Limits...
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.

Show MeSH
Segmentation procedure for the knee joint by the Laplacian of Gaussian operator algorithm (where input parameters σ = 4).
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4265700&req=5

fig2: Segmentation procedure for the knee joint by the Laplacian of Gaussian operator algorithm (where input parameters σ = 4).

Mentions: The segmentation process using the three algorithms as described above is shown in Figures 1, 2, and 3, respectively.


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)

Segmentation procedure for the knee joint by the Laplacian of Gaussian operator algorithm (where input parameters σ = 4).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2: Segmentation procedure for the knee joint by the Laplacian of Gaussian operator algorithm (where input parameters σ = 4).
Mentions: The segmentation process using the three algorithms as described above is shown in Figures 1, 2, and 3, respectively.

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