Image analysis for biomedical and healthcare applications

Authors:
Linda G. Shapiro
Affiliations:
University of Washington, Seattle, Washington, U.S.A.
Venue:
Proceedings of the 1st ACM international workshop on Multimedia indexing and information retrieval for healthcare
Year:
2013

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Abstract

Multimodality data is now commonly produced in healthcare environments and requires sophisticated algorithms to analyze. Image data is varied and includes such modalities as CT, MRI, functional MRI, and DTI, as well as 3D meshes extracted from multi-view stereo. Research at the University of Washington has been carried out in multiple different aspects of biomedical and healthcare applications including craniofacial image analysis from CT and 3D mesh data, brain image analysis from DTI and fMRI data, and tremor analysis from video data. This presentation will describe work in each of these areas. The algorithms described all involve complex feature vectors that are explicitely related to the conditions being analyzed or the goals of the analyses. They also involve the use of sophisticated machine learning algorithms, in some cases in multiple stages of the process.