An architecture for Naval telemedicine

Navy fleets have a defined overall objective for mission readiness impacted by the health of personnel aboard the ships. Medical treatment facilities on the ships determines the degree of mission readiness. The paper describes the concepts and technologies necessary to establish a Naval telemedicine system, which can drastically improve health care delivery. It consists of various combinations of the following components: Fleet Naval Medical Consultation and Diagnostic Centers, Shipboard Naval Medical Consultation and Diagnostic Centers (hospital ship or combatant ships with medical specialists on board), and Remote Medical Referring Centers such as a ship, a small Naval station annex, or a field hospital. This Naval telemedicine architecture delivers clinical medicine and continuing medical education (CME) by means of computers, video-conferencing systems, or telephony to enhance the quality of care through improved access to research, medical and nonmedical imaging, remote consultations, patient clinical data, and multimedia medical education programs. It integrates the informatics infrastructure and provides a medical telepresence among participants     

Non-Iterative Hierarchical Registration for Medical Images

Digital images from diverse medical imaging modalities and from different imaging times are becoming an indispensable information resource for making clinical decisions. Image registration is an enabling technique for more fully utilizing the embedded heterogeneous image information. However, in addition to the complex differences and deformations inherent in the medical images, the increasing scope, resolution, and dimensionality of imaging pose significant challenges in this medical arena. Wavelets have shown great potential in multi-scale registration due to their superior capacity for representing image information at different resolutions and spatial frequencies. However, the application of wavelets in registration is hindered by their lack of rotation- and translation-invariance. To overcome this obstacle, this paper proposes a non-iterative hierarchical registration method based on points of interest which are extracted automatically from wavelet decompositions. The proposed algorithm for two-dimensional monomodal medical images has been validated by experiments on phantom data and clinical imaging data. This proposed non-iterative method provides a computationally efficient registration, as well as assists in avoiding the non-convergence problem.     

基于深度学习的色彩迁移生物医学成像技术

传统病理学检测中,由于复杂的染色流程和单一的观察形式等限制着病情的诊断速度,而染色过程实质上是将颜色信息与形态特征关联,效果等同于现代数字技术的生物医学图像的图义分割,这使得研究者们可以通过计算后处理的方式,大大降低生物医学成像处理样品的步骤,实现与传统医学染色金标准一致的成像效果。近些年人工智能深度学习领域的发展促成了计算机辅助分析领域与临床医疗的有效结合,人工智能色彩迁移技术在生物医学成像分析上也逐渐表现出较高的发展潜力。文中回顾了深度学习色彩迁移的技术原理,列举此类技术在生物医学成像领域中的部分应用,并展望了人工智能色彩迁移在生物医学成像领域的研究现状和可能的发展趋势。     

PET/CT图像分割及其发展现状

随着精准医疗技术的快速进步,PET/CT图像中病灶区域分割已在医疗计划制定中显现出重要作用.PET/CT将PET（功能代谢显像）和CT（解剖结构显像）两种先进的影像技术有机地结合在一起,是影像诊断学的一个重要进展.结合当前分割方法,本文详细介绍了PET/CT成像原理以及PET/CT图像的特点,对分割方法进行分类,深入分析各种方法的现状及其在肿瘤学中的应用.最后,进一步阐述了PET/CT图像分割技术的核心问题和发展趋势.     

Curvelet Transform Based Watermarking for Telemedicine

Now a days telemedicine is a proactive research area and gaining more engrossment. Digital transmission of medical imaging, remote evaluation and diagnosis together are termed as Telemedcine and it has increasingly gained prominence in the recent times. Remote specialist reckons intemperately on scan images or medicinal images and data of patient for devising diagnostic conclusion. While imparting a scan image (clinical picture) to remote authority, the significant part (ROI) in it may be altered by interlopers. The remote authority must recover the ROI in a clinical picture on the off chance that it has been altered. This paper presents a novel robust watermarking method founded on Curvelet Transform to recover the ROI in medical image in case it is altered. The proposed method hides ROI information inside diagnostically insignificant part in medical image through Curvelet Transform. Experiments carried out using this novel technique have proven that the ROI in medical image is restored to its original state.

