Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Hessian-polar context: a descriptor for microfilaria recognition

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

This paper presents a new effective descriptor for microfilaria. Since microfilaria is a thin elastic object, the proposed descriptor handles it locally. At each medial point of the microfilaria, the local structure of the microfilaria votes for a given shape. Accumulating these votes in the polar domain yields a rich descriptor. Experimental results show the effectiveness of the proposed approach when compared to a set of different well-established methods.

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
$34.99 /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 excludes VAT (USA)
Tax calculation will be finalised during checkout.

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

Similar content being viewed by others

Notes

  1. https://github.com/fmstam/HPC

References

  1. AL-Tam, F., dos Anjos, A., Pion, S., Boussinesq, M., Shahbazkia, H.R.: Microfilariae classification using multiple classifiers for color and shape features. Open Eng. 6(1), 2016 (2016)

    Article  Google Scholar 

  2. Alexe, B., Deselaers, T., Ferrari, V.: What is an object? In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). San Francisco, CA, USA (2010)

  3. Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 509–522 (2002). https://doi.org/10.1109/34.993558

    Article  Google Scholar 

  4. Boussinesq, M.: Loiasis. Ann. Trop. Med. Parasitol. 100(8), 715–731 (2006)

    Article  Google Scholar 

  5. Chang, C.C., Lin, C.J.: Libsvm: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 27 (2011)

    Article  Google Scholar 

  6. Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M., Goldbaum, M.: Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans. Med. Imaging 8(3), 263–269 (1989)

    Article  Google Scholar 

  7. Comaniciu, D., Meer, P.: Cell image segmentation for diagnostic pathology. In: Suri, J., Setarehdan, S., Singh, S. (eds.) Advanced Algorithmic Approaches to Medical Image Segmentation, Advances in Computer Vision and Pattern Recognition, pp. 541–558. Springer, London (2002)

    Google Scholar 

  8. D’Ambrosio, M.V., Bakalar, M., Bennuru, S., Reber, C., Skandarajah, A., Nilsson, L., Switz, N., Kamgno, J., Pion, S., Boussinesq, M., Nutman, T.B., Fletcher, D.A.: Point-of-care quantification of blood-borne filarial parasites with a mobile phone microscope. Sci. Transl. Med. 7(286), 286 (2015). https://doi.org/10.1126/scitranslmed.aaa3480

    Article  Google Scholar 

  9. Figueiredo, M., Leitao, J.: A nonsmoothing approach to the estimation of vessel contours in angiograms. IEEE Trans. Med. Imaging 14(1), 162–172 (1995)

    Article  Google Scholar 

  10. Frangi, A., Niessen, W., Vincken, K., Viergever, M.: Multiscale vessel enhancement filtering. In: Wells, W., Colchester, A., Delp, S. (eds.) Medical Image Computing and Computer-Assisted Intervention—MICCAI-98. Lecture Notes in Computer Science, vol. 1496, pp. 130–137. Springer, Berlin (1998)

    Google Scholar 

  11. Hladůvka, J., König, A., Gröller, E.: Exploiting eigenvalues of the Hessian matrix for volume decimation. Tech. Rep. TR-186-2-00-19 (2000)

  12. Kirbas, C., Quek, F.: A review of vessel extraction techniques and algorithms. ACM Comput. Surv. 36(2), 81–121 (2004)

    Article  Google Scholar 

  13. Li, J., Allinson, N.M.: A comprehensive review of current local features for computer vision. Neurocomputing 71(10–12), 1771–1787 (2008). https://doi.org/10.1016/j.neucom.2007.11.032

    Article  Google Scholar 

  14. Li, K., Lu, Z., Liu, W., Yin, J.: Cytoplasm and nucleus segmentation in cervical smear images using radiating GVF snake. Pattern Recognit. 45(4), 1255–1264 (2012)

    Article  Google Scholar 

  15. Lindeberg, T.: Edge detection and ridge detection with automatic scale selection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 465–470. San Francisco, CA, USA (1996)

  16. Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  17. Meijster, A., Roerdink, J.B., Hesselink, W.H.: A general algorithm for computing distance transforms in linear time. In: Mathematical Morphology and its Applications to Image and Signal Processing, pp. 331–340. Springer (2002)

  18. Morard, V., Dokládal, P., Decenciere, E.: Parsimonious path openings and closings. IEEE Trans. Image Process. 23(4), 1543–1555 (2014)

