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Computer Methods and Programs in Biomedicine
Noise reduction and edge detection via kernel anisotropic diffusion
Pattern Recognition Letters
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IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
Joint Detection of the Carotid Boundaries in Longitudinal B-Mode Images
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Automatic noise estimation in images using local statistics. Additive and multiplicative cases
Image and Vision Computing
Coefficient-Tracking Speckle Reducing Anisotropic Diffusion
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Segmentation of the carotid intima-media region in B-mode ultrasound images
Image and Vision Computing
Ultrasound speckle reduction by a SUSAN-controlled anisotropic diffusion method
Pattern Recognition
SRAD and optical flow based external energy for echocardiograms with primitive shape priors
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
IEEE Transactions on Image Processing
Segmentation of ultrasound images of the carotid using RANSAC and cubic splines
Computer Methods and Programs in Biomedicine
Heart cavity detection in ultrasound images with SOM
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Despeckling low SNR, low contrast ultrasound images via anisotropic level set diffusion
Multidimensional Systems and Signal Processing
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The instantaneous coefficient of variation (ICOV) edge detector, based on normalized gradient and Laplacian operators, has been proposed for edge detection in ultrasound images. In this paper, the edge detection and localization performance of the ICOV-squared (ICOVS) detector are examined. First, a simplified version of the ICOVS detector, the normalized gradient magnitude squared, is scrutinized in order to reveal the statistical performance of edge detection and localization in speckled ultrasound imagery. Both the probability of detection and the probability of false alarm are evaluated for the detector. Edge localization is characterized by the position of the peak and the 3-dB width of the detector response. Then, the speckle-edge response of the ICOVS as applied to a realistic edge model is studied. Through theoretical analysis, we reveal the compensatory effects of the normalized Laplacian operator in the ICOV edge detector for edge-localization error. An ICOV-based edge-detection algorithm is implemented in which the ICOV detector is embedded in a diffusion coefficient in an anisotropic diffusion process. Experiments with real ultrasound images have shown that the proposed algorithm is effective in extracting edges in the presence of speckle. Quantitatively, the ICOVS provides a lower localization error, and qualitatively, a dramatic improvement in edge-detection performance over an existing edge-detection method for speckled imagery.