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A Semi-Supervised Learning Approach for Automatic Segmentation of Retinal Lesions Using SURF Blob Detector and Locally Adaptive Binarization

  • Tathagata Bandyopadhyay
  • Jan KubicekEmail author
  • Marek Penhaker
  • Juraj Timkovic
  • David Oczka
  • Ondrej Krejcar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11432)

Abstract

In the clinical ophthalmology, the retinal area is routinely investigated from the retinal images, by the naked eyes. Such subjective assessment may be apparently influenced by ineligible inaccuracies. Therefore, objective assessment of the retinal image records plays an important role for the clinical evaluation and treatment planning. Retinal lesions in premature born children represent one of the most frequent retinal findings which may endanger their vison. These findings are mostly connected with the Retinopathy of Prematurity (RoP). In this paper, we have proposed a novel segmentation model utilizing the SURF blob detector and locally adaptive binarization. The proposed model is able to autonomously detect, and consequently classify retinal lesions. In the result, we obtain a segmentation model of the retinal lesions, where the retinal posterior is effectively separated. As a part of the proposed analysis, we have done objectification and quantitative comparison of the proposed method against some of the state of the art segmentation models by selected evaluating parameters. The proposed method has a potential to be used in the clinical practice as a feedback for the automatic evaluation of the retinal lesions, and also for dynamic retinal lesion’s features extraction.

Keywords

Semi-Supervised learning Image segmentation Retinal lesions RetCam 3 Optical disc 

Notes

Acknowledgment

The work and the contributions were supported by the project SV4508811/2101Biomedical Engineering Systems XIV’. This study was also supported by the research project The Czech Science Foundation (GACR) 2017 No. 17-03037S Investment evaluation of medical device development run at the Faculty of Informatics and Management, University of Hradec Kralove, Czech Republic. This study was supported by the research project The Czech Science Foundation (TACR) ETA No. TL01000302 Medical Devices development as an effective investment for public and private entities.

References

  1. 1.
    Adal, K.M., et al.: An automated system for the detection and classification of retinal changes due to red lesions in longitudinal fundus images. IEEE Trans. Biomed. Eng. 65(6), 1382–1390 (2018)CrossRefGoogle Scholar
  2. 2.
    Kubicek, J., et al.: Optical nerve segmentation using the active shape method. Lékař a technika-Clinician Technol. 46(1), 13–20 (2016)Google Scholar
  3. 3.
    Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 13(1), 146–166 (2004)CrossRefGoogle Scholar
  4. 4.
    Zhang, J., Hu, J.: Image segmentation based on 2D Otsu method with histogram analysis. In: 2008 International Conference on Computer Science and Software Engineering. IEEE (2008)Google Scholar
  5. 5.
    Sauvola, J., Pietikäinen, M.: Adaptive document image binarization. Pattern Recogn. 33(2), 225–236 (2000)CrossRefGoogle Scholar
  6. 6.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006).  https://doi.org/10.1007/11744023_32CrossRefGoogle Scholar
  7. 7.
    Tuytelaars, T., Mikolajczyk, K.: Local invariant feature detectors: a survey. Found. Trends Comput. Graph. Vis. 3(3), 177–280 (2008)CrossRefGoogle Scholar
  8. 8.
    Chapelle, O., Scholkopf, B., Zien, A.: Semi-supervised learning. IEEE Trans. Neural Networks 20(3), 542 (2009). (Chapelle, O., et al. (eds.) 2006) [book reviews]CrossRefGoogle Scholar
  9. 9.
    Zhu, X.: Semi-supervised learning literature survey. Comput. Sci. Univ. Wis. Madison 2(3), 4 (2006)Google Scholar
  10. 10.
    Bandyopadhyay, T., Mitra, S., Mitra, S., Rato, L.M., Das, N.: Analysis of pancreas histological images for glucose intolerance identification using wavelet decomposition. In: Satapathy, S.C., Bhateja, V., Udgata, S.K., Pattnaik, P.K. (eds.) Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications. AISC, vol. 515, pp. 653–661. Springer, Singapore (2017).  https://doi.org/10.1007/978-981-10-3153-3_65CrossRefGoogle Scholar
  11. 11.
    Hartigan, J.A., Wong, M.A.: Algorithm AS 136: a k-means clustering algorithm. J. Roy. Stat. Soc. Ser. C (Appl. Stat.) 28(1), 100–108 (1979)Google Scholar
  12. 12.
    Colomer, A., Naranjo, V., Janvier, T., Mossi, J.M.: Evaluation of fractal dimension effectiveness for damage detection in retinal background. J. Comput. Appl. Math. 337, 341–353 (2018)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Sreng, S., Maneerat, N., Hamamoto, K., Panjaphongse, R.: Automated diabetic retinopathy screening system using hybrid simulated annealing and ensemble bagging classifier. Appl. Sci. 8(7), 1198 (2018)CrossRefGoogle Scholar
  14. 14.
    Baltatescu, A., et al.: Detection of perimacular red dots and blots when screening for diabetic retinopathy: refer or not refer? Diab. Vasc. Dis. Res. 15(4), 356–359 (2018)CrossRefGoogle Scholar
  15. 15.
    Kubicek, J., Timkovic, J., Krestanova, A., Augustynek, M., Penhaker, M., Bryjova, I.: Morphological segmentation of retinal blood vessels and consequent tortuosity extraction. J. Telecommun. Electron. Comput. Eng. 10(1–4), 73–77 (2018)Google Scholar
  16. 16.
    Kubicek, J., Kosturikova, J., Penhaker, M., Augustynek, M., Kuca, K.: Segmentation based on gabor transformation with machine learning: Modeling of retinal blood vessels system from retcam images and tortuosity extraction. Front. Artif. Intell. Appl. 297, 270–283 (2017)Google Scholar
  17. 17.
    Hu, J., Chen, Y., Zhong, J., Ju, R., Yi, Z.: Automated analysis for retinopathy of prematurity by deep neural networks. IEEE Trans. Med. Imaging 38, 269–279 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Tathagata Bandyopadhyay
    • 1
  • Jan Kubicek
    • 1
    Email author
  • Marek Penhaker
    • 1
  • Juraj Timkovic
    • 2
  • David Oczka
    • 1
  • Ondrej Krejcar
    • 3
  1. 1.FEECSVSB-Technical University of OstravaOstrava-PorubaCzech Republic
  2. 2.Clinic of OphthalmologyUniversity Hospital OstravaOstravaCzech Republic
  3. 3.Faculty of Informatics and Management, Center for Basic and Applied ResearchUniversity of Hradec KraloveHradec KraloveCzech Republic

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