Науковий вісник НГУ

Decomposition of images of aggregated elements in dispersive formations by their structure

• 
• 

User Rating:  / 1

PoorBest 

Details

Category: Information technologies, systems analysis and administration

Last Updated on 21 July 2014

Published on 24 December 2013

Hits: 3855

Authors:

Т.А. Ruzova, Cand. Sci. (Tech.), O. Gonchar Dnepropetrovsk National University, Senior Research Scientist of Research Laboratory of Fluid Mechanics and Heat-Mass Exchange Processes Modeling, Dnipropetrovsk, Ukraine

V.I. Yeliseyev, Cand. Sci. (Phys.-Math.),Senior Research Fellow, Dnepropetrovsk National University of O. Gonchar, Senior Research Scientist of Research Laboratory of Fluid Mechanics and Heat-Mass Exchange Processes Modeling, Dnepropetrovsk, Ukraine

A.P. Tolstopyat, Cand. Sci. (Tech.), Senior Research Fellow, O. Gonchar Dnepropetrovsk National University, Senior Research Scientist of Research Laboratory of Fluid Mechanics and Heat-Mass Exchange Processes Modeling, Dnipropetrovsk, Ukraine

L.A. Fleyer, O. Gonchar Dnepropetrovsk National University, Senior Research Scientist of Research Laboratory of Fluid Mechanics and Heat-Mass Exchange Processes Modeling, Dnipropetrovsk, Ukraine

Abstract:

The main problems of the micro objects measuring by their video images are: low contrast, lack of lighting, background noisiness, and the presence of aggregated structures. The incorrect processing leads to substantial distortion of the measuring results.  

Purpose. To create the method for segmentation of aggregated structures of spherical particles (emulsion drops) in dispersive formations. The method is to be resistant to image noises and to allow processing aggregates of complex shape.

Methodology. The method includes several steps: source image filtering, converting it to monochrome mode, evaluating coordinates of aggregate’s boundary points, and making skeleton on the base of Zhang–Suen algorithm. The connecting points are defined as the narrowest places of examined aggregate (necks). To find connecting points, we propose to work in special function on each skeleton branch. This function characterizes the width of aggregate area corresponding to the examined branch in given point. The particles connecting points are defined as points of local minimum for mentioned function. Low-frequency filter have been used for smoothing function and decrease boundary noises influence. We have developed the method for correcting this function in order to smooth errors of object’s boundary raster representation.

The effect of the method is illustrated at model images as well as at the images of real emulsion fragments (type II emulsions, water in oil).

References:

1. Гонсалес Р. Цифровая обработка изображений / Р. Гонсалес, Р. Вудс – М.: Техносфера, 2005. – 1072 с.

Gonzales, R.C. and Woods, R.E. (2008), Digital image processing, Upper Saddle River, Nj: Pearson Prentice Hall.

2. Singh, V., Marinescu, D.C. and Baker T.S. (2004), “Image segmentation for automatic particle identification in electron micrographs based on hidden Markov random field models and expectation maximization”, Journal of Structural Biology, no. 145. pp. 123–141.

3. Модифицированный рэлеевский детектор слабоконтрастных границ двумерных объектов на изображении / А.А. Кравцов, О.С.Сидоркина, Д.В. Юрин // 16-я международная конференция по компьютерной графике и ее приложениям ГрафиКон’2006. – Новосибирск: Академгородок. – 2006. – С. 351−354.

Kravtsov, A.A., Sidorkina, O.S. and Yurin, D.V. “Modified Rayleigh Detector of Low Contrast 2D Objects Boundaries on Image”, The 16th International Conference “Computer Graphics and Applications (GraphiCon’2006)”, Akademgorodok, Novosibirsk, Russia, pp. 351−354.

4. Хисамутдинов М.В. Фильтрация изображений с целью выделения эллипсов / М.В Хисамутдинов // Искусственный интеллект. – 2008. – № 4. – С. 429–437.

Hisamutdinov, M.V. (2008), “The filtration of images for the purpose of the isolation of ellipses”, Iskusstvenniy intellekt, no. 4, pp. 429–437.

5. Honkanen, M. (2007), “Analysis of the overlapping images of irregularly-shaped particles, bubbles and droplets”, The International Conference on Multiphase Flow, Leipzig, pp. 370–382.

6. Kutalik, Z., Razaz, M. and Baranyi, J. (2004), “Occluding convex image segmentation for e.coli microscopy images”, XII European Signal Processing Conference EUSIPCO, Viena, pp. 937–940.

7. Ritter, N., and Cooper, J. (2007), “Segmentation and Border Identification of Cells in Images of Peripheral Blood Smear Slides”, Proceedings of the 30th Australasian Computer Science Conference (ACSC2007), Ballarat, pp. 161–169.

8. Рузова Т.А. Модель пороговой классификации видеоизображений дисперсных образований / Т.А. Рузова // Збірник наукових праць НГУ. – 2007. – С. 162–167.

Ruzova, T.A. (2007), “Thresholding model of dispersive structures videoimages”, Zbirnyk naukovyx prats NGU, Dnipropetrovs: RVK NGU, pp. 162–167.

9. Рузова Т.А. Построение скелетов изображений агрегированных объектов дисперсий / Т.А. Рузова // Науковий вісник Національного гірничого університету. – 2012. – № 1(127). – С. 107–112.

Ruzova, T.A, (2012), “Dispersion aggregated objects images skeletonization”, NaukovyiVisnykNatsionalnohoHirnychohoUniversytetu, Dnipropetrovsk, no. 1(127), pp. 107–112.

2013_6_ruzova
2014-07-21  331.95 KB  1007

• Next >