Oriented feature-based coupled ellipse fitting for soft tissue quantification in ultrasound images

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, being contrast invariant. An oriented edge map can be derived from feature asymmetry, resulting from combining different scales of the local phase, and local orientation. This paper proposes a novel feature-based approach, based on a modified Hough transform framework, for the detection of coupled ellipses for soft tissue quantification in US images using oriented edge maps, derived from the monogenic signal. Quantitative and qualitative results are illustrated on US images of the fetal arm across gestation, the object of interest being the adipose tissue layer, which is an indicator of fetal nutrition.