Background: The two-dimensional videographic analysis of vibrissal movements in behaving rodents has become a standard method to estimate the degree of functional impairment and recovery after facial nerve injuries quantitatively. The main limitation of the method is the time consuming, uneconomic process of manually tracking the vibrissae in video sequences. New method: We developed a novel tool allowing automated detection of untagged vibrissae (two on each side of the snout). To compare the new method with the standard manual tracking approach, we used videos of unrestrained rats with unilateral section and immediate suture of the facial nerve performed two months earlier. Results: Measurement agreement analyses showed that the two methods are equivalent for both normal? high-amplitude vibrissal movements (non-operated side) and low-amplitude whisking (reinnervated side). Spectral analysis revealed a significant deviation in the power spectra on the control and injured side, indicating that bilaterally coordinated whisker movements are not present two months after surgery. Comparison with existing method(s): The novel method yields results equal to those of the manual tracking approach. An advantage of our tool is the possibility to significantly increase sample size without additional labor cost. Conclusions The novel tool can increase the efficacy and spectrum of functional measures used in facial nerve regeneration research.

One of the most important and challenging tasks in bio-medical image analysis is the localization, identification, and discrimination of salient objects or structures. While to date human experts are performing these tasks manually at the expense of time and reliability, methods for automation of these processes are evidently called for. This paper outlines a novel technique for geometric clustering of related object evidence called regularized geometric hulls (RGH) and gives three exemplary real-world application scenarios. Several experiments performed on real-world data highlight a set of useful advantages, such as robustness, reliability, as well as efficient runtime behavior.

A robust segmentation is the most important part of an automatic character recognition system (e.g. document pro- cessing, license plate recognition etc.). In our contribution we present an efficient segmentation framework using a pre- processing step for shadow suppression combined with a local thresholding technique. The method is based on a combination of difference of boxes filters and a new ternary segmentation, which are both simple low-level image oper- ations. We also draw parallels to a recently published work on a ganglion cell model and show that our approach is theoret- ically more substantiated as well as more robust and more efficient in practice. Systematic evaluation of noisy input data as well as results on a large dataset of license plate images 1 show the robustness and efficiency of our proposed method. Our results can be applied easily to any optical char- acter recognition system resulting in an impressive gain of robustness against nonlinear illumination.