Development of algorithms for biomedical image segmentation based on preliminary markup and texture attributes

Authors

DOI:

https://doi.org/10.15587/1729-4061.2017.119299

Keywords:

histological and cytological images, segmentation, preliminary markup, spatial moments, segmentation estimation

Abstract

Biomedical images are used for diagnosis and treatment of malignant neoplasms. Images of normal and pathological cells and tissues are derived using light microscopes. These images are the objects of study in histology and cytology. One of the most important stages in the automation of measuring optical and geometrical parameters of images is the segmentation of micro objects. Analysis of biomedical images is complicated due to the highly variable parameters and weak contrast in most micro objects.

The use of point connections for segmenting the images has several advantages: processing images of any type, splitting micro objects that are in contact, insensitivity to noise. A method of image segmentation based on preliminary markup implies splitting a color image into homogeneous regions, calculating the coefficient of relation between adjacent points, and merging points into homogeneous regions. The algorithm allows for the automated segmentation.

A texture segmentation method involves computing values of spatial moments for each point of the image. A feature space, obtained in this way, is segmented by the algorithm. The algorithm calculates thresholds based on mathematical expectation. This makes it possible to identify such complex micro objects as the layers of cells, cross-sections of blood vessels and ducts.

The quality of segmentation was estimated using a metric approach. The developed segmentation algorithms made it possible to improve quality of the biomedical image segmentation by 18−21 % on average

Author Biographies

Yuriy Batko, Ternopil National Economic University Lvivska str., 11, Ternopil, Ukraine, 46020

PhD, Senior Lecturer

Department of Computer Engineering

Natalia Batryn, Ternopil National Economic University Lvivska str., 11, Ternopil, Ukraine, 46020

PhD, Associate Professor

Department of International Economic Relations

Grygoriy Melnyk, Ternopil National Economic University Lvivska str., 11, Ternopil, Ukraine, 46020

PhD, Associate Professor

Department of Computer Engineering

Serhiy Verbovyy, Ternopil National Economic University Lvivska str., 11, Ternopil, Ukraine, 46020

Lecturer

Department of Computer Engineering

Tamara Datsko, I. Horbachevsky Ternopil State Medical University Voli sq., 1, Ternopil, Ukraine, 46001

PhD, Associate Professor

Department of Pathological Anatomy with section course and Forensic medicine

Petro Selskyy, I. Horbachevsky Ternopil State Medical University Voli sq., 1, Ternopil, Ukraine, 46001

