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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 6/2018

03-01-2017 | Original Article

Arrow detection in biomedical images using sequential classifier

Authors: K. C. Santosh, Partha Pratim Roy

Published in: International Journal of Machine Learning and Cybernetics | Issue 6/2018

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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 \(\text{F}_1\) score.
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Metadata
Title
Arrow detection in biomedical images using sequential classifier
Authors
K. C. Santosh
Partha Pratim Roy
Publication date
03-01-2017
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 6/2018
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-016-0623-y

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