Biomedical images are ever increasing in quantity and importance yet effective computing solutions for managing images and understanding their content are lacking. Image understanding is a key limiting factor in advancing these endeavors. Major challenges remain in understanding the capabilities of the human visual system with respect to biomedical imaging and in extracting and utilizing tacit knowledge of domain experts. To meet these challenges, we propose an innovative, multidisciplinary approach which combines methods of user centered design, visual perception and computer imaging research to interact with domain experts and to elicit and use their extrinsic and intrinsic knowledge. We will use a novel contextual design approach to inspection of dermatology images to discover relationships between perceptually- relevant visual content of images and users'conceptual understanding as expressed through natural language. Analysis of users'eye movements and verbal descriptions, together with mapping to domain medical ontologies, will allow us to integrate visual data with a user-specified language model to define perceptual categories and inform image classification. This is a fundamental and challenging data to knowledge problem that has not been solved. This study will provide proof of concept of the value of eliciting tacit knowledge from domain experts through multiple perceptually relevant modes in order to integrate data and knowledge models for better image understanding and may help enact a paradigm shift in how we conceptualize and develop biomedical information systems, in general.

Public Health Relevance

Biomedical images are ever increasing in quantity yet their usefulness for research, medicine, and teaching is limited by the design of current computing systems. Discoveries and concrete advances made in this study will contribute to solutions for effective use of digital images-a problem that is central to research and application across science, technology, and medicine. Advancements in our understanding of the design of useful and usable information systems will benefit society at large and contribute to the public health.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21LM010039-01A1
Application #
7896281
Study Section
Biomedical Library and Informatics Review Committee (BLR)
Program Officer
Sim, Hua-Chuan
Project Start
2010-06-01
Project End
2012-05-31
Budget Start
2010-06-01
Budget End
2011-05-31
Support Year
1
Fiscal Year
2010
Total Cost
$163,457
Indirect Cost
Name
Rochester Institute of Technology
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
002223642
City
Rochester
State
NY
Country
United States
Zip Code
14623
Guo, Xuan; Yu, Qi; Alm, Cecilia Ovesdotter et al. (2014) From spoken narratives to domain knowledge: mining linguistic data for medical image understanding. Artif Intell Med 62:79-90