     

Rethinking pre-training on medical imaging

Transfer learning from natural image datasets, such as ImageNet, is common for applying deep learning to medical imaging. However, the modalities of natural and medical images differ considerably, and the reason for the latest medical research preferring ImageNet to medical data is questionable. In this study, we investigated the properties of medical pre-training and its transfer effectiveness on various medical tasks. Through an intuitive convolution-based analysis, we determined the modality characteristics of images. Surprisingly, medical pre-training showed exceptional performance for a classification task but not for a segmentation task since medical data are visually homogeneous and lack morphological information. Using data with diverse modalities helped overcome such drawbacks, resulting in medical pre-training achieving performance comparable to pre-training with ImageNet with considerably fewer samples than ImageNet for both aforementioned tasks. Finally, a study of learned representations and realistic scenarios indicated that while ImageNet is the best choice for medical imaging, medical pre-training has significant potential.     

Web-PACS for multicenter clinical trials.

Medical information systems are not designed for clinical trials using clinical imaging. This paper presents a conceptual model for clinical trials based on medical imaging from two complementary points of view: a technical model and a business model. A Web information system (WIS) for supporting multicenter clinical trials has been designed to implement the proposed model. We show that our approach overcomes the actual limitations by facilitating medical image management in the context of clinical trials or cooperative research.     

Volume rendering of 3D medical ultrasound data using direct feature mapping

The authors explore the application of volume rendering in medical ultrasonic imaging. Several volume rendering methods have been developed for X-ray computed tomography (X-CT), magnetic resonance imaging (MRI) and positron emission tomography (PET). Limited research has been done on applications of volume rendering techniques in medical ultrasound imaging because of a general lack of adequate equipment for 3D acquisitions. Severe noise sources and other limitations in the imaging system make volume rendering of ultrasonic data a challenge compared to rendering of MRI and X-CT data. Rendering algorithms that rely on an initial classification of the data into different tissue categories have been developed for high quality X-CT and MR-data. So far, there is a lack of general and reliable methods for tissue classification in ultrasonic imaging. The authors focus on volume rendering methods which are not dependent on any classification into different tissue categories. Instead, features are extracted from the original 3D data-set, and projected onto the view plane. The authors found that some of these methods may give clinically useful information which is very difficult to get from ordinary 2D ultrasonic images, and in some cases renderings with very fine structural details. The authors have applied the methods to 3D ultrasound images from fetal examinations. The methods are now in use as clinical tools at the National Center of Fetal Medicine in Trondheim, Norway.     

Toward a decision informatics paradigm: a real-time, information-based approach to decision making

The focus of this paper is on decision making; more specifically, on what decision making requirements are needed in the future. We augur for a decision informatics paradigm; it is a real-time, information-based approach to decision making. The paradigm is supported by two sets of technologies (i.e., information and decision technologies) and underpinned by three disciplines (i.e., data fusion/analysis, decision modeling, and systems engineering). We begin by considering the context - and needs - for decision making as the economies of the world change and evolve, especially in regard to emerging services; then our proposed decision informatics paradigm is detailed and illustrated, together with an in-depth review of a critical, underpinning research area (dealing with real-time fusion and analysis of multiple nonhomogeneous data sources), followed by several concluding remarks.     

Three-dimensional modeling for functional analysis of cardiac images: a review

Three-dimensional (3-D) imaging of the heart is a rapidly developing area of research in medical imaging. Advances in hardware and methods for fast spatio-temporal cardiac imaging are extending the frontiers of clinical diagnosis and research on cardiovascular diseases. In the last few years, many approaches have been proposed to analyze images and extract parameters of cardiac shape and function from a variety of cardiac imaging modalities. In particular, techniques based on spatio-temporal geometric models have received considerable attention. This paper surveys the literature of two decades of research on cardiac modeling. The contribution of the paper is three-fold: 1) to serve as a tutorial of the field for both clinicians and technologists, 2) to provide an extensive account of modeling techniques in a comprehensive and systematic manner, and 3) to critically review these approaches in terms of their performance and degree of clinical evaluation with respect to the final goal of cardiac functional analysis. From this review it is concluded that whereas 3-D model-based approaches have the capability to improve the diagnostic value of cardiac images, issues as robustness, 3-D interaction, computational complexity and clinical validation still require significant attention.     

