Details

Autor(en) / Beteiligte

• Pearlman, P.C.
• Tagare, H.D.
• Lin, B.A.
• Sinusas, A.J.
• Duncan, J.S.

Titel

Segmentation of 3D radio frequency echocardiography using a spatio-temporal predictor

Ist Teil von

• Medical image analysis, 2012-02, Vol.16 (2), p.351-360

Ort / Verlag

Netherlands: Elsevier B.V

Erscheinungsjahr

2012

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• [Display omitted] ► We segment left ventricular endocardial boundaries from RF ultrasound. ► Our M.A.P. segmentation uses a joint spatial model and a multiframe conditional. ► The conditional model relates neighboring frames using a linear predictor. ► The linear predictor exploits spatio-temporal coherence in the data. ► We overcome problems due to image inhomogeneities amplified in B-mode segmentation. This paper presents an algorithm for segmenting left ventricular endocardial boundaries from RF ultrasound. Our method incorporates a computationally efficient linear predictor that exploits short-term spatio-temporal coherence in the RF data. Segmentation is achieved jointly using an independent identically distributed (i.i.d.) spatial model for RF intensity and a multiframe conditional model that relates neighboring frames in the image sequence. Segmentation using the RF data overcomes challenges due to image inhomogeneities often amplified in B-mode segmentation and provides geometric constraints for RF phase-based speckle tracking. The incorporation of multiple frames in the conditional model significantly increases the robustness and accuracy of the algorithm. Results are generated using between 2 and 5 frames of RF data for each segmentation and are validated by comparison with manual tracings and automated B-mode boundary detection using standard (Chan and Vese-based) level sets on echocardiographic images from 27 3D sequences acquired from six canine studies.