Affine Invariant Surface Evolutions for 3d Image Segmentation

In this paper we present an algorithm for 3D medical image segmentation based on an affine invariant flow. The algorithm is simple to implement and semi-automatic. The technique is based on active contours evolving in time according to intrinsic geometric measures of the image. The surface flow is obtained by minimizing a global energy with respect to an affine invariant metric. Affine invariant edge detectors for 3-dimensional objects are also computed which have the same qualitative behavior as the Euclidean edge detectors. This algorithm yields better segmentation results, since the affine symmetry group is much larger than the Euclidean group. Results on artificial and real MRI images show that the algorithm performs well, both in terms of accuracy and robustness to noise.