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Ungureanu VI, Negirla P, Korodi A. Image-Compression Techniques: Classical and "Region-of-Interest-Based" Approaches Presented in Recent Papers. SENSORS (BASEL, SWITZERLAND) 2024; 24:791. [PMID: 38339507 PMCID: PMC10857028 DOI: 10.3390/s24030791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 01/18/2024] [Accepted: 01/23/2024] [Indexed: 02/12/2024]
Abstract
Image compression is a vital component for domains in which the computational resources are usually scarce such as automotive or telemedicine fields. Also, when discussing real-time systems, the large amount of data that must flow through the system can represent a bottleneck. Therefore, the storage of images, alongside the compression, transmission, and decompression procedures, becomes vital. In recent years, many compression techniques that only preserve the quality of the region of interest of an image have been developed, the other parts being either discarded or compressed with major quality loss. This paper proposes a study of relevant papers from the last decade which are focused on the selection of a region of interest of an image and on the compression techniques that can be applied to that area. To better highlight the novelty of the hybrid methods, classical state-of-the-art approaches are also analyzed. The current work will provide an overview of classical and hybrid compression methods alongside a categorization based on compression ratio and other quality factors such as mean-square error and peak signal-to-noise ratio, structural similarity index measure, and so on. This overview can help researchers to develop a better idea of what compression algorithms are used in certain domains and to find out if the presented performance parameters are of interest for the intended purpose.
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Affiliation(s)
- Vlad-Ilie Ungureanu
- Automation and Applied Informatics Department, University Politehnica Timisoara, 300006 Timisoara, Romania;
| | - Paul Negirla
- Automation and Applied Informatics Department, University Politehnica Timisoara, 300006 Timisoara, Romania;
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Sinhal R, Ansari IA. Machine learning based multipurpose medical image watermarking. Neural Comput Appl 2023:1-22. [PMID: 37362569 PMCID: PMC10036986 DOI: 10.1007/s00521-023-08457-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Accepted: 03/03/2023] [Indexed: 03/26/2023]
Abstract
Digital data security has become an exigent area of research due to a huge amount of data availability at present time. Some of the fields like medical imaging and medical data sharing over communication platforms require high security against counterfeit access, manipulation and other processing operations. It is essential because the changed/manipulated data may lead to erroneous judgment by medical experts and can negatively influence the human's heath. This work offers a blind and robust medical image watermarking framework using deep neural network to provide effective security solutions for medical images. During watermarking, the region of interest (ROI) data of the original image is preserved by employing the LZW (Lampel-Ziv-Welch) compression algorithm. Subsequently the robust watermark is inserted into the original image using IWT (integer wavelet transform) based embedding approach. Next, the SHA-256 algorithm-based hash keys are generated for ROI and RONI (region of non-interest) regions. The fragile watermark is then prepared by ROI recovery data and the hash keys. Further, the LSB replacement-based insertion mechanism is utilized to embed the fragile watermark into RONI embedding region of robust watermarked image. A deep neural network-based framework is used to perform robust watermark extraction for efficient results with less computational time. Simulation results verify that the scheme has significant imperceptibility, efficient robust watermark extraction, correct authentication and completely reversible nature for ROI recovery. The relative investigation with existing schemes confirms the dominance of the proposed work over already existing work.
