Text-guided visual representation learning for medical image retrieval systems

PAC system is extending increasingly from the now well-established radiology applications into a hospital-wide PACS. New challenges are accompanying its spread into other clinical fields. With awareness of the importance of PAC systems among various medical experts, this system has been enhanced through the PAC system's pipeline, and simplification of image display for analysis via an interaction with the user. Generally PAC system consists of medical image, patients' data acquisition, storage, and display subsystems integrated by digital networks and application software. PAC system facilitates the systematic utilization of medical imaging for patient care. However, even though PAC system consist of medical image, data acquisition, storage and display subsystems, most of available PAC system does not have image analysis as required by the clinical expert. If the PAC system do have this element, it need interaction or interference from the clinical expert as user, and the PAC system storage mostly is an unstructured storage with no analysis element and report modules. And unfortunately in most cases, for the web-based PAC system, there are delayed in retrieval and visualize the required image from outside of the hospital. Most of the PACS with function of 3D display, it did not communicate information clearly and efficiently to users (clinical expert). Most of the visualization did not visual accurate information as required by the clinical expert. These listed constraints limited the clinical expert perspective regarding his/her decision. From market validation observation we concluded that most of the PAC system available in market does not have medical image processing functions for the purposes of decision analysis with minimum user interaction. Research towards this limitation has been conducted in accordance with the needs of clinical experts. Among these studies are: (i) angiography image processing for stenosis position detection and measurement of its dimensions, (ii) echocardiography image processing for detection of ventricular cardiac abnormalities (walls and volume) and (iii) 2D angiography images reconstructed to 3D images for display purposes and to identify the location of artery tree. With the result of these studies, the PAC system will integrate with extra modules, which are: i) 3D reconstruction function from single image angiogram with identification of the stenosis location, ii) Identification of abnormality heart wall chamber, iii) 3D reconstruction echocardiography left and right ventricular heart, and iv) 3D fused within CTA, angiography and MRI. As stated above, the common limitation for the web-based PAC system is the delayed in retrieval and visualize the required image from outside of the hospital/clinics. To overcome this limitation, we proposed a technique and integrated a related function for faster transmission of the processed image without sacrifice any importance information. And to complete the PAC system so that the new PAC system able to compete with current PAC system that available in market, we link the PACS with our Patients Clinical Record Database with report modules as required by the medical expert. The outputs of all those said researches will be integrated with the PAC system where each research output has been tested and validate by numbers of cardiac expert, the patients clinical record database has been tested in UKMMC and the PAC system currently being beta tested in a Private Clinic in Kelang, Selangor, Malaysia. Eventually the PAC system with the Patients Clinical Record Database will be integrated with these image analysis and 3D image visualization and it is plan to be tested in Veterinary Hospital of Universiti Putra Malaysia. To forward this project to commercialization activity, we have distributed questioners to Clinics in area Bangi (Selangor, Malaysia) and Nilai (Negeri Sembilan, Malaysia). There are 58 Medical, Veterinary and Dental Clinics received the questioner (currently we expand the distribution towards Serdang and Kajang (Selangor, Malaysia). Out of 58 clinics; a) 16% interested to collaborate and looking forward to see PAC system, b)8% interested with the PAC system but do not willing to have any demo, c) 44% not interested but open for demo of system, d) 28% not interested and not willing to have demo system and e) 4% return the form without answered for that particular question. To secure and protect the ownership, each research output has been submitted for Patent filed in Malaysia and with three chosen country, where 2 of the patent filed has been granted in Malaysia. We also copyrighted each module. This project has been selected by Universiti Putra Malaysia to be commercialized by a startup company seeded by UPM (CASD Medical Private Limited) under program INNOHUB. We realize to implement the complete PAC system in Hospital there are 10 main problems exist that we might need to overcome or try to minimize the consequences. These problems are; i) Integration with the Hospital Information System. Although a lack of inter-vendor device and IT integration can often make the problem worse, the market is improving as providers and meaningful use demand greater integration. Unfortunately, still, many radiologists and PACS administrators prefer to make full use of hospital IT to configure their own systems and achieve a bit more autonomy, ii) Every system has downtime where we need to establish alternate workflows. Both scheduled and unscheduled, but they need not have to be too serious to minimize the effect on patient care, iii) Non-standardized hanging protocol display is a common and pesky challenge for PACS users. Images from different modalities are not organized by default even though each of them generally will be transmitted through DICOM format getaway, each study takes a little longer to read. As the number of scanners increases and the sample of vendors expands, the problem grows worse, iv) Integration problems concern hardware, from digitizing pre-DICOM modalities to integrating systems for advanced image reconstruction. Add-ons like a DICOM converter can help squeeze out additional value from older CT, angiography and fluoroscopy systems, v) As with downtime, failures are unavoidable, there is a need to demonstrate strong support activities, vi) Effective training can be a cost-effective way to demonstrate to administrators and physicians many of PACS' underused and undervalued features. Training wil help to expose staff to what the system can do to make their jobs easier and more efficient, vii) The migration of data to the new PACS is often the most challenging part of the process, both in negotiating the release of data from the current PACS and in sorting out all the data entry errors that have accumulated over the lifespan of the system, viii) As other specialties realize the value of PACS, the system is slowly being taken out of radiologists' hands. PACS has become a mission-critical enterprise-wide tool used by nearly all specialties. With this change, decision-making for PACS-related purchases, upgrades and configurations has, in some cases, shifted from radiologists to a more central process, ix) Hiring a certified public ergonomist to evaluate the department's workstations can ease radiologists' repetitive stress symptoms and contribute substantially to productivity. Despite accelerating advances in technology, many interface tools have changed little since the introduction of PACS, and finally x) Like business continuity, disaster recovery can prevent a painful experience from becoming fatal. Many hospitals opt for either redundant servers, cloud storage or both. At the very least, preparation for downtime can spare physicians and patients from experiencing significant losses. To minimize the consequences of the listed 10 main problems, this project (in early stage) targeting potential customers among the owner of the small private clinics where the numbers of patients is less and the bureaucracy of the administration is limited.

