An image feature-Based approach to automatically find images for application to clinical decision support
The illustrations in biomedical publications often provide useful information in aiding clinicians' decisions when full text searching is performed to find evidence in support of a clinical decision. In this research, image analysis and classification techniques are explored to automatically extract information for differentiating specific modalities to characterize illustrations in biomedical publications, which may assist in the evidence finding process. Global, histogram-based, and texture image illustration features were compared to basis function luminance histogram correlation features for modality-based discrimination over a set of 742 manually annotated images by modality (radiological, photo, etc.) selected from the 2004-2005 issues of the British Journal of Oral and Maxillofacial Surgery. Using a mean shifting supervised clustering technique, automatic modality-based discrimination results as high as 95.57% were obtained using the basis function features. These results compared favorably to other feature categories examined. The experimental results show that image-based features, particularly correlation-based features, can provide useful modality discrimination information.
R. J. Stanley et al., "An image feature-Based approach to automatically find images for application to clinical decision support," Computerized Medical Imaging and Graphics, vol. 35, no. 5, pp. 365 - 372, Elsevier, Jul 2011.
The definitive version is available at https://doi.org/10.1016/j.compmedimag.2010.11.008
Electrical and Computer Engineering
Intramural Research Program of the National Institutes of Health
Lister Hill National Center for Biomedical Communications
National Library of Medicine
Keywords and Phrases
Computer-assisted; Image Interpretation; Information Storage and Retrieval; Medical Informatics Computing; Image Processing; Image analysis; Image processing
International Standard Serial Number (ISSN)
Article - Journal
© 2011 Elsevier, All rights reserved.
01 Jul 2011