Content-based Retrieval and Management of Multi-dimensional Biomedical Imaging Data

Summary

Efficient content-based image retrieval and management for fully exploiting massive and complex multi-dimensional biomedical imaging data.

Supervisor(s)

Associate Professor Tom Weidong Cai, Professor David Feng

Research Location

Information Technologies

Program Type

N/A

Synopsis

Healthcare services today rely heavily on diverse biomedical imaging data which have been expanded exponentially in quantity, content and dimension – due to an enormous increase in the number of clinical exams perform daily; due to the use of diverse range of imaging modalities for different clinical studies; and due to great improvements in spatial and temporal resolutions, in parallel with the expanding ability to integrate a variety of structural, biochemical, physiological and genetic information derived from combined modalities. Many medical Picture Archiving and Communication Systems (PACS) have so far been widely adopted to streamline the storage and retrieval of these data, but with standardized alphanumerical descriptive fields which are unable to sufficiently describe the rich visual properties and biological contents within image data – thus posing significant deficiencies on the access of enormous knowledge and information residing in these image repositories. This project aims to develop a framework for intelligent content representation and management of biomedical imaging data based on spatial / temporal / functional content analysis and modeling, to support efficient imaging data search / retrieval and to potentially open up many new vistas in intelligent image-based medical services such as disease tracking, differential diagnosis, non-invasive surgical planning, and clinical decision support and training

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Keywords

Computer Applications, Life and medical sciences, Content-based image retrieval, Multidimensional biomedical imaging, Multi-modal feature extraction, Spatial and temporal data modeling, Functional content analysis, Computer assisted diagnosis (CAD)

Opportunity ID

The opportunity ID for this research opportunity is: 311

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