In this paper, we present a novel approach for medical content based image retrieval (searching for images which are pathologically similar to a given example) and demonstrate its performance on a dataset containing maxillofacial lesions. Recently, distributed databases at hospitals and CT scanning centers are related through Picture Archiving and Communication Systems (PACS). Hence, a content based image retrieval system is helpful for radiologists in medical diagnosis. In our proposed framework, a feature vector is extracted for each image and SIFT sparse codes are employed in this step. After feature extraction based on sparse coding and maximum pooling, we have utilized different similarity measures such as Euclidean norm, Manhattan distance and SVM classifier to choose most relevant images to the query image. We have evaluated our proposed framework on a dataset containing 2023 images belonging to 5 different categories: cleft, impaction, fracture, maxillary sinus cyst and healthy. Classification rate of 97.3% and precision versus recall curve indicate the effectiveness of sparse coding in content-based medical image retrieval.

The picture archiving and communication system (PACS) originated as an image management system for improving the efficiency of radiologic practices. It has evolved into a hospital-integrated system that stores information media in many forms, including voice, text, medical records, waveform images, and video recordings. The integration of these various types of information requires the technology of multimedia, including hardware platforms, information systems and databases, communication protocols, display technology, and system interfacing and integration. The purpose of this book is to discuss the conceptual and technologic advances in PACS development for all of these previously mentioned technologies. The book also emphasizes the growing field of PACS-based imaging informatics, where existing PACS resources are used for largescale horizontal and longitudinal clinical service, research, and education applications. The book is intended for those who are involved in a health care environment that manages the everyday practical realities of PACS planning, operation, and maintenance. The book will also serve as excellent curriculum material for the training of PACS information technology personnel because the author provides comprehensive concepts that tie medical imaging, medical physics, information technology, and clinical workflow with enterprise PACS (multiple, hospital-wide PACS). The author, H. K. Huang, has been involved in PACS development since the 1980s and is a leading scientist in the field. The book is divided into five parts and 23 chapters. Part 1, titled “Medical Imaging Principles,” summarizes the history of PACS, the fundamental concepts of digital medical imaging, and the different types of modalities integrated into PACS. Part 2, titled “PACS Fundamentals,” describes in detail the PACS subsystems and industry standards (Digital Imaging and Communications in Medicine and Health Level 7) that are used to integrate PACS with the hospital information system and radiology information system. Part 3, titled “PACS Operation,” describes the practical problems encountered by PACS administrators and users and discusses different techniques to solve these problems, with many examples from installed PACS sites around the world. Because the author developed PACS at the University of California, Los Angeles, in 1991 and the University of California, San Francisco, in 1995, he is able to draw from his expertise on the subject. The author also uses his experience from developing many state-of-theart prototypes (eg, fault tolerant storage system) funded by research grants. Part 4, titled “PACS-based Imaging Informatics,” presents image-processing techniques that can be used to mine the huge image and clinical data resources stored in the PACS archive. The analysis of lung nodules by using temporal computed tomographic and contentbased image indexing are some of the examples of advanced processing applications applied to PACS data mining. Part 5, titled “Enterprise PACS,” describes different models for implementing an enterprise PACS in terms of the architectural design of the system and financial and business models. This new book builds on two previous volumes on PACS that were written by the author. The text, image collections, and schematic diagrams in this current work achieve the author’s goal of providing up-to-date material concerning the technologic advances in PACS and imaging informatics. The text serves as a guide for those who are planning an enterprise PACS and provides teaching material for training PACS developers in industry and health care. The author has used portions of this book as lecture material for graduate courses at leading universities in the United States and abroad. The author also uses many examples to emphasize that the integration of heterogeneous health care information systems is becoming more complex and that, as more radiologists and clinicians expect higher standards in the reliability of the data, a systemic understanding of Digital Imaging and Communications in Medicine, Health Level 7, and Integrating the Healthcare Enterprise becomes ever more important to optimize enterprise workflow. The strength of the book lies in the vast experience that the author, who has implemented PACS at numerous institutions in the United States and abroad, has transmitted into this new book. Huang has conducted first-hand analysis of the various systems from PACS manufacturers that are used in clinical operation at large medical institutions and has performed up-to-date research in data mining of the huge image and data resources stored in the PACS repository.

Content-based image retrieval (CBIR) has been heralded as a mechanism to cope with the increasingly larger volumes of information present in medical imaging repositories. However, generic, extensible CBIR frameworks that work natively with Picture Archive and Communication Systems (PACS) are scarce. In this article we propose a methodology for parametric CBIR based on similarity profiles. The architecture and implementation of a profiled CBIR system, based on query by example, atop Dicoogle, an open-source, full-fletched PACS is also presented and discussed. In this solution, CBIR profiles allow the specification of both a distance function to be applied and the feature set that must be present for that function to operate. The presented framework provides the basis for a CBIR expansion mechanism and the solution developed integrates with DICOM based PACS networks where it provides CBIR functionality in a seamless manner.

Objective To evaluate the teaching effects of case introduced method based on pic- ture archiving and communication system （ PACS ） system in medical imaging course. Methods Totally 148 students （ 2009 grade ） majored in five-year clinical medicine from Xiangya Medical College were divided into 10 groups; the 10 groups were taken randomly as control group （ n=75 ） and experimental group （ n=73 ）. Control group used traditional teaching methods （ film teaching ） while experimental group used case introduced method based on PACS system. Effects of teaching between were evaluated by questionnaire and test. χ^2 test was used to compare students＇ theoretical test scores, with α level = 0.05. Results Based on results of questionnaires, more students in experimental group than in control group hold that case introduced method based on PACS system can achieve the purpose of teaching, improve interests in learning, and strengthen the mastery of knowledge. Test scores showed that the percentage of students scored 90-100 and 80-89 （ respectively 14%, 75% ） in experimental group was significantly higher than that in control group （respectively 5%, 66% ） （χ^2 =17.932, P=O.O00 ）. Conclusions Application of case introduced method based on PACS system in medical imaging course not only improve students＇ motivation to learn and their clinica~ ability-, but also allow teachers to find and correct short- comings of students timely, therefore, it is worth promoting. Key words: Picture archiving and communication system; Case introduced method; Prac-tice teaching

