Improvement in cytoarchitectonic mapping by combining electrodynamic modeling with local orientation in high-resolution images of the cerebral cortex

Abstract

To detect changes of cortical cytoarchitectonics, digital images of cortical laminations are analyzed. Cortical regions are transformed into a rectangular grid for subsequent evaluations. Transformations are realized by stepwise scanning using perpendicular testlines. 3D cytoarchitectonic data of the human brain at a histological resolution are not available and 2D sections deliver partial information only. The problem is to find an optimal scanning-technique that introduces a minimum of distortions and noise by the transformation of the curvilinear cortex to a rectangular presentation. In the past this was solved by constructing testlines dependent on the outlined cortical contours only. An advanced approach was to model the contours as electrically charged surfaces and to use the resulting field lines as testlines. However, local information of cell distributions were not considered. Hence a novel hybrid approach was developed which is able to construct significantly better testlines in cortical images with mixtures of columnar rich (local orientation rich) and orientation poor parts of strongly curved and large regions of the cerebral cortex. The novel hybrid approach is based on the computer vision methods such as the structure tensor and constrained anisotropic diffusion. In addition, the introduction of projective transformations yields a significant improvement of cortical fingerprints, thereby offering the possibility for detecting weakly pronounced regions of cytoarchitectonic transitions. The statistical evaluation of the novel hybrid approach confirms robustness. This technique can be generalized and applied to different types of cerebral cortex with any kind and amount of local orientation information.