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Conf Proc IEEE Eng Med Biol Soc. 2008;2008:390-3. doi: 10.1109/IEMBS.2008.4649172.

A graph-based approach to the retrieval of dual-modality biomedical images using spatial relationships.

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  • 1Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Info. Tech., University of Sydney, and Department of PET and Nuclear Medicine, Royal Prince Alfred Hospital, Sydney, Australia.

Abstract

The increasing size of medical image archives and the complexity of medical images have led to the development of medical content-based image retrieval (CBIR) systems. These systems use the visual content of images for image retrieval in addition to conventional textual annotation, and have become a useful technique in biomedical data management. Existing CBIR systems are typically designed for use with single-modality images, and are restricted when multi-modal images, such as co-aligned functional positron emission tomography and anatomical computed tomography (PET/CT) images, are considered. Furthermore, the inherent spatial relationships among adjacent structures in biomedical images are not fully exploited. In this study, we present an innovative retrieval system for dual-modality PET/CT images by proposing the use of graph-based methods to spatially represent the structural relationships within these images. We exploit the co-aligned functional and anatomical information in PET/CT, using attributed relational graphs (ARG) to represent both modalities spatially and applying graph matching for similarity measurements. Quantitative evaluation demonstrated that our dual-modal ARG enabled the CBIR of dual-modality PET/CT. The potential of our dual-modal ARG in clinical application was also explored.

PMID:
19162675
[PubMed - indexed for MEDLINE]
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