Picture archiving and communication systems (PACS) is a current research topic in both the United States and Japan. During the past 6 years, our department has been working in this field very closely with our colleagues in Japan. In July 1 986, we attended the Third International Symposium on Picture Archiving and Communication Systems/Personal Health Data Recording Systems (JPACS) in conjunction with the Fifth Symposium on Medical Imaging and Technology (JAMIT) at the National Cancer Center in Tokyo. After the meeting, we visited various PACS research and development laboratories at major imaging manufacturers and hospitals. We summarized our impressions in a commentary: “Picture Archiving and Communication Systems in Japan” [1 ]. Since then, we have successfully implemented two PACS modules, one in the pediatric radiology section and one in the coronary care unit [2, 3]. More than 200 visitors from Japan have come to see our facilities. Among these visitors was Professor T. Takahasi from Kyoto University Hospital, who was the program chairman of the Eighth Symposium of JAMIT/Sixth International Symposium on PACS in July 1 989. After his visit, he extended an invitation to us to give the keynote speech, “Recent Progress in PACS” for the meeting. We took this opportunity to go back to Japan and repeat the hospital and laboratory tour in order to observe their research and development progress in PACS during the past 3 years. This paper is a summary of our opinion ofthe changes that have occurred in Japan since our last visit of the status of PACS in that country compared with that in ours. The JAMIT Meeting and the JPACS Meeting

In recent times, technological advancements have remarkably improved picture archiving and communication system(PACS) capabilities beyond their conventional use in radiology departments. Researchers and instructors have started employing PACS functionalities to improve medical research processes, promote interdisciplinary collaborations, and facilitate learning. To illustrate this point further, the PACS enables researchers to handle and analyze huge amounts of imaging data with superior precision and speed, supporting innovative studies in areas like disease progression, treatment outcomes, and imaging modalities. Moreover, a PACSintegrated with artificial intelligence (AI) algorithms leads to significant improvements in image processing, diagnostic accuracy, and personalized treatment, thus marking a new approach to medical imaging. The PACSsupported by AI is mostly transformative since they allow for improved early disease detection capabilities as well as automated image processing and decision assistance, which increase diagnostic accuracy and clinical outcomes. Such systems can rapidly process large quantities of visual data with an accuracy rate surpassing earlier endeavors. In medical research, however, combining PACSwith AI allows challenging imaging datasets to be examined, thereby making findings that were not previously possible. The capacity to combine imaging outcomes with clinical information is valuable for medical students and professionals in the field of education. They can access extensive medical image collections and case studies using PACS. This link is critical for teaching and learning as it allows students to interact with concrete events and improve their diagnostic accuracy in a controlled environment. This review discusses how the PACS affects educational courses and clinical outcomes based on the available literature. Our aim was not only to outline recent research or developments but also to present a comprehensive overview regarding the growing role played by PACS in the modern healthcare system and academics. Similarly, we look at the challenges and opportunities associated with the wide adoption of PACS, highlighting possible future areas of study or teaching methodologies. Issues such as data security, interoperability, and the need for defined protocols are included to give an exhaustive understanding of what PACS can and cannot do. Through this study, we stress PACS's revolutionary potential in advancing research methodology and educational practices, eventually contributing to enhanced patient care and knowledge dissemination in healthcare areas. The continual growth of PACS technology and its applications is expected to reshape the landscape of medical research and education, making it a vital component in the quest for medical excellence. By knowing the present trends and future potential, stakeholders in healthcare and education may better employ PACS to reach their objectives and boost overall results.

Introduction: Content-based image retrieval (CBIR) retrieves the images from the vast image repositories. Research in the CBIR domain gets attention due to the massive number of images generated by the mobiles and various image-capturing machines. Objectives: Any image retrieval system’s primary goal is to reduce the semantic gap between low-level features and high-level perception. Methods: The CBIR techniques are classified into multiple categories based on the feature extraction and retrieval mechanism. These categories are feature-based, machine-learning-based, and deep-learning-based methods. The pioneer techniques for each category are explained in detail in this chapter. Results: The comparative analysis has been done to highlight the advantages of techniques over others. Conclusion: The various application area of the CBIR has been described. The most evolving application is the content-based medical image retrieval (CBMIR) system. The applicability of the CBMIR system in different medical science domains is explained in detail.

