Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2016, Special Issue (2016), 277 - 281, 01.12.2016
https://doi.org/10.18100/ijamec.270453

Öz

Kaynakça

  • [1] Seçil Erden, Stem cells and clinical applications, Journal of New Results in Engineering and Natural Science, No: 3, pp.1-8, 2014.
  • [2] Geisa Martins Faustino et. al., Automatic embryonic stem cells detection and counting in fluorescence microscopy images, Monografias em Ciência da Computação, No. 04/09 ISSN: 0103-9741, 2009.
  • [3] J.M. Geusebroek et al., Segmentation of cell clusters by nearest neighbor graphs, Proceedings of the third annual conference of the Advanced School for Computing and Imaging, pp. 248–252, 1997.
  • [4] V. Meas-Yedid et al. Quantitative microscopic image analysis by active contours, in Vision Interface Annual Conference 2001 – Medical Applications, 2001.
  • [5] J. Kittler and J. Illingworth, “Minimum error thresholding,” Pattern Recognition, vol. 19, no. 1, pp. 41–47, 1986.
  • [6] N. Otsu, A threshold selection method from gray-level histograms, IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62–66, 1979.
  • [7] K. Wu, D. Gauthier, and M. Levine, Live cell image segmentation, IEEE Transactions on Biomedical Engineering, vol. 42, no. 1, pp. 1–12, 1995.
  • [8] T. Markiewicz et al. Myelogenous leukemia cell image preprocessing for feature generation, in 5th International Workshop on Computational Methods in Electrical Engineering, pp. 70–73, 2003.
  • [9] K. Althoff, J. Degerman, and T Gustavsson. Combined segmentation and tracking of neural stemcells. In Image Analysis, 2005.
  • [10] C. Tang and E. Bengtsson. Segmentation and tracking of neural stem cell. In Advances in Intelligent Computing, pages 851–859. 2005.
  • [11] N. N. Kachouie, P. Fieguth, and E. Jervis. Stem-cell localization: A deconvolution problem. In EMBS, 5525 – 5528, 2007.
  • [12] N. N. Kachouie, Paul Fieguth, John Ramunas, and Eric Jervis. Probabilisticmodel-based cell tracking. Int. Journal of Biomedical Imaging, pages 1 – 10, 2006.
  • [13] N. N. Kachouie, L. J. Lee, and P. Fieguth. A probabilistic living cell segmentation model. In ICIP, pages 137 – 140, 2005.
  • [14] C. Gonzalez & R. E. Woods. Gonzalez. Digital Image Processing, 3rd ed.2008.
  • [15] Yingmao Li, Asif Iqbal and Nicholas R. Gans, Multiple lane boundary detection using a combination of low-level image features, IEEE 17th International Conference on Intelligent Transportation Systems (ITSC) October 8-11, 2014.
  • [16] Mark A. Foltz. Connected components in binary images. 6.866: Machine Vision, December 1997.
  • [17] Çetinel G., Kamanlı A. F, Automatic Embryonic Stem Cell Counting Method Isıtes international Conference 2015
  • [18] W.K.Pratt Digital Image Processing: PIKS Scientific Inside ISBN-13: 978-0471767770, ISBN-10: 0471767778

Detection and Counting of Embryonic Stem Cells in Fluorescence Microscopy Images by a Fully Automatic Method

Yıl 2016, Special Issue (2016), 277 - 281, 01.12.2016
https://doi.org/10.18100/ijamec.270453

Öz

 In this
paper, an automatic cell counting method under microscopy is proposed. The cell
counting process can be performed in two ways: The manual counting in which a
specialist counts the cells with naked eye, and the automatic counting that
utilizes the computer-based techniques. In manual counting, there are several
techniques for dying the cells to turn them visible with naked eye. However, if
the concentration is more than normal the cells can overlap. Overlap and
incorrect adjusted microscopy parameters are the main factors that cause
inaccurate counting results. Furthermore, in manual counting inter-observer
variability is high. Even though the same cell image is taken into account by
the different specialist, different counting results can be obtained. Because
of the above mentioned problems, the cell counting process must be performed
automatically.

    The proposed automatic stem cell counting process
is based on image processing techniques that appropriate the frame of method.
At first, stem cell sections were obtained under the fluorescence microscopy.
In the following pre-processing step Gaussian filtering and background
extraction are performed. Before applying watershed algorithm histogram of the
image is partitioned in to four parts and the best combination is determined to
obtain the most exact counting results. The aim of using watershed algorithm is
to make the boundaries and maximum points of the cells more clear. Finally, spherical
contours corresponding to the stem cells are counted.





    The effectiveness of the proposed method is
evaluated by performing numerous computer simulations. It is shown that the
proposed method gives promising results and can eliminate the subjectivity
originated from the manual counting. The method is tested on a database
contains two image groups at different noise levels validated by the
specialists.

