Arrow detection in biomedical images using sequential classifier

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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Acknowledgements

The authors would like to acknowledge Mr. Naved Alam for his work during his stay (Research Work) at the Department of Computer Science, Indian Institute of Technology (IIT) Roorkee.

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Correspondence to K. C. Santosh.

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Santosh, K.C., Roy, P.P. Arrow detection in biomedical images using sequential classifier. Int. J. Mach. Learn. & Cyber. 9, 993–1006 (2018). https://doi.org/10.1007/s13042-016-0623-y

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Keywords

  • Arrow detection
  • Document images
  • Biomedical publications
  • Image region labeling
  • Content-based image retrieval