Computing human image annotation.
An image annotation is the explanatory or descriptive information about the pixel data of an image that is generated by a human (or machine) observer. An image markup is the graphical symbols placed over the image to depict an annotation. In the majority of current, clinical and research imaging practice, markup is captured in proprietary formats and annotations are referenced only in free text radiology reports. This makes these annotations difficult to query, retrieve and compute upon, hampering their integration into other data mining and analysis efforts. This paper describes the National Cancer Institute's Cancer Biomedical Informatics Grid's (caBIG) Annotation and Image Markup (AIM) project, focusing on how to use AIM to query for annotations. The AIM project delivers an information model for image annotation and markup. The model uses controlled terminologies for important concepts. All of the classes and attributes of the model have been harmonized with the other models and common data elements in use at the National Cancer Institute. The project also delivers XML schemata necessary to instantiate AIMs in XML as well as a software application for translating AIM XML into DICOM S/R and HL7 CDA. Large collections of AIM annotations can be built and then queried as Grid or Web services. Using the tools of the AIM project, image annotations and their markup can be captured and stored in human and machine readable formats. This enables the inclusion of human image observation and inference as part of larger data mining and analysis activities.
Channin
David S
DS
Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA. dsc@northwestern.edu
Mongkolwat
Pattanasak
P
Kleper
Vladimir
V
Rubin
Daniel L
DL
D001698:Biomedical Engineering; D019295:Computational Biology; D016208:Databases, Factual; D003952:Diagnostic Imaging / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D014584:User-Computer Interface