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Computed tomographic image analysis based on FEM performance comparison of segmentation on knee joint reconstruction.

Jang SW, Seo YJ, Yoo YS, Kim YS - ScientificWorldJournal (2014)

Bottom Line: The demand for an accurate and accessible image segmentation to generate 3D models from CT scan data has been increasing as such models are required in many areas of orthopedics.In this paper, to find the optimal image segmentation to create a 3D model of the knee CT data, we compared and validated segmentation algorithms based on both objective comparisons and finite element (FE) analysis.For comparison purposes, we used 1 model reconstructed in accordance with the instructions of a clinical professional and 3 models reconstructed using image processing algorithms (Sobel operator, Laplacian of Gaussian operator, and Canny edge detection).

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, Korea University of Technology and Education, 330-708 Cheonan, Republic of Korea.

ABSTRACT
The demand for an accurate and accessible image segmentation to generate 3D models from CT scan data has been increasing as such models are required in many areas of orthopedics. In this paper, to find the optimal image segmentation to create a 3D model of the knee CT data, we compared and validated segmentation algorithms based on both objective comparisons and finite element (FE) analysis. For comparison purposes, we used 1 model reconstructed in accordance with the instructions of a clinical professional and 3 models reconstructed using image processing algorithms (Sobel operator, Laplacian of Gaussian operator, and Canny edge detection). Comparison was performed by inspecting intermodel morphological deviations with the iterative closest point (ICP) algorithm, and FE analysis was performed to examine the effects of the segmentation algorithm on the results of the knee joint movement analysis.

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Construction of the tunnel and ACL graft.
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig6: Construction of the tunnel and ACL graft.

Mentions: In this section, we explain the process of generating a FE model, which was applied to the four segmentation algorithms to compare the effects of FE analysis. To generate a solid model with the same tunnel as is made in a real operation of the femur and tibia, 10 mm diameter tunnels were drilled in the center between the anteromedial (AM) bundle and the posterolateral (PL) bundle, as shown in Figure 6. The cylinder 9 mm in diameter at the center of the tunnel penetrating the solid model is the ACL bundle model. The Fillet function was applied to provide smooth contact between the ligament and bone.


Computed tomographic image analysis based on FEM performance comparison of segmentation on knee joint reconstruction.

Jang SW, Seo YJ, Yoo YS, Kim YS - ScientificWorldJournal (2014)

Construction of the tunnel and ACL graft.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4265700&req=5

fig6: Construction of the tunnel and ACL graft.
Mentions: In this section, we explain the process of generating a FE model, which was applied to the four segmentation algorithms to compare the effects of FE analysis. To generate a solid model with the same tunnel as is made in a real operation of the femur and tibia, 10 mm diameter tunnels were drilled in the center between the anteromedial (AM) bundle and the posterolateral (PL) bundle, as shown in Figure 6. The cylinder 9 mm in diameter at the center of the tunnel penetrating the solid model is the ACL bundle model. The Fillet function was applied to provide smooth contact between the ligament and bone.

Bottom Line: The demand for an accurate and accessible image segmentation to generate 3D models from CT scan data has been increasing as such models are required in many areas of orthopedics.In this paper, to find the optimal image segmentation to create a 3D model of the knee CT data, we compared and validated segmentation algorithms based on both objective comparisons and finite element (FE) analysis.For comparison purposes, we used 1 model reconstructed in accordance with the instructions of a clinical professional and 3 models reconstructed using image processing algorithms (Sobel operator, Laplacian of Gaussian operator, and Canny edge detection).

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, Korea University of Technology and Education, 330-708 Cheonan, Republic of Korea.

ABSTRACT
The demand for an accurate and accessible image segmentation to generate 3D models from CT scan data has been increasing as such models are required in many areas of orthopedics. In this paper, to find the optimal image segmentation to create a 3D model of the knee CT data, we compared and validated segmentation algorithms based on both objective comparisons and finite element (FE) analysis. For comparison purposes, we used 1 model reconstructed in accordance with the instructions of a clinical professional and 3 models reconstructed using image processing algorithms (Sobel operator, Laplacian of Gaussian operator, and Canny edge detection). Comparison was performed by inspecting intermodel morphological deviations with the iterative closest point (ICP) algorithm, and FE analysis was performed to examine the effects of the segmentation algorithm on the results of the knee joint movement analysis.

Show MeSH