 1, pp. 231-241, Sep. 2017.
[24] A. Hanon Hassin AlAsadi, “Contourlet Transform based Method for Medical Image Denoising”, JQCM, vol. 7, no. 1, pp. 146-159, Aug. 2017.
[25] R. M. Ghadban, “Medical Image Enhancement based on Adaptive Histogram Equalization and Contrast Stretching”, JQCM, vol. 6, no. 1, pp. 28-37, Aug. 2017.
[26] H. Z. Neima, “Brain MRI Enhancement using Brightness Preserving Dynamic Fuzzy Histogram Equalization”, JQCM, vol. 6, no. 1, pp. 38-51, Aug. 2017.
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Published
2021-10-13
How to Cite
Al-Salamee, B. A., & Al-Shammary, D. (2021). Semi-lossless Fractal MRI Image Compression Based on Fixed length Technique. Journal of Al-Qadisiyah for Computer Science and Mathematics, 13(3), Comp Page 130 – 141. https://doi.org/10.29304/jqcm.2021.13.3.854
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Computer Articles
