A Hybrid Progressive Image Compression, Transmission, and Reconstruction Architecture

Medical images are of high volume and contain valuable diagnostic information, which have to be preserved against any archival or transmission error. Progressive transmission of data is favourable due to the fact that the region of interest (RoI) within the image can be coded without any visible error and that it speeds up the transmission of desired information due to the hierarchical region relation & transmission. In addition, this modality of communication has been modified to be interactive so the physicians can select the RoI to prioritize transmission of various regions. Wireless transmission of medical images involves construction of an effective source-channel coding to not only preserve the diagnostic information but also to enable progressively streaming the data from host to receiver [1]. A number of large medical images have been compressed and progressively transmitted with low bitrate. The blocking effect, as can be seen in JPEG coded images with the same compression ratio, has been highly reduced and the quality of the decoded images enhanced. A MATLAB-based TCP/IP connection has been established to demonstrate the proposed interactive progressive transmission system.

In this paper, we present a new reversible decorrelation method of three-dimensional (3-D) medical images for progressive transmission. Progressive transmission of an image permits gradual improvement of image quality while being displayed. When the amount of image data is very large, as a 3-D medical image, the progressive transmission plays an important role in viewing or browsing the image. The data structure presented in this paper takes account of interframe correlation as well as intraframe correlation of the 3-D image. This type of data structure has been termed the 3-D hierarchy embedded differential image (3-D-HEDI) as was derived from the earlier HEDI structure. Experiments were conducted to verify the performance of 3-D HEDI in terms of the decorrelation efficiency as well as the progressive transmission efficiency. It is compared with those of conventional hierarchy interpolation (HINT), two-dimensional (2-D) HEDI and differential pulse code modulation (DPCM). Experimental results indicate that 3-D HEDI outperforms HINT, 2-D HEDI and DPCM in both decorrelation efficiency as well as the progressive transmission efficiency on 3-D medical images.

A new method for compact storage and transmission of medical images with concealed patient information in noisy environment is evinced. Digital watermarking is the technique adapted here for interleaving patient information with medical images. The patient information, which comprises of text data and signal graph, is encrypted to prevent unauthorized access of data. The latest encryption algorithm (Rijndael) is used for encrypting the text information. Signal graphs (ECG, EEG EMG etc.) are compressed using DPCM technique. To enhance the robustness of the embedded information, the patient information is coded by error correcting codes (ECC) such as (7,4) Hamming, Bose-Chaudhuri-Hocquenghem (BCH) and Reed Solomon (RS) codes .The noisy scenario is simulated by adding salt and pepper (S&P) noise to the embedded image. For different signal to noise ratio (SNR) of the image, bit error rate (BER) and number of character altered (NOCA) for text data and percentage distortion (PDIST) for the signal graph are evaluated. The performance comparison based on the above parameters is conducted for three types of ECC. It is elicited that coded systems can perform better than the uncoded systems.

The modern healthcare systems consider the secure transmission and storage of medical images as vital because of the sensitivity of patient data and the strict privacy regulations. This paper presents a secure and reversible RGB medical image scrambling and encryption scheme that aims to defend diagnostic images from unauthorized access, while, guaranteeing the integrity of the data. A 512- bit master key is generated through the memory-hard Argon2i algorithm which makes the system extremely resistant to brute-force and dictionary attacks. Domain-separated subkeys are generated using master key via AES in counter (CTR) mode. These subkeys are utilized at confusion and diffusion stages of the encryption and decryption process. The image confusion is performed with the use of a Fisher-Yates permutation which is controlled by cryptographically secure keystreams thereby disrupting spatial pixel relationships. The diffusion is done at both single colour channel and total colour channel levels utilizing a feedback-based mechanism that combines XOR operation, modular addition, and bit-level permutation. Both confusion and diffusion process ensures a strong avalanche effect across the entire image. Performance analysis shows that the proposed method provides an effective balance between security and computational efficiency, making it suitable for secure storage and transmission of medical images in telemedicine and healthcare information systems.

