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We address the task of object localisation in 2D fetal ultrasound images, where invariance to factors such as image contrast and object orientation is desirable. We build on recent methods for rotation-invariant detection and combine them with oriented measures of image structure derived from the monogenic signal. We test our approach on images containing the fetal heart. Our results suggest that...
Soft tissue quantification from ultrasound (US) images is a challenging task due to signal dropouts, missing boundaries, and presence of speckle. Typically, intensity and intensity gradient-based methods do not suffice to obtain a good representation of the object of interest. Prior work has shown that local phase, derived from the monogenic signal, extracts structural information from US images,...
Accurate and robust image analysis software is crucial for assessing the quality of ultrasound images of fetal biometry. In this work, we present the result of our automated image analysis method based on a machine learning algorithm in detecting important anatomical landmarks employed in manual scoring of ultrasound images of the fetal abdomen. Experimental results on 2384 images are promising and...
Ultrasound (US) image segmentation can be a challenging task due to signal dropouts, missing boundaries, and presence of speckle. Typically, purely intensity-based methods do not lead to a good segmentation of the structures of interest. Prior work has shown that local phase and feature asymmetry, derived from the monogenic signal, extract structural information from US images. This paper proposes...
Introduction of automated methods for heart function assessment have the potential to minimize the variance in operator assessment. This paper considers automated classification of rest and stress echocardiography. One previous attempt has been made to combine information from rest and stress sequences utilizing a Hidden Markov Model (HMM), which has proven to be the best performing approach to date...
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