Like most patient information, medical imaging data is subject to strict data protection and confidentiality requirements. This raises the issue of sending the data which contains a medical image on an open network as per the above issue, also there might be a leakage of information. Encrypting an Image and hiding the information in it is the Potential way of avoiding this problem. But there might be many problems when we try restoring the original image. As a solution to that, an algorithm dealing with region of intrest (ROI) in medical images based on the pixels of interest and histogram peak technique. Firstly Image histogram peak technique is used for calculating peaks in medical images. Then set the Threshold value to segregate the pixels of interest in the medical images. The threshold value can be calculated by taking an average of all peaks in the histogram. These pixels are encrypted with the help of the Sudoku matrix. The proposed scheme will be evaluated using a various test based on statistics along with those results which will be compared to benchmarks of the existing work. We can see the better performance in terms of security from the proposed technique.

1. Satoh, Hitoshi, Noboru Niki, Kenji Eguchi, Hironobu Ohmatsu, Masahiko Kusumoto, Masahiro Kaneko, and Noriyuki Moriyama. “Teleradiology network system on cloud using the web medical image conference system with a new information security solution.” In Medical Imaging 2013: Advanced PACS-based Imaging Informatics and Therapeutic Applications, vol. 8674, pp. 264-272. SPIE, 2013.
2. Avudaiappan, T., R. Balasubramanian, S. Sundara Pandiyan, M. Saravanan, S. K. Lakshmanaprabu, and K. Shankar. “Medical image security using dual encryption with oppositional based optimization algorithm.” Journal of medical systems42, no. 11 (2018): 1-11.
3. Wang, Chunpeng, Xingyuan Wang, Zhiqiu Xia, and Chuan Zhang. “Ternary radial harmonic Fourier moments based robust stereo image zero-watermarking algorithm.” Information Sciences470 (2019): 109-120.
4. Xie, Dong. “Public key image encryption based on compressed sensing.” IEEE Access7 (2019): 131672-131680.
5. Zhu, Shuqin, and Congxu Zhu. “Plaintext-related image encryption algorithm based on block structure and five-dimensional chaotic map.” IEEE Access7 (2019): 147106-147118.
6. Binjubeir, Mohammed, Abdulghani Ali Ahmed, Mohd Arfian Bin Ismail, Ali Safaa Sadiq, and Muhammad Khurram Khan. “Comprehensive survey on big data privacy protection.” IEEE Access8 (2019): 20067-20079.
7. Li, Ming, Pengcheng Wang, Yanfang Liu, and Haiju Fan. “Cryptanalysis of a novel bit-level color image encryption using improved 1D chaotic map.” IEEE Access7 (2019): 145798-145806.
8. Manjula, G., and H. S. Mohan. “Probability based selective encryption scheme for fast encryption of medical images.” In ICAICR’19: Proceedings of the Third International Conference on Advanced Informatics for Computing Research, Article, vol. 17, pp. 15-16. 2019.
9. Jang, Wonyoung, and Sun-Young Lee. “Partial image encryption using format-preserving encryption in image processing systems for Internet of things environment.” International Journal of Distributed Sensor Networks16, no. 3 (2020): 1550147720914779.
10. Sankaradass, Veeramalai, P. Murali, and M. Tholkapiyan. “Region of Interest (ROI) based image encryption with sine map and lorenz system.” In International Conference on ISMAC in Computational Vision and Bio-Engineering, pp. 493-502. Springer, Cham, 2018.
11. Mousavi, Seyed Mojtaba, Alireza Naghsh, and S. A. R. Abu-Bakar. “A heuristic automatic and robust ROI detection method for medical image warermarking.” Journal of digital imaging28, no. 4 (2015): 417-427.
12. Zhou, Jian, Jinqing Li, and Xiaoqiang Di. “A novel lossless medical image encryption scheme based on game theory with optimized ROI parameters and hidden ROI position.” IEEE Access8 (2020): 122210-122228.
13. Rashmi, P., and M. C. Supriya. “Encryption of Color image to enhance security using Permutation and Diffusion Techniques.” International Journal of Advanced Science and Technology28, no. 12 (2019): 375-384.
14. Ni, Zhicheng, Yun-Qing Shi, Nirwan Ansari, and Wei Su. “Reversible data hiding.” IEEE Transactions on circuits and systems for video technology16, no. 3 (2006): 354-362.
