Literature DB >> 12575874

Automatic segmentation of echocardiographic sequences by active appearance motion models.

Johan G Bosch1, Steven C Mitchell, Boudewijn P F Lelieveldt, Francisca Nijland, Otto Kamp, Milan Sonka, Johan H C Reiber.   

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.

Entities:  

Mesh:

Year:  2002        PMID: 12575874     DOI: 10.1109/TMI.2002.806427

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  30 in total

1.  Rapid and accurate LV surface generation from 3D echocardiography by a catalog based method.

Authors:  Milan Sonka
Journal:  Int J Cardiovasc Imaging       Date:  2003-02       Impact factor: 2.357

2.  Assessment of left ventricular volume and function by integration of simplified 3D echocardiography, tissue harmonic imaging and automated extraction of endocardial borders.

Authors:  Donato Mele; Roberta Teoli; Corrado Cittanti; Giovanni Pasanisi; Gabriele Guardigli; Robert A Levine; Roberto Ferrari
Journal:  Int J Cardiovasc Imaging       Date:  2004-06       Impact factor: 2.357

3.  Echocardiography without electrocardiogram using nonlinear dimensionality reduction methods.

Authors:  Ahmad Shalbaf; Zahra AlizadehSani; Hamid Behnam
Journal:  J Med Ultrason (2001)       Date:  2014-11-09       Impact factor: 1.314

4.  Simulating cardiac ultrasound image based on MR diffusion tensor imaging.

Authors:  Xulei Qin; Silun Wang; Ming Shen; Guolan Lu; Xiaodong Zhang; Mary B Wagner; Baowei Fei
Journal:  Med Phys       Date:  2015-09       Impact factor: 4.071

5.  Segmentation in echocardiographic sequences using shape-based snake model combined with generalized Hough transformation.

Authors:  Chen Sheng; Yang Xin; Yao Liping; Sun Kun
Journal:  Int J Cardiovasc Imaging       Date:  2005-12-20       Impact factor: 2.357

6.  Artificial neural network: border detection in echocardiography.

Authors:  Eduardo Jyh Herng Wu; Márcio Luiz De Andrade; Denys E Nicolosi; Sérgio C Pontes
Journal:  Med Biol Eng Comput       Date:  2008-07-15       Impact factor: 2.602

7.  Automatic Segmentation of Right Ventricle on Ultrasound Images Using Sparse Matrix Transform and Level Set.

Authors:  Xulei Qin; Zhibin Cong; Luma V Halig; Baowei Fei
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2013-03-13

8.  Cardiac motion recovery via active trajectory field models.

Authors:  Andrew D Gilliam; Frederick H Epstein; Scott T Acton
Journal:  IEEE Trans Inf Technol Biomed       Date:  2009-01-20

9.  A Dynamical Shape Prior for LV Segmentation from RT3D Echocardiography.

Authors:  Yun Zhu; Xenophon Papademetris; Albert J Sinusas; James S Duncan
Journal:  Med Image Comput Comput Assist Interv       Date:  2009

10.  Segmentation of the left ventricle from cardiac MR images using a subject-specific dynamical model.

Authors:  Yun Zhu; Xenophon Papademetris; Albert J Sinusas; James S Duncan
Journal:  IEEE Trans Med Imaging       Date:  2009-09-29       Impact factor: 10.048

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.