Active Contour Models for Shape Description using Multiscale Differential Invariants.

Classic curvature-minimizing active contour models are often incapable of extracting complex shapes with points of high curvature. This paper presents a new active contour model which overcomes this problem and which can be applied to image segmentation as well as shape description in order to allow for quantitative and qualitative studies of shape measurements at multiple scales. Multiscale differential operators, which are invariant to linear intensity transformations such as contrast or brightness adjustments and independent of coordinate transformations, are integrated into the model's spline energy functional. Whereas the image intensity gradient attracts the spline contour to image features, the isophote curvature of the image intensity function is used for matching the contour curvature. This novel curvature matching approach appears to be very useful for the extraction of very complex and strongly curved objects such as brain contours, results of which will be presented in this paper.