Methods of processing medical images for the detection of pathologies in craniofacial surgery

Authors

  • Оксана Сергеевна Шкурат National Technical University of Ukraine «Kyiv Polytechnic Institute» Pobedyi ave, 37, Kyiv, Ukraine, 3056, Ukraine https://orcid.org/0000-0001-7633-9121
  • Андрей Вячеславович Соломин National Technical University of Ukraine «Kyiv Polytechnic Institute» Pobedyi ave, 37, Kyiv, Ukraine, 3056, Ukraine https://orcid.org/0000-0002-5226-8813

DOI:

https://doi.org/10.15587/1729-4061.2015.43334

Keywords:

smoothing filters, differential operators, distinguishing the boundaries, the Serra mathematical morphology

Abstract

Analyzing the algorithms for filtering digital images, we have pointed out dimensional image processing techniques. We have studied the performance of smoothing filters, the work of differential operators, and the efficiency of the Canny method. We have determined the peculiarities of applying the algorithms for identifying CT scans boundaries. We have researched the possibility of applying the Serra mathematical morphology operations for determining the most significant and muting uninformative image objects. While specifying the boundaries, we have visually assessed the quality of noise reduction operations, the efficiency of various operators and techniques as well as the rate of performance and the minimum average quadratic deviation.

As a result of the study, we have suggested an algorithm for obtaining the morphological features of pathologies at the stage of diagnostics in craniofacial surgery. The essence of the algorithm lies in distinguishing the original information from the resulting model. Obtaining the original information means getting rid of the noise from a set of tomographic slices with the Gaussian filter, identifying the most informative image objects by means of the operation of mathematical morphology Close, and, depending on to the type of pathology, outlining the shot boundaries with the Sobel, Roberts and Laplace differential operators as well as by the Canny method. The model construction includes mirroring the healthy side and processing the obtained information with the help of the above listed methods.

Author Biographies

Оксана Сергеевна Шкурат, National Technical University of Ukraine «Kyiv Polytechnic Institute» Pobedyi ave, 37, Kyiv, Ukraine, 3056

The department of biosafety and human health

Андрей Вячеславович Соломин, National Technical University of Ukraine «Kyiv Polytechnic Institute» Pobedyi ave, 37, Kyiv, Ukraine, 3056

Candidate of physics and mathematics sciences, associate professor

The department of biosafety and human health

References

  1. Lo, L.-J., Chen, Y.-R. (2003). Three-Dimensional Computed Tomography Imaging in Craniofacial Surgery: Morphological Study and Clinical Applications. Chang Gung Med J, 26 (1), 1–11.
  2. Rafael, C. Gonzalez, Richard E. Woods. (2002). Digital Image Processing. Available at: http://users.dcc.uchile.cl/~jsaavedr/libros/dip_gw.pdf.
  3. Dougherty, G. (2010). Digital Image Processing for Medical Applications. Cambridge university press.
  4. Boyat, A., Joshi, B. K. (2013). Image Denoising using Wavelet Transform and Median Filtering. IEEE Nirma University International Conference on Engineering. Ahmedabad. doi: 10.1109/nuicone.2013.6780128
  5. Luisier, F., Blu, T., Unser, M. (2011). Image Denoising in Mixed Poisson–Gaussian Noise. IEEE Transactions on Image Processing, 20 (3), 696–708. doi: 10.1109/tip.2010.2073477
  6. Patil, J., Jadhav, S. (2013). A Comparative Study of Image Denoising Techniques. International Journal of Innovative Research in Science, Engineering and Technology, 2-3, 787–793.
  7. Kamboj, P., Rani, V. (2013) A Brief study of various noise models and filtering techniques. Journal of Global Research in Computer Science, 4 (4).
  8. Boyat, A. K., Joshi, B. K. (2015). A Review Paper : Noise Models in Digital Image Processing. SIPIJ, 6 (2), 63–75. doi: 10.5121/sipij.2015.6206
  9. Chen, J. S., Huertas, A., Medioni, G. (1987). Fast Convolution with Laplacian-of-Gaussian Masks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9 (4), 584–590. doi: 10.1109/tpami.1987.4767946
  10. Sharif, M., Mohsin, S. (2012). Single Image Face Recognition Using Laplacian of Gaussian and Discrete Cosine Transforms. The International Arab Journal of Information Texnology, 9 (6), 562–570.
  11. Shrivakshan, G., Chandrasekar, C. (2012). A Comparison of various Edge Detection Techniques used in Image Processing. IJCSI International Journal of Computer Science Issues, 9 (1), 269–276.
  12. Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on pattern analysis and machine intelligence, 8 (6), 679–698. doi: 10.1109/tpami.1986.4767851
  13. Sharifzadeh, S., Hassanpour, H. (2008). Edge Detection Techniques: Evaluations and Comparisons. Applied Mathematical Sciences, 2 (31), 1507–1520.
  14. Vlasov, A. V., Tsapko, I. V. (2013). Modifikatsiya algoritma Kanni primenitelno k obrabotke rentgenograficheskih izobrazheniy. Vestnik nauki Sibiri, 4 (10), 120–127.
  15. Zhou. P., Ye, W., Xia, Y., Wang, Q. (2011). An Improved Canny Algorithm for Edge Detection. Available at: http://www.jofcis.com/publishedpapers/2011_7_5_1516_1523.pdf
  16. Serra, J., Vincent, L. (1992). An overview of morphological filtering. Circuits Systems and Signal Process, 11 (1), 47–108. doi: 10.1007/bf01189221
  17. Baghshah, M. S., Kasaei, S. (2008). An FPCA-Based Color Morphological Filter for Noise Removal. Scientia Iranica, 16 (1), 8–18.
  18. Afonasenko, A. V. (2006). Byistryie morfologicheskie preobrazovaniya dlya zadach korrektsii i preobrazovaniya binarnyih izobrazheniy. Izvestiya TPU, 8, 122–126.
  19. Bucha, V. V., Ablameyko, S. V. (2006). Matematicheskaya morfologiya na szhatom binarnom rastre: primenenie v GIS. Nauchno-teoreticheskiy zhurnal "Iskusstvennyiy intellekt", 2, 21–24.
  20. Subsol, G., Mafart, B., Delingette, H. (2002). 3D Image Processing for the Study of the Evolution of the Shape of the Human Skull: Presentation of the Tools and Preliminary Results. Three-Dimensional Imaging in Paleoanthropology and Prehistoric Archaeology, 37–45.
  21. Muraev, A. A., Dyimnikov, A. B., Korotkova, N. L., Kobets, K. K., Ivanov, S. Yu. (2013). Optimizatsiya metoda planirovaniya plasticheskih operatsiy v chelyustno-litsevoy oblasti. Sovremennyie tehnologii v meditsine, 3, 57–62.

Downloads

Published

2015-06-29

How to Cite

Шкурат, О. С., & Соломин, А. В. (2015). Methods of processing medical images for the detection of pathologies in craniofacial surgery. Eastern-European Journal of Enterprise Technologies, 3(2(75), 35–41. https://doi.org/10.15587/1729-4061.2015.43334

Issue

Vol. 3 No. 2(75) (2015): Information technology. Industry control systems

Section

Information technology

License

Copyright (c) 2015 Оксана Сергеевна Шкурат, Андрей Вячеславович Соломин

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.

A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.