Pairwise Latent Semantic Association for Similarity Computation in Medical Imaging

Citation:

Fan Zhang, Yang Song, Weidong Cai, Sidong Liu, Siqi Liu, Sonia Pujol, Ron Kikinis, Yong Xia, Michael Fulham, and David Feng. 2016. “Pairwise Latent Semantic Association for Similarity Computation in Medical Imaging.” IEEE Trans Biomed Eng, 63, 5, Pp. 1058-69.

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.

Last updated on 01/25/2017