International Journal of Computer Vision
Multiview RT3D Echocardiography Image Fusion
FIMH '09 Proceedings of the 5th International Conference on Functional Imaging and Modeling of the Heart
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Real-time active shape models for segmentation of 3D cardiac ultrasound
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Adaptive multiscale ultrasound compounding using phase information
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Performance divergence with data discrepancy: a review
Artificial Intelligence Review
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Real-time 3-dimensional echocardiography (RT3DE) permits the acquisition and visualization of the beating heart in 3D. Despite a number of efforts to automate the left ventricle (LV) delineation from RT3DE images, this remains a challenging problem due to the poor nature of the acquired images usually containing missing anatomical information and high speckle noise. Recently, there have been efforts to improve image quality and anatomical definition by acquiring multiple single-view RT3DE images with small probe movements and fusing them together after alignment. In this work, we evaluate the quality of the multiview fused images using an image-driven semi-automatic LV segmentation method. The segmentation method is based on an edge-driven level set framework, where the edges are extracted using a local-phase inspired feature detector for low-contrast echocardiography boundaries. This totally image-driven segmentation method is applied for the evaluation of end-diastolic (ED) and end-systolic (ES) single-view and multiview fused images. Experiments were conducted on 17 cases and the results show that multiview fused images have better image segmentation quality, but large failures were observed on ED (88.2%) and ES (58.8%) single-view images.