Work

Pairwise Latent Semantic Association for Similarity Computation in Medical Imaging

Year: 2015

Type: article

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.

Source: IEEE Transactions on Biomedical Engineering

DOI: 10.1109/tbme.2015.2478028

Authors: Fan Zhang, Yang Song, Weidong Cai, Sidong Liu, Siqi Liu, Sonia Pujol, Ron Kikinis, Yong Xia, Michael J. Fulham, David Dagan Feng, Alzheimers Disease Neuroimaging Initiative

Institutions: Brigham and Women's Hospital, The University of Sydney, Harvard University, Northwestern Polytechnical University, Royal Prince Alfred Hospital, Shanghai Jiao Tong University

Language: English

Topic: Image Retrieval and Classification Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: U.S. Department of Defense, Alzheimer's Disease Neuroimaging Initiative, Dementia Australia Research Foundation, National Arts Council of South Africa, National Natural Science Foundation of China, Natural Science Foundation of Shanxi Province, National Institutes of Health, Fundamental Research Funds for the Central Universities, National Institute on Aging, National Institute of Biomedical Imaging and Bioengineering, National Center for Research Resources, Division of Arctic Sciences

Awards: G104586243, 基于结构深度学习群的空间目标识别技术研究, Neuroimaging Analysis Center (NAC), National Alliance for Medical Image Computing (NAMIC), G4931591408, G497257379, G5320264224, EFFECTS OF TRAUMATIC BRAIN INJURY (TBI) AND POST TRAUMATIC STRESS DISORDER (PTSD) ON ALZHEIMER'S DISEASE (AD) IN VETERANS USING IMAGING AND BIOMARKERS IN THE AD NEUROIMAGING INITIATIVE (ADNI)", Alzheimer's Disease Neuroimaging Initiative, Neuroimaging Analysis Center, G7293919616, G9000039018

OpenAlex ID: W2341362397

FWCI: 3.157

Cites: 63

Cited by: 26

Related to: 10