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

03.01.2017 | Original Article

Arrow detection in biomedical images using sequential classifier

verfasst von: K. C. Santosh, Partha Pratim Roy

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 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.
Vorheriger Artikel Rule number and approximation of the hybrid fuzzy system based on binary tree hierarchy
Nächster Artikel Parallel knowledge acquisition algorithms for big data using MapReduce

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Metadaten
Titel
Arrow detection in biomedical images using sequential classifier
verfasst von
K. C. Santosh
Partha Pratim Roy
Publikationsdatum
03.01.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 6/2018
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-016-0623-y

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