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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

3D echocardiography dataset preparation including 3DE full volume acquisition (a) and 4-slices encompassing the RV of long-axis view using MPR mode in 3D QLAB software (b).
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Related In: Results  -  Collection


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fig1: 3D echocardiography dataset preparation including 3DE full volume acquisition (a) and 4-slices encompassing the RV of long-axis view using MPR mode in 3D QLAB software (b).

Mentions: Echocardiography is the application of diagnostic ultrasound imaging to the heart. It has been received in the evaluation of cardiac disease and in characterizing the structure and function of the heart. One advantage over other imaging modalities is its ability to generate real-time images of anatomy without using ionizing radiation. Conventionally, RV echocardiographic data acquisition is very challenging due to its anterior position, complex geometry, and morphology with prominent trabeculations. In our work, all datasets of 3D echocardiography were obtained from a Malaysian hospital, IJN (National Heart Institute). Four stages of dataset preparation process are required; firstly, a live 3D full volume dataset encompassing the RV were acquired using a matrix array X2-7t transducer (TEE). In the second stage, all the acquired 3D datasets were transferred from the online medical system “Philips” directly to an “Xcelera” server in the offline workstation by running QLAB software. In the third stage, the 3D RV full volume dataset was viewed as orthogonal slices using the “MPR” mode (multiplanar reconstruction) and 3D quantification (3DQ) plug-in. Hence, 4-slices from 3D full volume encompassing RV in long-axis view are identified based on inflow-outflow view using “2 × 2 iSlices” plug-in. Finally, these 4-slices were stored individually as videos with complete cardiac cycle in AVI format. Then, each video was converted to a fixed number of frames (F1, F2, F3, …, Fn) according to frame rate, where each frame is represented as a BMP image. The 4-slices dataset preparation from 3D echocardiography full volume is presented in Figure 1.


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)

3D echocardiography dataset preparation including 3DE full volume acquisition (a) and 4-slices encompassing the RV of long-axis view using MPR mode in 3D QLAB software (b).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig1: 3D echocardiography dataset preparation including 3DE full volume acquisition (a) and 4-slices encompassing the RV of long-axis view using MPR mode in 3D QLAB software (b).
Mentions: Echocardiography is the application of diagnostic ultrasound imaging to the heart. It has been received in the evaluation of cardiac disease and in characterizing the structure and function of the heart. One advantage over other imaging modalities is its ability to generate real-time images of anatomy without using ionizing radiation. Conventionally, RV echocardiographic data acquisition is very challenging due to its anterior position, complex geometry, and morphology with prominent trabeculations. In our work, all datasets of 3D echocardiography were obtained from a Malaysian hospital, IJN (National Heart Institute). Four stages of dataset preparation process are required; firstly, a live 3D full volume dataset encompassing the RV were acquired using a matrix array X2-7t transducer (TEE). In the second stage, all the acquired 3D datasets were transferred from the online medical system “Philips” directly to an “Xcelera” server in the offline workstation by running QLAB software. In the third stage, the 3D RV full volume dataset was viewed as orthogonal slices using the “MPR” mode (multiplanar reconstruction) and 3D quantification (3DQ) plug-in. Hence, 4-slices from 3D full volume encompassing RV in long-axis view are identified based on inflow-outflow view using “2 × 2 iSlices” plug-in. Finally, these 4-slices were stored individually as videos with complete cardiac cycle in AVI format. Then, each video was converted to a fixed number of frames (F1, F2, F3, …, Fn) according to frame rate, where each frame is represented as a BMP image. The 4-slices dataset preparation from 3D echocardiography full volume is presented in Figure 1.

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