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), 2014. Published by Science Publishing Group

Keywords

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

References

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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