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Affiliation(s)
- Rishi Sinhal
- Electronics and Communication Engineering, PDPM Indian Institute of Information Technology Design and Manufacturing, Jabalpur, Madhya Pradesh 482005 India
| | - Irshad Ahmad Ansari
- Electronics and Communication Engineering, PDPM Indian Institute of Information Technology Design and Manufacturing, Jabalpur, Madhya Pradesh 482005 India
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Chacko A, Chacko S. Deep learning‐based robust medical image watermarking exploiting DCT and Harris hawks optimization. INT J INTELL SYST 2022. [DOI: 10.1002/int.22742] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Affiliation(s)
- Anusha Chacko
- Research Scholar, Department of Electronics and Communication Engineering Karunya Institute of Technology and Science Coimbatore Tamil Nadu India
- Department of Electronics and Communication Engineering Vimal Jyothi Engineering College Chemperi Kerala India
| | - Shanty Chacko
- Department of Electrical and Electronics Engineering Karunya Institute of Technology and Science Coimbatore Tamil Nadu India
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Sinhal R, Sharma S, Ansari IA, Bajaj V. Multipurpose medical image watermarking for effective security solutions. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:14045-14063. [PMID: 35233177 PMCID: PMC8874744 DOI: 10.1007/s11042-022-12082-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 11/23/2021] [Accepted: 01/04/2022] [Indexed: 06/14/2023]
Abstract
Digital medical images contain important information regarding patient's health and very useful for diagnosis. Even a small change in medical images (especially in the region of interest (ROI)) can mislead the doctors/practitioners for deciding further treatment. Therefore, the protection of the images against intentional/unintentional tampering, forgery, filtering, compression and other common signal processing attacks are mandatory. This manuscript presents a multipurpose medical image watermarking scheme to offer copyright/ownership protection, tamper detection/localization (for ROI (region of interest) and different segments of RONI (region of non-interest)), and self-recovery of the ROI with 100% reversibility. Initially, the recovery information of the host image's ROI is compressed using LZW (Lempel-Ziv-Welch) algorithm. Afterwards, the robust watermark is embedded into the host image using a transform domain based embedding mechanism. Further, the 256-bit hash keys are generated using SHA-256 algorithm for the ROI and eight RONI regions (i.e. RONI-1 to RONI-8) of the robust watermarked image. The compressed recovery data and hash keys are combined and then embedded into the segmented RONI region of the robust watermarked image using an LSB replacement based fragile watermarking approach. Experimental results show high imperceptibility, high robustness, perfect tamper detection, significant tamper localization, and perfect recovery of the ROI (100% reversibility). The scheme doesn't need original host or watermark information for the extraction process due to the blind nature. The relative analysis demonstrates the superiority of the proposed scheme over existing schemes.
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Affiliation(s)
- Rishi Sinhal
- Electronics and Communication Engineering, PDPM Indian Institute of Information Technology Design and Manufacturing, Jabalpur, MP 482005 India
| | - Sachin Sharma
- Research Division, Jagadish Chandra Bose Research Organisation, Gautam Budh Nagar, Uttar Pradesh 203207 India
| | - Irshad Ahmad Ansari
- Electronics and Communication Engineering, PDPM Indian Institute of Information Technology Design and Manufacturing, Jabalpur, MP 482005 India
| | - Varun Bajaj
- Electronics and Communication Engineering, PDPM Indian Institute of Information Technology Design and Manufacturing, Jabalpur, MP 482005 India
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Pourasad Y, Cavallaro F. A Novel Image Processing Approach to Enhancement and Compression of X-ray Images. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18136724. [PMID: 34206486 PMCID: PMC8297375 DOI: 10.3390/ijerph18136724] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/17/2021] [Accepted: 06/19/2021] [Indexed: 11/28/2022]
Abstract
At present, there is an increase in the capacity of data generated and stored in the medical area. Thus, for the efficient handling of these extensive data, the compression methods need to be re-explored by considering the algorithm’s complexity. To reduce the redundancy of the contents of the image, thus increasing the ability to store or transfer information in optimal form, an image processing approach needs to be considered. So, in this study, two compression techniques, namely lossless compression and lossy compression, were applied for image compression, which preserves the image quality. Moreover, some enhancing techniques to increase the quality of a compressed image were employed. These methods were investigated, and several comparison results are demonstrated. Finally, the performance metrics were extracted and analyzed based on state-of-the-art methods. PSNR, MSE, and SSIM are three performance metrics that were used for the sample medical images. Detailed analysis of the measurement metrics demonstrates better efficiency than the other image processing techniques. This study helps to better understand these strategies and assists researchers in selecting a more appropriate technique for a given use case.
