{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T21:54:54Z","timestamp":1759960494148},"reference-count":18,"publisher":"World Scientific Pub Co Pte Lt","issue":"06","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2017,6]]},"abstract":"<jats:p> We present a novel technique to separate panels from stitched multipanel figures appearing in biomedical research articles. 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. The method applies local line segment detection based on the gray-level pixel changes. It then applies a line vectorization process that connects prominent broken lines along the panel boundaries while eliminating insignificant line segments within the panels. 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\u00ae repository, and achieved precision and recall of 87.16% and 83.51%, respectively, in less than 0.461[Formula: see text]s per image, on average. We also reported the recent ImageCLEF 2015 competition results that highlight the usefulness of the proposed work. <\/jats:p>","DOI":"10.1142\/s0218001417570038","type":"journal-article","created":{"date-parts":[[2016,11,29]],"date-time":"2016-11-29T12:16:25Z","timestamp":1480421785000},"page":"1757003","source":"Crossref","is-referenced-by-count":12,"title":["Line Segment-Based Stitched Multipanel Figure Separation for Effective Biomedical CBIR"],"prefix":"10.1142","volume":"31","author":[{"given":"K. 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