  In this paper we present a new method for motion tracking of tumors in liver
ultrasound image sequences. Our algorithm has two main steps. In the first
step, we apply mean shift algorithm with multiple features to estimate the
center of the target in each frame. Target in the first frame is defined using
an ellipse. Edge, texture, and intensity features are extracted from the first
frame, and then mean shift algorithm is applied to each feature separately to
find the center of ellipse related to that feature in the next frame. The
center of ellipse will be the weighted average of these centers. By using mean
shift actually we estimate the target movement between two consecutive frames.
Once the correct ellipsoid in each frame is known, in the second step we apply
the Dynamic Directional Gradient Vector Flow (DDGVF) version of active contour
models, in order to find the correct boundary of tumors. We sample a few points
on the boundary of active contour then translate those points based on the
translation of the center of ellipsoid in two consecutive frames to determine
the target movement. We use these translated sample points as an initial guess
for active contour in the next frame. Our experimental results show that, the
suggested method provides a reliable performance for liver tumor tracking in
ultrasound image sequences.
