A neuroinformatics database system for disease-oriented neuroimaging research

Wong, Stephen T.C.; Hoo, Kent Soo; Cao, Xinhua; Tjandra, Donny; Fu, Jachih; Dillon, William P.

doi:10.1016/s1076-6332(03)00676-7

Cited by 18 publications

(14 citation statements)

References 7 publications

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“…We found experimentally using the FastBit software [24] that the interval encoding provided better performance results when compared to the intervalequality encoding and the binary encoding for indexing the current imageDW. We also found experimentally that 5 bins is the best value among the values of 5,10,15,20,25,30,35,40,45, and 50 bins for the current imageDW.…”

Section: Methodssupporting

confidence: 59%

“…[25][26][27][28]), but they differ from our work on their purpose and applicability. Wong et al [25] propose a database system based on the use of a DW and multimedia data, which enables clinical and scientific studies of temporal lobe-epilepsy patients through the analysis and processing of images. This work also allows the execution of queries over patient reports.…”

Section: Use Of Medical Images In Dwesmentioning

confidence: 93%

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A similarity-based data warehousing environment for medical images

Teixeira

Annibal

Felipe

et al. 2015

Computers in Biology and Medicine

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A core issue of the decision-making process in the medical field is to support the execution of analytical (OLAP) similarity queries over images in data warehousing environments. In this paper, we focus on this issue. We propose imageDWE, a non-conventional data warehousing environment that enables the storage of intrinsic features taken from medical images in a data warehouse and supports OLAP similarity queries over them. To comply with this goal, we introduce the concept of perceptual layer, which is an abstraction used to represent an image dataset according to a given feature descriptor in order to enable similarity search. Based on this concept, we propose the imageDW, an extended data warehouse with dimension tables specifically designed to support one or more perceptual layers. We also detail how to build an imageDW and how to load image data into it. Furthermore, we show how to process OLAP similarity queries composed of a conventional predicate and a similarity search predicate that encompasses the specification of one or more perceptual layers. Moreover, we introduce an index technique to improve the OLAP query processing over images. We carried out performance tests over a data warehouse environment that consolidated medical images from exams of several modalities. The results demonstrated the feasibility and efficiency of our proposed imageDWE to manage images and to process OLAP similarity queries. The results also demonstrated that the use of the proposed index technique guaranteed a great improvement in query processing.

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Section: Methodssupporting

confidence: 59%

Section: Use Of Medical Images In Dwesmentioning

confidence: 93%

A similarity-based data warehousing environment for medical images

Teixeira

Annibal

Felipe

et al. 2015

Computers in Biology and Medicine

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“…Original information is not displayed to users, and with the exception of the unique identifiers mentioned above, not stored in the image archive. However, it should be noted that automatic pseudonymization (2). The CRDAS main user designs and carries out the transfer of patient information from other hospital information systems with the respective system administrator (3)(4).…”

Section: Construction Of Research Pacsmentioning

confidence: 99%

“…Some initiatives have been taken to develop information systems that can integrate biomedical data from multiple laboratories and hospitals. These include the American College of Radiology Imaging Network for cancer studies [9]; the Medical Imaging Resource Center (MIRC) of the Radiological Society of North America, a central repository for clinical trials, research data, and materials [10,11]; and a Neuroinformatics Database System that supports both prospective and retrospective neuroscience studies [2].…”

mentioning

confidence: 99%

Development of a Research Dedicated Archival System (TARAS) in a University Hospital

et al. 2010

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Recent healthcare policies have influenced the manner in which patient data is handled in research projects, and the regulations concerning protected health information have become significantly tighter. Thus, new procedures are needed to facilitate research while protecting the confidentiality of patient data and ensuring the integrity of clinical work in the expanding environment of electronic files and databases. We have addressed this problem in a university hospital setting by developing the Tampere Research Archival System (TARAS), an extensive data warehouse for research purposes. This dynamic system includes numerous integrated and pseudonymized imaging studies and clinical data. In a pilot study on asthma patients, we tested and improved the functionality of the data archival system. TARAS is feasible to use in retrieving, analyzing, and processing both image and non-image data. In this paper, we present a detailed workflow of the implementation process of the data warehouse, paying special attention to administrative, ethical, practical, and data security concerns. The establishment of TARAS will enhance and accelerate research practice at Tampere University Hospital, while also improving the safety of patient information as well as the prospects for national and international research collaboration. We hope that much can be learned from our experience of planning, designing, and implementing a research data warehouse combining imaging studies and medical records in a university hospital.

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“…Even with the severe computational burdens that are typical in this field, it has engendered successful research environments, especially through the linking of medical, genetic and other information (Weimer et al, 2003;Wong et al, 2004). Much of the current impetus in the area of neuroinformatics stems from the federally funded Human Brain Project and the advances it has brought to imaging and image data (Brinkley & Rosse, 2002).…”

Section: Introductionmentioning

confidence: 99%

Autism Post-Mortem Neuroinformatic Resource: The Autism Tissue Program (ATP) Informatics Portal

Brimacombe

Pickett

Pickett³

2006

J Autism Dev Disord

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The Autism Tissue Program (ATP) was established to oversee and manage brain donations related to neurological research in autism. The ATP Informatics Portal (www.atpportal.org) is an integrated data access system based on Oracle technology, developed to provide access for researchers to information on this rare tissue resource. It also permits sorting of existing cases based on donor ante-mortem history as well as agonal states and post-mortem tissue conditions. Phase II of development established administrative tracking of registrants intending to donate, as well as management of tissue requests and the awarding and tracking of tissue. Phase III is the ongoing assimilation of data sets derived from research on a core group of donors with searchable access by investigators.

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A neuroinformatics database system for disease-oriented neuroimaging research

Cited by 18 publications

References 7 publications

A similarity-based data warehousing environment for medical images

A similarity-based data warehousing environment for medical images

Development of a Research Dedicated Archival System (TARAS) in a University Hospital

Autism Post-Mortem Neuroinformatic Resource: The Autism Tissue Program (ATP) Informatics Portal

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