Canny, A computational approach to edge detection, IEEE Trans. Wagner-Mann, If a picture is worth a thousand words, what is a trauma computerized tomography panel worth?, Am. Thoma, Image retrieval from scientific publications: Text and image content processing to separate multipanel figures, J.
Acar, Content-based image retrieval in radiology: Current status and future directions, J. Petkovic, Content-based representation and retrieval of visual media: A state-of-the-art review, Multimedia Tools Appl. We also reported the recent ImageCLEF 2015 competition results that highlight the usefulness of the proposed work. We validated our fully automatic technique on a set of stitched multipanel biomedical figures extracted from articles within the Open Access subset of PubMed Central® repository, and achieved precision and recall of 87.16% and 83.51%, respectively, in less than 0.461 s per image, on average.
It then applies a line vectorization process that connects prominent broken lines along the panel boundaries while eliminating insignificant line segments within the panels. The method applies local line segment detection based on the gray-level pixel changes. Since such figures may comprise images from different imaging modalities, separating them is a crucial first step for effective biomedical content-based image retrieval (CBIR): multimodal biomedical document classification and/or retrieval, for instance. We present a novel technique to separate panels from stitched multipanel figures appearing in biomedical research articles.
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