Computer-Aided Diagnosis of Pneumoconiosis X-ray Images Scanned with a Common CCD Scanner

Koji Abe; Takeshi Tahori; Masahide Minami; Munehiro Nakamura; Haiyan Tian

doi:doi:10.11648/j.acis.20130102.12

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Computer-Aided Diagnosis of Pneumoconiosis X-ray Images Scanned with a Common CCD Scanner

Koji Abe, Takeshi Tahori, Masahide Minami, Munehiro Nakamura, Haiyan Tian

Received: Accepted: Published: 2 April 2013

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Abstract

This paper presents a discrimination of pneumoconiosis X-ray images obtained with a common CCD scanner. Current computer-aided diagnosis systems of pneumoconiosis have been proposed to images obtained with a special scanner such as a drum scanner or a film scanner for X-ray pictures. However, since the special scanners need a large storage space and the scanners and commitment of the imaging need high-priced costs, the systems are not practical in small clinics. In this paper, we propose features for measuring abnormalities of pneumoconiosis as variables for the discrimination. Devices in the proposed system are only a tablet PC and a CCD scanner. In images obtained with CCD scanner, abnormal levels of pneumoconiosis could depend on density distribution in rib areas. Therefore, the proposed method measures the abnormalities by extracting characteristics of the distribution in the areas. Besides, using the abnormalities, the proposed method discriminates chest X-ray images into normal or abnormal cases of pneumoconiosis. Experimental results of the discriminations for 59 right-lung images have shown that the proposed abnormalities are well extracted for the discrimination.

DOI	10.11648/j.acis.20130102.12
Published in	Automation, Control and Intelligent Systems (Volume 1, Issue 2, April 2013)
Page(s)	24-33
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

Computer-Aided Diagnosis; Pneumoconiosis; Chest X-Ray Images; Medical Image Processing

References

[1]	R. P. Kruger, W. B. Thompson, and A. F. Turner, "Computer diagnosis of pneumoconiosis," Trans. on Systems, Man. And Cybernetics, vol. SMC-4, no. 1, pp. 40–49, Jan. 1974.
[2]	A. M. Savol, C. C. Li, and R. J. Hoy, "Computer aided recognition of small rounded pneumoconiosis opacities in chest X-rays," IEEE Trans Pattern Anal. March. Intell., vol. 2, no. 5, pp. 479-482, Sep. 1980.
[3]	J. Wei and H. Kobatake, "Detection of rounded opacities on chest radiographs using convergence index filter," Proc. ICIAP, pp. 757-761, Sep. 1999.
[4]	H. Kondo and T. Kouda, "Computer-aided diagnosis for pneumoconiosis using neural network," Proc. IEEE Symposium on Computer-Based Medical Systems, pp. 467-472, Jul. 2001
[5]	M. Nakamura, K. Abe, and M. Minami, "Quantitative evaluation of pneumoconiosis in chest radiographs obtained with a CCD scanner," Proc. of ICADIWT 2009, pp. 673-678, London, UK, Ang. 2009.
[6]	M. Nakamura, K. Abe, and M. Minami, "Extraction of features for diagnosing pneumoconiosis from chest radiographs obtained with a CCD scanner," Journal of Digital Information Management, vol. 8, no. 3, pp. 147-152, Jun. 2010.
[7]	M. Loog and B. Ginneken, "Segmentation of the posterior ribs in chest radiographs using iterated contextual pixel classification," IEEE Trans. on Medical Imaging, vol. 25, no. 5, pp. 602-611, May 2006.
[8]	F. Mosteller, "A k-sample slippage test for an extreme population," The Annals of Mathematical Statistics, vol. 19, no. 1, pp. 58-65, Mar. 1948.

