Sascha Seifert; Michael Kelm; Manuel Möller; Saikat Mukherjee; Alexander Cavallaro; Martin Huber; Dorin Comaniciu
In: Proceedings of SPIE Medical Imaging. SPIE Medical Imaging, February 13-18, San Diego, CA, USA, 2010.
Abstract
Diagnosis and treatment planning for patients can be signi cantly improved by comparing with clinical images of other patients with similar anatomical and pathological characteristics. This requires the images to be annotated using common vocabulary from clinical ontologies. Current approaches to such annotation are typically manual, consuming extensive clinician time, and cannot be scaled to large amounts of imaging data in hospitals. On the other hand, automated image analysis while being very scalable do not leverage standardized semantics and thus cannot be used across specific applications. In our work, we describe an automated and context-sensitive work based on an image parsing system complemented by an ontology-based context-sensitive annotation tool. An unique characteristic of our framework is that it brings together the diverse paradigms of machine learning based image analysis and ontology based modeling for accurate and scalable semantic image annotation.
@inproceedings{pub4690,
author = {
Seifert, Sascha
and
Kelm, Michael
and
Möller, Manuel
and
Mukherjee, Saikat
and
Cavallaro, Alexander
and
Huber, Martin
and
Comaniciu, Dorin
},
title = {Semantic Annotation of Medical Images},
booktitle = {Proceedings of SPIE Medical Imaging. SPIE Medical Imaging, February 13-18, San Diego, CA, United States},
year = {2010}
}
German Research Center for Artificial Intelligence Deutsches Forschungszentrum für Künstliche Intelligenz