Database Management System for a Digitized Medical Image

Medical images are critical component of the healthcare system with great impact on the society’s welfare. Traditionally, medical images were stored on films in developing country, but the advances in modern imaging modalities made it possible to store them electronically. Thus, this paper gave and developed a novel framework for storing, retrieving and processing digitized medical images. Digital medical informatics and images are commonly used in hospitals today because  of the interrelatedness of the radiology department and other departments, especially the intensive care unit and emergency department, the transmission and sharing of medical images has become a critical issue. This work provides vivid solution to the problem encountered and the difficulties associated with the challenges of large memory utilization attributing to storing patient’s medical image information conveniently. Keyword: Database Management System, Digitized Medical images, Memory utilizatio

Distributed Object Medical Imaging Model

Abstract- Digital medical informatics and images are commonly used in hospitals today,. Because of the interrelatedness of the radiology department and other departments, especially the intensive care unit and emergency department, the transmission and sharing of medical images has become a critical issue. Our research group has developed a Java-based Distributed Object Medical Imaging Model(DOMIM) to facilitate the rapid development and deployment of medical imaging applications in a distributed environment that can be shared and used by related departments and mobile physiciansDOMIM is a unique suite of multimedia telemedicine applications developed for the use by medical related organizations. The applications support realtime patients’ data, image files, audio and video diagnosis annotation exchanges. The DOMIM enables joint collaboration between radiologists and physicians while they are at distant geographical locations. The DOMIM environment consists of heterogeneous, autonomous, and legacy resources. The Common Object Request Broker Architecture (CORBA), Java Database Connectivity (JDBC), and Java language provide the capability to combine the DOMIM resources into an integrated, interoperable, and scalable system. The underneath technology, including IDL ORB, Event Service, IIOP JDBC/ODBC, legacy system wrapping and Java implementation are explored. This paper explores a distributed collaborative CORBA/JDBC based framework that will enhance medical information management requirements and development. It encompasses a new paradigm for the delivery of health services that requires process reengineering, cultural changes, as well as organizational changes

MammoApplet: an interactive Java applet tool for manual annotation in medical imaging

Web-based applications in computational medicine have become increasingly important during the last years. The rapid growth of the World Wide Web supposes a new paradigm in the telemedicine and eHealth areas in order to assist and enhance the prevention, diagnosis and treatment of patients. Furthermore, training of radiologists and management of medical databases are also becoming increasingly important issues in the field. In this paper, we present MammoApplet , an interactive Java applet interface designed as a web-based tool. It aims to facilitate the diagnosis of new mammographic cases by providing a set of image processing tools that allow a better visualization of the images, and a set of drawing tools, used to annotate the suspicious regions. Each annotation allows including the attributes considered by the experts when issuing the final diagnosis. The overall set of overlays is stored in a database as XML files associated with the original images. The final goal is to obtain a database of already diagnosed cases for training and enhancing the performance of novice radiologistsPeer ReviewedPostprint (author's final draft

Large-scale analysis, management, and retrieval of biological and medical images

Biomedical image data have been growing quickly in volume, speed, and complexity, and there is an increasing reliance on the analysis of these data. Biomedical scientists are in need of efficient and accurate analyses of large-scale imaging data, as well as innovative retrieval methods for visually similar imagery across a large-scale data collection to assist complex study in biological and medical applications. Moreover, biomedical images rely on increased resolution to capture subtle phenotypes of diseases, but this poses a challenge for clinicians to sift through haystacks of visual cues to make informative diagnoses. To tackle these challenges, we developed computational methods for large-scale analysis of biological and medical imaging data using simulated annealing to improve the quality of image feature extraction. Furthermore, we designed a Big Data infrastructure for the large-scale image analysis and retrieval of digital pathology images and conducted a longitudinal study of clinician's usage patterns of an image database management system (MDID) to shed light on the potential adoption of new informatics tools. This research also resulted in image analysis, management, and retrieval applications relevant to dermatology, radiology, pathology, life sciences, and palynology disciplines. These tools provide the potential to answer research questions that would not be answerable without our novel innovations that take advantage of Big Data technologies

Feeling Around in the Dark: Defining the Library’s Role in a Campus-Wide Digitization Project

