Secured Compression for 2D Medical Images Through the Manifold and Fuzzy Trapezoidal Correlation Function

Year 2022, Volume: 35 Issue: 4, 1372 - 1391, 01.12.2022

P Naga Srinivasu , Norita Norwawi , Shanmuk Srinivas Amiripalli , P Deepalakshmi

https://doi.org/10.35378/gujs.884880

Cited By: 13

https://izlik.org/JA75CZ26GN

Abstract

In biomedical imaging, the imaging of secured storage and maintaining medical images like MRI, CT, and ultrasound scans are challenging with ever-growing tremendous image data. This article has proposed a systematic approach for secured compression of the image data that would compress the image data at multiple levels at each instance that would substitute with a smaller size data block through dictionary mechanism. The resultant image is encrypted through a 256-bit symmetric key dynamically generated through the hashing-based technique for multiple rounds. In each round, a 16-bit key sequence obtained from the hashing-based technique is an integral part of the 256-bit key used in the encryption process, and the same key sequence is being used in the decryption phase. Finally, the resultant image is stored for future reference for further medical examinations. In reconstructing the original image, the same approach is performed in reverse order to get back the original image without any significant impact on the image standard through the Fuzzy Trapezoidal correlation method. The proposed mechanism is being practically implemented over the medical images, and the outcome seems to be very pleasing compared to the counterparts. It is observed on implementation. The medical images are compressed to 58% of their original size without significant impact on the quality of the image that is being reconstructed. The approximated entropy in the majority of the cases is less than zero has proven the proposed mechanism is robust for secured compression of the medical images for secured storage.

Keywords

Lossless compression , Image encryption , Image decryption , Fuzzy trapezoidal , Correlation function

