TY  - JOUR
AU  - Sokac, Mario
AU  - Budak, Igor
AU  - Katić, Marko
AU  - Jakovljević, Živana
AU  - Santosi, Željko
AU  - Vukelić, Đorđe
PY  - 2020
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3368
AB  - The paper demonstrates a novel methodology for surface extraction of multi-material components (MMCs) on industrial X-ray computed tomography (CT) datasets. The methodology is based on a combination of fuzzy C-means clustering (FCM) and region growing (RG) methods. FCM, used as a preprocessing step, allows proper classification and improvement of different objects boundaries present on industrial X-ray CT datasets. Afterwards, application of RG method enables accurate segmentation of classified and improved X-ray CT datasets. The performance of presented approach has been tested on two CT datasets acquired on an industrial X-ray CT system NIKON XT H 225. It was also compared against two commercial industrial software VGStudio Max v3.1 and GOM Inspect v2018. Obtained results from application of the proposed approach show significant improvement in surface extraction of MMCs in CT datasets, especially in cases of low-density materials such as polymers. Verification has been conducted by obtaining reference measurements using contact coordinate measuring machine (CMM) Contura G2 by CARL ZEISS.
PB  - Elsevier Sci Ltd, Oxford
T2  - Measurement
T1  - Improved surface extraction of multi-material components for single-source industrial X-ray computed tomography
VL  - 153
DO  - 10.1016/j.measurement.2019.107438
ER  - 

@article{
author = "Sokac, Mario and Budak, Igor and Katić, Marko and Jakovljević, Živana and Santosi, Željko and Vukelić, Đorđe",
year = "2020",
abstract = "The paper demonstrates a novel methodology for surface extraction of multi-material components (MMCs) on industrial X-ray computed tomography (CT) datasets. The methodology is based on a combination of fuzzy C-means clustering (FCM) and region growing (RG) methods. FCM, used as a preprocessing step, allows proper classification and improvement of different objects boundaries present on industrial X-ray CT datasets. Afterwards, application of RG method enables accurate segmentation of classified and improved X-ray CT datasets. The performance of presented approach has been tested on two CT datasets acquired on an industrial X-ray CT system NIKON XT H 225. It was also compared against two commercial industrial software VGStudio Max v3.1 and GOM Inspect v2018. Obtained results from application of the proposed approach show significant improvement in surface extraction of MMCs in CT datasets, especially in cases of low-density materials such as polymers. Verification has been conducted by obtaining reference measurements using contact coordinate measuring machine (CMM) Contura G2 by CARL ZEISS.",
publisher = "Elsevier Sci Ltd, Oxford",
journal = "Measurement",
title = "Improved surface extraction of multi-material components for single-source industrial X-ray computed tomography",
volume = "153",
doi = "10.1016/j.measurement.2019.107438"
}

Sokac, M., Budak, I., Katić, M., Jakovljević, Ž., Santosi, Ž.,& Vukelić, Đ.. (2020). Improved surface extraction of multi-material components for single-source industrial X-ray computed tomography. in Measurement
Elsevier Sci Ltd, Oxford., 153.
https://doi.org/10.1016/j.measurement.2019.107438

Sokac M, Budak I, Katić M, Jakovljević Ž, Santosi Ž, Vukelić Đ. Improved surface extraction of multi-material components for single-source industrial X-ray computed tomography. in Measurement. 2020;153.
doi:10.1016/j.measurement.2019.107438 .

Sokac, Mario, Budak, Igor, Katić, Marko, Jakovljević, Živana, Santosi, Željko, Vukelić, Đorđe, "Improved surface extraction of multi-material components for single-source industrial X-ray computed tomography" in Measurement, 153 (2020),
https://doi.org/10.1016/j.measurement.2019.107438 . .