Literature-based biomedical image classification and retrieval. (January 2015)

• Record Type: Journal Article
• Authors: Simpson, Matthew S.; You, Daekeun; Rahman, Md Mahmudur; Xue, Zhiyun; Demner-Fushman, Dina; Antani, Sameer; Thoma, George
• Is Part Of: Computerized medical imaging and graphics. Volume 39(2015)
• Page Start: 3
• Page End: 13
• Publication Date: 2015-01
• Subjects: Image-based retrieval -- Case-based retrieval -- Modality classification -- Compound figure separation; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2014.06.006
• Languages: English
• ISSNs: 0895-6111

Abstract

Abstract : Highlights: We describe a method for retrieving images from biomedical articles. We describe a method for segmenting the multi-panel figures in biomedical articles. We describe a method for classifying biomedical images according to medical modality. We describe an optimization strategy for search engine parameters. We describe our participation in the 2013 medical track of ImageCLEF. Abstract: Literature-based image informatics techniques are essential for managing the rapidly increasing volume of information in the biomedical domain. Compound figure separation, modality classification, and image retrieval are three related tasks useful for enabling efficient access to the most relevant images contained in the literature. In this article, we describe approaches to these tasks and the evaluation of our methods as part of the 2013 medical track of ImageCLEF. In performing each of these tasks, the textual and visual features used to represent images are an important consideration often left unaddressed. Therefore, we also describe a gradient-based optimization strategy for determining meaningful combinations of features and apply the method to the image retrieval task. An evaluation of our optimization strategy indicates the method is capable of producing statistically significant improvements in retrieval performance. Furthermore, the results of the 2013 ImageCLEF evaluation demonstrate the effectiveness of our techniques. In particular, our text-based and mixed image retrieval methods ranked first among all the participating groups.