title: Thoracic image matching with appearance and spatial distribution.
abstract: 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.
authors.affiliation: BMIT Research Group, School of Information Technologies, University of Sydney, Australia.
authors.forename: Yang
authors.initials: Y
authors.lastname: Song
authors.forename: Weidong
authors.initials: W
authors.lastname: Cai
authors.forename: Stefan
authors.initials: S
authors.lastname: Eberl
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Fulham
authors.forename: David Dagan
authors.initials: DD
authors.lastname: Feng
mesh_terms: D002289:Carcinoma, Non-Small-Cell Lung / Q000000981:diagnostic imaging*; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D064847:Multimodal Imaging; D049268:Positron-Emission Tomography; D014057:Tomography, X-Ray Computed