RadSem: Semantic Annotation and Retrieval for Medical Images
M. Möller, S. Regel, and M. Sintek. Proc. of The 6th Annual European Semantic Web Conference (ESWC2009), (June 2009)
Abstract
We present a platform for semantic medical image annotation and retrieval.
It leverages on the MEDICO ontology which covers formal background
information from various biomedical ontologies such as the Foundational
Model of Anatomy (FMA), terminologies like ICD-10 and RadLex and
covers various aspects of clinical procedures. This ontology is used
during several steps of annotation and retrieval: (1) We developed
an ontology-driven metadata extractor for the medical image format
DICOM. Its output contains, e. g., person name, age, image acquisition
parameters, body region etc. (2) The output from (1) is used to simplify
the manual annotation by providing intuitive visualizations and to
provide a preselected subset of annotation concepts. Furthermore,
the extracted metadata is linked together with anatomical annotations
and clinical ndings to generate a unied view on a patient’s medical
history. (3) On the search side we perform query expansion based
on the structure of the medical ontologies. (4) Our ontology for
clinical data management allows to link and combine patients, medical
images and annotations together in a comprehensive result list. (5)
The medical annotations are further extended by links to external
sources like Wikipedia to provide additional information
%0 Conference Paper
%1 Moeller2009a
%A Möller, Manuel
%A Regel, Sven
%A Sintek, Michael
%B Proc. of The 6th Annual European Semantic Web Conference (ESWC2009)
%D 2009
%K medico
%T RadSem: Semantic Annotation and Retrieval for Medical Images
%X We present a platform for semantic medical image annotation and retrieval.
It leverages on the MEDICO ontology which covers formal background
information from various biomedical ontologies such as the Foundational
Model of Anatomy (FMA), terminologies like ICD-10 and RadLex and
covers various aspects of clinical procedures. This ontology is used
during several steps of annotation and retrieval: (1) We developed
an ontology-driven metadata extractor for the medical image format
DICOM. Its output contains, e. g., person name, age, image acquisition
parameters, body region etc. (2) The output from (1) is used to simplify
the manual annotation by providing intuitive visualizations and to
provide a preselected subset of annotation concepts. Furthermore,
the extracted metadata is linked together with anatomical annotations
and clinical ndings to generate a unied view on a patient’s medical
history. (3) On the search side we perform query expansion based
on the structure of the medical ontologies. (4) Our ontology for
clinical data management allows to link and combine patients, medical
images and annotations together in a comprehensive result list. (5)
The medical annotations are further extended by links to external
sources like Wikipedia to provide additional information
@inproceedings{Moeller2009a,
abstract = {We present a platform for semantic medical image annotation and retrieval.
It leverages on the MEDICO ontology which covers formal background
information from various biomedical ontologies such as the Foundational
Model of Anatomy (FMA), terminologies like ICD-10 and RadLex and
covers various aspects of clinical procedures. This ontology is used
during several steps of annotation and retrieval: (1) We developed
an ontology-driven metadata extractor for the medical image format
DICOM. Its output contains, e. g., person name, age, image acquisition
parameters, body region etc. (2) The output from (1) is used to simplify
the manual annotation by providing intuitive visualizations and to
provide a preselected subset of annotation concepts. Furthermore,
the extracted metadata is linked together with anatomical annotations
and clinical ndings to generate a unied view on a patient’s medical
history. (3) On the search side we perform query expansion based
on the structure of the medical ontologies. (4) Our ontology for
clinical data management allows to link and combine patients, medical
images and annotations together in a comprehensive result list. (5)
The medical annotations are further extended by links to external
sources like Wikipedia to provide additional information},
added-at = {2010-03-12T13:33:40.000+0100},
author = {M\"oller, Manuel and Regel, Sven and Sintek, Michael},
biburl = {https://www.bibsonomy.org/bibtex/2a52cf977f7b149ff2d6d94b46c97c694/manuelm},
booktitle = {Proc. of The 6th Annual European Semantic Web Conference ({ESWC2009})},
interhash = {72a9f67b6c93c961437075e99086e1ea},
intrahash = {a52cf977f7b149ff2d6d94b46c97c694},
keywords = {medico},
month = {June},
timestamp = {2010-03-12T13:33:40.000+0100},
title = {RadSem: Semantic Annotation and Retrieval for Medical Images},
year = 2009
}