Framework for parsing, visualizing and scoring tissue microarray images.

Increasingly automated techniques for arraying, immunostaining, and imaging tissue sections led us to design software for convenient management, display, and scoring. Demand for molecular marker data derived in situ from tissue has driven histology informatics automation to the point where one can envision the computer, rather than the microscope, as the primary viewing platform for histopathological scoring and diagnoses. Tissue microarrays (TMAs), with hundreds or even thousands of patients' tissue sections on each slide, were the first step in this wave of automation. Via TMAs, increasingly rapid identification of the molecular patterns of cancer that define distinct clinical outcome groups among patients has become possible. TMAs have moved the bottleneck of acquiring molecular pattern information away from sampling and processing the tissues to the tasks of scoring and results analyses. The need to read large numbers of new slides, primarily for research purposes, is driving continuing advances in commercially available automated microscopy instruments that already do or soon will automatically image hundreds of slides per day. We reviewed strategies for acquiring, collating, and storing histological images with the goal of streamlining subsequent data analyses. As a result of this work, we report an implementation of software for automated preprocessing, organization, storage, and display of high resolution composite TMA images.     

跨模态医学图像预测综述

医学影像技术与设备的进步在生物医学领域的各项研究中发挥着重要作用.跨模态医学图像预测旨在由一种模态图像预测另一种模态图像.本文详细综述了由MRI预测CT图像、7T-Like图像重构、PET预测及其他医学模态预测研究,阐述了各类模态预测的必要性及存在的挑战,说明各类预测方法的特点并进行性能比较,最终得出结论：基于深度学习的跨模态预测在预测精度和预测时间两方面更具优势.     

Radiologic image compression-a review

The objective of radiologic image compression is to reduce the data volume of and to achieve a low bit rate in the digital representation of radiologic images without perceived loss of image quality. However, the demand for transmission bandwidth and storage space in the digital radiology environment, especially picture archiving and communication systems (PACS) and teleradiology, and the proliferating use of various imaging modalities, such as magnetic resonance imaging, computed tomography, ultrasonography, nuclear medicine, computed radiography, and digital subtraction angiography, continue to outstrip the capabilities of existing technologies. The availability of lossy coding techniques for clinical diagnoses further implicates many complex legal and regulatory issues. This paper reviews the recent progress of lossless and lossy radiologic image compression and presents the legal challenges of using lossy compression of medical records. To do so, we first describe the fundamental concepts of radiologic imaging and digitization. Then, we examine current compression technology in the field of medical imaging and discuss important regulatory policies and legal questions facing the use of compression in this field. We conclude with a summary of future challenges and research directions     

基于数字水印的图像完整性保护研究

随着网络技术的发展,图像等多媒体信息的使用达到空前广度和深度,使用各种处理软件可以轻松的对图像等进行修改,这对通过网络等方式传输的图像等多媒体的完整性提出了巨大挑战。研究当前用于保护图像完整性的各种数字水印技术,重点分析了无损数字水印技术和有损数字水印技术典型算法,并比较了它们的优缺点,最后总结并提出了用于图像完整性保护的数字水印技术的发展方向。     

How business intelligence maturity enabling hospital agility

Executives of information officers polled agree that rapid and accurate decision-making are essential to organizational agility and data plays an important role in decision making process. With Advanced information technologies, collecting data can be ubiquitously. However, the current volume of data accumulated in hospitals has exceeded the capacity of their medical information systems, not to mention using the data to make decisions. Hospitals started to employ business intelligence systems (BIS) to extract correct, timely, and useful information for hospital decision-makers. Most studies in the area focus on the establishment and related benefits of BIS. This research aims to evaluate the BIS maturity and its influences on decision quality to reveal the BIS impacts on hospital agility. To test the research model, opinions were collected by distributing questionnaires to clinical and administrative decision-makers who had experiences of using BIS in hospitals. The results showed that medical information quality was significantly influenced by BIS maturity. Furthermore, medical information quality exerted a significant effect on medical decision quality, BIS usage, and user satisfaction. The positive influence of user satisfaction on medical decision quality is also verified.     