    Article  MathSciNet  Google Scholar 

  19. Organization, W.H.: Bench aids for the diagnosis of filarial infections (1997)

  20. Peng, Y., Dharssi, S., Chen, Q., Keenan, T.D., Agrón, E., Wong, W.T., Chew, E.Y., Lu, Z.: Deepseenet: A deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs. Ophthalmology 126(4), 565–575 (2019)

    Article  Google Scholar 

  21. Perkins Simon, A.W., Wolfart, E.: Hypermedia Image Processing Reference. Wiley, New York (1996)

    Google Scholar 

  22. Platt, J.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv. Large Margin Classif. 10(3), 61–74 (1999)

    Google Scholar 

  23. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)

    Article  MathSciNet  Google Scholar 

  24. van de Sande, K., Gevers, T., Snoek, C.: Evaluating color descriptors for object and scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1582–1596 (2010)

    Article  Google Scholar 

  25. Soille, P., Talbot, H.: Directional morphological filtering. IEEE Trans. Pattern Anal. Mach. Intell. 23(11), 1313–1329 (2001). https://doi.org/10.1109/34.969120

    Article  Google Scholar 

  26. Steger, C.: An unbiased detector of curvilinear structures. IEEE Trans. Pattern Anal. Mach. Intell. 20(2), 113–125 (1998)

    Article  Google Scholar 

  27. Swain, M.J., Ballard, D.H.: Color indexing. Int. J. Comput. Vis. 7(1), 11–32 (1991)

    Article  Google Scholar 

  28. Tankyevych, O., Talbot, H., Dokladal, P.: Curvilinear Morpho-Hessian filter. In: The 5th IEEE International Symposium on Biomedical Imaging: From Nano (2008)

  29. Telea, A., Van Wijk, J.J.: An augmented fast marching method for computing skeletons and centerlines. In: Proceedings of the symposium on Data Visualisation 2002, pp. 251–ff. Eurographics Association (2002)

  30. Vedaldi, A., Fulkerson, B.: Vlfeat: An open and portable library of computer vision algorithms. In: Proceedings of the International Conference on Multimedia, MM’10, pp. 1469–1472. ACM, New York, NY, USA (2010)

  31. Wang, Z., Fan, B., Wu, F.: Local intensity order pattern for feature description. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 603–610 (2011)

  32. Yamada, H., Yamamoto, K., Hosokawa, K.: Directional mathematical morphology and reformalized Hough transformation for the analysis of topographic maps. IEEE Trans. Pattern Anal. Mach. Intell. 15(4), 380–387 (1993). https://doi.org/10.1109/34.206957

    Article  Google Scholar 

  33. Zhang, Y., Koydemir, H.C., Shimogawa, M.M., Yalcin, S., Guziak, A., Liu, T., Oguz, I., Huang, Y., Bai, B., Luo, Y., et al.: Motility-based label-free detection of parasites in bodily fluids using holographic speckle analysis and deep learning. Light Sci. Appl. 7(1), 108 (2018)

    Article  Google Scholar 

  34. Zhu, W., Liu, C., Fan, W., Xie, X.: Deeplung: deep 3d dual path nets for automated pulmonary nodule detection and classification. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 673–681 (2018)

  35. Zhu, W., Lou, Q., Vang, Y.S., Xie, X.: Deep multi-instance networks with sparse label assignment for whole mammogram classification. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 603–611. Springer (2017)

Download references

Acknowledgements

The authors are grateful to Thamar university and Infectiopôle Sud, Marseille-France, and the Bill & Melinda Gates foundation.

Author information

Authors and Affiliations

  1. Department of Computer Science, Aberystwyth University, Aberystwyth, SY23 3DB, UK

    Faroq AL-Tam & António dos Anjos

  2. CEOT, Universidade do Algarve, 8005-139, Faro, Portugal

    Faroq AL-Tam

  3. NOVA LINCS, Universidade de Évora, Évora, Portugal

    António dos Anjos

  4. Departamento de Engenharia Electrónica e Informática, Universidade do Algarve, 8005-139, Faro, Portugal

    Hamid Reza Shahbazkia

Authors
  1. Faroq AL-Tam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. António dos Anjos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hamid Reza Shahbazkia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Faroq AL-Tam.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

AL-Tam, F., dos Anjos, A. & Shahbazkia, H.R. Hessian-polar context: a descriptor for microfilaria recognition. Machine Vision and Applications 32, 25 (2021). https://doi.org/10.1007/s00138-020-01154-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00138-020-01154-6

Keywords

Search

Navigation