The ever-growing numbers of medical digital images and the need to share them among specialists and hospitals for better and more accurate diagnosis require that patients' privacy be protected. During the transmission of medical images between hospitals or specialists through the network, the main priority is to protect a patient's documents against any act of tampering by unauthorised individuals. Because of this, there is a need for medical image authentication scheme to enable proper diagnosis on patient. In addition, medical images are also susceptible to salt and pepper impulse noise through the transmission in communication channels. This noise may also be intentionally used by the invaders to corrupt the embedded watermarks inside the medical images. A common drawback of existing watermarking methods is their weakness against salt and pepper noise. The research carried out in this work addresses the issue of designing a new watermarking method that can withstand high density of salt and pepper noise for brain MRI images. For this purpose, combination of a spatial domain watermarking method, channel coding and noise filtering schemes are used. The region of non-interest (RONI) of MRI images from five different databases are used as embedding area and electronic patient record (EPR) is considered as embedded data. The quality of watermarked image is evaluated using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), and the accuracy of the extracted watermark is assessed in terms of Bit Error Rate (BER).

The compression and transmission of medical image are the two key technology to realize the remote consultation system. It takes much energy in compression and transmission of medical image. Image collection, the ROI compression algorithm and JPEG2000, color image processing, and the network transmission technology based on TCP/IP are discussed in detail. It can realize the conversation function using HDP protocol. Using the technology of TCP/IP protocol and multi-thread it finished the image transmission function. It is difficult to make the image both seen by sender and receiver same in general transmission. So it takes all those technologies to send the whole image after compression the collection image, in order to avoid the different phenomenon. And it can transmit rapidly and steadily in some local region image. The remote consultation system called RDS2000 is developed successfully based those technologies. The system integrates the functions of video communication, character communication, image communication and remote control, and meanwhile it also verifies the validity and dependability of those technologies.

Current progressive image transmission (PIT) systems can not control both bit rate and resolution constraints in images. Due to the partially localised nature of the wavelet transform, the value of any pixel in the image depends on only a small number of wavelet coefficients. Thus, it is possible to specify an arbitrary region of the image and prevent that region from being badly degraded during the compression process. This can be done by representing coefficients corresponding to those regions in the wavelet space more accurately. This is accomplished by multiplying those values by an arbitrary factor before applying the embedded zerotree wavelets (EZW) coding technique. The EZW algorithm is then effectively applied to, wavelet coefficients to refine and encode the most significant ones in the wavelet space. The information of the image is thus transmitted in several successive stages. It is shown that in a PIT system not only any region of interest (ROI) can be sent and received progressively, but also bitrate and resolution constraints can be controlled simultaneously. This option becomes very effective in interactive communication channels where the available bandwidth is at a premium.

Digital Imaging and Communications in Medicine (DICOM) communication mainly focuses on the transmission of medical images, storing the information in medical image and also for printing and securing the image. A medical image communication is mainly for secured medical facilities for the physicians and the patient. The medical image is compressed under the JPEG 2000 format. The hash value is found out using additive hash function (AHF) and it is encrypted using RSA to form the digital signature. Combination of digital signature and text will be the watermark. This text consists of patient information, doctor information, disease information, and prescription. Reversible watermarking is a technique in which watermark is embedded and watermarked images passes through the authentication process, the original image is extracted along with the watermark. Strict authentication is provided in order to have high security for accessing the secure medical images by implementing Kerberos technique.

