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JACIII Vol.16 No.1 pp. 69-75
doi: 10.20965/jaciii.2012.p0069
(2012)

Paper:

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Discrimination of Pneumoconiosis X-Ray Images Scanned with a CCD Scanner

Masahide Minami*, KojiAbe**,
and Munehiro Nakamura***

*Health Control Department, Awazu Plant, Komatsu Ltd., 23 Tsu, Futsu, Komatsu 923-0392, Japan

**Department of Informatics, School of Science and Engineering, Kinki University, 3-4-1 Kowakae, Higashi-Osaka 577-8502, Japan

***Faculty of Electrical and Computer Engineering, Institute of Science and Engineering, Kanazawa University, Kakuma, Kanazawa 920-1192, Japan

Received:
June 24, 2011
Accepted:
October 12, 2011
Published:
January 20, 2012
Creative Commons License
Keywords:
computer-aided diagnosis, pneumoconiosis, chest X-ray images, medical image processing
Abstract
This paper presents a discrimination of pneumoconiosis X-ray images obtained with a common CCD scanner. Since the current computer-aided diagnosis systems of pneumoconiosis are not practical due to high costs of usage, features for measuring abnormalities of pneumoconiosis are proposed as variables for the discrimination in this paper. In the images, abnormal levels of pneumoconiosis could depend on density distribution in each of intercostal and 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 56 right-lung images have shown that the proposed abnormalities are well extracted for the discrimination.
Cite this article as:
M. Minami, KojiAbe, and M. Nakamura, “Discrimination of Pneumoconiosis X-Ray Images Scanned with a CCD Scanner,” J. Adv. Comput. Intell. Intell. Inform., Vol.16 No.1, pp. 69-75, 2012.
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References
  1. [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. [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. Mach. Intell., Vol.2, No.5, pp. 479-482, Sep. 1980.
  3. [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. [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. [5] M. Pattichis, H. Muralldharan, C. Pattichis, and P. Soliz, “New image processing models for opacity image analysis in chest radiographs,” Proc. of IEEE Southwest Symposium on Image Analysis and Interpretation, pp. 260-264, Apr. 2002.
  6. [6] S. Kido, S. Adachi, and R. Tachibana, “Computer-aided Diagnosis of Pneumoconiosis Chest Radiographs,” Proc. of Radiological Society of North America Scientific Assembly and Annual Meeting, pp. 249-250, Dec. 2004.
  7. [7] K. Doi, “Current status and future potential of computer-aided diagnosis in medical imaging,” British J. of Radiology, Special Issue 2005, pp. S3-S19, 2005.
  8. [8] S. Katsuragawa and K. Doi, “Computer-aided diagnosis in chest radiography,” Computerized Medical Imaging and Graphics, Vol.31, Issues 4-5, pp. 212-223, 2007.
  9. [9] V. Murray, M. S. Pattichis, H. Davis, E. S. Barriga, and P. Soliz, “Multiscale AM-FM analysis of pneumoconiosis x-ray images,” Proc. of 16th IEEE Int. Conf. on Image Processing, pp. 4201-4204, Nov. 2009.
  10. [10] P. Yu, J. Zhao, H. Xu, C. Yang, X. Sun, S. Chen, and L. Mao, “Computer Aided Detection for Pneumoconiosis Based on Histogram Analysis,” Proc. of the 1st Int. Conf. on Information Science and Engineering, pp. 3625-3628, Dec. 2009.
  11. [11] H. Xu, X. Tao, R. Sundararajan, W. Yan, P. Annangi, X. Sun, and L. Mao, “Computer Aided Detection for Pneumoconiosis Screening on Digital Chest Radiographs,” Proc. of Third Int. Workshop on Pulmonary Image Analysis, pp. 129-138, Sep. 2010.
  12. [12] P. Yu, H. Xu, Y. Zhu, C. Yang, X. Sun, and J. Zhao, “An Automatic Computer-Aided Detection Scheme for Pneumoconiosis on Digital Chest Radiographs,” J. of Digital Imaging, Vol.24, No.3, pp. 382-393, Jun. 2011.
  13. [13] 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.
  14. [14] 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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