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APA Style

Koji Abe, Takeshi Tahori, Masahide Minami, Munehiro Nakamura, Haiyan Tian. (2013). Computer-Aided Diagnosis of Pneumoconiosis X-ray Images Scanned with a Common CCD Scanner. Automation, Control and Intelligent Systems, 1(2), 24-33. https://doi.org/10.11648/j.acis.20130102.12

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ACS Style

Koji Abe; Takeshi Tahori; Masahide Minami; Munehiro Nakamura; Haiyan Tian. Computer-Aided Diagnosis of Pneumoconiosis X-ray Images Scanned with a Common CCD Scanner. Autom. Control Intell. Syst. 2013, 1(2), 24-33. doi: 10.11648/j.acis.20130102.12

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AMA Style

Koji Abe, Takeshi Tahori, Masahide Minami, Munehiro Nakamura, Haiyan Tian. Computer-Aided Diagnosis of Pneumoconiosis X-ray Images Scanned with a Common CCD Scanner. Autom Control Intell Syst. 2013;1(2):24-33. doi: 10.11648/j.acis.20130102.12

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@article{10.11648/j.acis.20130102.12,
  author = {Koji Abe and Takeshi Tahori and Masahide Minami and Munehiro Nakamura and Haiyan Tian},
  title = {Computer-Aided Diagnosis of Pneumoconiosis X-ray Images Scanned with a Common CCD Scanner},
  journal = {Automation, Control and Intelligent Systems},
  volume = {1},
  number = {2},
  pages = {24-33},
  doi = {10.11648/j.acis.20130102.12},
  url = {https://doi.org/10.11648/j.acis.20130102.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20130102.12},
  abstract = {This paper presents a discrimination of pneumoconiosis X-ray images obtained with a common CCD scanner. Current computer-aided diagnosis systems of pneumoconiosis have been proposed to images obtained with a special scanner such as a drum scanner or a film scanner for X-ray pictures. However, since the special scanners need a large storage space and the scanners and commitment of the imaging need high-priced costs, the systems are not practical in small clinics. In this paper, we propose features for measuring abnormalities of pneumoconiosis as variables for the discrimination. Devices in the proposed system are only a tablet PC and a CCD scanner. In images obtained with CCD scanner, abnormal levels of pneumoconiosis could depend on density distribution in rib areas. Therefore, the proposed method measures the abnormalities by extracting characteristics of the distribution in the areas. Besides, using the abnormalities, the proposed method discriminates chest X-ray images into normal or abnormal cases of pneumoconiosis. Experimental results of the discriminations for 59 right-lung images have shown that the proposed abnormalities are well extracted for the discrimination.},
 year = {2013}
}

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TY  - JOUR
T1  - Computer-Aided Diagnosis of Pneumoconiosis X-ray Images Scanned with a Common CCD Scanner
AU  - Koji Abe
AU  - Takeshi Tahori
AU  - Masahide Minami
AU  - Munehiro Nakamura
AU  - Haiyan Tian
Y1  - 2013/04/02
PY  - 2013
N1  - https://doi.org/10.11648/j.acis.20130102.12
DO  - 10.11648/j.acis.20130102.12
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 24
EP  - 33
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20130102.12
AB  - This paper presents a discrimination of pneumoconiosis X-ray images obtained with a common CCD scanner. Current computer-aided diagnosis systems of pneumoconiosis have been proposed to images obtained with a special scanner such as a drum scanner or a film scanner for X-ray pictures. However, since the special scanners need a large storage space and the scanners and commitment of the imaging need high-priced costs, the systems are not practical in small clinics. In this paper, we propose features for measuring abnormalities of pneumoconiosis as variables for the discrimination. Devices in the proposed system are only a tablet PC and a CCD scanner. In images obtained with CCD scanner, abnormal levels of pneumoconiosis could depend on density distribution in rib areas. Therefore, the proposed method measures the abnormalities by extracting characteristics of the distribution in the areas. Besides, using the abnormalities, the proposed method discriminates chest X-ray images into normal or abnormal cases of pneumoconiosis. Experimental results of the discriminations for 59 right-lung images have shown that the proposed abnormalities are well extracted for the discrimination.
VL  - 1
IS  - 2
ER  -

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Author Information

Koji Abe

Kinki University, Osaka, Japan
Takeshi Tahori

Contec EMS, Co. Ltd, Japan
Masahide Minami

the University of Tokyo, Japan
Munehiro Nakamura

Kanazawa University, Kanazawa, Japan
Haiyan Tian

Chongqing University, Chongqing, China

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APA Style

Koji Abe, Takeshi Tahori, Masahide Minami, Munehiro Nakamura, Haiyan Tian. (2013). Computer-Aided Diagnosis of Pneumoconiosis X-ray Images Scanned with a Common CCD Scanner. Automation, Control and Intelligent Systems, 1(2), 24-33. https://doi.org/10.11648/j.acis.20130102.12

