Surgical Planning Laboratory - Brigham & Women's Hospital - Boston, Massachusetts USA - a teaching affiliate of Harvard Medical School

Surgical Planning Laboratory

The Publication Database hosted by SPL

All Publications | Upload | Advanced Search | Gallery View | Download Statistics | Help | Import | Log in

Pairwise Latent Semantic Association for Similarity Computation in Medical Imaging

1Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, Sydney, Australia.
2Surgical Planning Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
IEEE Engineering in Medicine and Biology Society
Publication Date:
IEEE Trans Biomed Eng
Volume Number:
Issue Number:
IEEE Trans Biomed Eng. 2016 May;63(5):1058-69.
PubMed ID:
Medical image retrieval, latent topic, semantic association
Appears in Collections:
P41 EB015902/EB/NIBIB NIH HHS/United States
P41 RR013218/RR/NCRR NIH HHS/United States
U01 AG024904/AG/NIA NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Zhang F., Song Y., Cai W., Liu S., Liu S., Pujol S., Kikinis R., Xia Y., Fulham M., Feng D. Pairwise Latent Semantic Association for Similarity Computation in Medical Imaging. IEEE Trans Biomed Eng. 2016 May;63(5):1058-69. PMID: 26372117. PMCID: PMC4850117.
Downloaded: 792 times. [view map]
Paper: Download, View online
Export citation:
Google Scholar: link

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.

Additional Material
1 File (170.603kB)
Zhang-IEEE_TBE2016-fig6.jpg (170.603kB)