AUTOMATIC MATCHING OF HOMOLOGOUS HISTOLOGICAL SECTIONS

The role of neuroanatomical atlases is undergoing a significant redefi nition as digital atlases become available. These have the potential t o serve as more than passive guides and to hold the role of directing segmentation and multimodal fusion of experimental data. Key elements needed to support these new tasks are registration algorithms. For ima ges derived from histological procedures, the need is for techniques t o map the two-dimensional (2-D) images of the sectional material into the reference atlas which may be a full three-dimensional (3-D) data s et or one consisting of a series of 2-D images. A variety of 2-D-2-D r egistration methods are available to align experimental images with th e atlas once the corresponding plane of section through the atlas has been identified. Methods to automate the identification of the homolog ous plane, however, have not been previously reported. In this paper w e use the external section contour to drive the identification and reg istration procedure. For this purpose, me model the contours by B-spli nes because of their attractive properties the most important of which are: 1) smoothness and continuity; 2) local controllability which imp lies that local changes in shape are confined to the B-spline paramete rs local to that change; 3) shape invariance under affine transformati on, which means that the affine transformed curve is still a B-spline whose control points are related to the object control points through the transformation, In this paper we present a fast algorithm for esti mating the control points of the B-spline which is robust to nonunifor m sampling, noise, and local deformations. Curve matching is achieved by using a similarity measure that depends directly on the parameters of the B-spline, Performance tests are reported using histological mat erial from rat brains.