Skip to main content
SpringerLink
Log in

Morphological operations with iterative rotation of structuring elements for segmentation of retinal vessel structures

  • Published:
Multidimensional Systems and Signal Processing Aims and scope Submit manuscript

Abstract

The development of computer aided diagnosis system has a great impact on early and accurate disease diagnosis. The segmentation of retinal blood vessels aids in identifying the alteration in vessel structure and hence helps to diagnose many diseases such as diabetic retinopathy, glaucoma, hypertension along with some cardiovascular diseases. In this research work, a method is presented for the segmentation of retinal vessel structure from retinal fundus images. 2D wavelet transform assisted morphological gradient operation based ‘Contrast Limited Adaptive Histogram Equalization’ technique has been introduced for the preprocessing of the low contrast fundus images. Morphological gray level hit-or-miss transform with multi-structuring element with varying orientation has been proposed for the separation of blood vessel from its background. Finally a hysteresis thresholding, guided by some morphological operations has been employed to obtain the binary image excluding other unwanted areas. The proposed methodology has been tested on DRIVE database and a maximum accuracy and an average accuracy of 95.65 and 94.31% respectively have been achieved.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  • Abràmoff, M. D., Garvin, M. K., & Sonka, M. (2010). Retinal imaging and image analysis. IEEE Transactions on Medical Imaging, 1(3), 169–208.

    Google Scholar 

  • Al-Diri, B., Hunter, A., & Steel, D. (2009). An active contour model for segmenting and measuring retinal vessels. IEEE Transactions on Medical Imaging, 28, 1488–1497.

    Article  Google Scholar 

  • Bai, X., Wang, T., & Zhou, F. (2015). Linear feature detection based on the multi-scale, multi-structuring element, gray-level hit-or-miss transform. Comp. Elect. Engg., 46, 487–499.

    Article  Google Scholar 

  • Bankhead, P., Scholfield, C. N., McGeown, J. G., & Curtis, T. M. (2012). Fast retinal vessel detection and measurement using wavelets and edge location refinement. PLoS ONE, 7, e32435.

    Article  Google Scholar 

  • Bechar, M. E. A., Settouti, N., Berra, V., & Chikh, M. A. (2017). Semi-supervised superpixel classification for medical images segmentation: application to detection of glaucoma disease. Multidimensional Systems and Signal Processing. https://doi.org/10.1007/s11045-017-0483-y.

    Google Scholar 

  • Chatterjee, S., Dey, D., & Munshi, S. (2017a). Optimal Selection of features using wavelet fractal descriptors and automatic correlation bias reduction for classifying skin lesions. Biomedical Signal Processing and Control, 40, 252–262.

    Article  Google Scholar 

  • Chatterjee, S., Dey, D., & Munshi, S. (2017b). Studies on a formidable dot and globule related feature extractiontechnique for detection of melanoma from dermoscopic images. In Proceedings of the computer, communication and electrical technology (pp. 337–341). (Taylor & Francis Group).

  • Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M., & Goldbaum, M. (1989). Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Transactions on Medical Imaging, 8, 263–269.

    Article  Google Scholar 

  • Cinsdikici, M. G., & Aydin, D. (2009). Detection of blood vessels in ophthalmoscope images using MF/ant (matched filter/ant colony) algorithm. Computer Methods and Programs in Biomedicine, 96, 85–95.

    Article  Google Scholar 

  • DRIVE database for retinal fundus images. http://www.isi.uu.nl/Research/Databases/DRIVE.

  • Fraz, M. M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A. R., Owen, C. G., et al. (2012). An ensemble classification-based approach applied to retinal blood vessel segmentation. IEEE Transaction on BioMedical Engineering, 59, 2538–2548.

    Article  Google Scholar 

  • Gonzalez, R. C., & Woods, R. E. (2014). Digital image processing (3rd ed.). London: Pearson.

    Google Scholar 

  • Hoover, A., Kouznetsova, V., & Goldbaum, M. (2000). Locating blood vessels in retinal images by piuecewise threshold probing of a matched filter response. IEEE Transactions on Medical Imaging, 19, 203–210.

    Article  Google Scholar 

  • Lam, B. S., Gao, Y., & Liew, A.-C. (2010). General retinal vessel segmentation using regularization-based multiconcavity modeling. IEEE Transactions on Medical Imaging, 29, 1369–1381.

    Article  Google Scholar 

  • Lam, B. S., & Yan, H. (2008). A novel vessel segmentation algorithm for pathological retina images based on the divergence of vector fields. IEEE Transactions on Medical Imaging, 27, 237–246.

    Article  Google Scholar 

  • Lee, C., Shih, C., Lee, S., & Hong, W. (2012). Enhancement of blood vessels in retinal imaging using the nonsubsampled contourlet transform. Multidimensional Systems and Signal Processing, 23, 423–436. https://doi.org/10.1007/s11045-011-0167-y.

    Article  MathSciNet  MATH  Google Scholar 

  • Lupascu, C. A., Tegolo, D., & Trucco, E. (2010). Fabc: retinal vessel segmentation using adaboost”. IEEE Transactions on Information Technology in Biomedicine, 14(5), 1267–1274.

    Article  Google Scholar 

  • Marín, D., Aquino, A., Gegúndez-Arias, M. E., & Bravo, J. M. (2011). A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Transactions on Medical Imaging, 30, 146–158.