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Affiliation(s)
- Yaghoub Pourasad
- Department of Electrical Engineering, Urmia University of Technology, Urmia 17165-57166, Iran
- Correspondence:
| | - Fausto Cavallaro
- Department of Economics, University of Molise, Via De Sanctis, 86100 Campobasso, Italy;
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Que Y, Chen D, Tong L, Chen C. [A new lossy compression method for fetal heart rate signals-Convolutional Codec Network]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2021; 41:279-284. [PMID: 33624603 DOI: 10.12122/j.issn.1673-4254.2021.02.17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
In order to reduce the energy loss during data transmission and storage in the Internet of Things system and improve the transmission efficiency of fetal heart rate data to allow real-time monitoring of the fetus, we used a convolutional codec network (CC-Net) to compress the data. The network has two modules: the encoding and decoding modules. The original data are compressed in the encoding module and reconstructed in the decoding module. The internal parameters are continuously updated using the mean square error of the original and the reconstructed signals to minimize the error to obtain effectively compressed data in the encoding module. In this study, the compression ratio of fetal heart rate signals using this method reached 12.07%, and the error between the reconstructed and original signals was 0.03. The proposed CC-Net can achieve a very low compression ratio for fetal heart rate compression while ensuring a high similarity between the reconstructed and the original signals to retain important information in fetal heart rate signals.
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Affiliation(s)
- Y Que
- College of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - D Chen
- College of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - L Tong
- Guangdong Vocational College of Mechanical and Electrical Technology, Guangzhou 510550, China
| | - C Chen
- College of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
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Aldemir E, Gezer NS, Tohumoglu G, Barış M, Kavur AE, Dicle O, Selver MA. Reversible 3D compression of segmented medical volumes: usability analysis for teleradiology and storage. Med Phys 2020; 47:1727-1737. [PMID: 31994208 DOI: 10.1002/mp.14053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Revised: 01/20/2020] [Accepted: 01/21/2020] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND DICOM standard does not have modules that provide the possibilities of two-dimensional Presentation States to three-dimensional (3D). Once the final 3D rendering is obtained, only video/image exporting or snapshots can be used. To increase the utility of 3D Presentation States in clinical practice and teleradiology, the storing and transferring the segmentation results, obtained after tedious procedures, can be very effective. PURPOSE To propose a strategy for preserving interaction and mobility of visualizations for teleradiology by storing and transferring only binary segmented data, which is effectively compressed by modern adaptive and context-based reversible methods. MATERIAL AND METHODS A diverse set of segmented data, which include four abdominal organs (liver, spleen, right, and left kidneys) from 20 T1-DUAL and 20 T2-SPIR MRI, liver from 20 CT, and abdominal aorta with aneurysms (AAA) from 19 computed tomography-angiography datasets, are collected. Each organ is segmented manually by expert physicians, and binary volumes are created. The well-established reversible binary compression methods PNG, JPEG-LS, JPEG-XR, CCITT-G4, LZW, JBIG2, and ZIP are applied to medical datasets. Recently proposed context-based (3D-RLE) and adaptive (ABIC) algorithms are also employed. The performance assessment has been presented in terms of the compression ratio that is a universal compression metric. RESULTS Reversible compression of binary volumes results with substantial decreases in file size such as 254 to 2.14 MB for CT-AAA, 56.7 to 0.3 MB for CT-liver. Moreover, compared to the performance of well-established methods (i.e., mean 76.14%), CR is observed to be increased significantly for all segmented organs from both CT and MRI datasets when ABIC (95.49%) and 3D-RLE (94.98%) are utilized. The hypothesis is that morphological coherence of scanning procedure and adaptation between the segmented organs, that is, bi-level images, contributes to compression performance. Although the performance of well-established techniques is satisfactory, the sensitivity of ABIC to modality type and the advantage of 3D-RLE when the spatial coherence between the adjacent slices are high results with up to 10 times more CR performance. CONCLUSION Adaptive and context-based compression strategies allow effective storage and transfer of segmented binary data, which can be used to re-produce visualizations for better teleradiology practices preserving all interaction mechanisms.
