MAPPING: MAnagement and Processing of Images for Population ImagiNG


Book Description

Several recent papers underline methodological points that limit the validity of published results in imaging studies in the life sciences and especially the neurosciences (Carp, 2012; Ingre, 2012; Button et al., 2013; Ioannidis, 2014). At least three main points are identified that lead to biased conclusions in research findings: endemic low statistical power and, selective outcome and selective analysis reporting. Because of this, and in view of the lack of replication studies, false discoveries or solutions persist. To overcome the poor reliability of research findings, several actions should be promoted including conducting large cohort studies, data sharing and data reanalysis. The construction of large-scale online databases should be facilitated, as they may contribute to the definition of a “collective mind” (Fox et al., 2014) facilitating open collaborative work or “crowd science” (Franzoni and Sauermann, 2014). Although technology alone cannot change scientists’ practices (Wicherts et al., 2011; Wallis et al., 2013, Poldrack and Gorgolewski 2014; Roche et al. 2014), technical solutions should be identified which support a more “open science” approach. Also, the analysis of the data plays an important role. For the analysis of large datasets, image processing pipelines should be constructed based on the best algorithms available and their performance should be objectively compared to diffuse the more relevant solutions. Also, provenance of processed data should be ensured (MacKenzie-Graham et al., 2008). In population imaging this would mean providing effective tools for data sharing and analysis without increasing the burden on researchers. This subject is the main objective of this research topic (RT), cross-listed between the specialty section “Computer Image Analysis” of Frontiers in ICT and Frontiers in Neuroinformatics. Firstly, it gathers works on innovative solutions for the management of large imaging datasets possibly distributed in various centers. The paper of Danso et al. describes their experience with the integration of neuroimaging data coming from several stroke imaging research projects. They detail how the initial NeuroGrid core metadata schema was gradually extended for capturing all information required for future metaanalysis while ensuring semantic interoperability for future integration with other biomedical ontologies. With a similar preoccupation of interoperability, Shanoir relies on the OntoNeuroLog ontology (Temal et al., 2008; Gibaud et al., 2011; Batrancourt et al., 2015), a semantic model that formally described entities and relations in medical imaging, neuropsychological and behavioral assessment domains. The mechanism of “Study Card” allows to seamlessly populate metadata aligned with the ontology, avoiding fastidious manual entrance and the automatic control of the conformity of imported data with a predefined study protocol. The ambitious objective with the BIOMIST platform is to provide an environment managing the entire cycle of neuroimaging data from acquisition to analysis ensuring full provenance information of any derived data. Interestingly, it is conceived based on the product lifecycle management approach used in industry for managing products (here neuroimaging data) from inception to manufacturing. Shanoir and BIOMIST share in part the same OntoNeuroLog ontology facilitating their interoperability. ArchiMed is a data management system locally integrated for 5 years in a clinical environment. Not restricted to Neuroimaging, ArchiMed deals with multi-modal and multi-organs imaging data with specific considerations for data long-term conservation and confidentiality in accordance with the French legislation. Shanoir and ArchiMed are integrated into FLI-IAM1, the national French IT infrastructure for in vivo imaging.




Computational Vision and Medical Image Processing


Book Description

This book contains extended versions of papers presented at the international Conference VIPIMAGE 2009 – ECCOMAS Thematic Conference on Computational Vision and Medical Image, that was held at Faculdade de Engenharia da Universidade do Porto, Portugal, from 14th to 16th of October 2009. This conference was the second ECCOMAS thematic conference on computational vision and medical image processing. It covered topics related to image processing and analysis, medical imaging and computational modelling and simulation, considering their multidisciplinary nature. The book collects the state-of-the-art research, methods and new trends on the subject of computational vision and medical image processing contributing to the development of these knowledge areas.




