Component extraction on CT volumes of assembled products using geometric template matching

Katsutoshi Muramatsu; Yutaka Ohtake; Hiromasa Suzuki; Yukie Nagai

doi:10.1117/12.2266221

14 May 2017 Component extraction on CT volumes of assembled products using geometric template matching

Katsutoshi Muramatsu, Yutaka Ohtake, Hiromasa Suzuki, Yukie Nagai

Proceedings Volume 10338, Thirteenth International Conference on Quality Control by Artificial Vision 2017; 103381E (2017) https://doi.org/10.1117/12.2266221
Event: The International Conference on Quality Control by Artificial Vision 2017, 2017, Tokyo, Japan

Abstract

As a method of non-destructive internal inspection, X-ray computed tomography (CT) is used not only in medical applications but also for product inspection. Some assembled products can be divided into separate components based on density, which is known to be approximately proportional to CT values. However, components whose densities are similar cannot be distinguished using the CT value driven approach. In this study, we proposed a new component extraction algorithm from the CT volume, using a set of voxels with an assigned CT value with the surface mesh as the template rather than the density. The method has two main stages: rough matching and fine matching. At the rough matching stage, the position of candidate targets is identified roughly from the CT volume, using the template of the target component. At the fine matching stage, these candidates are precisely matched with the templates, allowing the correct position of the components to be detected from the CT volume. The results of two computational experiments showed that the proposed algorithm is able to extract components with similar density within the assembled products on CT volumes.

Citation Download Citation

Katsutoshi Muramatsu, Yutaka Ohtake, Hiromasa Suzuki, and Yukie Nagai "Component extraction on CT volumes of assembled products using geometric template matching", Proc. SPIE 10338, Thirteenth International Conference on Quality Control by Artificial Vision 2017, 103381E (14 May 2017); https://doi.org/10.1117/12.2266221

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KEYWORDS

X-ray computed tomography

Clouds

Feature extraction

Inspection

3D image processing

Detection and tracking algorithms

Computed tomography

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