title: Pairwise Latent Semantic Association for Similarity Computation in Medical Imaging.
abstract: Retrieving medical images that present similar diseases is an active research area for diagnostics and therapy. However, it can be problematic given the visual variations between anatomical structures. In this paper, we propose a new feature extraction method for similarity computation in medical imaging. Instead of the low-level visual appearance, we design a CCA-PairLDA feature representation method to capture the similarity between images with high-level semantics. First, we extract the PairLDA topics to represent an image as a mixture of latent semantic topics in an image pair context. Second, we generate a CCA-correlation model to represent the semantic association between an image pair for similarity computation. While PairLDA adjusts the latent topics for all image pairs, CCA-correlation helps to associate an individual image pair. In this way, the semantic descriptions of an image pair are closely correlated, and naturally correspond to similarity computation between images. We evaluated our method on two public medical imaging datasets for image retrieval and showed improved performance.
authors.affiliation: Biomedical and Multimedia Information Technology Research Group, School of Information Technologies, University of Sydney, Sydney, N.S.W., Australia.
authors.forename: Fan
authors.initials: F
authors.lastname: Zhang
authors.affiliation: Biomedical and BMIT Research Group, School of Information Technologies, University of Sydney.
authors.forename: Yang
authors.initials: Y
authors.lastname: Song
authors.affiliation: Biomedical and Multimedia Information Technology Research Group, School of Information Technologies, University of Sydney.
authors.forename: Weidong
authors.initials: W
authors.lastname: Cai
authors.affiliation: Biomedical and BMIT Research Group, School of Information Technologies, University of Sydney.
authors.forename: Sidong
authors.initials: S
authors.lastname: Liu
authors.affiliation: Biomedical and Multimedia Information Technology Research Group, School of Information Technologies, University of Sydney.
authors.forename: Siqi
authors.initials: S
authors.lastname: Liu
authors.affiliation: Surgical Planning Lab, Brigham & Women's Hospital, Harvard Medical School.
authors.forename: Sonia
authors.initials: S
authors.lastname: Pujol
authors.affiliation: Surgical Planning Lab, Brigham & Women's Hospital, Harvard Medical School.
authors.forename: Ron
authors.initials: R
authors.lastname: Kikinis
authors.affiliation: Shaanxi Key Lab of Speech and Image Information Processing, School of Computer Science and Technology, Northwestern Polytechnical University.
authors.forename: Yong
authors.initials: Y
authors.lastname: Xia
authors.affiliation: Department of PET and Nuclear Medicine, Royal Prince Alfred Hospital.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Fulham
authors.affiliation: BMIT Research Group, School of Information Technologies, University of Sydney.
authors.forename: David Dagan
authors.initials: DD
authors.lastname: Feng
authors.lastname: Alzheimers Disease Neuroimaging Initiative
mesh_terms: D000465:Algorithms; D001932:Brain Neoplasms / Q000175:diagnosis; D016208:Databases, Factual; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008175:Lung Neoplasms / Q000175:diagnosis; D012660:Semantics