Fine-grained correlation analysis for medical image retrieval. (March 2021)

• Record Type: Journal Article
• Authors: Wang, Xiaoqin; Lan, Rushi; Wang, Huadeng; Liu, Zhenbing; Luo, Xiaonan
• Is Part Of: Computers & electrical engineering. Volume 90(2021)
• Publication Date: 2021-03
• Subjects: Feature fusion -- K-means clustering -- Medical image retrieval -- Canonical correlation analysis; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.106992
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Feature fusion in medical image retrieval remains a challenging task because of the high-dimensional data and massive amount of irrelevant information in images. To solve these issues, we propose a novel feature fusion method, called fine-grained correlation analysis (FGCA), for medical image retrieval. First, we analyze the problem that there are many irrelevant local regions in a category. To solve this problem, an image is partitioned into some fine-grained samples. Then, the fine-grained samples with similar characteristics are tagged with the same label by the k-means clustering algorithm. Finally, we investigate how the correlation relationship extracted from the fine-grained samples helps fuse different features and obtain the more discriminative and less redundant information for medical image retrieval. Experiments on three medical image datasets show that our proposed FGCA approach works better than the conventional methods. Graphical abstract: Highlights: Propose a fine-grained correlation analysis (FGCA) algorithm. FGCA can fuse different features based on fine-grained samples. FGCA can find discriminative features with low dimensions. FGCA can improve performance for medical images retrieval.