Intensity-based Choroidal Registration Using Regularized Block Matching

Detecting and monitoring changes in the human choroid play a crucial role in treating ocular diseases such as myopia. However, reliable segmentation of optical coherence tomography (OCT) images at the choroid-sclera interface (CSI) is notoriously difficult due to poor contrast, signal loss and OCT artefacts. In this paper we present blockwise registration of successive scans to improve stability also during complete loss of the CSI-signal. First, we formulated the problem as minimization of a regularized energy functional. Then, we tested our automated method for piecewise Intensity-based Choroidal rigid Registration using regularized block matching (ICR) on 20 OCT 3D-volume scan-rescan data set pairs. Finally, we used these data set pairs to determine the precision of our method, while the accuracy was determined by comparing our results with those using manually annotated scans

Dose-compatible grating-based phase-contrast mammography on mastectomy specimens using a compact synchrotron source

With the introduction of screening mammography, the mortality rate of breast cancer has been reduced throughout the last decades. However, many women undergo unnecessary subsequent examinations due to inconclusive diagnoses from mammography. Two pathways appear especially promising to reduce the number of false-positive diagnoses. In a clinical study, mammography using synchrotron radiation was able to clarify the diagnosis in the majority of inconclusive cases. The second highly valued approach focuses on the application of phase-sensitive techniques such as grating-based phasecontrast and dark-field imaging. Feasibility studies have demonstrated a promising enhancement of diagnostic content, but suffer from dose concerns. Here we present dose-compatible grating-based phase-contrast and dark-field images as well as conventional absorption images acquired with monochromatic x-rays from a compact synchrotron source based on inverse Compton scattering. Images of freshly dissected mastectomy specimens show improved diagnostic content over ex-vivo clinical mammography images at lower or equal dose. We demonstrate increased contrast-to-noise ratio for monochromatic over clinical images for a well-defined phantom. Compact synchrotron sources could potentially serve as a clinical second level examination

Object segmentation by fitting statistical shape models : a Kernel-based approach with application to wisdom tooth segmentation from CBCT images

Image segmentation is an important and challenging task in medical image analysis. Especially from low-quality images, segmentation algorithms have to cope with misleading background clutter, insufficient object boundaries and noise in the image. Statistical shape models are a powerful tool to tackle these problems. However, their construction as well as their application for segmentation remain challenging. In this thesis, we focus on the wisdom-tooth shape and its segmentation from Cone Beam Computed Tomography images. The large shape variation leads to difficult registration problems and an often too restrictive shape model, while the challenging appearance of the wisdom tooth makes the model fitting difficult. To tackle these problems, we follow on kernel-based approaches to registration and shape modeling. We introduce a kernel, which considers landmarks as an additional prior in image registration. This allows to locally improve the registration accuracy. We present a Demons-like registration method with an inhomogeneous regularization which allows to apply such a landmark kernel. For modeling the shape variation, we construct a kernel comprising a generic smoothness and an empirical sample covariance. With this combined kernel, we increase the flexibility of the statistical shape model. We make use of a reproducing kernel Hilbert space framework for registration, where we apply this combined kernel as reproducing kernel. To make the approach computationally feasible, we perform a low-rank approximation of the specific kernel function. Because of a heterogeneous appearance inside the wisdom tooth, fitting the statistical model to plain intensity images is difficult. We build a nonparametric appearance model, based on random forest regression, which abstracts the raw images to semantic probability maps. Hence, the misleading structures become semantic values, which greatly simplificates the shape model fitting

Variational image registration using inhomogeneous regularization

We present a generalization of the convolution basedvariational image registration approach, in which differentregularizers can be implemented by conveniently exchangingthe convolution kernel, even if it is nonseparableor nonstationary. Nonseparable kernels pose a challenge becausethey cannot be efficiently implemented by separate1D convolutions. We propose to use a low-rank tensor decompositionto efficiently approximate nonseparable convolution.Nonstationary kernels pose an even greater challengebecause the convolution kernel depends on, and needs tobe evaluated for, every point in the image. We propose topre-compute the local kernels and efficiently store them inmemory using the Tucker tensor decomposition model. Inour experiments we use the nonseparable exponential kerneland a nonstationary landmark kernel. The exponential kernelreplicates desirable properties of elastic image registration,while the landmark kernel incorporates local prior knowledgeabout corresponding points in the images.We examinethe trade-off between the computational resources neededand the approximation accuracy of the tensor decompositionmethods. Furthermore, we obtain very smooth displacementfields even in the presence of large landmark displacements

Common Ground. HOTEL GELEM partizipatives Kunstprojekt – kollektive Prozesse und eigene Praxen im Prekären

Common Ground. The participatory Art project HOTEL GELEM – collective processes and own practices in precarious living conditions Due to the culture capitalistic principle, artistic, creative work no longer brings a position of personal independence or a development of critical faculties. Creative capabilities are nowadays a general skill that matters for all different kinds of occupations. At the same time, the success of every creative undertaking is reduced to its commercial success. Art could do more. The project HOTEL GELEM leads us to a fundamental understanding how the given preconditions and the social expectations shape our specific opportunities for participation and expression. Many Roma families suffer from expulsion and exclusion, and try to survive in the outmost precarious living conditions on the fringes of our society. Together with these families, HOTEL GELEM attempts to break the dreadful vicious circle of stigmatization and poverty. The potential of the artistic practice lays in the possibility of overcoming predetermined views

Using object probabilities in deformable model fitting

We present a novel image segmentation method based on statistical shape model fitting. Instead of fitting the model to raw intensity values we consider object probabilities. The abstraction from the plain intensity images to probability maps makes the segmentation more robust against misleading texture inside the object or surrounding background. The target object probability is predicted based on random forest regression trained with neighborhood dependent features of sample images. In contrast to similar approaches, both, the object boundary as well as the whole object and background region are considered for segmentation. We apply our approach to a 3D cone beam computed tomography image dataset of the jaw region where we segment the wisdom tooth shape. Compared to a boundary and a region-based method we obtain superior segmentation performance