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Title: Arrow detection in biomedical images using sequential classifier
Authors: Santosh K.C.
Pratim Roy, Partha
Published in: International Journal of Machine Learning and Cybernetics
Abstract: Biomedical images are often complex, and contain several regions that are annotated using arrows. Annotated arrow detection is a critical precursor to region-of-interest (ROI) labeling, which is useful in content-based image retrieval (CBIR). In this paper, we propose a sequential classifier comprising of bidirectional long short-term memory (BLSTM) classifier followed by convexity defect-based arrowhead detection. Different image layers are first segmented via fuzzy binarization. Candidate regions are then checked whether they are arrows by using BLSTM classifier, where Npen++ features are used. In case of low confidence score (i.e., BLSTM classifier score), we take convexity defect-based arrowhead detection technique into account. Our test results on biomedical images from imageCLEF 2010 collection outperforms the existing state-of-the-art arrow detection techniques, by approximately more than 3% in precision, 12% in recall, and therefore 8% in F 1 score. © 2016, Springer-Verlag Berlin Heidelberg.
Citation: International Journal of Machine Learning and Cybernetics (2018), 9(6): 993-1006
Issue Date: 2018
Publisher: Springer Verlag
Keywords: Arrow detection
Biomedical publications
Content-based image retrieval
Document images
Image region labeling
ISSN: 18688071
Author Scopus IDs: 14831502300
Author Affiliations: Santosh, K.C., Department of Computer Science, University of South Dakota, 414 E Clark St, Vermillion, SD 57069, United States
Roy, P.P., Indian Institute of Technology Roorkee, Department of Computer Science, Roorkee, India
Corresponding Author: Santosh, K.C.; Department of Computer Science, University of South Dakota, 414 E Clark St, United States; email:
Appears in Collections:Journal Publications [CS]

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