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A Hybrid Method for Endocardial Contour Extraction of Right Ventricle in 4-Slices from 3D Echocardiography Dataset.

Dawood FA, Rahmat RW, Kadiman SB, Abdullah LN, Zamrin MD - Adv Bioinformatics (2014)

Bottom Line: Speckle noise reduction and contrast enhancement were implemented in Phase II as preprocessing tasks.The accuracy of experimental results obtained by the proposed method was evaluated qualitatively and quantitatively.It has been done by comparing the segmentation results of RV cavity based on endocardial contour extraction with the ground truth.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, College of Science, University of Baghdad, Baghdad, Iraq ; Department of Multimedia, Faculty of Computer Science and Information Technology, UPM, 43400 Serdang, Selangor, Malaysia.

ABSTRACT
This paper presents a hybrid method to extract endocardial contour of the right ventricular (RV) in 4-slices from 3D echocardiography dataset. The overall framework comprises four processing phases. In Phase I, the region of interest (ROI) is identified by estimating the cavity boundary. Speckle noise reduction and contrast enhancement were implemented in Phase II as preprocessing tasks. In Phase III, the RV cavity region was segmented by generating intensity threshold which was used for once for all frames. Finally, Phase IV is proposed to extract the RV endocardial contour in a complete cardiac cycle using a combination of shape-based contour detection and improved radial search algorithm. The proposed method was applied to 16 datasets of 3D echocardiography encompassing the RV in long-axis view. The accuracy of experimental results obtained by the proposed method was evaluated qualitatively and quantitatively. It has been done by comparing the segmentation results of RV cavity based on endocardial contour extraction with the ground truth. The comparative analysis results show that the proposed method performs efficiently in all datasets with overall performance of 95% and the root mean square distances (RMSD) measure in terms of mean ± SD was found to be 2.21 ± 0.35 mm for RV endocardial contours.

No MeSH data available.


Related in: MedlinePlus

An example of manual-to-automatic contour extraction (a) and the comparative analysis based on the radial distances from RVCCP (b).
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fig9: An example of manual-to-automatic contour extraction (a) and the comparative analysis based on the radial distances from RVCCP (b).

Mentions: For both methods, the RV contour points are saved in two scalars C and , respectively. In this context, the RMSD was measured based on the distance calculation as in the following formula:(6)RMSD=1N  ∑i=1Ndistance(Ci,Ç´i),where N is the length in pixels of each contour which is equal to 360 points in our implementation. Each contour point has x, y coordinates as {(xj,1, yj,1), (xj,2, yj,2), …, (xj,n, yj,n)}, where the distance has been calculated by Euclidian distance using the following formula:(7)distance(Ci,Ç´i)=(xCi−xÇ´i)²+(yCi−yÇ´i)².Figure 9(a) depicts an example of the manual-to-automatic contours with identified RVCCP. The comparative analysis of the paired contours are done using the polar format based on the radial distances started from 0° to 360° for each contour as shown in Figure 9(b).


A Hybrid Method for Endocardial Contour Extraction of Right Ventricle in 4-Slices from 3D Echocardiography Dataset.

Dawood FA, Rahmat RW, Kadiman SB, Abdullah LN, Zamrin MD - Adv Bioinformatics (2014)

An example of manual-to-automatic contour extraction (a) and the comparative analysis based on the radial distances from RVCCP (b).
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4209758&req=5

fig9: An example of manual-to-automatic contour extraction (a) and the comparative analysis based on the radial distances from RVCCP (b).
Mentions: For both methods, the RV contour points are saved in two scalars C and , respectively. In this context, the RMSD was measured based on the distance calculation as in the following formula:(6)RMSD=1N  ∑i=1Ndistance(Ci,Ç´i),where N is the length in pixels of each contour which is equal to 360 points in our implementation. Each contour point has x, y coordinates as {(xj,1, yj,1), (xj,2, yj,2), …, (xj,n, yj,n)}, where the distance has been calculated by Euclidian distance using the following formula:(7)distance(Ci,Ç´i)=(xCi−xÇ´i)²+(yCi−yÇ´i)².Figure 9(a) depicts an example of the manual-to-automatic contours with identified RVCCP. The comparative analysis of the paired contours are done using the polar format based on the radial distances started from 0° to 360° for each contour as shown in Figure 9(b).

Bottom Line: Speckle noise reduction and contrast enhancement were implemented in Phase II as preprocessing tasks.The accuracy of experimental results obtained by the proposed method was evaluated qualitatively and quantitatively.It has been done by comparing the segmentation results of RV cavity based on endocardial contour extraction with the ground truth.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, College of Science, University of Baghdad, Baghdad, Iraq ; Department of Multimedia, Faculty of Computer Science and Information Technology, UPM, 43400 Serdang, Selangor, Malaysia.

ABSTRACT
This paper presents a hybrid method to extract endocardial contour of the right ventricular (RV) in 4-slices from 3D echocardiography dataset. The overall framework comprises four processing phases. In Phase I, the region of interest (ROI) is identified by estimating the cavity boundary. Speckle noise reduction and contrast enhancement were implemented in Phase II as preprocessing tasks. In Phase III, the RV cavity region was segmented by generating intensity threshold which was used for once for all frames. Finally, Phase IV is proposed to extract the RV endocardial contour in a complete cardiac cycle using a combination of shape-based contour detection and improved radial search algorithm. The proposed method was applied to 16 datasets of 3D echocardiography encompassing the RV in long-axis view. The accuracy of experimental results obtained by the proposed method was evaluated qualitatively and quantitatively. It has been done by comparing the segmentation results of RV cavity based on endocardial contour extraction with the ground truth. The comparative analysis results show that the proposed method performs efficiently in all datasets with overall performance of 95% and the root mean square distances (RMSD) measure in terms of mean ± SD was found to be 2.21 ± 0.35 mm for RV endocardial contours.

No MeSH data available.


Related in: MedlinePlus