Abstract

Terabytes of medical image data are everyday used in the industry of healthcare through advanced imaging modalities as Ultrasound (US), Magnetic Resonance Imaging (MRI), Computerized Tomography (CT), X-rays, and Mammograms, among others. It is difficult to detect and recover the pixels because of the highly complicated, which makes it hard to store and transmit the immense amount of data that is created. Compressing an image decrease its size, which thusly requires less storage space, allowing for the storing of more images in a given amount of space. Such photos once they have been compressed will eventually use less bandwidth for transmission and thus shorten the download time. Due to these intrinsic worth image compression is considered as a necessity for multimedia technology, so we want to design and test neural networks states like (deep neural networks, artificial neural networks, recurrent neural networks, and convolution neural networks) as one of the most thrilling deep learning techniques, which is a subset of machine learning, to track down a representation to compress images well. In order to make improvements in the performance of medical images compression. This review can be categorized into the following two subsections namely compression based on medical images, compression based on deep learning during the latest 4 years,describing each section in efficient manner then open issues to highlightin.

References

• Ammah, Paul Nii Tackie, and Ebenezer Owusu. "Robust medical image compression based on wavelet transform and vector quantization." Informatics in Medicine Unlocked 15 (2019): 100183.
• Sadeeq, Haval T., et al. "Image Compression Using Neural Networks: A Review." International Journal of Online & Biomedical Engineering 17.14 (2021).
• Zhang, Quan-shi, and Song-Chun Zhu. "Visual interpretability for deep learning: a survey." Frontiers of Information Technology & Electronic Engineering 19.1 (2018): 27-39.
• Habib, Rafi Ullah. "Optimal compression of medical images." Int. J. Adv. Comput. Sci. Appl. IJACSA 10.4 (2019).
• Boopathy, G., and S. Arockiasamy. "Implementation of vector quantization for image compression-A Survey." Global Journal of Computer Science and Technology (2010).
• Anwar, Syed Muhammad, et al. "Medical image analysis using convolutional neural networks: a review." Journal of medical systems 42.11 (2018): 1-13.
• Toderici, George, et al. "Full resolution image compression with recurrent neural networks." Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. 2017.
• UmaMaheswari, S., and V. SrinivasaRaghavan. "Lossless medical image compression algorithm using tetrolet transformation." Journal of Ambient Intelligence and Humanized Computing 12.3 (2021): 4127-4135.
• Fred, A. Lenin, et al. "Bat optimization based vector quantization algorithm for medical image compression." Nature Inspired Optimization Techniques for Image Processing Applications. Springer, Cham, 2019. 29-54.
• Kumar, Pardeep, and Ashish Parmar. "Versatile approaches for medical image compression: A review." Procedia Computer Science 167 (2020): 1380-1389.
• Choi, Rene Y., et al. "Introduction to machine learning, neural networks, and deep learning." Translational Vision Science & Technology 9.2 (2020): 14-14.
• Kong, Fanzhi. "Facial expression recognition method based on deep convolutional neural network combined with improved LBP features." Personal and Ubiquitous Computing 23.3 (2019): 531-539.
• Zeebaree, D. Qader, et al. "Multi-level fusion in ultrasound for cancer detection based on uniform LBP features." Computers, Materials & Continua 66.3 (2021): 3363-3382.
• Mohammed, Soleen Basim, and Adnan Mohsin Abdulazeez. "Deep Convolution Neural Network for Facial Expression Recognition." PalArch's Journal of Archaeology of Egypt/Egyptology 18.4 (2021): 3578-3586.
• Yasin, Hajar Maseeh, and Adnan Mohsin Abdulazeez. "Image Compression Based on Deep Learning: A."
• Cai, Han, Ji Lin, and Song Han. "Efficient methods for deep learning." Advanced Methods and Deep Learning in Computer Vision. Academic Press, 2022. 159-190.
• Choi, Rene Y., et al. "Introduction to machine learning, neural networks, and deep learning." Translational Vision Science & Technology 9.2 (2020): 14-14.
• Krishnaraj, N., et al. "Deep learning model for real-time image compression in Internet of Underwater Things (IoUT)." Journal of Real-Time Image Processing 17.6 (2020): 2097-2111.
• Liu, Huanhua, et al. "Deep learning-based picture-wise just noticeable distortion prediction model for image compression." IEEE Transactions on Image Processing 29 (2019): 641-656.Jiang F, Tao W, Liu S, Ren J, Guo X, Zhao D. An end-to-end compression framework based on convolutional neural networks,” IEEE Trans. Circuits Syst Video Technol. 2018;28(10):3007–3018.
• Hoang, Trinh Man, Jinjia Zhou, and Yibo Fan. "Image compression with encoder-decoder matched semantic segmentation." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. 2020.
• Li, Mu, et al. "Efficient and effective context-based convolutional entropy modeling for image compression." IEEE Transactions on Image Processing 29 (2020): 5900-5911.
• Guo, Pengfei, Dawei Li, and Xingde Li. "Deep OCT image compression with convolutional neural networks." Biomedical Optics Express 11.7 (2020): 3543-3554.
• Somassoundaram, T., and N. P. Subramaniam. "High performance angiogram sequence compression using 2D bi-orthogonal multiwavelet and hybrid SPIHT-DEFLATE algorithm." Indian Journal of Science and Technology 8.14 (2015): 1.
• Rani, M. Mary Shanthi, and P. Chitra. "A hybrid medical image coding method based on haar wavelet transform and particle swarm optimization technique." International Journal of Pure and Applied Mathematics 118.8 (2018): 3056-3067.