This paper presents an image representation and matching framework for image categorization in medical image archives. Categorization enables one to determine automatically, based on the image content, the examined body region and imaging modality. It is a basic step in content-based image retrieval (CBIR) systems, the goal of which is to augment text-based search with visual information analysis. CBIR systems are currently being integrated with picture archiving and communication systems for increasing the overall search capabilities and tools available to radiologists. The proposed methodology is comprised of a continuous and probabilistic image representation scheme using Gaussian mixture modeling (GMM) along with information-theoretic image matching via the Kullback-Leibler (KL) measure. The GMM-KL framework is used for matching and categorizing X-ray images by body regions. A multidimensional feature space is used to represent the image input, including intensity, texture, and spatial information. Unsupervised clustering via the GMM is used to extract coherent regions in feature space that are then used in the matching process. A dominant characteristic of the radiological images is their poor contrast and large intensity variations. This presents a challenge to matching among the images, and is handled via an illumination-invariant representation. The GMM-KL framework is evaluated for image categorization and image retrieval on a dataset of 1500 radiological images. A classification rate of 97.5% was achieved. The classification results compare favorably with reported global and local representation schemes. Precision versus recall curves indicate a strong retrieval result as compared with other state-of-the-art retrieval techniques. Finally, category models are learned and results are presented for comparing images to learned category models.

Objective: This research investigates the needs and opinions of radiologists on the use of enhanced information technologies and approaches to improve the functionality of Picture Archiving and Communication Systems (PACS). Method: Six interviews were conducted in the main governmental hospital in Kuwait (AL-Sabah Hospital) with radiologists, including two senior radiologists, two junior radiologists, and two trainee radiologists undertaking the Irish radiology board. Results: The radiologists identified a number of limitations that exist in current PACS and requirements to enhance usability and functionality. However, it was the case that some of the radiologists had little knowledge about the advanced trends in PACS. Four themes emerged from the thematic analysis of the data: (1) limitations of traditional PACS; (2) Features and requirements that can increase PACS functionality; (3) web based solutions of PACS; (4) PACS in mobile phones. Conclusion: It is widely recognized that PACS has limitations. This research has identified themes that, when incorporated, will enhance the functionality of PACS and provide better quality clinical practice. This research has determined the important future trends of PACS. Primarily web based solutions and use in mobile phones. The findings from this research can be used as recommendations to vendors, for product development and medical institutes to consider when undertaking implementation of PACS and training future radiologists.

Picture archiving and communication system (PACS) has become a mature technology over the past few years and has been widely implemented in several developed countries, in health care delivery for daily clinical imaging service and data management. A PACS is an integrated workflow system for managing images and related data which is designed to streamline operations throughout the whole patient care delivery process. Use of PACS has changed the medical image interpretation from conventional hard copy images to soft-copy studies viewed on the systems workstations. Computer-aided detection and diagnosis (CAD) utilizes computer methods to obtain quantitative measurements from medical images and clinical information to assist clinicians. The CAD needs image input and related information from PACS to improve its accuracy and PACS benefits from CAD results online and available at the PACS workstation (WS) as a second reader to assist physicians in the decision making process. Although yet these two technologies remain as two separate independent systems with only minimal system integration. The use of the CAD-PACS software toolkit package has revolutionized PACS WS and thus clinical workflow from single event patient-based queries to longitudinal-based queries. The advantages of query/retrieving content-based imaging data can be a great benefit for medical imaging research and clinical practice. This paper describes a general method to integrate CAD results with PACS in clinical environment.

