Abstract
Asbestos-related illnesses become a nationwide problem in Japan. Now human inspectors check whether asbestos is contained in building material or not. To judge whether the specimen contains asbestos or not, 3,000 particles must be counted from microscope images. This is a major labor-intensive bottleneck. In this paper, we propose an automatic particle counting method for automatic judgement system whether the specimen is hazardous or not. However, the size, shape and color of particles are not constant. Therefore, it is difficult to model the particle class. On the other hand, the non-particle class is not varied much. In addition, the area of non-particles is wider than that of particles. Thus, we use One-Class Support Vector Machine (OCSVM). OCSVM identifies “outlier” from input samples. Namely, we model the non-particle class to detect the particle class as outlier. In experiments, the proposed method gives higher accuracy and smaller number of false positives than a preliminary method of our project.
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Kuba, H., Hotta, K., Takahashi, H. (2009). Automatic Particle Detection and Counting by One-Class SVM from Microscope Image. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03040-6_44
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DOI: https://doi.org/10.1007/978-3-642-03040-6_44
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