Parallel Low-Complexity Lossless Coding of Three-Dimensional Medical Images

Steganography which is an art as well as a science of invisible transmission arrived as a saviour by providing utmost security through various techniques. Medical Image Steganography is a special subcategory of Image Steganography. It has come a long way to make the transmission of medical images more and more safe and to make sure that the diagnostic reading of the image does not get affected. Security is the most crucial aspect of communication in today’s world. While sharing documents on the internet, one needs assurance of privacy and secrecy, as these days’ communication is not only restricted to chatting but plays a vital role in almost all sectors. This paper discusses various algorithms used in medical image steganography and analyse the best out of all based on various characteristics of the algorithms like robustness, PSNR (Peak signal-to-noise ratio) value, MSE (Mean Squared Error) value, payload capacity etc.

Lossless compression schemes with secure transmission play a key role in telemedicine applications that helps in accurate diagnosis and research. Traditional cryptographic algorithms for data security are not fast enough to process vast amount of data. Hence a novel secured lossless compression approach proposed in this paper is based on reversible integer wavelet transform, EZW algorithm arithmetic coding and selective bit scrambling. The use of the lifting scheme allows to generate truly lossless integer-to-integer wavelet transforms. Images are compressed/decompressed by well-known EZW algorithm and arithmetic coding is used to improve the compression performance and also increases the security level. The proposed approach employs scrambling method, which is fast, simple to implement, and it provides security. Lossless compression ratios and distortion performance of this proposed methods are found to be better than other lossless techniques.

Medical images must be compressed before transmission due to bandwidth and storage limitations. The set partitioning in hierar- chical trees (SPIHT) algorithm is an efficient method for lossy and loss- less coding of medical images. We propose some modifications to the SPIHT algorithm. It is based on the idea of the insignificant correlation of wavelet coefficients among medium- and high-frequency subbands. In this scheme, insignificant wavelet coefficients that correspond to the same spatial location in the medium subbands can be used to reduce the redundancy by a combined function proposed in associated with the modified SPIHT. In high-frequency subbands, the modified SPIHT pro- poses a dictator to reduce the interband redundancy. Experimental re- sults indicate that the proposed technique improves the quality of the reconstructed medical image in terms of both the peak signal-to-noise ratio (PSNR) and the perceptual results over JPEG2000 and the original SPIHT at the same bit rate. © 2003 Society of Photo-Optical Instrumentation Engi- neers. (DOI: 10.1117/1.1578645)

Three-dimensional wavelet coding of volumetric medical images provides better coding performance compared to corresponding 2D methods by exploiting the inter-slice correlation that exists in such data. It introduces however latencies when it comes to transmitting specific parts of the volume. This paper presents an extension to 3D-SPlHT which allows 3D region of interest (ROI) coding. ROl coding enables faster reconstruction of diagnostically useful regions in volumetric datasets by assigning higher priority to them in the bitstream. It also introduces the possibility for increased compression performance, by allowing certain parts of the volume to be coded in a lossy manner while others are coded losslessly. The necessary modifications to 3D-SPIHT for ROI coding are described and methods for specifying a 3D ROI without adding a significant overhead are suggested. Results are presented highlighting the benefits of the ROl extension.

Three-dimensional wavelet coding of volumetric medical images provides better coding performance compared to corresponding 2D methods by exploiting the inter-slice correlation that exists in such data. It introduces however latencies when it comes to retrieving/transmitting specific parts of the volume. This paper presents an extension to 3D-SPIHT which allows 3D region of interest (ROI) coding. ROI coding enables faster reconstruction of diagnostically useful regions in volumetric datasets by assigning higher priority to them in the bitstream. It also introduces the possibility for an increased compression performance, by allowing certain parts of the image to be coded in a lossy manner while others are coded losslessly. The necessary modifications to 3D-SPIHT for ROI coding are described and methods for specifying a 3D ROI without adding a significant overhead are suggested. Results are presented highlighting the benefits of the ROI extension.

BI&T Tell me about the Medical Imaging and Technology Alliance. David Fisher The Medical Imaging & Technology Alliance (MITA), a division of the National Electrical Manufacturers Association (NEMA), is the leading organization and collective voice of medical imaging equipment manufacturers, innovators, and product developers. It represents companies whose sales comprise more than 90 percent of the global market for medical imaging technology. The goals of MITA are to: • Increase the awareness and understanding of the value of medical imaging • Achieve efficient and reasonable regulation of medical imaging technologies • Interact with appropriate government agencies on reimbursement and technology assessment policies • Expand the global acceptance of the digital communications standard (DICOM) that allows digital imaging technologies to interact seamlessly • Improve regulatory harmonization of the global market for medical imaging products • Develop and represent industry positions in technical, trade and other issues • Provide market data unique to this industry

