Towards medical ultrasound image segmentation with limited prior knowledge

Though a popular imaging technique, ultrasound is known for producing images filled with noise, distortions and shadowing effects. As a result, segmentation of ultrasound images require significant prior knowledge, often inserted into algorithms interactively or through shape information of the region of interest. This type of prior knowledge puts limitations on current approaches. This paper presents a different approach to ultrasound image segmentation that relies mainly on the physical properties of ultrasonic imaging. Robust intensity-based external energy formulations are incorporated into an Active Contour framework that is tolerant of the noise common to ultrasound images. By initializing the contour through an ellipse fitting procedure, an autonomous ultrasound image segmentation system is created that that can generalize to objects of varying shapes and sizes. The segmentation system was tested on ultrasound images of neonatal kidneys with results comparable to current methods.