Skip to main content
SpringerLink
Log in

An Automatic Stopping Criterion for Contrast Enhancement Using Multi-scale Top-Hat Transformation

  • Original Paper
  • Published:
Sensing and Imaging Aims and scope Submit manuscript

Abstract

Image contrast enhancement is frequently referred to as one of the most important issues in image processing because it is a necessary pre-processing step in many computer vision and image processing algorithms. Contrast enhancement is normally required to increase the quality of low contrast images by expanding the dynamic range of input gray level. However, image contrast enhancement without disturbing other parameters of the image is one of the difficult tasks in image processing. To efficiently enhance images, algorithms based on multi-scale top-hat morphological transform (MSTH) have been proposed. However, scale selection to stop the algorithm is very subjective and empirical. In order to automatically select the iterations number required by MSTH algorithm, an automatic stopping criterion based on the contrast improvement ratio revisited is proposed in this paper.

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
Fig. 7

Similar content being viewed by others

References

  1. Bai, X., & Zhou, F. (2010). Multi Scale Top-hat Transform Based Algorithm for Image Enhancement. In ICSP2010 Proceedings, pp. 797–800.

  2. Bai, X., Li, Y., & Zhou, F. (2012). Measure of image clarity using image features extracted by the multiscale top-hat transform. Journal of Optics, 14(4), 045402.

    Article  Google Scholar 

  3. Bai, X., & Zhou, F. (2013). A unified form of multi-scale top-hat transform based algorithms for image processing. Optik (Stuttg), 124(13), 1614–1619.

    Article  Google Scholar 

  4. Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI–8(6), 679–698.

    Article  Google Scholar 

  5. Firoz, R., Ali, M. Shahjahan, Khan, M. Nasir Uddin, Hossain, M. Khalid, Islam, M. Khairul, & Shahinuzzaman, M. (2016). Medical image enhancement using morphological transformation. Journal of Data Analysis and Information Processing, 4(4), 1–12.

    Article  Google Scholar 

  6. Hassanpour, H., Samadiani, N., & Salehi, S. M. Mahdi. (2015). Using morphological transforms to enhance the contrast of medical images. The Egyptian Journal of Radiology and Nuclear Medicine, 46(2), 481–489.

    Article  Google Scholar 

  7. Jagannath, H. S., Virmani, J., & Kumar, V. (2012). Morphological enhancement of microcalcifications in digital mammograms. Journal of The Institution of Engineers (India): Series B, 93(3), 163–172.

    Article  Google Scholar 

  8. João, A., Gambaruto, A. M., Tiago, J., & Sequeira, A. (2016). Computational advances applied to medical image processing: an update. Open Access Bioinformatics, 8, 1–15.

    Google Scholar 

  9. Kamra, A., Jain, V. K., & Pragya, (2015). Contrast enhancement of masses in mammograms using multiscale morphology. International Journal of Medical, Health, Biomedical, Bioengineering and Pharmaceutical Engineering, 9(7), 546–549.

    Google Scholar 

  10. Kimori, Y. (2013). Morphological image processing for quantitative shape analysis of biomedical structures: Effective contrast enhancement. Journal of Synchrotron Radiation, 20(6), 848–853.

    Article  Google Scholar 

  11. Le, T. K. (2013). Segmentation of lung vessels together with nodules in CT images using morphological operations and level set. Journal of Medical and Bioengineering, 2(1), 5–10.

    Article  Google Scholar 

  12. Maragatham, G., & Roomi, M. (2015). A Review of Image Contrast Enhancement Methods and Techniques. Research Journal of Applied Sciences, Engineering and Technology, 9(5), 309–326.

    Article  Google Scholar 

  13. Maragos, P. (2005). Morphological Filtering for image enhancement and feature detection. In The image and video processing handbook (2nd ed.), 2nd ed., A. C. Bovik, Ed. Elsevier Academic Press, pp. 135–156.

    Chapter  Google Scholar 

  14. Mukhopadhyay, S., & Chanda, B. (2000). Local contrast enhancement of grayscale images using multiscale morphology. In ICVGIP, pp. 1–8.

  15. Pesaresi, M., & Benediktsson, J. A. (2001). A new approach for the morphological segmentation of high-resolution satellite imagery. IEEE Transactions on Geoscience and Remote Sensing, 39(2), 309–320.

    Article  Google Scholar 

  16. Renieblas, G. Prieto, Nogués, A. Turrero, González, A. Muñoz, Gómez-Leon, N., & del Castillo, E. Guibelalde. (2017). Structural similarity index family for image quality assessment in radiological images. Journal of Medical Imaging, 4(3), 1–11.

    Article  Google Scholar 

  17. Ritika, (2012). A novel approach for local contrast enhancement of medical images using mathematical morphology. International Journal of Computer Science, Information Technology, and Security, 2(2), 392–397.

    Google Scholar 

  18. Serra, J. P. (1982). Image analysis and mathematical morphology (2nd ed., Vol. 1). New York: Academic Press.

    MATH  Google Scholar 

  19. Serra, J. P., & Soille, P. (1994). Mathematical morphology and its applications to image processing (Vol. 2). Berlin: Springer Science and Business Media.

    Book  Google Scholar 

  20. Serra, J. P. (2006). A lattice approach to image segmentation. Journal of Mathematical Imaging and Vision, 24(1), 83–130.

    Article  MathSciNet  Google Scholar 

  21. Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(379–423), 623–656.

    Article  MathSciNet  Google Scholar 

  22. Soille, P. (2004). Morphological image analysis: Principles and applications, Second. Berlin: Springer.

    Book  Google Scholar 

  23. Wang, Y.-P., Wu, Q., Castleman, K. R., & Xiong, Z. (2003). Chromosome image enhancement using multiscale differential operators. IEEE Transactions on Medical Imaging, 22(5), 685–693.

    Article  Google Scholar 

  24. Zadorozny, A., & Zhang, H. (2009). Contrast enhancement using morphological scale space. In IEEE international conference on automation and logistics, pp. 804–807.

Download references

Author information

Authors and Affiliations

  1. Department of Systems Engineering, Pontificia Universidad Javeriana, Bogotá D.C., Colombia

    Cesar Bustacara-Medina & Leonardo Flórez-Valencia

Authors
  1. Cesar Bustacara-Medina
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Leonardo Flórez-Valencia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cesar Bustacara-Medina.

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

Bustacara-Medina, C., Flórez-Valencia, L. An Automatic Stopping Criterion for Contrast Enhancement Using Multi-scale Top-Hat Transformation. Sens Imaging 20, 26 (2019). https://doi.org/10.1007/s11220-019-0239-x

Download citation

  • Received:

  • Revised:

  • Published:

  • DOI: https://doi.org/10.1007/s11220-019-0239-x

Keywords

Search

Navigation