DocumentCode :
1131134
Title :
Automatic segmentation of echocardiographic sequences by active appearance motion models
Author :
Bosch, Johan G. ; Mitchell, Steven C. ; Lelieveldt, Boudewijn P F ; Nijland, Francisca ; Kamp, Otto ; Sonka, Milan ; Reiber, Johan H C
Author_Institution :
Departmentof Radiol., Leiden Univ. Med. Center, Netherlands
Volume :
21
Issue :
11
fYear :
2002
Firstpage :
1374
Lastpage :
1383
Abstract :
A novel extension of active appearance models (AAMs) for automated border detection in echocardiographic image sequences is reported. The active appearance motion model (AAMM) technique allows fully automated robust and time-continuous delineation of left ventricular (LV) endocardial contours over the full heart cycle with good results. Nonlinear intensity normalization was developed and employed to accommodate ultrasound-specific intensity distributions. The method was trained and tested on 16-frame phase-normalized transthoracic four-chamber sequences of 129 unselected infarct patients, split randomly into a training set (n=65) and a test set (n=64). Borders were compared to expert drawn endocardial contours. On the test set, fully automated AAMM performed well in 97% of the cases (average distance between manual and automatic landmark points was 3.3 mm, comparable to human interobserver variabilities). The ultrasound-specific intensity normalization proved to be of great value for good results in echocardiograms. The AAMM was significantly more accurate than an equivalent set of two-dimensional AAMs.
Keywords :
echocardiography; edge detection; image segmentation; image sequences; medical image processing; modelling; 16-frame phase-normalized transthoracic four-chamber sequences; 3.3 mm; active appearance motion models; echocardiographic sequences segmentation; expert drawn endocardial contours; full heart cycle; human interobserver variabilities; medical diagnostic imaging; nonlinear intensity normalization; test set; time sequences; training set; unselected infarct patients; Active appearance model; Biomedical imaging; Heart; Humans; Image segmentation; Image sequences; Robustness; Testing; Ultrasonic imaging; Visualization; Algorithms; Echocardiography; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Models, Statistical; Motion; Myocardial Infarction; Observer Variation; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Single-Blind Method;
fLanguage :
English
Journal_Title :
Medical Imaging, IEEE Transactions on
Publisher :
ieee
ISSN :
0278-0062
Type :
jour
DOI :
10.1109/TMI.2002.806427
Filename :
1175086
Link To Document :
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