An antidictionary is particularly useful for data compression, and on-line electrocardiogram (ECG) lossless compression algorithms using antidictionaries have been proposed. They work in real-time with constant memory and give better compression ratios than traditional lossless data compression algorithms, while they only deal with ECG data on a binary alphabet. This paper proposes on-line ECG lossless compression for a given data on a finite alphabet. The proposed algorithm gives not only better compression ratios than those algorithms but also uses less computational space than they do. Moreover, the proposed algorithm work in real-time. Its effectiveness is demonstrated by simulation results.

tailored to support and assist individuals on the autism spectrum, helping them improve their social skills and overall well-being. Nowadays, they are crucial tools for therapy that can improve social skills in a short period.In conclusion, this editorial paper highlights the remarkable progress made in AI and neural networks, shedding light on their applications in human activity recognition, medical image steganography, lower limb prosthetics, assistive robots for ASD, and wearable assistive devices for visually impaired individuals. The discussed advancements in spiking neural P systems, geneticbased steganography algorithms, systematic analysis of prosthetic technology, energy optimization of assistive robots, and the Tiny-Yolov3 architecture demonstrate the transformative potential of AI in improving various aspects of human life. A systematic literature review of the current technologies for developing lower limb prosthetics reveals a need for a specific, generalized structure for new developments. Notable gaps exist in energy management and improve, smoother patient interaction, thereby highlighting areas for future research and improvement. The application of socially assistive robots for autism therapies is increasing. A recent study presents the integration of an output feedback adaptive controller for trajectory tracking and energetic autonomy of a mobile socially assistive robot for ASD under an event-driven control scheme. Energy optimization is crucial as it allows therapists to extend and adapt sessions with autistic children, enhancing effectiveness during therapies. Finally, wearable assistive devices based on video camera technology pose unique challenges for visually impaired individuals. A proposed Tiny You Only Look Once (YOLO) architecture for pedestrian detection can be implemented in low-cost wearable devices, providing a potential solution for assistive technologies for the visually impaired. In conclusion, advancements in artificial intelligence and neural networks continue to revolutionize numerous fields, improving both the efficiency and quality of solutions. However, continued research and improvements are necessary to harness these technological breakthroughs and optimize their benefits. These five research papers illuminate the cutting-edge advancements and future potentials of artificial intelligence (AI) and neural networks in various fields, ranging from human activity recognition to medical image confidentiality, prosthetics, assistive robots for autism spectrum disorder (ASD), and wearable devices for the visually impaired. Each paper individually contributes to the broader landscape of AI, offering innovative solutions to existing challenges and opening new avenues for exploration. They reveal the transformative power of AI in enhancing real-time applications, increasing computational efficiency, improving user interactions, ensuring data confidentiality, and optimizing energy management. Yet, collectively, they tell a more profound story of how interconnected these AI applications are and how common threads weave through them. They reinforce the notion that the evolution of AI is not happening in silos but is a holistic, intertwined process where advancements in one area can catalyze growth in others. As AI continues to develop, researchers, developers, and practitioners must consider this interconnectedness. This approach will allow us to fully realize the potential of AI, not only to solve individual problems but also to create systemic changes that ripple across sectors and applications. AI is not only a tool or a participant in the process but a fundamental shift in how we perceive, interact with, and shape our world. The progress discussed in these papers is inspiring, yet it is equally a reminder of the endless journey ahead in AI research. The opportunities for AI to further revolutionize healthcare, accessibility, and our daily interactions are vast, and our collective pursuit of these advancements is what will continue to drive progress in this exciting, transformative field.

The increasing need for efficient image storage in hospitals imposes heavy requirements on the design of picture archiving and communication systems. Thus new methods are needed to improve the medical image compression performance. In this paper, we propose an efficient coding algorithm called OSS (Optimal Subband Shift) scheme based on the RB-IWT (Reversible Biorthogonal Integer Wavelet Transform). In the new scheme, the original image is first decomposed by the RB-IWT. Then, the image coefficients of every subband are multiplied by the powers of two. Finally, the SPECK coding is applied. Experimental results show that the OSS scheme does not only provide the PSNR performance better than SPECK using the original RB-IWT without the subband shift, but also has the low coding complexity. So this idea is valuable for future research in medical image coding and its applications.

In information security, image steganography is one of the information securing process. Image steganography is mostly used in medical fields for hiding medical prescription data behind the medical images. Designing steganographic algorithms for empirical cover sources, such as digital images, is very perplexing due to the lack of accurate models. The most successful approach today avoids estimating the cover source distribution because this task is infeasible for complex and non-stationary sources. Instead, the steganography problem is formulated as source coding with fidelity constraint the sender entrenches the message with minimizing the distortion. Practical algorithms that entrench near the theoretical payload–distortion bound are available for a very general class of distortion functions. With the current framework, the only task left to the sender is essential for the design of the distortion function. Here information is which wants to be confidential that information's are stored behind the image pixels. However, due to increasing crypt analysis attacks and binary image de stenographic process image steganography has very less security. So improving the security in image steganography process, the current system provide the asymmetric encryption standard for improving more security in steganography.