Handbook of Medical Imaging


Book Description

In recent years, the remarkable advances in medical imaging instruments have increased their use considerably for diagnostics as well as planning and follow-up of treatment. Emerging from the fields of radiology, medical physics and engineering, medical imaging no longer simply deals with the technology and interpretation of radiographic images. The limitless possibilities presented by computer science and technology, coupled with engineering advances in signal processing, optics and nuclear medicine have created the vastly expanded field of medical imaging. The Handbook of Medical Imaging is the first comprehensive compilation of the concepts and techniques used to analyze and manipulate medical images after they have been generated or digitized. The Handbook is organized in six sections that relate to the main functions needed for processing: enhancement, segmentation, quantification, registration, visualization as well as compression storage and telemedicine. * Internationally renowned authors(Johns Hopkins, Harvard, UCLA, Yale, Columbia, UCSF) * Includes imaging and visualization * Contains over 60 pages of stunning, four-color images







Encyclopedia of Image Processing


Book Description

The Encyclopedia of Image Processing presents a vast collection of well-written articles covering image processing fundamentals (e.g. color theory, fuzzy sets, cryptography) and applications (e.g. geographic information systems, traffic analysis, forgery detection). Image processing advances have enabled many applications in healthcare, avionics, robotics, natural resource discovery, and defense, which makes this text a key asset for both academic and industrial libraries and applied scientists and engineers working in any field that utilizes image processing. Written by experts from both academia and industry, it is structured using the ACM Computing Classification System (CCS) first published in 1988, but most recently updated in 2012.




Imaging Floods and Glacier Geohazards with Remote Sensing


Book Description

Remote sensing plays a pivotal role in understanding where and how floods and glacier geohazards occur; their severity, causes and types; and the risk that they may pose to populations, activities and properties. By providing a spectrum of imaging capabilities, resolutions and temporal and spatial coverage, remote sensing data acquired from satellite, aerial and ground-based platforms provide key geo-information to characterize and model these processes. This book includes research papers on novel technologies (e.g., sensors, platforms), data (e.g., multi-spectral, radar, laser scanning, GPS, gravity) and analysis methods (e.g., change detection, offset tracking, structure from motion, 3D modeling, radar interferometry, automated classification, machine learning, spectral indices, probabilistic approaches) for flood and glacier imaging. Through target applications and case studies distributed globally, these articles contribute to the discussion on the current potential and limitations of remote sensing in this specialist research field, as well as the identification of trends and future perspectives.







Acoustical Imaging


Book Description

Contains 131 papers presented at the September 1995 symposium. Arrangement is in sections on the mathematics and physics of acoustical imaging, novel approaches in biomedical imaging, tissue characterization, flow imaging, transducers and arrays, imaging systems and techniques, underwater and indust




Research Awards Index


Book Description




HIMSS Dictionary of Health Information and Technology Terms, Acronyms and Organizations


Book Description

This significantly expanded and newest edition of the bestselling HIMSS Dictionary of Health Information and Technology Terms, Acronyms and Organizations has been developed and extensively reviewed by a robust team of industry experts. The fifth edition of this dictionary serves as a quick reference for students, health information and technology (IT) professionals, and healthcare executives to better navigate the ever-growing health IT field. This valuable resource includes more than 3,400 definitions, organizations, credentials, acronyms and references. Definitions of terms for the health IT, medical and nursing informatics fields are updated and included. This fifth edition also includes an acronyms list with cross references to current definitions and a list of health IT-related associations and organizations, including contact information, mission statements and web addresses. Academic and professional certification credentials are also included. As a mission driven non-profit, HIMSS offers a unique depth and breadth of expertise in health innovation, public policy, workforce development, research and analytics to advise global leaders, stakeholders and influencers on best practices in health information and technology. Through our innovation companies, HIMSS delivers key insights, education and engaging events to healthcare providers, governments and market suppliers, ensuring they have the right information at the point of decision. As an association, HIMSS encompasses more than 72,000 individual members and 630 corporate members. We partner with hundreds of providers, academic institutions and health services organizations on strategic initiatives that leverage innovative information and technology. Together, we work to improve health, access and the quality and cost-effectiveness of healthcare. HIMSS Vision Better health through information and technology. HIMSS Mission Globally, lead endeavors optimizing health engagements and care outcomes through information and technology.




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