The authors present a new method for the segmentation of autoradiographic brain image stacks, as well as the automatic extraction and 3D visualization of functional information encoded in the image gray values. The difficulty in the evaluation of these so-called "2 1/2 D" data sets is that they do not inherently represent a continuous 3D data volume (as MRI or CT), but consist of a series of consecutive, strongly distorted gray value images of single tissue slices, while the exact position and direction of the original slices varies among different data sets. This complicates the comparison of different experiments. In this work, in addition to providing an enhanced active contour based segmentation algorithm, the concept of "map space" is introduced, which allows the automatic functional evaluation using a shape adaptive, rectangular, normalized, parametric representation of the cortex. Results are presented for a rat brain under acoustical stimulation.

Nonlinear (cid:12)ltering can solve very complex problems, but typically involve very time consuming calculations. Here we show that for (cid:12)lters that are constructed as a RBF network with Gaussian basis functions, a decomposition into linear (cid:12)lters exists, which can be computed e(cid:14)ciently in the frequency domain, yielding dramatic improvement in speed. We present an application of this idea to image processing. In electron micrograph images of photoreceptor terminals of the fruit (cid:13)y, Drosophila, synaptic vesicles containing neurotransmitter should be detected and labeled automatically. We use hand labels, provided by human experts, to learn a RBF (cid:12)lter using Support Vector Regression with Gaussian kernels. We will show that the resulting nonlinear (cid:12)lter solves the task to a degree of accuracy, which is close to what can be achieved by human experts. This allows the very time consuming task of data evaluation to be done e(cid:14)ciently.

In this work, we focus on methods for the preprocessing of neurons from three-dimensional (3-D) confocal microscopy images, which are needed for a subsequent detailed morphologic analysis. Due to the specific image properties of confocal microscopy scans, we had to include several heuristic approaches which are based on multiscale edges to guarantee meaningful results: (1) a reliable segmentation of objects of different sizes independent of image contrast, and, based on it, (2) the computation of skeleton points along the branch central axes, and (3) the reliable detection of branching points and of problematic regions. These are preprocessing steps to gather information which is needed by the subsequent construction of a graph representing the geometry of the neuron and a final surface reconstruction.

The accurate comparison of inter-individual 3D image datasets of brains requires warping techniques to reduce geometric variations. In this study we use a point-based method of warping with weighted sums of displacement vectors, which is extended by an optimization process. To improve the practicability of 3D warping, we investigate 3D operators as landmark detectors for the applicability to our image datasets. The combined approach was tested on 3D autoradiographs of brains of Mongolian gerbils. The warping function is distance-weighted with landmark-specific weighting factors. These weighting factors are optimized by a computational evolution strategy. Within this optimization process the quality of warping is quantified by the sum of spatial differences of manually predefined registration points (registration error). The described approach combines a highly suitable procedure to detect landmarks in brain images and a point-based warping technique, which optimizes local weighting factors.