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Visual system-based object tracking using image segmentation for biomedical applications

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Abstract

The main idea of proposed paper is to specify and verify the segmentation method (or a group of segmentation techniques) ideal for tracking a moving object in a scene. The object is represented by an animal (guinea pig) in a laboratory cage. Object tracking based on visual system uses and compares different techniques of segmentation: global thresholding, adaptive thresholding, differential operators used for motion detection in a video sequence and color matching algorithm implemented from industrial applications. Animal activity is a significant marker corresponding with health status. Trajectory of an animal is a good quantitative parameter for evaluation of its activity. In our case, animal tracking was used for induced gastrointestinal diseases (gastric ulcer) in the group of guinea pigs. The results serve as a supporting tool in pharmacology and pathological physiology to set optimal therapy and medication for selected groups of diseases and also help to understand the mechanisms of diseases in the medical research. Experimental measurements were taken in the large database of healthy (reference) animals and “non-healthy” ones. The visual system-based animal (object) tracking could be used as a non-expensive and flexible (reconfigurable) replacement of radio-frequency identifiers. The paper also discusses the possibility to use introduced methods in real-time applications.

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Abbreviations

\(\mu \) :

Mean value

AND:

Logical multiplication

B(i; j):

Pixel at position [i; j] in background corrected image

CF:

Current (actual) frame (of video sequence)

CoefVar :

Coefficient of variation

d(n):

n-th frame of differential image

E(k) :

Error of pixel misclassification

f(n) :

n-th frame of video sequence

\(f_\mathrm{ref }\) :

Reference frame of video sequence (or empty background image)

\(H_\mathrm{b}\) :

Entropy of background (black) pixels

HSI :

Hue/saturation/intensity color space

\(H_\mathrm{w}\) :

Entropy of foreground (white) pixels

I(i; j):

Pixel at position [i; j] in original image

IP:

Internet protocol

IR:

Infra red

k :

Image threshold value

m(i; j):

Mean value of pixel region centered at position [i; j]

MAX:

Maximal value of given set of numbers

MIN:

Minimal value of given set of numbers

NF:

Next frame (of video sequence)

p(i; j) :

Pixel value at position [i; j]

\(P_\mathrm{b}\) :

Probability of occurrence of background (black) pixels

PF:

Previous frame (of video sequence)

\(P_\mathrm{w}\) :

Probability of occurrence of foreground (white) pixels

RFID:

Radio-frequency identification

RGB:

Red/green/blue color space

ROI:

Region of interest

StdDev:

Standard deviation of given set of numbers

\(\Delta x\) :

Difference of trajectory in x-direction

\(\Delta y\) :

Difference of trajectory in y-direction

References

  1. Halička J, Tatár M (2013) Upper gastrointenstinal visceral pain models in Guinea-pigs (mini-review). Acta Medica Martiniana, Suppl. 1. doi:10.2478/acm-2013-0005

  2. Catarinucci L, Colella R, Mainetti L, Patrono L, Pieretti S, Sergi I, Tarricone L (2014) Smart RFID antenna system for indoor tracking and behaviour analysis of small animal in colony cages. IEEE Sens J 14(4):1198–1206

    Article  Google Scholar 

  3. Catarinucci L, Colella R, Mainetti L, Mighali V, Patrono L, Sergi I, Tarricone L (2012) An innovative animals tracking system based on passive UHF RFID technology. 2012 20th international conference on software, telecommunications and computer networks (SoftCOM). ISBN: 978-1-4673-2710-7

  4. Frivaldsky M, Cuntala J, Spanik P et al (2016) Investigation of thermal effects and lifetime estimation of electrolytic double layer capacitors during repeated charge and discharge cycles in dedicated application. Electr Eng. ISSN: 0948-7921. doi:10.1007/s00202-016-0482-2

  5. Miková L’, Trebuňa F, Kelemen M (2013) Concept of locmotion mobile undercarriage structure control for the path tracking. Solid State Phenom 198:79–83

    Article  Google Scholar 

  6. Cuntala J, Kondelova A, Hock O, Pridala M (2016) Electro-thermal modeling of power LED using COMSOL environment. In: ELEKTRO 11th international conference, pp. 127–130. ISBN:978-1-4673-8698-2