Kaynakça

  • [1] Seçil Erden, Stem cells and clinical applications, Journal of New Results in Engineering and Natural Science, No: 3, pp.1-8, 2014.
  • [2] Geisa Martins Faustino et. al., Automatic embryonic stem cells detection and counting in fluorescence microscopy images, Monografias em Ciência da Computação, No. 04/09 ISSN: 0103-9741, 2009.
  • [3] J.M. Geusebroek et al., Segmentation of cell clusters by nearest neighbor graphs, Proceedings of the third annual conference of the Advanced School for Computing and Imaging, pp. 248–252, 1997.
  • [4] V. Meas-Yedid et al. Quantitative microscopic image analysis by active contours, in Vision Interface Annual Conference 2001 – Medical Applications, 2001.
  • [5] J. Kittler and J. Illingworth, “Minimum error thresholding,” Pattern Recognition, vol. 19, no. 1, pp. 41–47, 1986.
  • [6] N. Otsu, A threshold selection method from gray-level histograms, IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62–66, 1979.
  • [7] K. Wu, D. Gauthier, and M. Levine, Live cell image segmentation, IEEE Transactions on Biomedical Engineering, vol. 42, no. 1, pp. 1–12, 1995.
  • [8] T. Markiewicz et al. Myelogenous leukemia cell image preprocessing for feature generation, in 5th International Workshop on Computational Methods in Electrical Engineering, pp. 70–73, 2003.
  • [9] K. Althoff, J. Degerman, and T Gustavsson. Combined segmentation and tracking of neural stemcells. In Image Analysis, 2005.
  • [10] C. Tang and E. Bengtsson. Segmentation and tracking of neural stem cell. In Advances in Intelligent Computing, pages 851–859. 2005.
  • [11] N. N. Kachouie, P. Fieguth, and E. Jervis. Stem-cell localization: A deconvolution problem. In EMBS, 5525 – 5528, 2007.
  • [12] N. N. Kachouie, Paul Fieguth, John Ramunas, and Eric Jervis. Probabilisticmodel-based cell tracking. Int. Journal of Biomedical Imaging, pages 1 – 10, 2006.
  • [13] N. N. Kachouie, L. J. Lee, and P. Fieguth. A probabilistic living cell segmentation model. In ICIP, pages 137 – 140, 2005.
  • [14] C. Gonzalez & R. E. Woods. Gonzalez. Digital Image Processing, 3rd ed.2008.
  • [15] Yingmao Li, Asif Iqbal and Nicholas R. Gans, Multiple lane boundary detection using a combination of low-level image features, IEEE 17th International Conference on Intelligent Transportation Systems (ITSC) October 8-11, 2014.
  • [16] Mark A. Foltz. Connected components in binary images. 6.866: Machine Vision, December 1997.
  • [17] Çetinel G., Kamanlı A. F, Automatic Embryonic Stem Cell Counting Method Isıtes international Conference 2015
  • [18] W.K.Pratt Digital Image Processing: PIKS Scientific Inside ISBN-13: 978-0471767770, ISBN-10: 0471767778
Toplam 18 adet kaynakça vardır.

Ayrıntılar

Konular Mühendislik
Bölüm Research Article
Yazarlar

Gökçen Çetinel

Ali Furkan Kamanlı Bu kişi benim

Yayımlanma Tarihi 1 Aralık 2016
Yayımlandığı Sayı Yıl 2016 Special Issue (2016)

Kaynak Göster

APA Çetinel, G., & Kamanlı, A. F. (2016). Detection and Counting of Embryonic Stem Cells in Fluorescence Microscopy Images by a Fully Automatic Method. International Journal of Applied Mathematics Electronics and Computers(Special Issue-1), 277-281. https://doi.org/10.18100/ijamec.270453
AMA Çetinel G, Kamanlı AF. Detection and Counting of Embryonic Stem Cells in Fluorescence Microscopy Images by a Fully Automatic Method. International Journal of Applied Mathematics Electronics and Computers. Aralık 2016;(Special Issue-1):277-281. doi:10.18100/ijamec.270453
Chicago Çetinel, Gökçen, ve Ali Furkan Kamanlı. “Detection and Counting of Embryonic Stem Cells in Fluorescence Microscopy Images by a Fully Automatic Method”. International Journal of Applied Mathematics Electronics and Computers, sy. Special Issue-1 (Aralık 2016): 277-81. https://doi.org/10.18100/ijamec.270453.
EndNote Çetinel G, Kamanlı AF (01 Aralık 2016) Detection and Counting of Embryonic Stem Cells in Fluorescence Microscopy Images by a Fully Automatic Method. International Journal of Applied Mathematics Electronics and Computers Special Issue-1 277–281.
IEEE G. Çetinel ve A. F. Kamanlı, “Detection and Counting of Embryonic Stem Cells in Fluorescence Microscopy Images by a Fully Automatic Method”, International Journal of Applied Mathematics Electronics and Computers, sy. Special Issue-1, ss. 277–281, Aralık 2016, doi: 10.18100/ijamec.270453.
ISNAD Çetinel, Gökçen - Kamanlı, Ali Furkan. “Detection and Counting of Embryonic Stem Cells in Fluorescence Microscopy Images by a Fully Automatic Method”. International Journal of Applied Mathematics Electronics and Computers Special Issue-1 (Aralık 2016), 277-281. https://doi.org/10.18100/ijamec.270453.
JAMA Çetinel G, Kamanlı AF. Detection and Counting of Embryonic Stem Cells in Fluorescence Microscopy Images by a Fully Automatic Method. International Journal of Applied Mathematics Electronics and Computers. 2016;:277–281.
MLA Çetinel, Gökçen ve Ali Furkan Kamanlı. “Detection and Counting of Embryonic Stem Cells in Fluorescence Microscopy Images by a Fully Automatic Method”. International Journal of Applied Mathematics Electronics and Computers, sy. Special Issue-1, 2016, ss. 277-81, doi:10.18100/ijamec.270453.
Vancouver Çetinel G, Kamanlı AF. Detection and Counting of Embryonic Stem Cells in Fluorescence Microscopy Images by a Fully Automatic Method. International Journal of Applied Mathematics Electronics and Computers. 2016(Special Issue-1):277-81.

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