Active appearance motion models for endocardial contour detection in time sequences of echocardiograms

2001 ◽  
Author(s):  
Hans G. Bosch ◽  
Steven C. Mitchell ◽  
Boudewijn P. F. Lelieveldt ◽  
Francisca Nijland ◽  
Otto Kamp ◽  
...  
Keyword(s):  
Contour Detection ◽  
Endocardial Contour ◽  
Motion Models ◽  
Active Appearance ◽  
Time Sequences

Active Appearance–Motion Models for fully automated endocardial contour detection in time sequences of echocardiograms

2001 ◽  
Vol 1230 ◽  
pp. 941-947 ◽  
Author(s):  
Hans G. Bosch ◽  
Steven C. Mitchell ◽  
Boudewijn P.F. Lelieveldt ◽  
Francisca Nijland ◽  
Otto Kamp ◽  
...  
Keyword(s):  
Contour Detection ◽  
Endocardial Contour ◽  
Motion Models ◽  
Active Appearance ◽  
Time Sequences

Time-Continuous Segmentation of Cardiac Image Sequences Using Active Appearance Motion Models

2001 ◽  
pp. 446-452 ◽  
Author(s):  
Boudewijn P.F. Lelieveldt ◽  
Rob J. van der Geest ◽  
Johan H.C. Reiber ◽  
Johan G. Bosch ◽  
Steven C. Mitchell ◽  
...  
Keyword(s):  
Image Sequences ◽  
Motion Models ◽  
Cardiac Image ◽  
Active Appearance

Left ventricle contour detection in x-ray angiograms using multi-view active appearance models

2003 ◽  
Author(s):  
Elco Oost ◽  
Boudewijn P. F. Lelieveldt ◽  
Gerhard Koning ◽  
Milan Sonka ◽  
Johan H. C. Reiber
Keyword(s):  
Left Ventricle ◽  
Contour Detection ◽  
Active Appearance Models ◽  
X Ray ◽  
Appearance Models ◽  
Active Appearance

scholarly journals LV Challenge LKEB Contribution: Fully Automated Myocardial Contour Detection

2009 ◽  
Author(s):  
Jeroen Wijnhout ◽  
Dennis Hendriksen ◽  
Hans Van Assen ◽  
Rob Van der geest
Keyword(s):  
Detection Method ◽  
User Interaction ◽  
Clinical Applications ◽  
Automated Detection ◽  
Contour Detection ◽  
Data Sets ◽  
3D Segmentation ◽  
Endocardial Contour ◽  
Ventricle Segmentation ◽  
Myocardial Contour

In this paper a contour detection method is described and evaluated on the evaluation data sets of the Cardiac MR Left Ventricle Segmentation Challenge as part of MICCAI 2009’s 3D Segmentation Challenge for Clinical Applications. The proposed method, using 2D AAM and 3D ASM, performs a fully automated detection of the myocardial contours, not requiring any user interaction. The algorithm’s performance is reported using the metrics provided by the LV Challenge organization. Endocardial contour detection was classified as successful in 86% of the images and epicardial contours in 94%. The average perpendicular distance (APD) of the successful contours was 2.28 mm and 2.29 mm for the endo- and epicardial contours, respectively.

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

2014 ◽  
Vol 2014 ◽  
pp. 1-15
Author(s):  
Faten A. Dawood ◽  
Rahmita W. Rahmat ◽  
Suhaini B. Kadiman ◽  
Lili N. Abdullah ◽  
Mohd D. Zamrin
Keyword(s):  
Hybrid Method ◽  
Search Algorithm ◽  
Region Of Interest ◽  
Ground Truth ◽  
Speckle Noise ◽  
Contour Detection ◽  
3D Echocardiography ◽  
Phase Iii ◽  
Contour Extraction ◽  
Endocardial Contour

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.

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