Song Yang, Cai Weidong, Eberl Stefan, Fulham Michael J, Feng David Dagan
BMIT Research Group, School of Information Technologies, University of Sydney, Australia.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:4469-72. doi: 10.1109/IEMBS.2011.6091108.
Positron emission tomography--computed tomography (PET-CT) produces co-registered anatomical (CT) and functional (PET) patient information (3D image set) from a single scanning session, and is now accepted as the best imaging technique to accurately stage the most common form of primary lung cancer--non-small cell lung cancer (NSCLC). This paper presents a content-based image retrieval (CBIR) method for retrieving similar images as a reference dataset to potentially aid the physicians in PET-CT scan interpretation. We design a spatial distribution to describe the spatial information of each region-of-interest (ROI), and a pairwise ROI mapping scheme between images to compute the image matching level. Similar images are then retrieved based on the local and spatial information of the detected ROIs, and a learned weighted sum of ROI distances. Our evaluation on clinical data shows good image retrieval performance.
