Human Cell Detection in Microscopic Images through Discrete Cosine Transform and Gaussian Mixture Model

Faezeh Rohani; Hamid Hassannia; Mohammad Amin MoghaddasiFar; Elham Sagheb

doi:doi:10.11648/j.cbb.20140204.11

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Human Cell Detection in Microscopic Images through Discrete Cosine Transform and Gaussian Mixture Model

Faezeh Rohani, Hamid Hassannia, Mohammad Amin MoghaddasiFar, Elham Sagheb

Published in Computational Biology and Bioinformatics (Volume 2, Issue 4)

Received: 19 August 2014 Accepted: 4 September 2014 Published: 20 September 2014

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Abstract

Automatic detection of human cell is still one of the most common investigation methods that may be used as part of a computer aided medical decision making system[1]. In this paper a statistical method based on Gaussian Mixture Model is applied to human cell detection in microscopic images[2]. 120 normal microscopic images of human cell from our research laboratory were used for analysis. Texture and grayscale features extracted from blocks of these images are given to Gaussian Mixture Model as input. It is used to model this data into three classes which are cell, extra cellular space and cell membrane [3]. Our proposed algorithm is applied on a sample dataset and experimental results show that this model is both accurate and fast with overall detection rate of around 91.23%. Error rate for cell detection was 1.82%.

Published in	Computational Biology and Bioinformatics (Volume 2, Issue 4)
DOI	10.11648/j.cbb.20140204.11
Page(s)	52-56
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Human Cell Detection, Cell Segmentation, DCT, Gaussian Mixture Model

References

[1]	Musen, Mark A., Blackford Middleton, and Robert A. Greenes. "Clinical decision-support systems." Biomedical informatics. Springer London, 2014. 643-674.
[2]	Nogueira, Pedro A., and Luís Filipe Teófilo. "Automatic analysis of Leishmania infected microscopy images via Gaussian mixture models." Advances in Artificial Intelligence-SBIA 2012. Springer Berlin Heidelberg, 2012. 82-91.
[3]	Mathivanan, Suresh, Hong Ji, and Richard J. Simpson. "Exosomes: extracellular organelles important in intercellular communication." Journal of proteomics 73.10 (2010): 1907-1920.
[4]	Camazine, Scott. Self-organization in biological systems. Princeton University Press, 2003.
[5]	K Wu, D Gauthier, MD Levine, “Live cell image segmentation,” IEEE Transaction on Biomedical Engineering, vol 42, pp. 1-12, 1995.
[6]	HS Wu, J Berba, J Gil, “Iterative thresholding for segmentation of cells from noisy images,” Journal of Microscopy, vol 197, pp. 296-304, 2000.
[7]	Tafti, A. P., Naji, H. R. Malakooti, M. V., “An Efficient Algorithm for Human Cell Detection in Electron Microscope Images based on Cluster Analysis and Vector Quantization,” Second International Conference on Digital Information and Communication Technology and it's Applications (DICTAP), pp. 125 – 129. IEEE, Bangkok, 2012.
[8]	AA Aly, SB Deris, N Zaki, “Research review for digital image segmentation techniques,”International journal of compute science & Information technology, vol3, No 5, 2011.
[9]	Bovik, A.C., The essential guide to image processing, Academic Press, 2009.
[10]	McNicholas, Paul D., and Thomas Brendan Murphy. "Model-based clustering of microarray expression data via latent Gaussian mixture models." Bioinformatics 26.21 (2010): 2705-2712.
[11]	SH Han, E Ackerstaff, R Stoyanova, S Carlin, W. Huang, J. A. Koutcher, J. K. Kim, G. Cho, G. Jang, and H. Cho, “Gaussian mixture model-based classification of dynamic contrast enhanced MRI data for identifying diverse tumor microenvironments: preliminary results, ” NMR in Biomedicine, vol 26, pp. 519-532, 2013.
[12]	P Mayorga, C Druzgalski, RL Morelos, O. H. Gonzalez, and J. Vidales, “Acoustics based assessment of respiratory diseases using GMM classification,”Annual international conference of the IEEE Engineering in Medicine and Biology Society , Buenos Aires, 2010.
[13]	Pun, Chi-Man, and Hong-Min Zhu. "Image Segmentation Using Discrete Cosine Texture Feature." Rn 1 (2010): 1.
[14]	Rubel, Aleksey, Vladimir Lukin, and Oleksiy Pogrebnyak. "Efficiency of DCT-Based Denoising Techniques Applied to Texture Images." Pattern Recognition. Springer International Publishing, 2014. 261-270.
[15]	Glob GH, Van Loan, Matrix computation. 2nd ed. Baltimore, Johns Hopkins University Press, 1989.
[16]	http://www.cs.waikato.ac.nz/ml/weka/
[17]	Tafti, A. P., Rohani, F., Moghadasifar, M., “Towards a scalable G2G framework for customs information system through N-Tier architecture,” International Conference on e-Learning and e-Technologies in Education , pp. 175 – 179. IEEE, Lodz, 2012.
[18]	Tafti, A. P., Janosepah, S., Modiri, N., “Development of a Framework for Applying ASYCUDA System with N-Tier Application Architecture,” Second International Conference, ICSECS, Part III. Communications in Computer and Information Science, vol 181, pp. 533-541. Springer, 2011.

