Marc Hensel

Robust and Fast Estimation of Signal-Dependent Noise in Medical X-Ray Image Sequences

We present a practice-oriented, i.e. fast and robust, estimator for strong signal-dependent noise in medical low-dose X-ray images. Structure estimation by median filtering has shown to be superior to linear binomial filtering. Falsifications due to remaining structure in the estimated noise image are significantly reduced by iterative outlier removal.

Real-Time denoising of medical x-ray image sequences: three entirely different approaches

Low-dose X-ray image sequences exhibit severe signal-dependent noise that must be reduced in real-time while, at the same time, preserving diagnostic structures and avoiding artifacts. We propose three different methods with applications beyond medical image processing. Major contributions are innovative motion detection based on independent binarization of positive and negative temporal differences, real-time multiscale nonlinear diffusion in the presence of severe signal-dependent noise, and multi-resolution inter-scale correlation in shift-dependent pyramids. All methods exhibit excellent performance over a broad range of noise, detail, and contrast levels. As performance in medical imaging depends to a large degree on the type of intervention and individual preferences of medical staff, no method is generally superior and all methods are considered for the next generation of fluoroscopy systems.

Ruler-Based Automatic Stitching of Spatially Overlapping Radiographs

We present an algorithm for fast automatic registration of spatially overlapping radiographs. It possesses strong robustness against noise, feature masking and feature displacement. Pivotal for this algorithm is an actual interpretation of the stitching feature instead of a simple detection. The proposed method has been evaluated on 3000 clinical radiographs and proved to be a powerful enhancement of established automatic registration algorithms.

Noise Reduction with Edge Preservation by Multiscale Analysis of Medical X-Ray Image Sequences

Real-time visualization of digital X-ray image sequences requires the reduction of severe noise while preserving diagnostic details. We introduce a noise reduction method for X-ray image sequences using products of Laplacian pyramid coefficients. The method features SNR improvement comparable to the Wiener filter, however, being superior in the preservation of fine structures and generating a more stable image impression in sequences.

Motion Detection for Adaptive Spatio-temporal Filtering of Medical X-Ray Image Sequences

Spatio-temporal filters are used to improve the quality of X-ray image sequences exhibiting severe noise in real-time. The spatial and temporal ratios have to be adapted locally in order to avoid artifacts. We propose a method processing the positive and negative pixel values of difference images independently in order to detect regions dominated by motion and single pixels dominated by noise. In the context of noise-adaptive binarization using Euler numbers, the influence of noise and motion on Euler curves is investigated.

LAST Filter for Artifact-Free Noise Reduction of Fluoroscopic Sequences in Real-Time

We present a spatio-temporal filter for real-time noise reduction of strongly corrupted X-ray image sequences. It possesses efficient noise reduction while, at the same time, preventing typical artifacts of state-of-the-art methods. Decisive for these features are, in particular, innovative motion detection as well as noise-adaptive filter parametrization. Motion detection based on twofold signed binarization proved to be a powerful method for pixelwise separation of motion and strong noise. Drawbacks of threshold determination by Euler curve analysis as applied previously were eliminated by integration of signal-dependent noise estimation.