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Affiliation(s)
- Erdoğan Aldemir
- The Graduate School of Natural and Applied Sciences, Dokuz Eylül University, Kuruçeşme Mahallesi, DEÜ Tinaztepe Campus No: 22, 35390, Buca, İzmir, Turkey
| | - Naciye Sinem Gezer
- Dokuz Eylül University Medical School, Department of Radiology, İnciraltı Mahallesi, Mithatpaşa Street, İnciraltı Campus, No:1606, 35340, Narlıdere/İzmir, Turkey
| | - Gulay Tohumoglu
- Electrical and Electronics Engineering Department, Dokuz Eylül University, Kuruçeşme Mahallesi, DEÜ Kaynaklar Campus No: 22, 35090, Buca, İzmir, Turkey
| | - Mustafa Barış
- Dokuz Eylül University Medical School, Department of Radiology, İnciraltı Mahallesi, Mithatpaşa Street, İnciraltı Campus, No:1606, 35340, Narlıdere/İzmir, Turkey
| | - A Emre Kavur
- The Graduate School of Natural and Applied Sciences, Dokuz Eylül University, Kuruçeşme Mahallesi, DEÜ Tinaztepe Campus No: 22, 35390, Buca, İzmir, Turkey
| | - Oguz Dicle
- Dokuz Eylül University Medical School, Department of Radiology, İnciraltı Mahallesi, Mithatpaşa Street, İnciraltı Campus, No:1606, 35340, Narlıdere/İzmir, Turkey
| | - M Alper Selver
- Electrical and Electronics Engineering Department, Dokuz Eylül University, Kuruçeşme Mahallesi, DEÜ Kaynaklar Campus No: 22, 35090, Buca, İzmir, Turkey
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Li D, Deng L, Bhooshan Gupta B, Wang H, Choi C. A novel CNN based security guaranteed image watermarking generation scenario for smart city applications. Inf Sci (N Y) 2019. [DOI: 10.1016/j.ins.2018.02.060] [Citation(s) in RCA: 174] [Impact Index Per Article: 34.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Badshah G, Liew SC, Zain JM, Ali M. Watermarking of ultrasound medical images in teleradiology using compressed watermark. J Med Imaging (Bellingham) 2016; 3:017001. [PMID: 26839914 DOI: 10.1117/1.jmi.3.1.017001] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2015] [Accepted: 12/08/2015] [Indexed: 11/14/2022] Open
Abstract
The open accessibility of Internet-based medical images in teleradialogy face security threats due to the nonsecured communication media. This paper discusses the spatial domain watermarking of ultrasound medical images for content authentication, tamper detection, and lossless recovery. For this purpose, the image is divided into two main parts, the region of interest (ROI) and region of noninterest (RONI). The defined ROI and its hash value are combined as watermark, lossless compressed, and embedded into the RONI part of images at pixel's least significant bits (LSBs). The watermark lossless compression and embedding at pixel's LSBs preserve image diagnostic and perceptual qualities. Different lossless compression techniques including Lempel-Ziv-Welch (LZW) were tested for watermark compression. The performances of these techniques were compared based on more bit reduction and compression ratio. LZW was found better than others and used in tamper detection and recovery watermarking of medical images (TDARWMI) scheme development to be used for ROI authentication, tamper detection, localization, and lossless recovery. TDARWMI performance was compared and found to be better than other watermarking schemes.
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Affiliation(s)
- Gran Badshah
- Universiti Malaysia Pahang , Faculty of Computer System and Software Engineering, Gambang 26300, Kuantan Pahang, Malaysia
| | - Siau-Chuin Liew
- Universiti Malaysia Pahang , Faculty of Computer System and Software Engineering, Gambang 26300, Kuantan Pahang, Malaysia
| | - Jasni Mohamad Zain
- Universiti Malaysia Pahang , Faculty of Computer System and Software Engineering, Gambang 26300, Kuantan Pahang, Malaysia
| | - Mushtaq Ali
- Universiti Malaysia Pahang , Faculty of Computer System and Software Engineering, Gambang 26300, Kuantan Pahang, Malaysia
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