Few networks offer sufficient bandwidth for the transmission of high resolution two- and three-dimensional medical image sets without incurring significant latency. Traditional compression methods achieve bit-rate reduction based on pixel statistics and ignore visual cues that are important in identifying visually informative regions. This paper describes an approach to managing image transmission in which spatial regions are selected and prioritized for transmission so that visually informative data is received in a timely manner. This context-based image transmission (CBIT) scheme is a lossless form of progressive image transmission (PIT) in which gross structure, represented by an approximate iconic image, is transmitted first. Each part of this iconic image is progressively updated, using a simple set of rules that take into account viewing requirements. CBIT is realized using knowledge about image composition to segment, label, prioritize, and fit geometric models to regions of an image. Tests, using neurological images, show that, with CBIT, a valuable transmitted image is received with a latency that is about one-tenth that of traditional PIT schemes. Frequently, the necessary regions of the image are transmitted in about half the time taken to transmit the full image.

Currently, much work has been done in the area of image storage and retrieval. However, the overall performance has been far from practical. A highly integrated wavelet-based image management system is proposed in this paper. By integrating wavelet-based solutions for image compression and decompression, content-based retrieval and progressive transmission, much higher performance can be achieved. The multiresolution nature of the wavelet transform has been proven to be a powerful tool to represent images. The wavelet transform decomposes the image into a set of subimages with different resolutions. From here three solutions for key aspects of image management are reached. The content-based image retrieval (CBIR) features of our system include the color, contour, texture, sample, keyword and topic information of images. The first four features can be naturally extracted from the wavelet transform coefficients. By scoring the similarity of users' requests with images in the database, those who have higher scores are noted and the user receives feedback. Image compression and decompression. Assuming that details at high resolution and diagonal directions are less visible to the human eye, a good compression ratio can be achieved. In each subimage, the wavelet coefficients are vector quantized (VQ), using the LGB algorithm, which is improved in our approach to accelerate the process. Higher compression ratio can be achieved with DPCM and entropy coding method applied together. With YIQ representation, color images can also be effectively compressed. There is a very low load on the network bandwidth by transmitting compressed image data across the network. Progressive transmission is possible by employment of the multiresolution nature of the wavelet, which makes the system respond faster and the user-interface more friendly. The system shows a high overall performance by exploring the excellent features of wavelet, and integrating key aspects of image management. An image retrieval service using Java is also available on the world wide web (www), to demonstrate the system's features.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The Internet of Medical Things (IoMT) is reforming healthcare by enabling interconnected medical devices and systems to facilitate efficient data collection, transmission, and analysis. While IoMT has significantly improved real-time monitoring and personalized care, the transmission of high-resolution medical images remains a challenge due to bandwidth constraints, latency issues, data loss, and computational overhead. Efficient and secure medical image transmission is critical to ensuring reliable diagnostics and timely patient care in this ecosystem. This research presents an optimized Deep Learning (DL) architecture developed to overcome the limitations of medical image transmission in IoMT environments. The proposed solution incorporates Convolutional Neural Networks (CNNs) for spatial feature extraction and dimensionality reduction while preserving diagnostic-critical information, and Long Short-Term Memory (LSTM) networks to manage sequential data and mitigate transmission issues such as packet loss and latency. The framework incorporates robust encryption mechanisms to ensure data security without significantly increasing computational overhead. Once predictions are made, the data is securely transferred to the cloud for further analysis and storage. Furthermore, Hippopotamus Optimization is utilised to enhance the model's performance and fine-tune hyperparameters, improving both efficiency and accuracy. Performance evaluations were conducted using real-world medical image datasets under varying IoMT network conditions. The results demonstrate that the proposed CNN-LSTM framework delivers superior performance across key metrics, like Peak Signal-to-Noise Ratio (PSNR), accuracy, F1 score, specificity, and sensitivity. Additionally, the framework optimizes encryption and decryption times and reduces bandwidth consumption, ensuring efficient and secure data transmission. This approach showcases a significant advancement in IoMT-based medical imaging, paving the way for enhanced reliability and efficiency in healthcare delivery systems.

