• 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