  7. Collins RT, Gross R, Jianbo S (2002) Silhouette-based human identification from body shape and gait. In: Proceedings of 5th IEEE international conference on automatic face gesture recognition

  8. Nobuyuki O (1979) A threshold selection method from gray-level histograms. IEEE Trans Sys Man Cyber 9(1):62–66. doi:10.1109/TSMC.1979.4310076

    Article  Google Scholar 

  9. Sobral Andrews BGSLibrary (2013) An OpenCV C++ background subtraction library. IX Workshop de Visão Computacional (WVC’2013), Rio de Janeiro. https://github.com/andrewssobral/bgslibrary

  10. Piccardi M (2004) Background subtraction techniques: a review. In: 2004 IEEE international conference on systems, man and cybernetics, vol 4, pp 3099–3104. IEEE

  11. Zheng L, Zhang S, Zhang J, Zhang Y (2014) Dynamic image mosaic via SIFT and dynamic programming. Mach Vis Appl 25:1271–1282

    Article  Google Scholar 

  12. Luthon F, Liévin M (2002) Entropy power for thresholding technique in image processing. In: XI European signal processing conference (EUSIPCO’02), Sep 2002, Toulouse, France, pp 605–608

  13. Liscano R, Wong AKC (1995) A study into entropy-based thresholding for image edge detection. In: Proceedings of vision interface ’95, May 16–19, Québec

  14. Kapur JN, Sahoo P, Wong AKC (1980) A new method for gray-level picture thresholding using the entropy of the histogram. Comput Vis Graph Image Process 29(3):273–285. doi:10.1016/0734-189X(85)90125-2

    Article  Google Scholar 

  15. National Instruments (2013) NI vision concepts help

  16. Islam MM, Lee G, Hettiwatte SN (2016) A nearest neighbour clustering approach for incipient fault diagnosis of power transformers. Electr Eng. ISSN: 0948-7921. doi:10.1007/s00202-016-0481-3

  17. Gokozan H, Taskin S, Seker S et al (2015) A neural network based approach to estimate of power system harmonics for an induction furnace under the different load conditions. Electr Eng. ISSN: 0948-7921. doi:10.1007/s00202-014-0320-3

  18. Danikas MG, Gao N, Aro M (2003) Partial discharge recognition using neural networks: a review. Electr Eng. ISSN: 0948-7921. doi:10.1007/s00202-002-0151-5

  19. Cha S-H (2007) Comprehensive survey on distance/similarity measures between probability density functions. Int J Math Models Methods Appl Sci 1(4):300–307

    MathSciNet  Google Scholar 

Download references

Acknowledgements

Authors of this paper wish to kindly thank to all supporting bodies, especially to Grant APVV-15-0462: Research on sophisticated methods for analyzing the dynamic properties of respiratory epithelium’s microscopic elements and projects Centre of Experimental and Clinical Respirology,” IMTS code: 26220120004 and “Measurement of Respiratory Epithelium Cilium Kinematics,” IMTS code: 26220120019 funded by European Community, also for financial support to Slovak Research.

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Authors and Affiliations

  1. Department of Mechatronics and Electronics, University of Žilina, Univerzitná 1, 01026, Žilina, Slovakia

    Dušan Koniar, Libor Hargaš, Zuzana Loncová & Anna Simonová

  2. Institute of Robotics and Cybernetics, Slovak University of Technology in Bratislava, Ilkovičova 3, 812 19, Bratislava, Slovakia

    František Duchoň & Peter Beňo

Authors
  1. Dušan Koniar
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  2. Libor Hargaš
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  3. Zuzana Loncová
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  4. Anna Simonová
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  5. František Duchoň
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  6. Peter Beňo
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Corresponding author

Correspondence to Dušan Koniar.

Additional information

Paper is extended version for special issue ELEKTRO 2016.

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Koniar, D., Hargaš, L., Loncová, Z. et al. Visual system-based object tracking using image segmentation for biomedical applications. Electr Eng 99, 1349–1366 (2017). https://doi.org/10.1007/s00202-017-0609-0

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  • DOI: https://doi.org/10.1007/s00202-017-0609-0

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