A Brief Survey of Recent Edge-Preserving Smoothers

We introduce recent and very recent smoothing methods and discuss them in the common framework of `energy functions'. Focus is on the preservation of boundaries, spikes and canyons in presence of noise

Segmentierung des Gehirns auf der Basis von MR-Daten

Es wird ein Segmentierungsverfahren vorgestellt, das bei T1-gewichteten MR Aufnahmen Liquor, Cortex und weisse Materie trennt. Das Verfahren korrigiert in mehreren Schritten aufnahmetechnisch bedingte Artefakte und bestimmt die Substanzen durch 2 globale Schwellen. Das Verfahren erfordert an mehreren Stellen eine interaktive Justierung von Parametern und ist entsprechend flexibel

Noise Reduction in Images: Some Recent Edge-Preserving Methods

We introduce some recent and very recent smoothing methods which focus on the preservation of boundaries, spikes and canyons in presence of noise. We try to point out basic principles they have in common; the most important one is the robustness aspect. It is reflected by the use of `cup functions' in the statistical loss functions instead of squares; such cup functions were introduced early in robust statistics to down weight outliers. Basically, they are variants of truncated squares. We discuss all the methods in the common framework of `energy functions', i.e we associate to (most of) the algorithms a `loss function' in such a fashion that the output of the algorithm or the `estimate' is a global or local minimum of this loss function. The third aspect we pursue is the correspondence between loss functions and their local minima and nonlinear filters. We shall argue that the nonlinear filters can be interpreted as variants of gradient descent on the loss functions. This way we can show that some (robust) M-estimators and some nonlinear filters produce almost the same result

Kinematic simulation to investigate the influence of the cutting edge topography when ball end micro milling

During the ball end micro milling of material measures, the cutting edge topography is imaged on the machined workpiece. The influence of the chipping on the resulting surface quality is much more dominant than other kinematic effects. In this simulative study, a model is built that is able to predict the correlation between the cutting edge topography and the resulting workpiece topography. Thus, the mentioned correlation can be investigated without overlaying effects of material separation or measurement uncertainties, which are unavoidable in an experimental study. The model has been validated based on four artificial chippings

Evaluation of a new approach for semi-automatic segmentation of the cerebellum in patients with multiple sclerosis

Cerebellar dysfunction is an important contributor to disability in patients with multiple sclerosis (MS), however, few in vivo studies focused on cerebellar volume loss so far. This relates to technical challenges regarding the segmentation of the cerebellum. In this study, we evaluated the semi-automatic ECCET software for performing cerebellar volumetry using high-resolution 3D T1-MR scans in patients with MS and healthy volunteers. We performed test-retest as well as inter-observer reliability testing of cerebellar segmentation and compared the ECCET results with a fully automatic cerebellar segmentation using the FreeSurfer software pipeline in 15 MS patients. In a pilot matched-pair analysis with another data set from 15 relapsing-remitting MS patients and 15 age- and sex-matched healthy controls (HC), we assessed the feasibility of the ECCET approach to detect MS-related cerebellar volume differences. For total normalized cerebellar volume as well as grey and white matter volumes, intrarater (intraclass correlation coefficient (ICC)=0.99, 95% CI=0.98-0.99) and interobserver agreement (ICC=0.98, 95% CI=0.74-0.99) were strong. Comparison between ECCET and FreeSurfer results likewise yielded a good intraclass correlation (ICC=0.86, 95% CI=0.58-0.95). Compared to HC, MS patients had significantly reduced normalized total brain, total cerebellar, and grey matter volumes (p≤0.05). ECCET is a suitable tool for cerebellar segmentation showing excellent test-retest and inter-observer reliability. Our matched-pair analysis between MS patients and healthy volunteers suggests that the method is sensitive and reliable in detecting cerebellar atrophy in M

Recent Developments in Edge-Preserving Smoothing

Abstract. We introduce recent and very recent smoothing methods and discuss them in the common framework of `energy functions'. Focus is on the preservation of boundaries, spikes and canyons in presence of noise. 1 Introduction There is rapidly increasing interest in models and methods for discontinuous phenomena, both in the mathematical and statistical community. The focus is on the identification of discontinuities in data perturbed by noise. This is of particular importance in imaging, where noise has to be removed from image data while preserving relevant basic features like jumps, spikes and boundaries