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APA Style

Faezeh Rohani, Hamid Hassannia, Mohammad Amin MoghaddasiFar, Elham Sagheb. (2014). Human Cell Detection in Microscopic Images through Discrete Cosine Transform and Gaussian Mixture Model. Computational Biology and Bioinformatics, 2(4), 52-56. https://doi.org/10.11648/j.cbb.20140204.11

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ACS Style

Faezeh Rohani; Hamid Hassannia; Mohammad Amin MoghaddasiFar; Elham Sagheb. Human Cell Detection in Microscopic Images through Discrete Cosine Transform and Gaussian Mixture Model. Comput. Biol. Bioinform. 2014, 2(4), 52-56. doi: 10.11648/j.cbb.20140204.11

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AMA Style

Faezeh Rohani, Hamid Hassannia, Mohammad Amin MoghaddasiFar, Elham Sagheb. Human Cell Detection in Microscopic Images through Discrete Cosine Transform and Gaussian Mixture Model. Comput Biol Bioinform. 2014;2(4):52-56. doi: 10.11648/j.cbb.20140204.11

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@article{10.11648/j.cbb.20140204.11,
  author = {Faezeh Rohani and Hamid Hassannia and Mohammad Amin MoghaddasiFar and Elham Sagheb},
  title = {Human Cell Detection in Microscopic Images through Discrete Cosine Transform and Gaussian Mixture Model},
  journal = {Computational Biology and Bioinformatics},
  volume = {2},
  number = {4},
  pages = {52-56},
  doi = {10.11648/j.cbb.20140204.11},
  url = {https://doi.org/10.11648/j.cbb.20140204.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cbb.20140204.11},
  abstract = {Automatic detection of human cell is still one of the most common investigation methods that may be used as part of a computer aided medical decision making system[1]. In this paper a statistical method based on Gaussian Mixture Model is applied to human cell detection in microscopic images[2]. 120 normal microscopic images of human cell from our research laboratory were used for analysis. Texture and grayscale features extracted from blocks of these images are given to Gaussian Mixture Model as input. It is used to model this data into three classes which are cell, extra cellular space and cell membrane [3]. Our proposed algorithm is applied on a sample dataset and experimental results show that this model is both accurate and fast with overall detection rate of around 91.23%. Error rate for cell detection was 1.82%.},
 year = {2014}
}

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TY  - JOUR
T1  - Human Cell Detection in Microscopic Images through Discrete Cosine Transform and Gaussian Mixture Model
AU  - Faezeh Rohani
AU  - Hamid Hassannia
AU  - Mohammad Amin MoghaddasiFar
AU  - Elham Sagheb
Y1  - 2014/09/20
PY  - 2014
N1  - https://doi.org/10.11648/j.cbb.20140204.11
DO  - 10.11648/j.cbb.20140204.11
T2  - Computational Biology and Bioinformatics
JF  - Computational Biology and Bioinformatics
JO  - Computational Biology and Bioinformatics
SP  - 52
EP  - 56
PB  - Science Publishing Group
SN  - 2330-8281
UR  - https://doi.org/10.11648/j.cbb.20140204.11
AB  - Automatic detection of human cell is still one of the most common investigation methods that may be used as part of a computer aided medical decision making system[1]. In this paper a statistical method based on Gaussian Mixture Model is applied to human cell detection in microscopic images[2]. 120 normal microscopic images of human cell from our research laboratory were used for analysis. Texture and grayscale features extracted from blocks of these images are given to Gaussian Mixture Model as input. It is used to model this data into three classes which are cell, extra cellular space and cell membrane [3]. Our proposed algorithm is applied on a sample dataset and experimental results show that this model is both accurate and fast with overall detection rate of around 91.23%. Error rate for cell detection was 1.82%.
VL  - 2
IS  - 4
ER  -

