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We present a novel approach to the coarse segmentation of tubular structures in three-dimensional (3-D) image data. Our algorithm, which requires only few initial values and minimal user interaction, can be used to initialize complex deformable models and is based on an extension of the randomized hough transform (RHT), a robust method for low-dimensional parametric object detection. Tubular structures are modeled as generalized cylinders. By means of a discrete Kalman filter, they are tracked through 3-D space. Our extensions to the RHT are a feature adaptive selection of the sample size, expectation-dependent weighting of the input data, and a novel 3-D parameterization for straight elliptical cylinders. Experimental results obtained for 3-D synthetic as well as for 3-D medical images demonstrate the robustness of our approach w.r.t. image noise. We present the successful segmentation of tubular anatomical structures such as the aortic arc and the spinal cord.

Digital soil mapping as a tool to generate spatial soil information provides solutions for the growing demand for high‐resolution soil maps worldwide. Even in highly developed countries like Germany, digital soil mapping becomes essential due to the decreasing, time‐consuming, and expensive field surveys which are no longer affordable by the soil surveys of the individual federal states.This article summarizes the present state of soil survey in Germany in terms of digitally available soil data, applied digital soil mapping, and research in the broader field of pedometrics and discusses future perspectives.Based on the geomorphologic conditions in Germany, relief is a major driving force in soil genesis. This is expressed by the digital–soil mapping research which highlights the great importance of digital terrain attributes in combination with information on parent material in soil prediction.An example of digital soil mapping using classification trees in Thuringia is given as an introduction in digital soil‐class mapping based on correlations to environmental covariates within the scope of the German classification system.

Abstract. In this paper, we present a new approach for coarse segmentation of tubular anatomical structures in 3D image data. Our approach can be used to initialise complex deformable models and is based on an extension of the randomized Hough transform (RHT), a robust method for low-dimensional parametric object detection. In combination with a discrete Kalman filter, the object is tracked through 3D space. Our extensions to the RHT feature adaptive selection of the sample size, expectation-dependent weighting of the input data, and a novel 3D parameterisation for straight elliptical cylinders. For initialisation, only little user interaction is necessary. Experimental results obtained for 3D synthetic as well as for 3D medical images demonstrate the robustness of our approach w.r.t. image noise. We present the successful segmentation of tubular anatomical structures such as the aortic arc or the spinal chord.