Interpolation snakes for border detection in ultrasound images

Ultrasound images present major challanges to just about any segmentation algorithm, including active contour techniques, due to increased specularity, non-uniform edges along the boundaries of interest, incomplete and misleading visual support. Active contours that depend on a vector of parameters (e.g. B-splines), have been proposed in the literature, and have the advantage over traditional snakes and level-set snakes, that smoothness is built-in, which is a sine qua non requirement in border detection in medical images. We propose in this paper the use of interpolation splines as active contours for border detection in ultrasound images, which we term interpolation snakes. We argue that interpolation snakes are better suited for ultrasound than other snakes, because of the fact that the control points (parameters which control the shape of the snake) are on the curve. This allows for an initial arclength parameterization of the snake. In conjunction with interpolation snakes we define a new energy (measure of fit) which incorporates a term supposed to maintain arclength parameterization of the snake throughout the minimization process. A shape prior can also be introduced naturally, as a distribution on the control points.