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Sujatha, T.
- Lossless Image Compression using Different Encoding Algorithm for Various Medical Images
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Authors
T. Sujatha
1,
K. Selvam
1
Affiliations
1 Department of Computer Applications, Dr. MGR Educational and Research Institute, IN
1 Department of Computer Applications, Dr. MGR Educational and Research Institute, IN
Source
ICTACT Journal on Image and Video Processing, Vol 12, No 4 (2022), Pagination: 2704-2709Abstract
In the medical industry, the amount of data that can be collected and kept is currently increasing. As a result, in order to handle these large amounts of data efficiently, compression methods must be re-examined while taking the algorithm complexity into account. An image processing strategy should be explored to eliminate the duplication image contents, so boosting the capability to retain or transport data in the best possible manner. Image Compression (IC) is a method of compressing images as they are being stored and processed. The information is preserved in a lossless image compression technique which allows for exact image reconstruction from compressed data with retain the quality of image to higher possible extend but it does not significantly decrease the size of the image. In this research work, the encoding algorithm is applied to various medical images such as brain image, dental x-ray image, hand x ray images, breast mammogram images and skin image can be used to minimize the bit size of the image pixels based on the different encoding algorithm such as Huffman, Lempel-Ziv-Welch (LZW) and Run Length Encoding (RLE) for effective compression and decompression without any quality loss to reconstruct the image. The image processing toolbox is used to apply the compression algorithms in MATLAB. To assess the compression efficiency of various medical images using different encoding techniques and performance indicators such as Compression Ratio (CR) and Compression Factor (CF). The LZW technique compresses binary images; however, it fails to generate a lossless image in this implementation. Huffman and RLE algorithms have a lower CR value, which means they compress data more efficiently than LZW, although RLE has a larger CF value than LZW and Huffman. When fewer CR and more CF are recorded, RLE coding becomes more viable. Finally, using state-of-the-art methodologies for the sample medical images, performance measures such as PSNR and MSE is retrieved and assessed.Keywords
Lossless Image Compression, Huffman Coding, Lempel-Ziv-Weich, Run Length Encoding
References
- N. Karimi, R. Ranjbarzadeh Kondrood and T. Alizadeh, “An Intelligent System for Quality Measurement of Golden Bleached Raisins using Two Comparative Machine Learning Algorithms”, Journal of the International Measurement Confederation, Vol. 107, pp. 68-76, 2017.
- A.C.B. Monteiro, R.P. França and P.D.M. Negrete, “Metaheuristics Applied to Blood Image Analysis”, Lecture Notes in Electrical Engineering, Vol. 696, pp. 117-135, 2021.
- Z. Li, X. Zhang and S. Zhang, “Large-Scale Retrieval for Medical Image Analytics: A Comprehensive Review”, Medical Image Analysis, Vol. 43, pp. 66-84, 2018.
- R. Ranjbarzadeh and S. Baseri Saadi, “Automated Liver and Tumor Segmentation based on Concave and Convex Points using Fuzzy C-Means and Mean Shift Clustering”, Journal of the International Measurement Confederation, Vol. 151, pp. 1-18, 2020.
- W.A. Ali, K.N. Manasa and P. Sandhya, “A Review of Current Machine Learning Approaches for Anomaly Detection in Network Traffic”, Journal of Telecommunications and the Digital Economy, Vol. 8, No. 1, pp. 64-95, 2020.
- M.L. Hachemi, M. Omari and M. Baroudi, “Enhancement of DCT-Based Image Compression Using Trigonometric Functions”, Proceedings of International Conference on Computing Sciences and Engineering, pp. 1-5, 2018.
- N.V. Kousik and M. Saravanan, “A Review of Various Reversible Embedding Mechanisms”, International Journal of Intelligence and Sustainable Computing, Vol. 1, No. 3, pp. 233-266, 2021.
- Med Karim Abdmouleh, Atef Masmoudi and Med Salim Bouhlel. “A New Method which Combines Arithmetic Coding with RLE for Lossless Image Compression”, Scientific Research Publishing, Vol. 2021, pp. 1-7, 2021.
- J. Surendiran, S. Theetchenya and P.M. Benson Mansingh, “Segmentation of Optic Disc and Cup Using Modified Recurrent Neural Network”, BioMed Research International, Vol. 2022, pp.1-8, 2022.