Author: H. K. Huang, D.Sc., FRCR (Hon.)Publisher: Wiley-Liss Publication Date: July 2004ISBN: 0-471-25123-2Pages: 637Cost: $135The question is no longer, “Can picture archiving communication systems (PACS) be cost-justified?” but, “How can PACS best be implemented?” Reductions in hardware costs, case studies showing the benefits of PACS, and the maturing of all aspects of PACS have now put these systems or upgrades in the capital budget even at community hospitals. In his third book on PACS, Dr. Huang builds on his previous publications, PACS and Imaging Informatics and PACS: Basic Principles and Applications, by focusing on clinical best practices, the use of distributed PC-based high resolution workstations, enterprise systems, and other technological and practice changes.Audience: PACS planners and administrators, radiologists, information technology (IT) and systems engineers, clinical engineers, radiology administrators, high-level imaging technicians, and academics.Overview: The 23 chapters are grouped into five sections:The book starts off with a much-appreciated list of acronyms used in medical imaging, PACS, and IT. A historical perspective on PACS is offered, noting the concept's introduction in 1979 by Heinz U. Lemke. Pioneering efforts in PACS are covered, followed by a short presentation on PACS concepts and global implementation issues. The next section on digital imaging fundamentals includes image quality, compression, and major modalities, with an emphasis on digital attributes. The essential use of mathematical concepts in this section is not overwhelming.The PACS fundamentals section encompasses components, work flow, standards (e.g. DICOM, HL7, IHE), acquisition gateways, networking communication, controllers, servers, displays, and integration with radiology information systems, hospital information systems, and the electronic patient record (EHR). The section on PACS operation is especially helpful to those of us most familiar with the technical side of PACS. Data management, including web-based systems and tele-health, is discussed, along with the important areas of fault tolerance and security.Generic and case study presentations on implementation, installation, acceptance, upgrade, and evaluation are combined into well-constructed chapters. The remainder of the book includes coverage of informatics and enterprise PACS. Portions on image-assisted surgery and radiation therapy servers may be of the most interest to medical instrumentation enthusiasts. Finally, 21 pages of references, a glossary, and an index wrap up the publication.Assessment: His many years of experience in the field and his previous publications have led Dr. Huang to produce an inclusive, well-written book combining key concepts and practical, working solutions. Interesting elements include case studies showing cost justification, references for PACS requests for proposals, data mining by image matching, and the EHR. This is likely the most comprehensive work on PACS principles and applications, but at the same time is very readable and of practical value to anyone who may be involved with or is working with PACS.

As the use of digital media increases, effective retrieval and management techniques become more important. In order to extract useful information from this huge amount of data, many content based image retrieval (CBIR) systems have been developed. A typical CBIR system captures image features such as color, texture, or shape of objects in the query image and tries to retrieve images from the database with similar features. The main advantage of CBIR systems with relevance feedback is that these systems take into account the gap between the high-level concepts and low-level features and subjectivity of human perception of visual content. In this paper, a CBIR system based on color histogram has been presented and a new approach for image storage and retrieval called association-based image retrieval (ABIR) has been studied. The results of both systems have been shown.

This article provides an overview on the literature published on the topic of cybersecurity for PACS (Picture Archiving and Communications Systems) and medical imaging. From a practical perspective, PACS specific security measures must be implemented together with the measures applicable to the IT infrastructure as a whole, in order to prevent incidents such as PACS systems exposed to access from the Internet. Therefore, the article first offers an overview of the physical, technical and organizational mitigation measures that are proposed in literature on cybersecurity in healthcare information technology in general, followed by an overview on publications discussing specific cybersecurity topics that apply to PACS and medical imaging and present the “building blocks” for a secure PACS environment available in the literature. These include image de-identification, transport security, the selective encryption of the DICOM (Digital Imaging and Communications in Medicine) header, encrypted DICOM files, digital signatures and watermarking techniques. The article concludes with a discussion of gaps in the body of published literature and a summary.

Clinical Data Warehouses (DWHs) are used to provide researchers with simplified access to pseudonymized and homogenized clinical routine data from multiple primary systems. Experience with the integration of imaging and metadata from picture archiving and communication systems (PACS), however, is rare. Our goal was therefore to analyze the viability of integrating a production PACS with a research DWH to enable DWH queries combining clinical and medical imaging metadata and to enable the DWH to display and download images ad hoc. We developed an application interface that enables to query the production PACS of a large hospital from a clinical research DWH containing pseudonymized data. We evaluated the performance of bulk extracting metadata from the PACS to the DWH and the performance of retrieving images ad hoc from the PACS for display and download within the DWH. We integrated the system into the query interface of our DWH and used it successfully in four use cases. The bulk extraction of imaging metadata required a median (quartiles) time of 0.09 (0.03–2.25) to 12.52 (4.11–37.30) seconds for a median (quartiles) number of 10 (3–29) to 103 (8–693) images per patient, depending on the extraction approach. The ad hoc image retrieval from the PACS required a median (quartiles) of 2.57 (2.57–2.79) seconds per image for the download, but 5.55 (4.91–6.06) seconds to display the first and 40.77 (38.60–41.63) seconds to display all images using the pure web-based viewer. A full integration of a production PACS with a research DWH is viable and enables various use cases in research. While the extraction of basic metadata from all images can be done with reasonable effort, the extraction of all metadata seems to be more appropriate for subgroups.