Integrated Support for Medical Image Analysis Methods: From Development to Clinical Application

Computer-aided image analysis is becoming increasingly important to efficiently and safely handle large amounts of high-resolution images generated by advanced medical imaging devices. The development of medical image analysis (MIA) software with the required properties for clinical application, however, is difficult and labor-intensive. Such development should be supported by systems providing scalable computational capacity and storage space, as well as information management facilities. This paper describes the properties of distributed systems to support and facilitate the development, evaluation, and clinical application of MIA methods. First, the main characteristics of existing systems are presented. Then, the phases in a method's lifecycle are analyzed (development, parameter optimization, evaluation, clinical routine), identifying the types of users, tasks, and related computational issues. A scenario is described where all tasks are performed with the aid of computational tools integrated into an ideal supporting environment. The requirements for this environment are described, proposing a grid-oriented paradigm that emphasizes virtual collaboration among users, pieces of software, and devices distributed among geographically dispersed healthcare, research, and development enterprises. Finally, the characteristics of the existing systems are analyzed according to these requirements. The proposed requirements offer a useful framework to evaluate, compare, and improve the existing systems that support MIA development     

Component forensics

Visual sensor technologies have experienced tremendous growth in recent decades, and digital devices are becoming ubiquitous. Digital images taken by various imaging devices have been used in a growing number of applications, from military and reconnaissance to medical diagnosis and consumer photography. Consequently, a series of new forensic issues arise amidst such rapid advancement and widespread adoption of imaging technologies. For example, one can readily ask what kinds of hardware and software components as well as their parameters have been employed inside these devices? Given a digital image, which imaging sensor or which brand of sensor was used to acquire the image? How was the image acquired? Was it captured using a digital camera, cell phone camera, image scanner, or was it created artificially using an imageediting software? Has the image undergone any manipulation after capture? Is it authentic, or has it been tampered in any way? Does it contain any hidden information or steganographic data? Many of these forensic questions are related to tracing the origin of the digital image to its creation process. Evidence obtained from such analysis would provide useful forensic information to law enforcement, security, and intelligence agencies. Knowledge of image acquisition techniques can also help answer further forensic questions regarding the nature of additional processing that the image might have undergone after capture.     

Integrated fusion framework using hybrid domain and deep neural network for multimodal medical images

Multidimensional Systems and Signal Processing - Nowadays medical images are captured through various imaging modalities for clinical diagnosis. It is more complicated to process the images...     

从实验室到市场:光声成像的产业化进程

Photoacoustic imaging (PAI) or optoacoustic imaging, the modern application of an ancient physical discovery to biomedical imaging, is without doubt one of the most exciting imaging technologies that has drawn increasing attention from biomedical specialists. In PAI, the rich contrast of optical excitation is seamlessly combined with the high spatial resolution and large penetration depth of ultrasonic detection to produce clear images of optically scattering biological tissues. As a complementary imaging modality that surpasses the territory of traditional microscopic optical imaging, PAI has been explored for numerous biomedical studies, and hence enthusiastically embraced by researchers around the globe who have attested to its unique imaging capabilities, namely the deep penetration and functional sensitivity. Not surprisingly, as the market clearly sees the promise, the commercial production of PAI systems has grown apace with the technological advancements and clinical applications. The adoption of commercial PAI in research and clinical settings has however seen difficulties, majorly due to costs, regulatory blocks, and competition with mainstream technologies. Here, from a practical standpoint, a wide range of commercial PAI systems currently available in the market were introduced, their advantages and disadvantages were analyzed, and the design considerations for targeted applications were emphasized. The key technological, logistical, and clinical issues were also discussed that need to be solved to accelerate the technology translations. By doing so, it is hoped that a clearer picture of the future commercialization of PAI for clinicians, researchers, and industrial entrepreneurs will be presented.