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 . .

TY  - JOUR
AU  - Sokac, Mario
AU  - Vukelić, Đorđe
AU  - Jakovljević, Živana
AU  - Santosi, Željko
AU  - Hadzistević, Miodrag
AU  - Budak, Igor
PY  - 2019
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3240
AB  - This research proposes a hybrid method for improving the segmentation accuracy of reconstructed 3D models from computed tomography/magnetic resonance imaging (CT/MRI) data. A semi-automatic hybrid method based on combination of Fuzzy C-Means clustering (FCM) and region growing (RG) is proposed. In this approach, FCM is used in the first stage as a preprocessing step in order to classify and improve images by assigning pixels to the clusters for which they have the maximum membership, and manual selection of the membership intensity map with the best contrast separation. Afterwards, automatic seed selection is performed for RG, for which a new parameter standard deviation (STD) of pixel intensities, is included. It is based on the selection of an initial seed inside a region with maximum value of STD. To evaluate the performance of the proposed method, it was compared to several other segmentation methods. Experimental results show that the proposed method overall provides better results compared to other methods in terms of accuracy. The average sensitivity and accuracy rates for cone-beam computed tomography CBCT 1 and CBCT 2 datasets are 99 %, 98.4 %, 47.2 % and 89.9 %, respectively. For MRI 1 and MRI 2 datasets, the average sensitivity and accuracy values are 99.1 %, 100 %, 75.6 % and 99.6 %, respectively. The average values for the Dice coefficient and Jaccard index for the CBCT 1 and CBCT 2 datasets are 95.88, 0.88, 0.6, and 0.51, respectively, while for MRI 1 and MRI 2 datasets, average values are 0.96, 0.93, 0.81 and 0.7, respectively, which confirms the high accuracy of the proposed method.
PB  - Assoc Mechanical Engineers Technicians Slovenia, Ljubljana
T2  - Strojniski Vestnik-Journal of Mechanical Engineering
T1  - Fuzzy Hybrid Method for the Reconstruction of 3D Models Based on CT/MRI Data
EP  - 494
IS  - 9
SP  - 482
VL  - 65
DO  - 10.5545/sv-jme.2019.6136
ER  - 

@article{
author = "Sokac, Mario and Vukelić, Đorđe and Jakovljević, Živana and Santosi, Željko and Hadzistević, Miodrag and Budak, Igor",
year = "2019",
abstract = "This research proposes a hybrid method for improving the segmentation accuracy of reconstructed 3D models from computed tomography/magnetic resonance imaging (CT/MRI) data. A semi-automatic hybrid method based on combination of Fuzzy C-Means clustering (FCM) and region growing (RG) is proposed. In this approach, FCM is used in the first stage as a preprocessing step in order to classify and improve images by assigning pixels to the clusters for which they have the maximum membership, and manual selection of the membership intensity map with the best contrast separation. Afterwards, automatic seed selection is performed for RG, for which a new parameter standard deviation (STD) of pixel intensities, is included. It is based on the selection of an initial seed inside a region with maximum value of STD. To evaluate the performance of the proposed method, it was compared to several other segmentation methods. Experimental results show that the proposed method overall provides better results compared to other methods in terms of accuracy. The average sensitivity and accuracy rates for cone-beam computed tomography CBCT 1 and CBCT 2 datasets are 99 %, 98.4 %, 47.2 % and 89.9 %, respectively. For MRI 1 and MRI 2 datasets, the average sensitivity and accuracy values are 99.1 %, 100 %, 75.6 % and 99.6 %, respectively. The average values for the Dice coefficient and Jaccard index for the CBCT 1 and CBCT 2 datasets are 95.88, 0.88, 0.6, and 0.51, respectively, while for MRI 1 and MRI 2 datasets, average values are 0.96, 0.93, 0.81 and 0.7, respectively, which confirms the high accuracy of the proposed method.",
publisher = "Assoc Mechanical Engineers Technicians Slovenia, Ljubljana",
journal = "Strojniski Vestnik-Journal of Mechanical Engineering",
title = "Fuzzy Hybrid Method for the Reconstruction of 3D Models Based on CT/MRI Data",
pages = "494-482",
number = "9",
volume = "65",
doi = "10.5545/sv-jme.2019.6136"
}

Sokac, M., Vukelić, Đ., Jakovljević, Ž., Santosi, Ž., Hadzistević, M.,& Budak, I.. (2019). Fuzzy Hybrid Method for the Reconstruction of 3D Models Based on CT/MRI Data. in Strojniski Vestnik-Journal of Mechanical Engineering
Assoc Mechanical Engineers Technicians Slovenia, Ljubljana., 65(9), 482-494.
https://doi.org/10.5545/sv-jme.2019.6136

Sokac M, Vukelić Đ, Jakovljević Ž, Santosi Ž, Hadzistević M, Budak I. Fuzzy Hybrid Method for the Reconstruction of 3D Models Based on CT/MRI Data. in Strojniski Vestnik-Journal of Mechanical Engineering. 2019;65(9):482-494.
doi:10.5545/sv-jme.2019.6136 .

Sokac, Mario, Vukelić, Đorđe, Jakovljević, Živana, Santosi, Željko, Hadzistević, Miodrag, Budak, Igor, "Fuzzy Hybrid Method for the Reconstruction of 3D Models Based on CT/MRI Data" in Strojniski Vestnik-Journal of Mechanical Engineering, 65, no. 9 (2019):482-494,
https://doi.org/10.5545/sv-jme.2019.6136 . .