  We develop a generalized active contour formalism for image segmentation
based on shape templates. The shape template is subjected to a restricted
affine transformation (RAT) in order to segment the object of interest. RAT
allows for translation, rotation, and scaling, which give a total of five
degrees of freedom. The proposed active contour comprises an inner and outer
contour pair, which are closed and concentric. The active contour energy is a
contrast function defined based on the intensities of pixels that lie inside
the inner contour and those that lie in the annulus between the inner and outer
contours. We show that the contrast energy functional is optimal under certain
conditions. The optimal RAT parameters are computed by maximizing the contrast
function using a gradient descent optimizer. We show that the calculations are
made efficient through use of Green's theorem. The proposed formalism is
capable of handling a variety of shapes because for a chosen template,
optimization is carried with respect to the RAT parameters only. The proposed
formalism is validated on multiple images to show robustness to Gaussian and
Poisson noise, to initialization, and to partial loss of structure in the
object to be segmented.