References

• [1] Pavithra, V., Jeyamala, C., "A Survey on the Techniques of Medical Image Encryption", IEEE International Conference on Computational Intelligence and Computing Research, Madurai, India, 1-8, (2018).
• [2] Zuo, Z., Lan, X., Deng, L., Yao, S., Wang, X., "An improved medical image compression technique with the lossless region of interest", Optik, 126(21): 2825-2831, (2015).
• [3] Chamberlin, P., Balasubramanian, S., "Near lossless medical image compression using block BWT–MTF and hybrid fractal compression techniques", Cluster Computing, 22: 12929–12937, (2019).
• [4] Messaoudi, A., Benchabane, F., Srairi, K., "DCT-based color image compression algorithm using adaptive block scanning", Signal Image and Video Processing, 13: 1441–1449, (2019).
• [5] Brahimi, N., Bouden, T., Brahimi, T., Boubchir, L., "A novel and efficient 8-point DCT approximation for image compression", Multimed Tools and Applications, 79: 7615–763, (2020).
• [6] Boucetta, A., Melkemi, K.E., "DWT Based-Approach for Color Image Compression Using Genetic Algorithm", In: Elmoataz A., Mammass D., Lezoray O., Nouboud F., Aboutajdine D. (eds) Image and Signal Processing. Lecture Notes in Computer Science 7340, Springer, (2012).
• [7] Parkale, Y.V., Nalbalwar, S.L., "Application of 1-D discrete wavelet transform based compressed sensing matrices for speech compression", SpringerPlus, 5: 2048, (2016).
• [8] Sangeetha, M., Betty P., Kumar., G. S. N., "A biometric iris image compression using LZW and hybrid LZW coding algorithm", 2017 International Conference on Innovations in Information, Embedded and Communication Systems, Coimbatore, India, 1-6, (2017).
• [9] Wang, H., Xia, Y., Wang, Z., "Dictionary learning-based image compression", 2017 IEEE International Conference on Image Processing, Beijing, China, 3235-3239, (2017).
• [10] Joshi, M., Agarwal, A.K., Gupta, B., "Fractal Image Compression and Its Techniques: A Review", In: Ray K., Sharma T., Rawat S., Saini R., Bandyopadhyay A. (eds) Soft Computing: Theories and Applications, Advances in Intelligent Systems and Computing, 742, Springer, Singapore, (2019).
• [11] Masmoudi, A., Bouhlel, M., Puech, W., "Efficient Adaptive Arithmetic Coding Based on Updated Probability Distribution for Lossless Image Compression", Journal Electronic Imaging, 19(2), (2010).
• [12] Lin, S., Gao, Z., Han, Y. S., "Arithmetic Coding Based on Reflected Binary Codes", 2019 Ninth International Workshop on Signal Design and its Applications in Communications, Dongguan, China, 1-5, (2019).
• [13] Gong-bin, Q., Qing-feng, J., Shui-sheng, Q., "A new image encryption scheme based on DES algorithm and Chua's circuit", 2009 IEEE International Workshop on Imaging Systems and Techniques Shenzhen, China, 168-172, (2009).
• [14] Mohammad, O. F., Rahim, M. S., Zeebaree, S. R. M., Ahmed, F., "A Survey and Analysis of the Image Encryption Methods", International Journal of Applied Engineering Research, 12: 13265-13280, (2017).
• [15] Shakir, H.R., "An image encryption method based on selective AES coding of wavelet transform and chaotic pixel shuffling", Multimedia Tools Applications, 78: 26073–26087, (2019).
• [16] Zhao, G., Yang, X., Zhou, B., Wei, W., "RSA-based digital image encryption algorithm in wireless sensor networks", 2010 2nd International Conference on Signal Processing Systems, Dalian, V2-640-V2-643, (2010).
• [17] Alsaffar, D. M., Almutiri, A. S., Alqahtani, B., Alamri, R. M., Alqahtani, H. F., Alqahtani, N. N., Alshammari, G. N., Ali, A. A., "Image Encryption Based on AES and RSA Algorithms", 3rd International Conference on Computer Applications & Information Security (ICCAIS) Riyadh, Saudi Arabia, 1-5, (2020).
• [18] Xiao, C., Chun-Jie H., "Adaptive medical image encryption algorithm based on multiple chaotic mapping", Saudi Journal of Biological Sciences, 24(8): 1821-1827, (2017).
• [19] Dener, M., "A new gateway node for wireless sensor network applications", Scientific Research and Essays 11, 20: 213-220, (2016).
• [20] Dener, M., “Security Analysis in Wireless Sensor Networks”, International Journal of Distributed Sensor Networks, 1-9, (2014).
• [21] Patidar, V., Pareek, N., Sud, K., "A new substitution–diffusion based image cipher using chaotic standard and logistic maps", International Journal of Network Security & Its Applications, 4(7): 3056-3075, (2009).