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Author Information

Faezeh Rohani

Department of Computer Science, Sanabad Institute of Higher Education, Golbahar , IRAN
Hamid Hassannia

IEEE Member, Tehran, IRAN
Mohammad Amin MoghaddasiFar

Department of Computer Science, Sanabad Institute of Higher Education, Golbahar , IRAN
Elham Sagheb

Ferdowsi University of Mashhad, Mashhad, IRAN

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APA Style

Faezeh Rohani, Hamid Hassannia, Mohammad Amin MoghaddasiFar, Elham Sagheb. (2014). Human Cell Detection in Microscopic Images through Discrete Cosine Transform and Gaussian Mixture Model. Computational Biology and Bioinformatics, 2(4), 52-56. https://doi.org/10.11648/j.cbb.20140204.11

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ACS Style

Faezeh Rohani; Hamid Hassannia; Mohammad Amin MoghaddasiFar; Elham Sagheb. Human Cell Detection in Microscopic Images through Discrete Cosine Transform and Gaussian Mixture Model. Comput. Biol. Bioinform. 2014, 2(4), 52-56. doi: 10.11648/j.cbb.20140204.11

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AMA Style

Faezeh Rohani, Hamid Hassannia, Mohammad Amin MoghaddasiFar, Elham Sagheb. Human Cell Detection in Microscopic Images through Discrete Cosine Transform and Gaussian Mixture Model. Comput Biol Bioinform. 2014;2(4):52-56. doi: 10.11648/j.cbb.20140204.11

Copy | Download

@article{10.11648/j.cbb.20140204.11,
  author = {Faezeh Rohani and Hamid Hassannia and Mohammad Amin MoghaddasiFar and Elham Sagheb},
  title = {Human Cell Detection in Microscopic Images through Discrete Cosine Transform and Gaussian Mixture Model},
  journal = {Computational Biology and Bioinformatics},
  volume = {2},
  number = {4},
  pages = {52-56},
  doi = {10.11648/j.cbb.20140204.11},
  url = {https://doi.org/10.11648/j.cbb.20140204.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cbb.20140204.11},
  abstract = {Automatic detection of human cell is still one of the most common investigation methods that may be used as part of a computer aided medical decision making system[1]. In this paper a statistical method based on Gaussian Mixture Model is applied to human cell detection in microscopic images[2]. 120 normal microscopic images of human cell from our research laboratory were used for analysis. Texture and grayscale features extracted from blocks of these images are given to Gaussian Mixture Model as input. It is used to model this data into three classes which are cell, extra cellular space and cell membrane [3]. Our proposed algorithm is applied on a sample dataset and experimental results show that this model is both accurate and fast with overall detection rate of around 91.23%. Error rate for cell detection was 1.82%.},
 year = {2014}
}

Copy | Download

TY  - JOUR
T1  - Human Cell Detection in Microscopic Images through Discrete Cosine Transform and Gaussian Mixture Model
AU  - Faezeh Rohani
AU  - Hamid Hassannia
AU  - Mohammad Amin MoghaddasiFar
AU  - Elham Sagheb
Y1  - 2014/09/20
PY  - 2014
N1  - https://doi.org/10.11648/j.cbb.20140204.11
DO  - 10.11648/j.cbb.20140204.11
T2  - Computational Biology and Bioinformatics
JF  - Computational Biology and Bioinformatics
JO  - Computational Biology and Bioinformatics
SP  - 52
EP  - 56
PB  - Science Publishing Group
SN  - 2330-8281
UR  - https://doi.org/10.11648/j.cbb.20140204.11
AB  - Automatic detection of human cell is still one of the most common investigation methods that may be used as part of a computer aided medical decision making system[1]. In this paper a statistical method based on Gaussian Mixture Model is applied to human cell detection in microscopic images[2]. 120 normal microscopic images of human cell from our research laboratory were used for analysis. Texture and grayscale features extracted from blocks of these images are given to Gaussian Mixture Model as input. It is used to model this data into three classes which are cell, extra cellular space and cell membrane [3]. Our proposed algorithm is applied on a sample dataset and experimental results show that this model is both accurate and fast with overall detection rate of around 91.23%. Error rate for cell detection was 1.82%.
VL  - 2
IS  - 4
ER  -

Copy | Download