On the use of motion-based frame rejection in temporal averaging denoising for segmentation of echocardiographic image sequences

We have recently introduced an algorithm for semi-automatic segmentation of the left ventricular wall in short-axis echocardiographic images (EMBC 30:218-221). In its preprocessing stage, the algorithm uses temporal averaging for image denoising. Motion estimation is used to detect and reject frames...

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Published in2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vol. 2009; pp. 507 - 510
Main Authors do Carmo dos Reis, M., Carvalho, J.L.A., Macchiavello, B.L., Vasconcelos, D.F., da Rocha, A.F., Nascimento, F.A.O., Camapum, J.F.
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.01.2009
Subjects
Algorithm design and analysis
Algorithms
area variation
boundary detection
Discrete wavelet transforms
echocardiographic images
Echocardiography - methods
Filters
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image segmentation
Image sequences
Labeling
Motion
Motion detection
Motion estimation
Noise reduction
Reproducibility of Results
Segmentation
Sensitivity and Specificity
Subtraction Technique
USA Councils
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Summary:We have recently introduced an algorithm for semi-automatic segmentation of the left ventricular wall in short-axis echocardiographic images (EMBC 30:218-221). In its preprocessing stage, the algorithm uses temporal averaging for image denoising. Motion estimation is used to detect and reject frames that do not correlate well with the set of images being averaged. However, the process of estimating motion vectors is computationally intense, which increases the algorithm's computation time. In this work, we evaluate the viability of replacing the motion estimation stage with less computationally intense approaches. Two alternative techniques are evaluated. The ventricular contours obtained from each of the three algorithm variants were quantitatively and qualitatively compared with contours manually-segmented by a specialist. We show that it is possible to reduce the algorithm's computational load without significantly reducing the segmentation quality. The proposed algorithms are also compared with three other techniques from the literature.
ISSN:1094-687X
1557-170X
1558-4615
DOI:10.1109/IEMBS.2009.5333104