MD, Professor

Department of Pathological Anatomy with section course and Forensic medicine

References

  1. Argyris, G., Kapageridis, I., Triantafyllou, A. (2008). 3D Terrain Modelling of the Amyntaio – Ptolemais Basin. 2nd International Workshop in “Geoenvironment and Geotechnics”. Milos island.
  2. Berezsky, O. M., Batko, Y. M., Melnyk, G. M. (2009). Method of image segmentation based on previous layouts images. Proceedings of the 4th International Scientific-Technical Conference "Computer Science and Information Technology 2009". Lviv: "Tower and Co", 48–52.
  3. Berezsky, O. M., Berezska, K. M., Batko, Y. M., Melnyk, G. M. (2011). Vision-based medical expert system. 6th International Scientific and Technical Conference “Computer Sciences and Information Technologies”. Lviv, 49–50.
  4. Bieri, M., Wethmar, A., Wey, N. (2009). Quantitative analysis of Alzheimer plaques in mice using virtual microscopy. First European Workshop on Tissue Imaging and Analysis, 31–38.
  5. Muralidhar, G. S., Bovik, A. C., Giese, J. D., Sampat, M. P., Whitman, G. J., Haygood, T. M. et. al. (2010). Snakules: A Model-Based Active Contour Algorithm for the Annotation of Spicules on Mammography. IEEE Transactions on Medical Imaging, 29 (10), 1768–1780. doi: 10.1109/tmi.2010.2052064
  6. Berezsky, O. M., Batko, Y. M., Melnyk, G. M. (2009). Texture segmentation of biomedical images based on spatial moments. Proceedings of the 4th International Scientific-Technical Conference "Computer Science and Information Technology 2009". Lviv: "Tower and Co", 42–45.
  7. Berezsky, O., Batko, Y., Melnyk, G., Verbovyy, S., Haida, L. (2015). Segmentation of cytological and histological images of breast cancer cells. 2015 IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). doi: 10.1109/idaacs.2015.7340745
  8. Fu, K. S., Mui, J. K. (1981). A survey on image segmentation. Pattern Recognition, 13 (1), 3–16. doi: 10.1016/0031-3203(81)90028-5
  9. Pal, N. R., Pal, S. K. (1993). A review on image segmentation techniques. Pattern Recognition, 26 (9), 1277–1294. doi: 10.1016/0031-3203(93)90135-j
  10. Skarbek, W., Koschan, A. (1994). Color Image Segmentation – A Survey. Berlin, 81.
  11. Lucchese, L., Mitra, S. (2001). Color Image Segmentation: A State-of-the-Art Survey, Image Processing, Vision, and Pattern Recognition. Proceedingss of the Indian National Science Academy (INSA-A). New Delhi, India, 207–221.
  12. Jipkate, B. R., Gohokar, V. V. (2012). A Comparative Analysis of Fuzzy C-means Clustering and K-means Clustering Algorithms. International Journal of computational Engineering Research, 2 (3), 737–739.
  13. Thilagamani, S., Shanthi, N. (2011). A Survey on image segmentation through clustering. International journal of research and Information sciences, 1 (1).
  14. Sharma, N., Mishra, M., Shrivastava, M. (2012). Colour image segmentation techniques and issues: an approach. International Journal of Scientific & Technology Research, 1 (4), 9–12.
  15. Saini, S., Arora, K. (2014). A Study Analysis on the Different Image Segmentation Techniques. International Journal of Information & Computation Technology, 4 (14), 1445–1452.
  16. Belaid, L. J., Mourou, W. (2011). Image segmentation: a watershed transformation algorithm. Image Analysis & Stereology, 28 (2), 93. doi: 10.5566/ias.v28.p93-102
  17. Abirami, M. S., Sheela, T. (2014). Analysis of Image Segmentation Techniques for Medical Images. Proceedings of International Conference on Emerging Research in Computing, Information, Communication and Applications (ERCICA-14).
  18. Li, C., Huang, R., Ding, Z., Gatenby, J. C., Metaxas, D. N., Gore, J. C. (2011). A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities With Application to MRI. IEEE Transactions on Image Processing, 20 (7), 2007–2016. doi: 10.1109/tip.2011.2146190
  19. Berezsky, O. M., Berezska, K. M., Batko, Y. M., Melnyk, G. M. (2009). Design of computer systems for biomedical image analysis. Proceedings of the X-th International Conference "The Experience of Designing and Application of CAD Systems in Microelectronics" CADSM’2009. Lviv-Polyana, 186–192.
  20. Arbeláez, P., Maire, M., Fowlkes, C., Malik, J. (2011). Contour Detection and Hierarchical Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33 (5), 898–916. doi: 10.1109/tpami.2010.161
  21. Joshi, V. S., Shire, A. N. (2013). A Review of an Enhanced Algorithm for Color Image Segmentation. Journal of Advanced Research in Computer Science and Software Engineering, 3 (3), 435–441.
  22. Berezsky, O. N., Berezskaya, E. N. (2015). Quantitative evaluation of the quality of the segmentation of images based on metrics. Upravlyayushchie Sistemy i Mashiny, 6, 59–65.
  23. Atallah, M. J., Ribeiro, C. C., Lifschitz, S. (1991). A linear time algorithm for the computation of some distance functions between convex polygons. RAIRO – Operations Research, 25 (4), 413–424. doi: 10.1051/ro/1991250404131
  24. Berezsky, O., Pitsun, O. (2016). Automated Processing of Cytological and Histological Images. 2016 XII International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH). Lviv-Polyana, 51–53. doi: 10.1109/memstech.2016.7507518
  25. Berezky, O. M., Pitsun, O. Y., Verbovyi, S. O., Datsko, T. V. (2017). Relational database of intelligent automated microscopy system. Scientific Bulletin of UNFU, 27 (5), 125–129. doi: 10.15421/40270525

Downloads

Published

2017-12-25

How to Cite

Batko, Y., Batryn, N., Melnyk, G., Verbovyy, S., Datsko, T., & Selskyy, P. (2017). Development of algorithms for biomedical image segmentation based on preliminary markup and texture attributes. Eastern-European Journal of Enterprise Technologies, 6(4 (90), 35–44. https://doi.org/10.15587/1729-4061.2017.119299

Issue

Vol. 6 No. 4 (90) (2017): Mathematics and Cybernetics - applied aspects

Section

Mathematics and Cybernetics - applied aspects

License

Copyright (c) 2017 Yuriy Batko, Natalia Batryn, Grygoriy Melnyk, Serhiy Verbovyy, Tamara Datsko, Petro Selskyy

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.