Phase-based feature detection in fetal ultrasound images

Detection of image features is an essential step in many medical applications. However, it is very challenging to accurately extract important features from ultrasound data that is corrupted by various imaging artifacts. Traditional intensity-based methods generally have poor performance in detecting salient features from ultrasound images. In contrast, phase-base approaches have been shown to perform well in these images because they are theoretically intensity invariant. In this paper, we extend previous phase-based methods to the field of fetal ultrasound images to detect both symmetric and asymmetric features, which correspond to ridge-like and step edge-like object boundaries, respectively. This is achieved by exploiting local phase-based measures computed from a 2D isotropic analytic signal: monogenic signal. Experimental results in clinical images demonstrate the outperformance of the proposed approach.