%0 Conference Paper %1 möller2010combining %A Möller, Manuel %A Ernst, Patrick %A Sintek, Michael %A Seifert, Sascha %A Grimnes, Gunnar %A Cavallaro, Alexander %A Dengel, Andreas %B Proc. of the 14th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES) %C Cardiff, UK %D 2010 %K automatic combining extraction medico metadata patient %T Combining Patient Metadata Extraction and Automatic Image Parsing for the Generation of an Anatomic Atlas %X We present a system that integrates ontology-based metadata extraction from medical images with a state-of-the-art object recognition algorithm for 3D volume data sets generated by Computed Tomography scanners. Extracted metadata and automatically generated medical image annotations are stored as instances of OWL classes. This system is applied to a corpus of over 750 GB of clinical image data. A spatial database is used to store and retrieve 3D representations of the generated medical image annotations. Our integrated data representation allows to easily analyze our corpus and to estimate the quality of image metadata. A rule-based system is used to check the plausibility of the output of the automatic object recognition technique against the Foundational Model of Anatomy ontology. All combined, these methods are used to determine an appropriate set of metadata and image features for the automatic generation of a spatial atlas of human anatomy.

@inproceedings{möller2010combining, abstract = {We present a system that integrates ontology-based metadata extraction from medical images with a state-of-the-art object recognition algorithm for 3D volume data sets generated by Computed Tomography scanners. Extracted metadata and automatically generated medical image annotations are stored as instances of OWL classes. This system is applied to a corpus of over 750 GB of clinical image data. A spatial database is used to store and retrieve 3D representations of the generated medical image annotations. Our integrated data representation allows to easily analyze our corpus and to estimate the quality of image metadata. A rule-based system is used to check the plausibility of the output of the automatic object recognition technique against the Foundational Model of Anatomy ontology. All combined, these methods are used to determine an appropriate set of metadata and image features for the automatic generation of a spatial atlas of human anatomy.}, added-at = {2010-08-01T11:52:59.000+0200}, address = {Cardiff, UK}, author = {Möller, Manuel and Ernst, Patrick and Sintek, Michael and Seifert, Sascha and Grimnes, Gunnar and Cavallaro, Alexander and Dengel, Andreas}, biburl = {https://www.bibsonomy.org/bibtex/2a367a0b6ed304359f31f0ec276e43132/patrick6585}, booktitle = {Proc. of the 14th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES)}, interhash = {8c323c8735e0d504dbcb798625c1c74e}, intrahash = {a367a0b6ed304359f31f0ec276e43132}, keywords = {automatic combining extraction medico metadata patient}, month = {8--10 September}, timestamp = {2010-08-01T11:52:59.000+0200}, title = {Combining Patient Metadata Extraction and Automatic Image Parsing for the Generation of an Anatomic Atlas}, year = 2010 }