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ACS Style

Koji Abe; Takeshi Tahori; Masahide Minami; Munehiro Nakamura; Haiyan Tian. Computer-Aided Diagnosis of Pneumoconiosis X-ray Images Scanned with a Common CCD Scanner. Autom. Control Intell. Syst. 2013, 1(2), 24-33. doi: 10.11648/j.acis.20130102.12

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AMA Style

Koji Abe, Takeshi Tahori, Masahide Minami, Munehiro Nakamura, Haiyan Tian. Computer-Aided Diagnosis of Pneumoconiosis X-ray Images Scanned with a Common CCD Scanner. Autom Control Intell Syst. 2013;1(2):24-33. doi: 10.11648/j.acis.20130102.12

Copy | Download

@article{10.11648/j.acis.20130102.12,
  author = {Koji Abe and Takeshi Tahori and Masahide Minami and Munehiro Nakamura and Haiyan Tian},
  title = {Computer-Aided Diagnosis of Pneumoconiosis X-ray Images Scanned with a Common CCD Scanner},
  journal = {Automation, Control and Intelligent Systems},
  volume = {1},
  number = {2},
  pages = {24-33},
  doi = {10.11648/j.acis.20130102.12},
  url = {https://doi.org/10.11648/j.acis.20130102.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20130102.12},
  abstract = {This paper presents a discrimination of pneumoconiosis X-ray images obtained with a common CCD scanner. Current computer-aided diagnosis systems of pneumoconiosis have been proposed to images obtained with a special scanner such as a drum scanner or a film scanner for X-ray pictures. However, since the special scanners need a large storage space and the scanners and commitment of the imaging need high-priced costs, the systems are not practical in small clinics. In this paper, we propose features for measuring abnormalities of pneumoconiosis as variables for the discrimination. Devices in the proposed system are only a tablet PC and a CCD scanner. In images obtained with CCD scanner, abnormal levels of pneumoconiosis could depend on density distribution in rib areas. Therefore, the proposed method measures the abnormalities by extracting characteristics of the distribution in the areas. Besides, using the abnormalities, the proposed method discriminates chest X-ray images into normal or abnormal cases of pneumoconiosis. Experimental results of the discriminations for 59 right-lung images have shown that the proposed abnormalities are well extracted for the discrimination.},
 year = {2013}
}

Copy | Download

TY  - JOUR
T1  - Computer-Aided Diagnosis of Pneumoconiosis X-ray Images Scanned with a Common CCD Scanner
AU  - Koji Abe
AU  - Takeshi Tahori
AU  - Masahide Minami
AU  - Munehiro Nakamura
AU  - Haiyan Tian
Y1  - 2013/04/02
PY  - 2013
N1  - https://doi.org/10.11648/j.acis.20130102.12
DO  - 10.11648/j.acis.20130102.12
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 24
EP  - 33
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20130102.12
AB  - This paper presents a discrimination of pneumoconiosis X-ray images obtained with a common CCD scanner. Current computer-aided diagnosis systems of pneumoconiosis have been proposed to images obtained with a special scanner such as a drum scanner or a film scanner for X-ray pictures. However, since the special scanners need a large storage space and the scanners and commitment of the imaging need high-priced costs, the systems are not practical in small clinics. In this paper, we propose features for measuring abnormalities of pneumoconiosis as variables for the discrimination. Devices in the proposed system are only a tablet PC and a CCD scanner. In images obtained with CCD scanner, abnormal levels of pneumoconiosis could depend on density distribution in rib areas. Therefore, the proposed method measures the abnormalities by extracting characteristics of the distribution in the areas. Besides, using the abnormalities, the proposed method discriminates chest X-ray images into normal or abnormal cases of pneumoconiosis. Experimental results of the discriminations for 59 right-lung images have shown that the proposed abnormalities are well extracted for the discrimination.
VL  - 1
IS  - 2
ER  -

Copy | Download