    Article  Google Scholar 

  • Naegel, B., Passat, N., & Ronse, C. (2007a). Gray-level hit-or-miss transforms—Part I: Unified theory. Pattern Recognition, 40, 635–647.

    Article  MATH  Google Scholar 

  • Naegel, B., Passat, N., & Ronse, C. (2007b). Gray-level hit-or-miss transforms—Part I: Unified theory. Pattern Recognition, 40, 648–658.

    Article  MATH  Google Scholar 

  • Pal, S., & Chatterjee, S. (2017). Mathematical morphology aided optic disk segmentation from retinal images. In 2017 3rd IEEE international conference on condition assessment techniques in electrical systems (CATCON) (pp. 380–385).

  • Pizer, S. M., Amburn, E. P., Austin, J. D., et al. (1987). adaptive histogram equalization and its variations. Computer Vision Graphics and Image Processing, 39, 355–368.

    Article  Google Scholar 

  • Ramlugun, G. S., Nagarajan, V. K., & Chakraborty, C. (2012). Small retinal vessels extraction towards proliferative diabetic retinopathy screening. Expert Systems with Application, 39, 1141–1146.

    Article  Google Scholar 

  • Roychowdhury, S., Koozekanani, D. D., & Parhi, K. K. (2014). DREAM: diabetic retinopathy analysis using machine learning. IEEE Journal of Biomedical and Health Informatics, 18(5), 1717–1728.

    Article  Google Scholar 

  • Saffarzadeh, V. M., Osareh, A., & Shadgar, B. (2014). Vessel segmentation in retinal images using multi-scale line operator and Kmeans clustering. Journal of Medical Signals and Sensors, 4(2), 122–129.

    Google Scholar 

  • Serra, J. (1982). Image analysis and mathematical morphology. London: Academic Press.

    MATH  Google Scholar 

  • Shapiro, L. G., & Stockman, G. C. (2001). Computer vision (pp. 137–150). Upper Saddle River: Prentice Hall.

    Google Scholar 

  • Sigurðsson, E. M., Valero, S., Benediktsson, J. A., Chanussot, J., Talbot, H., & Stefánsson, E. (2014). Automatic retinal vessel extraction based on directional mathematical morphology and fuzzy classification. Pattern Recognition Letters, 47, 164–171.

    Article  Google Scholar 

  • Soille, P. (2002). Advances in the analysis of topographic features on discrete images. In A. Braquelaire, J.-O. Lachaud & A. Vialard (Eds.), Proceedings of the DGCI’02—10th international conference on discrete geometry for computer imagery. Lecture notes in Computer Science (Vol. 2301, pp. 175–186). Bordeaux: Springer.

  • Soille, P. (2004). Morphological image analysis principles and applications (2nd ed.). Berlin: Springer.

    Book  MATH  Google Scholar 

  • Sum, K., & Cheung, P. Y. (2008). Vessel extraction under non-uniform illumination: a level set approach. IEEE Transactions on Biomedical Engineering, 55, 358–360.

    Article  Google Scholar 

  • Wang, L., Bhalerao, A., & Wilson, R. (2007). Analysis of retinal vasculature using a multiresolution hermite model. IEEE Transactions on Medical Imaging, 26, 137–152.

    Article  Google Scholar 

  • Wang, S., Yin, Y., Cao, G., Wei, B., Zheg, Y., & Yang, G. (2015). Hierarchical retinal blood vessel segmentation based on feature and ensemble learning. Neurocomputing, 149, 708–717.

    Article  Google Scholar 

  • Xiaoyi, J., & Mojon, D. (2003). Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25, 131–137.

    Article  Google Scholar 

  • Yao, C., & Chen, H.-J. (2009). Automated retinal blood vessels segmentation based on simplified PCNN and fast 2D-Otsu algorithm. Journal of Central South University of Technology, 16, 640–646.

    Article  Google Scholar 

  • Zhao, Y., Rada, L., Chen, Ke, Harding, S. P., & Zheng, Y. (2015). Automated vessel segmentation using infinite perimeter active contour model with hybrid region information with application to retinal images. IEEE Transactions on Medical Imaging, 34(9), 1797–1807.

    Article  Google Scholar 

  • Zana, F., & Klein, J.-C. (2001). Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Transactions on Image Processing, 10(7), 1010–1019.

    Article  MATH  Google Scholar 

  • Zhang, L., Li, Q., You, J., & Zhang, D. (2009). A modified matched filter with double-sided thresholding for screening proliferative diabetic retinopathy. IEEE Transaction on Information Technology in Biomedicine, 13(4), 528–537.

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the Department of Electronics and IT, Govt. of India through ‘Visvesvaraya PhD scheme’ awarded to Jadavpur University, India.

Author information

Authors and Affiliations

  1. Electrical Engineering Department, Jadavpur University, Kolkata, India

    Soumyadeep Pal, Saptarshi Chatterjee, Debangshu Dey & Sugata Munshi

Authors
  1. Soumyadeep Pal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Saptarshi Chatterjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Debangshu Dey
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sugata Munshi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Saptarshi Chatterjee.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pal, S., Chatterjee, S., Dey, D. et al. Morphological operations with iterative rotation of structuring elements for segmentation of retinal vessel structures. Multidim Syst Sign Process 30, 373–389 (2019). https://doi.org/10.1007/s11045-018-0561-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11045-018-0561-9

Keywords

Search

Navigation