Model-Based Detection of Acoustically Dense Objects in Ultrasound

Traditional methods of detection tend to under perform in the presence of the strong and variable background clutter that characterize a medical ultrasound image. In this paper, we present a novel diffusion based technique to localize acoustically dense objects in an ultrasound image. The approach is premised on the observation that the topology of noise in ultrasound images is more sensitive to diffusion than that of any such physical object (≫ λ). We show that our method when applied to the problem of fetal head detection and automatic measurement of head circumference in 59 obstetric scans compares remarkably well with manually assisted measurements. Based on fetal age estimates and their bounds specified in Standard OB Tables, the Gestational Age predictions from automated measurements is found to be within ± 2SD in 95% and 98% of cases when compared with manual measurements by two experts. The framework is general and can be extended to object localization in diverse applications of ultrasound imaging.