Image compression is a well-established and extensively researched field. The huge interest in it has been aroused by the rapid enhancements introduced in imaging techniques and the various applications that use high-resolution images (e.g. medical, astronomical, Internet applications). The image compression algorithms should not only give state-of-art performance, they should also provide other features and functionalities such as progressive transmission. Often, a rough approximation (thumbnail) of an image is sufficient for the user to decide whether to continue the image transmission or to abort; which accordingly helps to reduce time and bandwidth. That in turn necessitated the development of multi-resolution image compression schemes. The existed multi-resolution schemes (e.g., Multi-Level Progressive method) have shown high computational efficiency, but with a lack of the compression performance, in general. In this thesis, a LOw Complexity Multi-resolution Image Compression (LOCMIC) based on the Hierarchical INTerpolation (HINT) framework is presented. Moreover, a novel integration of the Just Noticeable Distortion (JND) for perceptual coding with the HINT framework to achieve a visual-lossless multi-resolution scheme has been proposed. In addition, various prediction formulas, a context-based prediction correction model and a multi-level Golomb parameter adaption approach have been investigated. The proposed LOCMIC (the lossless and the visual lossless) has contributed to the compression performance. The lossless LOCMIC has achieved a 3% reduced bit rate over LOCO-I, about 1% over JPEG2000, 3% over SPIHT, and 2% over CALIC. The Perceptual LOCMIC has been better in terms of bit rate than near-lossless JPEG-LS (at NEAR=2) with about 4.7%. Moreover, the decorrelation efficiency of the LOCMIC in terms of entropy has shown an advance of 2.8%, 4.5% than the MED and the conventional HINT respectively.

In digital communication, the transmission of medical images over communication network is very explosive. We need a communication system to transmit the medical information rapidly and securely. In this manuscript, we propose a cryptosystem with novel encoding strategy and lossless compression technique. The chaos based DNA cryptography is used to enrich security of medical images. The lossless Discrete Haar Wavelet Transform is used to reduce space and time efficiency during transmission. The cryptanalysis proves that proposed cryptosystem is secure against different types of attacks. The compression ratio and pixel comparison is performed to verify the similarity of retained medical image.

Transmission of medical images among remote places is a general practice in telemedicine. Medical images may be modified intentionally or accidentally as the transmission of these may take place through unsecure networks such as internet. Before making any diagnostic decisions, the medical practitioner has to verify the integrity of region of interest (ROI) in the received medical image in order to avoid wrong diagnosis. Watermarking can be used for checking the integrity of medical images. In this study, the authors propose a novel medical image watermarking method based on integer wavelet transform (IWT). This proposal verifies the integrity of ROI, precisely identifies tampered blocks inside ROI, provides robustness to the data embedded inside region of non‐interest (RONI) and recovers original ROI. In the proposed method, the medical image is segmented into ROI and RONI regions. Hash value of ROI, recovery data of ROI and data of patient are embedded into RONI using IWT. Experimental results show that the proposed method provides robustness to the watermark data embedded inside RONI and accurately detects and localises tampered areas inside ROI and recovers the original ROI.

Medical images are more typical than any other ordinary images, since it stores patient’s information for diagnosis purpose. Such images need more security and confidentiality as total diagnosis depends on it. In telemedicine applications, transmission of medical image via open channel, demands strong security and copyright protection. In our proposed robust watermarking model, a double layer security is introduced to ensure the robustness of embedded data. The embedded data is scrambled using a unique key and then a transform domain based hybrid watermarking technique is used to embed the scrambled data into the transform coefficients of the host image. The data embedding in medical images involves more attention, so that the diagnosis part must not be affected by any modification. Therefore, Support Vector Machine (SVM) is used as a classifier, which classify a medical image into two regions i.e. Non Region of Interest (NROI) and Region of Interest (ROI) to embed watermark data into the NROI part of the medical image, using the proposed embedding algorithm. The objective of the proposed model is to avoid any quality degradation to the medical image. The simulation is performed to measure the Peak Signal to Noise Ratio (PSNR) for imperceptibility and Structural Similarity Index (SSIM) to test the robustness. The experimented result shows, robustness and imperceptibility with SSIM of more than 0.50 and PSNR of more than 35 dB for proposed watermarking model.