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Asbestos Detection Method with Frequency Analysis for Microscope Images

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Advances in Visual Computing (ISVC 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5876))

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Abstract

In this paper, we propose an asbestos detection method focusing on frequency distribution of microscopic images. In building construction, asbestos has been used for molding plates and heat insulation materials. However, increased injury caused by asbestos has become a problem in Japan. Removal of asbestos from building materials and rendering it harmless are common means of alleviating asbestos hazards. Nevertheless, those processes necessitate a judgment of whether asbestos is included in building materials. According to the JIS standards, it is necessary to count 3000 particles in microscopic images. We consider the asbestos shape, and define a new feature obtained through frequency analysis. The proposed method intensifies the low-brightness asbestos using its feature, so it can detect not only high-brightness particles and asbestos but also low-brightness asbestos. We underscore the effectiveness of the method by comparing its results with results counted by an expert.

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Author information

Authors and Affiliations

  1. Saitama University, 255 Shimo-Okubo, Sakura-ku, Saitama-shi, Saitama, Japan

    Hikaru Kumagai & Taketoshi Mishima

  2. The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa-shi, Chiba, Japan

    Soichiro Morishita & Hajime Asama

  3. RIKEN, 2-1, Hirosawa, Wako-shi, Saitama, Japan

    Kuniaki Kawabata

Authors
  1. Hikaru Kumagai
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  2. Soichiro Morishita
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  3. Kuniaki Kawabata
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  4. Hajime Asama
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  5. Taketoshi Mishima
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Graduate School of Science and Engineering, Saitama University, 255 Shimo-Okubo, Sakura-ku, Saitama-shi, 338-8570, 338-8570, Japan

    Yoshinori Kuno

  6. Microsoft Research, Redmond, WA, USA

    Junxian Wang

  7. Univ. of Zurich, Department of Informatics, Winterthurerstr. 190, P.O. Box, 8057, Zurich, Switzerland

    Renato Pajarola

  8. Lawrence Livermore National Laboratory, 94550, Livermore, CA, USA

    Peter Lindstrom

  9. University of Applied Sciences Bonn-Rhein-Sieg, 53754, Sankt Augustin, Germany

    André Hinkenjann

  10.  ,  

    Miguel L. Encarnação

  11. SCI Institute & School of Computing, University of Utah, 84112, Salt Lake City, UT, USA

    Cláudio T. Silva

  12. Desert Research Institute, 89512, Reno, NV, USA

    Daniel Coming

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© 2009 Springer-Verlag Berlin Heidelberg

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Kumagai, H., Morishita, S., Kawabata, K., Asama, H., Mishima, T. (2009). Asbestos Detection Method with Frequency Analysis for Microscope Images. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2009. Lecture Notes in Computer Science, vol 5876. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10520-3_40

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  • DOI: https://doi.org/10.1007/978-3-642-10520-3_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10519-7

  • Online ISBN: 978-3-642-10520-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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