- S.S. Sivasankari, J. Surendiran and M. Ramkumar, “Classification of Diabetes using Multilayer Perceptron”, Proceedings of IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, pp. 1-5, 2022.
- Gajendra Sharma, “Analysis of Huffman Coding and Lempel–Ziv–Welch (LZW) Coding as Data Compression Techniques”, International Journal of Scientific Research in Computer Science and Engineering, Vol. 8, No. 1, pp. 37-44, 2020.
- Yaghoub Pourasad and Fausto Cavallaro. “A Novel Image Processing Approach to Enhancement and Compression of X-ray Images”, International Journal of Environmental Research and Public Health, Vol. 18, pp. 1-12, 2021.
- R. Ahmad and N.S. Choubey, “Review on Image Enhancement Techniques using Biologically Inspired Artificial Bee Colony Algorithms and its Variants”, Lecture Notes in Computational Vision and Biomechanics, pp. 249-271, 2018.
- G. Kaur, N. Bhardwaj and P.K. Singh, “An Analytic Review on Image Enhancement Techniques based on Soft Computing Approach”, Proceedings of International Conference on Advances in Intelligent Systems and Computing, pp. 255-265, 2018.
- C.R. Nithyananda and A.C. Ramachandra, “Review on Histogram Equalization based Image Enhancement Techniques”, Proceedings of International Conference on Electrical, Electronics, and Optimization Techniques, pp. 1-6, 2016.
- R. Maini and H.A. Aggarwal, “Comprehensive Review of Image Enhancement Techniques”, International Journal of Innovative Research and Growth, Vol. 8. pp. 1-9, 2010.
- C.S.G. Dhas and T.D. Geleto, “D-PPSOK Clustering Algorithm with Data Sampling for Clustering Big Data Analysis”, Academic Press, 2022.
- N. Karthikeyan and S.R. Mugunthan, “Comparative Study of Lossy and Lossless Image Compression Techniques”, International Journal of Engineering and Technology, Vol. 7, pp. 950-953, 2018.
- Boopathiraja Subramanian, Kalavathi Palanisamy and V.B. Surya Prasath, “On a Hybrid Lossless Compression Technique for Three-Dimensional Medical Images”, Medical Imaging, Vol. 2020, pp. 1-12, 2020.
- R. Sowmyalakshmi, Mohamed IbrahimWaly, T. Mohamed Yacin Sikkandar, Jayasankar Sayed Sayeed Ahmad, Rashmi Rani and Suresh Chavhan, “An Optimal Lempel Ziv Markov Based Microarray Image Compression Algorithm”, Computers, Materials and Continua, Vol. 69, No. 2, pp. 1-14, 2021.
- M.S. Divya, J. Chandrashekhara, S. Vinay and A. Ramadevi, “Lossless Compression for Text Document Using Huffman Encoding, Run Length Encoding, and Lempel-Ziv Welch Coding Algorithms”, International Journal of Computer Science and Mobile Computing, Vol. 9, No. 3, pp. 100-105, 2020.
- J. Karam Lina, “Lossless Image Compression”, Academic Press, 2009.
- Bin Xiao, Gang Lu, Yanhong Zhang, Weisheng Li and Guoyin Wang, “Lossless Image Compression based on Integer Discrete Tchebichef Transform”, Neurocomputing, Vol. 214, pp. 587–593, 2016.
- Med Karim Abdmouleh, Atef Masmoudi and Med Salim Bouhlel, “A New Method which Combines Arithmetic Coding with RLE for Lossless Image Compression”, Scientific Research Publishing, Vol. 2021, pp. 1-12, 2021.
- S.W. Chiang and L.M. Po, “Adaptive Lossy LZW Algorithm for Palletized Image Compression”, Electronics Letters, Vol. 33, No. 10, pp. 852-854, 1997.
- Paul G. Howard and Jeffrey Scott Vitter, “Parallel Lossless Image Compression using Huffman and Arithmetic Coding”, Proceedings of International Conference on Data Compression, pp. 299-308, 1992.
- Yaghoub Pourasad and Fausto Cavallaro, “A Novel Image Processing Approach to Enhancement and Compression of X-ray Images”, International Journal of Environmental Research and Public Health, Vol. 18, pp. 6724-6744, 2021.