[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

Arrow detection in biomedical images using sequential classifier

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Demner-Fushman D, Antani S, Simpson M, Rahman M (2010) Combining text and visual features for biomedical information retrieval and ontologies. Tech. rep, LHNCBC Board of Scientific Counselors, National Institutes of Health, Bethesda

  2. Demner-Fushman D, Antani S, Simpson MS, Thoma GR (2012) Design and development of a multimodal biomedical information retrieval system. J Comput Sci Eng 6(2):168–177

    Article  Google Scholar 

  3. Dori D, Wenyin L (1999) Automated cad conversion with the machine drawing understanding system: concepts, algorithms, and performance. IEEE Trans Syst Man Cybern Part A Syst Humans 29:411–416

    Article  Google Scholar 

  4. Dori D, Member S, Liu W (1999) Sparse pixel vectorization: An algorithm and its performance evaluation. IEEE Trans Pattern Anal Mach Intell 21:202–215

    Article  Google Scholar 

  5. Park J, Rasheed W, Beak J (2008) Robot navigation using camera by identifying arrow signs. In: International Conference on Grid and Pervasive Computing—Workshops, pp 382–386

  6. Cheng B, Stanley RJ, De S, Antani S, Thoma GR (2011) Automatic detection of arrow annotation overlays in biomedical images. Int J Healthc Inf Syst Inf 6(4):23–41

    Article  Google Scholar 

  7. You D, Simpson MS, Antani S, Demner-Fushman D, Thoma GR (2013) A robust pointer segmentation in biomedical images toward building a visual ontology for biomedical article retrieval. In: Zanibbi R, Coüasnon B (eds) Document recognition and retrieval, vol 8658 of SPIE Proceedings. SPIE

  8. You D, Apostolova E, Antani S, Demner-Fushman D, Thoma GR (2009) Figure content analysis for improved biomedical article retrieval. In: Berkner K, Likforman-Sulem L (eds) Document recognition and retrieval, vol 7247 of SPIE Proceedings, SPIE, pp 1–10

  9. You D, Antani S, Demner-Fushman D, Rahman MM, Govindaraju V, Thoma GR (2010) Biomedical article retrieval using multimodal features and image annotations in region-based cbir. In: Likforman-Sulem L, Agam G (eds) Document recognition and retrieval, vol 7534 of SPIE Proceedings. SPIE, pp 1–10

  10. Hori O, Doermann DS (1995) Robust table-form structure analysis based on box-driven reasoning. Int Conf Document Anal Recogn 1:218–221

    Article  Google Scholar 

  11. Santosh KC, Wendling L, Antani S, Thoma G (2014) Scalable arrow detection in biomedical images. In: International Conference on Pattern Recognition. IEEE Computer Society, Stockholm, pp 3257–3262

  12. Cheng H, Chen Y-H (1999) Fuzzy partition of two dimensional histogram and its application to thresholding. Pattern Recogn 32:825–843

    Article  Google Scholar 

  13. Jaeger S, Manke S, Reichert J, Waibel A (2001) Online handwriting recognition: the npen++ recognizer. Int J Document Anal Recogn 3(3):169–180

    Article  Google Scholar 

  14. Deans SR (1983) The Radon transform and some of its applications. A Wiley-Interscience publication, Wiley, New York

    MATH  Google Scholar 

  15. Graves A, Liwicki M, Fernández S, Bertolami R, Bunke H, Schmidhuber J (2009) A novel connectionist system for unconstrained handwriting recognition. Pattern Anal Mach Intell IEEE Trans 31(5):855–868

    Article  Google Scholar 

  16. Liwicki M, Graves A, Bunke H, Schmidhuber J (2007) A novel approach to on-line handwriting recognition based on bidirectional long short-term memory networks. In: Proc. 9th Int. Conf. on Document Analysis and Recognition, vol 1, pp 367–371

  17. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  18. Santosh KC, Wendling L, Antani SK, Thoma GR (2016) Overlaid arrow detection for labeling regions of interest in biomedical images. IEEE Intell Syst 31(3):66–75

    Article  Google Scholar 

  19. Santosh KC, Alam N, Roy PP, Wendling L, Antani S, Thoma G (2016) Arrowhead detection in biomedical images. In: Electronic imaging, document recognition and retrieval XXIII, vol 7. Society for Imaging Science and Technology, pp 1–7

  20. Santosh KC, Alam N, Roy PP, Wendling L, Antani SK, Thoma GR (2016) A simple and efficient arrowhead detection technique in biomedical images. Int J Pattern Recogn Artif Intell 30(5):1–16

    Article  Google Scholar 

  21. Ramer U (1972) An iterative procedure for the polygonal approximation of plane curves. Comput Gr Image Process 1(3):244–256

    Article  Google Scholar 

  22. Douglas DH, Peucker TK (1973) Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Can Cartogr 10(2):112–122

    Article  Google Scholar 

  23. Prasad DK, Leung MK, Quek C, Cho S-Y (2012) A novel framework for making dominant point detection methods non-parametric. Image Vision Comput 30(11):843–859

    Article  Google Scholar 

  24. Sakoe H (1978) Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans Acoust Speech Signal Process 26:43–49

    Article  MATH  Google Scholar 

  25. Keogh EJ, Pazzani MJ (1999) Scaling up dynamic time warping to massive dataset. In: European PKDD, pp 1–11

  26. Müller H, Kalpathy-Cramer J, Eggel I, Bedrick S, Radhouani S, Bakke B, Kahn CE Jr, Hersh W (2010) Overview of the clef 2009 medical image retrieval track. Multilingual information access evaluation II. Multimedia Experiments. Springer, New York, pp 72–84

  27. Zhang D, Lu G (2002) Shape-based image retrieval using generic fourier descriptor. Signal Process Image Commun 17:825–848

    Article  Google Scholar 

  28. Belongie S, Malik J, Puzicha J (2002) Shape matching and object recognition using shape contexts. IEEE Trans Pattern Anal Mach Intell 24(4):509–522

    Article  Google Scholar 

  29. Kim W-Y, Kim Y-S (2000) A region-based shape descriptor using zernike moments. Signal Process Image Commun 16(1–2):95–102

    Article  Google Scholar 

  30. Hoang TV, Tabbone S (2012) The generalization of the r-transform for invariant pattern representation. Pattern Recogn 45(6):2145–2163

    Article  MATH  Google Scholar 

  31. Santosh KC, Lamiroy B, Wendling L (2013) Dtw-radon-based shape descriptor for pattern recognition. Int J Pattern Recogn Artif Intell 27(3):33

    Article  MathSciNet  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. Department of Computer Science, University of South Dakota, 414 E Clark St, Vermillion, SD, 57069, USA

    K. C. Santosh

  2. Indian Institute of Technology Roorkee, Department of Computer Science, Roorkee, India

    Partha Pratim Roy

Authors
  1. K. C. Santosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Partha Pratim Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. C. Santosh.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-016-0623-y

Keywords

Search

Navigation