Hans Meine

Microstructural analysis of lignocellulosic fiber networks

The structure of wood based medium density fiberboard (MDF) has been studied using synchrotron radiation-based x-ray microtomography (SRμCT.) Fully automated 3D segmentation and analysis routines have been developed in order to gain information about individual fibers, the distribution of the fiber material, fiber orientation, fiber surfaces and size and location of contact areas. Representative samples of the analyzed volume data are presented to demonstrate the results of the implemented methods using the VIGRA image processing library.

A topological sampling theorem for Robust boundary reconstruction and image segmentation

Existing theories on shape digitization impose strong constraints on admissible shapes, and require error-free data. Consequently, these theories are not applicable to most real-world situations. In this paper, we propose a new approach that overcomes many of these limitations. It assumes that segmentation algorithms represent the detected boundary by a set of points whose deviation from the true contours is bounded. Given these error bounds, we reconstruct boundary connectivity by means of Delaunay triangulation and @a-shapes. We prove that this procedure is guaranteed to result in topologically correct image segmentations under certain realistic conditions. Experiments on real and synthetic images demonstrate the good performance of the new method and confirm the predictions of our theory.

Topologically correct image segmentation using alpha shapes

Existing theories on shape digitization impose strong constraints on feasible shapes and require error-free measurements We use Delaunay triangulation and α-shapes to prove that topologically correct segmentations can be obtained under much more realistic conditions Our key assumption is that sampling points represent object boundaries with a certain maximum error Experiments on real and generated images demonstrate the good performance and correctness of the new method.

The GeoMap: a unified representation for topology and geometry

We propose the GeoMap abstract data type as a unified representation for image segmentation purposes. It manages both topology (based on XPMaps) and pixel-based information, and its interface is carefully designed to support a variety of automatic and interactive segmentation methods. We have successfully used the abstract concept of a GeoMap as a foundation for the implementation of well-known segmentation methods.

A new sub-pixel map for image analysis

Planar maps have been proposed as a powerful and easy-to-use representation for various kinds of image analysis results, but so far they are restricted to pixel accuracy. This leads to limitations in the representation of complex structures (such as junctions, triangulations, and skeletons) and discards the sub-pixel information available in grayvalue and color images. We extend the planar map formalism to sub-pixel accuracy and introduce various algorithms to create such a map, thereby demonstrating significant gains over the existing approaches.

Accurate CT-MR image registration for deep brain stimulation: a multi-observer evaluation study

Since the first clinical interventions in the late 1980s, Deep Brain Stimulation (DBS) of the subthalamic nucleus has evolved into a very effective treatment option for patients with severe Parkinsons disease. DBS entails the implantation of an electrode that performs high frequency stimulations to a target area deep inside the brain. A very accurate placement of the electrode is a prerequisite for positive therapy outcome. The assessment of the intervention result is of central importance in DBS treatment and involves the registration of pre- and postinterventional scans. In this paper, we present an image processing pipeline for highly accurate registration of postoperative CT to preoperative MR. Our method consists of two steps: a fully automatic pre-alignment using a detection of the skull tip in the CT based on fuzzy connectedness, and an intensity-based rigid registration. The registration uses the Normalized Gradient Fields distance measure in a multilevel Gauss-Newton optimization framework and focuses on a region around the subthalamic nucleus in the MR. The accuracy of our method was extensively evaluated on 20 DBS datasets from clinical routine and compared with manual expert registrations. For each dataset, three independent registrations were available, thus allowing to relate algorithmic with expert performance. Our method achieved an average registration error of 0.95mm in the target region around the subthalamic nucleus as compared to an inter-observer variability of 1.12 mm. Together with the short registration time of about five seconds on average, our method forms a very attractive package that can be considered ready for clinical use.

Provably correct edgel linking and subpixel boundary reconstruction

Existing methods for segmentation by edgel linking are based on heuristics and give no guarantee for a topologically correct result. In this paper, we propose an edgel linking algorithm based on a new sampling theorem for shape digitization, which guarantees a topologically correct reconstruction of regions and boundaries if the edgels approximate true object edges with a known maximal error. Experiments on real and generated images demonstrate the good performance of the new method and confirm the predictions of our theory.

Fast and Accurate Interactive Image Segmentation in the GEOMAP Framework

Although many interactive segmentation methods exists,none can be considered a silver bullet for all clinical tasks. Moreover, incompatible data representations prevent multiple algorithms from being combined as desired. We propose the GEOMAP as a unified representation for segmentation results and illustrate how it facilitates the design of an integrated framework for interactive medical image analysis. Results show the high flexibility and performance of the new framework.

Pixel approximation errors in common watershed algorithms

The exact, subpixel watershed algorithm delivers very accurate watershed boundaries based on a spline interpolation, but is slow and only works in 2D. On the other hand, there are very fast pixel watershed algorithms, but they produce errors not only in certain exotic cases, but also in real-world images and even in the most simple scenarios. In this work, we examine closely the source of these errors and propose a new algorithm that is fast, approximates the exact watersheds (with pixel resolution), and can be extended to 3D.