Objective: Describe the library\u27s leadership on a team with representatives from academic computing and the faculty to develop a database of medical images. The library will (1) add value to the project by offering expertise in methods of organization, indexing, cataloging and project management; (2) develop policies and procedures for participation; and (3) maintain visibility by promoting both the library and its staff. Method:Case study: The library has marketed the idea of an image database for the past four years. In early 2003, the project was funded and a campuswide task force was formed. The library took the lead in project management by holding weekly and monthly meetings, establishing milestones, setting deadlines, and drafting usage policies. The library played an important role by interviewing potential contributors and developing a database and record structure that meets the needs of users. The library also participates in the cataloging of images by designing workflow procedures that allow library staff to check all images for quality control and to assign MeSH terms. Task force members developed a training session on how to search the database and how to contribute images. Results: A campuswide database of over 150 digital assets (and growing) has been created. The weekly and monthly meetings helped to keep project assignments clear and document changes to roles and responsibilities. Setting deadlines and establishing milestones helped to keep the project on schedule and progressing forward. The database structure and record format first developed by the team is meeting the needs of participants, but the library anticipates making adjustments as the database becomes more popular. Having MeSH terms assigned to each digital asset has improved searching for database users. To date, seven faculty members have been trained and are contributing to the database. Conclusions: The library has a valuable role to play in campuswide digital initiatives. Collaborating with information services has allowed both departments to gain a greater appreciation of the skills and resources that each department has to offer and provided the library with greater visibility and new opportunities for outreach and education. Presented at the Medical Library Association Annual Meeting, Washington, DC, May 23, 2004

LNDb Dataset

The Lung Nodule Database (LNDb) was developed as an external dataset complimentary to LIDCIDRI. The publication of this database gives continuity to LIDC-IDRI and allows the community to perform an external and comparable validation of proposed computer-aided diagnosis (CAD) systems. The LNDb contains 294 CT scans collected retrospectively at the Centro Hospitalar e Universitário de São João (CHUSJ) in Porto, Portugal between 2016 and 2018. All data was acquired under approval from the CHUSJ Ethical Commitee and was anonymised prior to any analysis to remove personal information except for patient birth year and gender. The database served as the basis for the Grand Challenge on automatic lung cancer patient management, or LNDb challenge. THIS DATASET IS PUBLICALLY AVAILABLE UNDER A CREATIVE COMMONS CC BY-NC-ND LICENSE (ATTRIBUTION) ESSENCIALLY, YOU ARE GRANTED ACCESS TO THE DATASET FOR USE IN YOUR RESEARCH AS LONG AS YOU CREDIT OUR WORK/PUBLICATIONS(*) (*) Pedrosa, João, et al. "LNDb: a lung nodule database on computed tomography." arXiv preprint arXiv:1911.08434 (2019). (*) Pedrosa, João, et al. "LNDb challenge on automatic lung cancer patient management." Medical image analysis 70 (2021): 102027.This work was financed by the European Regional Development Fund (ERDF) through the Operational Programme for Competitiveness - COMPETE 2020 Programme and by National Funds through the Portuguese Funding agency, FCT Fundaçãoo para a Ciência e Tecnologia within project PTDC/EEI-SII/6599/2014 (POCI01-0145-FEDER-016673)

Medical image information representation: Gabor Filter solution for the Big Data

International audienceIn the health field, several thousand images are generated every day in medical imaging establishments. The volume of information involved is still far from being fully controlled. On the other hand, the development of machine learning tools today opens the way to a new generation of image analysis in this context of "BigData". Moreover, our approach is part of this research dynamic. In order to test the robustness of our algorithm and its degree of adaptation to BigData, we tested, in a first phase of analysis, our algorithm on an image-database containing 320 mammograms. The precision obtained is estimated at 75% for a recall of 33%. In a second analysis phase, we performed the test on an image database containing 1000 medical images. The precision obtained is estimated at nearly 70% for a recall of 33%. Although the precision obtained in this first step is far from perfect, our processing algorithm remains promising and shows a good adaptation to the management of "Digdat

Multi-regional Adaptive Image Compression (AIC) for hip fractures in pelvis radiography

High resolution digital medical images are stored in DICOM (Digital Imaging and Communications in Medicine) format that requires high storage space in database. Therefore reducing the image size while maintaining diagnostic quality can increase the memory usage efficiency in PACS. In this study, diagnostic regions of interest (ROI) of pelvis radiographs marked by the radiologist are segmented and adaptively compressed by using image processing algorithms. There are three ROIs marked by red, blue and green in every image. ROI contoured by red is defined as the most significant region in the image and compressed by lossless JPEG algorithm. Blue and green regions have less importance than the red region but still contain diagnostic data compared to the rest of the image. Therefore, these regions are compressed by lossy JPEG algorithm with higher quality factor than rest of the image. Non-contoured region is compressed by low quality factor which does not have any diagnostic information about the patient. Several compression ratios are used to determine sufficient quality and appropriate compression level. Compression ratio (CR), peak signal to noise ratio (PSNR), bits per pixel (BPP) and signal to noise ratio (SNR) values are calculated for objective evaluation of image quality. Experimental results show that original images can approximately be compressed six times without losing any diagnostic data. In pelvis radiographs marking multiple regions of interest and adaptive compression of more than one ROI is a new approach. It is believed that this method will improve database management efficiency of PACS while preserving diagnostic image content