DocumentCode :
2108955
Title :
A Graph-based approach to the retrieval of volumetric PET-CT lung images
Author :
Kumar, Ajit ; Jinman Kim ; Lingfeng Wen ; Dagan Feng
Author_Institution :
Biomed. & Multimedia Inf. Technol. (BMIT) Res. Group, Univ. of Sydney, Sydney, NSW, Australia
fYear :
2012
fDate :
Aug. 28 2012-Sept. 1 2012
Firstpage :
5408
Lastpage :
5411
Abstract :
Combined positron emission tomography and computed tomography (PET-CT) scans have become a critical tool for the diagnosis, localisation, and staging of most cancers. This has led to a rapid expansion in the volume of PET-CT data that is archived in clinical environments. The ability to search these vast imaging collections has potential clinical applications in evidence-based diagnosis, physician training, and biomedical research that may lead to the discovery of new knowledge. Content-based image retrieval (CBIR) is an image search technique that complements conventional text-based retrieval by the use of image features as search criteria. Graph-based CBIR approaches have been found to be exemplary methods for medical CBIR as they provide the ability to consider disease localisation during the similarity measurement. However, the majority of graph-based CBIR studies have been based on 2D key slice approaches and did not exploit the rich volumetric data that is inherent to modern medical images, such as multi-modal PET-CT. In this paper, we present a graph-based CBIR method that exploits 3D spatial features extracted from volumetric regions of interest (ROIs). We index these features as attributes of a graph representation and use a graph-edit distance to measure the similarity of PET-CT images based on the spatial arrangement of tumours and organs in a 3D space. Our study aims to explore the capability of these graphs in 3D PET-CT CBIR. We show that our method achieves promising precision when retrieving clinical PET-CT images of patients with lung tumours.
Keywords :
cancer; computerised tomography; feature extraction; image retrieval; medical image processing; positron emission tomography; tumours; 3D spatial feature; biomedical research; cancer diagnosis; cancer localisation; cancer staging; computed tomography; content based image retrieval; evidence based diagnosis; feature extraction; graph based CBIR method; graph based approach; graph edit distance; image feature; lung tumour; physician training; positron emission tomography; search criteria; text based retrieval; volumetric PET-CT lung image retrieval; Biomedical imaging; Computed tomography; Feature extraction; Image edge detection; Lungs; Positron emission tomography; Tumors; Computer Graphics; Humans; Lung; Multimodal Imaging; Positron-Emission Tomography; Reproducibility of Results; Tomography, X-Ray Computed;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE
Conference_Location :
San Diego, CA
ISSN :
1557-170X
Print_ISBN :
978-1-4244-4119-8
Electronic_ISBN :
1557-170X
Type :
conf
DOI :
10.1109/EMBC.2012.6347217
Filename :
6347217
Link To Document :
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