The caBIG™ Annotation and Image Markup Project

Finding the Meaning in Images: Annotation and Image Markup

Biomedical images and ontologies are closely related conceptually, yet currently they are studied in isolation. Biomedical ontologies provide a representation of the canonical entities considered in biomedical research and clinical observations, and the relations among them. Images reveal instances of those entities and, taken in aggregate, inform the construction of ontologies describing the p...

Medical Imaging on the Semantic Web: Annotation and Image Markup

Medical images are proliferating at an explosive pace, similar to other types of data in e-Science. Technological solutions are needed to enable machines to help researchers and physicians access and use these images optimally. While Semantic Web technologies are showing promise in tackling the information challenges in biomedicine, less attention is focused on leveraging similar technologies i...

Managing and Querying Image Annotation and Markup in XML.

Proprietary approaches for representing annotations and image markup are serious barriers for researchers to share image data and knowledge. The Annotation and Image Markup (AIM) project is developing a standard based information model for image annotation and markup in health care and clinical trial environments. The complex hierarchical structures of AIM data model pose new challenges for man...

Fuzzy Neighbor Voting for Automatic Image Annotation

With quick development of digital images and the availability of imaging tools, massive amounts of images are created. Therefore, efficient management and suitable retrieval, especially by computers, is one of themost challenging fields in image processing. Automatic image annotation (AIA) or refers to attaching words, keywords or comments to an image or to a selected part of it. In this paper,...

Semantic-Based Image Retrial in the VQ Compressed Domain using Image Annotation Statistical Models