• [22] Srinivasu, N. P., Seshadri, Ch. "A Multilevel Image Encryption based on Duffing map and Modified DNA Hybridization for Transfer over an Unsecured Channel", International Journal of Computer Applications, 20(4): 1-4, (2015).
• [23] Pan, H., Lei, Y., Jian, C., "Research on digital image encryption algorithm based on double logistic chaotic map", Journal Image Video Processing, 142, (2018).
• [24] Akkasaligar, P., Biradar, S., "Medical Image Encryption with Integrity Using DNA and Chaotic Map", Recent Trends in Image Processing and Pattern Recognition (RTIP2R) Solapur, India, Communications in Computer and Information Science, 1036, Springer, Singapore, (2018).
• [25] Nematzadeh, H., Enayatifar, R., Motameni, H., Guimarães, F.G., Coelho, V. N., "Medical image encryption using a hybrid model of modified genetic algorithm and coupled map lattices", Optics and Lasers in Engineering, 110: 24-32, (2018).
• [26] Viswanath, G., Krishna, P., V., "Hybrid encryption framework for securing big data storage in multi-cloud environment", Evolutionary Intelligence, (2020).
• [27] Dener, M., Bostancıoğlu, C., “Smart Technologies with Wireless Sensor Networks”, Procedia - Social and Behavioral Sciences, 195: 1915-1921, (2015).
• [28] Rehman, A. U., Wang, H., Shadid, M. M. A., Igbal S., Abbas, Z., Firdous, A., "A Selective Cross-Substitution Technique for Encrypting Color Images Using Chaos, DNA Rules and SHA-512", IEEE Access, 7: 162786-162802, (2019).
• [29] Gopalakrishnan, T., Srinivasan, R., "Chaotic Image Encryption with Hash Keying as Key Generator", Institute of Electronics and Telecommunications Engineers Journal of Research, 63(2): 172-187, (2017).
• [30] Singh, P. K., Singh, R. S., Rai, K. N., "An image encryption algorithm based on XOR operation with approximation com ponent in wavelet transform", 2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), Patna, India, 1-4, (2015).
• [31] Belazi, A., Abd El-Latif, A. A., Belghith, S., "A novel image encryption scheme based on substitution-permutation network and chaos", Signal Processing, 128: 155–170, (2016).
• [32] Srinivasu, N. P., Bhoi, K., Nayak, S., Bhutta, M., Woźniak, M., “Blockchain Technology for Secured Healthcare Data Communication among the Non-Terminal Nodes in IoT Architecture in 5G Network”, Electronics, 10(12): 1437, (2021).
• [33] Srinivasu, N. P., Lalitha, R., "An Efficient Data Encryption Through Image via Prime Order Symmetric Key and Bit Shuffle Technique", Lecture Notes in Networks and Systems 5. Springer, Singapore, (2017).
• [34] Wallace, B. C., Dahabreh, I. J., "Improving class probability estimates for imbalanced data", Knowledge Information System, 41: 33–52, (2014).
• [35] Hua, Z., Zhou, B., Pun, C., Chen, P., "Image encryption using 2D Logistic-Sine chaotic map", IEEE International Conference on Systems, Man, and Cybernetics (SMC) San Diego, CA, USA, 3229-3234, (2014).
• [36] Sayed, W. S., Fahmy, H. A. H., Rezk A. A. and Radwan, A. G., "Generalized Smooth Transition Map Between Tent and Logistic Maps", International Journal of Bifurcation and Chaos, 27(1), (2017).
• [37] Hua, Z., Zhou, B., Zhou, Y., "Sine Chaotification Model for Enhancing Chaos and Its Hardware Implementation", IEEE Transactions on Industrial Electronics, 66(2): 1273-1284, (2019).
• [38] Srinivasu, P., Balas, V. E., “Self-Learning Network-based segmentation for real-time brain M.R. images through HARIS”, PeerJ Computer Science, 7: e654, (2021).
• [39] Srinivasu, P., Rao, T. S., Balas, V. E., "Volumetric Estimation of the Damaged Area in the Human Brain from 2D M.R. Image", International Journal of Information System Modeling and Design (IJISMD), 11(1): 74-92, (2020).
• [40] Sundara, R., Priya, V., Fred, A.L. "An Efficient Compound Image Compression Using Optimal Discrete Wavelet Transform and Run Length Encoding Techniques", Journal of Intelligent Systems, 28(1): 87-101, (2019).
• [41] Devaraj, S. J., Ezra, K., Kasaraneni, K. "Survey on Image Compression Techniques: Using CVIP Tools", In: Meghanathan N., Chaki N., Nagamalai D. (eds) Advances in Computer Science and Information Technology. Computer Science and Information Technology, CCSIT 2012, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 86, Springer, Berlin, Heidelberg, (2012).
• [42] Hasanzadeh, E., Yaghoobi, M., "A novel color image encryption algorithm based on substitution box and hyper-chaotic system with fractal keys", Multimedia Tools and Applications, 79: 7279–7297, (2020).