Ultrasound image segmentation using an interacting multiple-model probabilistic data association filter

Abstract

This paper presents a novel segmentation technique to extract cavity contours from ultrasound images. The problem is first discretized by projecting equispaced radii from an arbitrary seed point inside the cavity towards its boundary. The distance of the cavity boundary from the seed point is modeled by the trajectory of a moving object. The motion of this moving object is assumed to be governed by a finite set of dynamical models subject to uncertainty. Candidate edge points obtained along each radius may include the actual object position and some false measurements. This modeling approach enables us to employ the interacting multiple model (IMM) estimator along with a probabilistic data association filter (PDAF) for contour extraction. Since the method does not employ any numerical optimization, convergence is very fast. The stability and accuracy of the method is demonstrated by segmenting contours from a series of ultrasound images with different levels of speckles and echo dropouts in the cavity contour. An application of the method in segmenting bone contours from CT images is also presented. We have demonstrated that in certain situations, such as wrist CT images, when the distance between two bones is very small and conventional thresholding techniques fail, our algorithm is able to segment bones successfully.