15. Kumar, C. Vinoth, V. Natarajan, and Deepika Bhogadi. “High capacity reversible data hiding based on histogram shifting for medical images.” In 2013 international conference on communication and signal processing, pp. 730-733. IEEE, 2013.
16. Yang, Yang, Weiming Zhang, and Nenghai Yu. “Improving visual quality of reversible data hiding in medical image with texture area contrast enhancement.” In 2015 international conference on intelligent information hiding and multimedia signal processing (IIH-MSP), pp. 81-84. IEEE, 2015.
17. Wu, Min-Hao, Jianyang Zhao, Bolun Chen, Yongjun Zhang, Yongtao Yu, and Jianhong Cheng. “Reversible data hiding based on medical image systems by means of histogram strategy.” In 2018 3rd international conference on information systems engineering (ICISE), pp. 6-9. IEEE, 2018.
18. Huang, Li-Chin, Lin-Yu Tseng, and Min-Shiang Hwang. “A reversible data hiding method by histogram shifting in high quality medical images.” Journal of Systems and Software86, no. 3 (2013): 716-727.
19. Yue, X. D., D. Q. Miao, N. Zhang, L. B. Cao, and Qiang Wu. “Multiscale roughness measure for color image segmentation.” Information Sciences216 (2012): 93-112.
20. Sastry, S. Sreehari, K. Mallika, B. Gowri Sankara Rao, Ha Sie Tiong, and S. Lakshminarayana. “Liquid crystal textural analysis based on histogram homogeneity and peak detection algorithm.” Liquid Crystals39, no. 4 (2012): 415-418.
21. Boukharouba, S., José Manuel Rebordão, and P. L. Wendel. “An amplitude segmentation method based on the distribution function of an image.” Computer vision, graphics, and image processing29, no. 1 (1985): 47-59.
22. Elguebaly, Tarek, and Nizar Bouguila. “Bayesian learning of finite generalized Gaussian mixture models on images.” Signal Processing91, no. 4 (2011): 801-820.
23. Azam, Muhammad, and Nizar Bouguila. “Unsupervised keyword spotting using bounded generalized Gaussian mixture model with ICA.” In 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp. 1150-1154. IEEE, 2015.
24. Wu, Yue, Yicong Zhou, Joseph P. Noonan, Karen Panetta, and Sos Agaian. “Image encryption using the sudoku matrix.” In Mobile Multimedia/Image Processing, Security, and Applications 2010, vol. 7708, pp. 222-233. SPIE, 2010.
25. Ahmad, Jawad, and Fawad Ahmed. “Efficiency analysis and security evaluation of image encryption schemes.” computing23 (2010): 25.
26. Zhang, Xuncai, Lingfei Wang, Guangzhao Cui, and Ying Niu. “Entropy-based block scrambling image encryption using DES structure and chaotic systems.” International Journal of Optics2019 (2019).
27. Wu, Yue, Joseph P. Noonan, and Sos Agaian. “NPCR and UACI randomness tests for image encryption.” Cyber journals: multidisciplinary journals in science and technology, Journal of Selected Areas in Telecommunications (JSAT)1, no. 2 (2011): 31-38.
28. Wang, Zhou, Alan C. Bovik, Hamid R. Sheikh, and Eero P. Simoncelli. “Image quality assessment: from error visibility to structural similarity.” IEEE transactions on image processing13, no. 4 (2004): 600-612.
29. Wang, Zhou, and Alan C. Bovik. “Modern image quality assessment.” Synthesis Lectures on Image, Video, and Multimedia Processing2, no. 1 (2006): 1-156.
30. Sajjad, Muhammad, Khan Muhammad, Sung Wook Baik, Seungmin Rho, Zahoor Jan, Sang-Soo Yeo, and Irfan Mehmood. “Mobile-cloud assisted framework for selective encryption of medical images with steganography for resource-constrained devices.” Multimedia Tools and Applications76, no. 3 (2017): 3519-3536.
31. Akkasaligar, Prema T., and Sumangala Biradar. “Selective medical image encryption using DNA cryptography.” Information Security Journal: A Global Perspective29, no. 2 (2020): 91-101.

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