In this work, we introduce a nonlinear geometric transform, called peak transform (PT), for efficient image representation and coding. The proposed PT is able to convert high-frequency signals into low-frequency ones, making them much easier to be compressed. Coupled with wavelet transform and subband decomposition, the PT is able to significantly reduce signal energy in high-frequency subbands and achieve a significant transform coding gain. This has important applications in efficient data representation and compression. To maximize the transform coding gain, we develop a dynamic programming solution for optimum PT design. Based on PT, we design an image encoder, called the PT encoder, for efficient image compression. Our extensive experimental results demonstrate that, in wavelet-based subband decomposition, the signal energy in high-frequency subbands can be reduced by up to 60% if a PT is applied. The PT image encoder outperforms state-of-the-art JPEG2000 and H.264 (INTRA) encoders by up to 2-3 dB in peak signal-to-noise ratio (PSNR), especially for images with a significant amount of high-frequency components. Our experimental results also show that the proposed PT is able to efficiently capture and preserve high-frequency image features (e.g., edges) and yields significantly improved visual quality. We believe that the concept explored in this work, designing a nonlinear transform to convert hard-to-compress signals into easy ones, is very useful. We hope this work would motivate more research work along this direction.

I2Cnet (Image Indexing by Content network) aims to provide services related to the content-based management of images in healthcare over the World-Wide Web. Each I2Cnet server maintains an autonomous repository of medical images and related information. The annotation service of I2Cnet allows specialists to interact with the contents of the repository, adding comments or illustrations to medical images of interest. I2Cnet annotations may be communicated to other users via e-mail or posted to I2Cnet for inclusion in its local repositories. This paper discusses the annotation service of I2Cnet and argues that such services pave the way towards the evolution of active digital medical image libraries.

Problem statement: Identifying the new lossless image compression algorithm for high performance applications like medical and satellite imaging; a high quality lossless image is most important when reproduction which leads to classify the data for decision making. Approach: A new lossless hybrid algorithm based on simple selective scan order with Bit Plane Slicing method is presented for lossless Image compression of limited bits/pixel images, such as medical images, satellite images and other still images common in the world. Efficient coding is achieved by run length and modified Huffman coding. This approach is combined with efficient selective scan order for entire image in one pass through. Results: The new hybrid algorithm achieves good compression rate, compared to the existing schemes of coding with different test images. Conclusion: Compared to the existing standards JPEG-LS and CALIC, the compression rate is reduced with our proposed algorithm for different standard test images.

As medical imaging facilities move towards film-less imaging technology, robust image compression systems are starting to play a key role. Conventional storage and transmission of large-scale raw medical image datasets can be very expensive and time-consuming. Recently, we proposed a memory-assisted lossless image compression algorithm based on Principal Component Analysis(PCA). In this paper, we further improve the performance of the algorithm in two different directions: Firstly, we replace PC A with NMF (Non Negative Matrix Factorization). NMF has several advantages in representing images with an image-like basis, results in sparse factors, and provides better user control over iterations. Secondly, we expand the single-level model with a new multilevel decomposition/projection framework to further reduce entropy of residual images. Our experimental results on X-ray images confirm that both modifications provide significant improvements over the single level PCA based algorithm as well as existing non-memory based techniques.

With the development of modern computer technology, the growth of purchase funds for storage equipment is far behind the growth of data. How to effectively use the limited storage resources to store data becomes the focus of our research and the data compression technology is the key to solve this problem. The traditional compression technology on the CPU platform could not meet the requirements for processing massive data in the compression rate and the energy cost. The GPU provides a new solution for its strong parallel computation ability. In this paper we present an implementation of Parallel Matching Lempel-Ziv-Storer-Szymanski (PMLZSS), a high speed lossless data compression algorithm by using CUDA framework. The basic idea of our implementation of the PMLZSS algorithm on GPUs is the introduction of a paralleled matrix matching. The data needed to be compressed are divided into multiple dictionary strings and pre-read strings as the vertical axis and horizontal axis of the matrices, respectively. All of the matrices are paralleled matched in the different blocks. Compared with the traditional serial CPU platform LZSS compression algorithm and BZIP2 compression algorithm, the experimental data shows that on the premise of the basic compression rate unchanged, relative to the serial LZSS, the compression speed of PMLZSS is improved about 16x, while to the BZIP2, about 12x.