Michael Balda

A Comparative Error Analysis of Current Time-of-Flight Sensors

Time-of-flight (ToF) cameras suffer from systematic errors, which can be an issue in many application scenarios. In this paper, we investigate the error characteristics of eight different ToF cameras. Our survey covers both well established and recent cameras including the Microsoft Kinect V2. We present up to six experiments for each camera to quantify different types of errors. For each experiment, we outline the basic setup, present comparable data for each camera, and discuss the respective results. The results discussed in this paper enable the community to make appropriate decisions in choosing the best matching camera for a certain application. This work also lays the foundation for a framework to benchmark future ToF cameras. Furthermore, our results demonstrate the necessity for correcting characteristic measurement errors. We believe that the presented findings will allow 1) the development of novel correction methods for specific errors and 2) the development of general data processing algorithms that are able to robustly operate on a wider range of cameras and scenes.

Real-Time Range Imaging in Health Care: A Survey

The recent availability of dynamic, dense, and low-cost range imaging has gained widespread interest in health care. It opens up new opportunities and has an increasing impact on both research and commercial activities. This chapter presents a state-of-the-art survey on the integration of modern range imaging sensors into medical applications. The scope is to identify promising applications and methods, and to provide an overview of recent developments in this rapidly evolving domain. The survey covers a broad range of topics, including guidance in computer-assisted interventions, operation room monitoring and workflow analysis, touch-less interaction and on-patient visualization, as well as prevention and support in elderly care and rehabilitation. We put emphasis on dynamic and interactive tasks where real-time and dense 3-D imaging forms the key aspect. While considering different range imaging modalities that fulfill these requirements, we particularly investigate the impact of Time-of-Flight imaging in this domain. Eventually, we discuss practical demands and limitations, and open research issues and challenges that are of fundamental importance for the progression of the field.

ROCHADE: Robust Checkerboard Advanced Detection for Camera Calibration

We present a new checkerboard detection algorithm which is able to detect checkerboards at extreme poses, or checkerboards which are highly distorted due to lens distortion even on low-resolution images. On the detected pattern we apply a surface fitting based subpixel refinement specifically tailored for checkerboard X-junctions. Finally, we investigate how the accuracy of a checkerboard detector affects the overall calibration result in multi-camera setups. The proposed method is evaluated on real images captured with different camera models to show its wide applicability. Quantitative comparisons to OpenCV’s checkerboard detector show that the proposed method detects up to 80% more checkerboards and detects corner points more accurately, even under strong perspective distortion as often present in wide baseline stereo setups.

Ground Truth for Evaluating Time of Flight Imaging

In this work, we systematically analyze how good ground truth (GT) datasets for evaluating methods based on Time-of-Flight (ToF) imaging data should look like. Starting from a high level characterization of the application domains and requirements they typically have, we characterize how good datasets should look like and discuss how algorithms can be evaluated using them. Furthermore, we discuss the two different ways of obtaining ground truth data: By measurement and by simulation.

Ray Contribution Masks for Structure Adaptive Sinogram Filtering

The patient dose in computed tomography (CT) imaging is linked to measurement noise. Various noise-reduction techniques have been developed that adapt structure preserving filters like anisotropic diffusion or bilateral filters to CT noise properties. We introduce a structure adaptive sinogram (SAS) filter that incorporates the specific properties of the CT measurement process. It uses a point-based forward projector to generate a local structure representation called ray contribution mask (RCM). The similarities between neighboring RCMs are used in an enhanced variant of the bilateral filtering concept, where the photometric similarity is replaced with the structural similarity. We evaluate the performance in four different scenarios: The robustness against reconstruction artifacts is demonstrated by a scan of a high-resolution-phantom. Without changing the modulation transfer function (MTF) nor introducing artifacts, the SAS filter reduces the noise level by 13.6%. The image sharpness and noise reduction capabilities are visually assessed on in vivo patient scans and quantitatively evaluated on a simulated phantom. Unlike a standard bilateral filter, the SAS filter preserves edge information and high-frequency components of organ textures well. It shows a homogeneous noise reduction behavior throughout the whole frequency range. The last scenario uses a simulated edge phantom to estimate the filter MTF for various contrasts: the noise reduction for the simple edge phantom exceeds 80%. For low contrasts at 55 Hounsfield units (HU), the mid-frequency range is slightly attenuated, at higher contrasts of approximately 100 HU and above, the MTF is fully preserved.

Value-based noise reduction for low-dose dual-energy computed tomography

We introduce a value-based noise reduction method for Dual-Energy CT applications. It is based on joint intensity statistics estimated from high- and low-energy CT scans of the identical anatomy in order to reduce the noise level in both scans. For a given pair of measurement values, a local gradient ascension algorithm in the probability space is used to provide a noise reduced estimate. As a consequence, two noise reduced images are obtained. It was evaluated with synthetic data in terms of quantitative accuracy and contrast to noise ratio (CNR)-gain. The introduced method allows for reducing patient dose by at least 30% while maintaining the original CNR level. Additionally, the dose reduction potential was shown with a radiological evaluation on real patient data. The method can be combined with state-of-the-art filter-based noise reduction techniques, and makes low-dose Dual-Energy CT possible for the full spectrum of quantitative CT applications.

Lookup table-based simulation of directly-converting counting X-ray detectors for computed tomography

Medical computed tomography (CT) can benefit from directly-converting counting detectors. As yet there is little expertise with this type of detectors in commercially available clinical CT systems, a precise detector model is required for developing such a system. We introduce a lookup table-based simulation of counting detectors on X-ray photon level that allows studying the influence of detector parameters and efficiently evaluating proposed designs. It uses energy-resolved sinograms of incoming X-ray photons as input data and generates photon counts for each channel and reading. The effects of Poisson noise, photon interactions, pulse generation, read-out electronics and electrode signal processing are covered. The photon interaction data as well as signal characteristics are provided in the form of detector-specific lookup tables. This approach offers the precision of Monte-Carlo simulations and the efficiency of model-based descriptions. Unlike standard Monte-Carlo simulations, it is capable of simulating whole CT-scans in a reasonable amount of time on a standard workstation. Due to this efficiency, the influence of detector parameters on image quality in the reconstructed image domain can be evaluated. The simulation is verified against measured data.

OCPAD — Occluded checkerboard pattern detector

Many camera calibration techniques require the detection of a pattern with known geometry, e.g., a checkerboard. Typically, the pattern must be fully contained in the field of view. This brings several limitations, one of which is that lens distortion can not reliably be estimated in outer image regions. This paper presents the occluded checkerboard pattern detector (OCPAD) to find checkerboards, even in a) low-resolution images, b) images with high lens distortion and if c) the pattern is partly occluded or not completely within the field of view. We exploit that checkerboards can easily be represented by a graph. We use graph matching to find the largest partial checkerboard in the image. Our detector complements a state-of-the-art calibration algorithm. Quantitatively, detection rates are considerably improved over the state-of-the-art. Additionally, estimation of lens distortion is greatly improved at outer image regions. Here, the reprojection error is improved by up to 50%.

Evaluation of an image-based algorithm for quantitative spectral CT applications

In this paper we describe and evaluate an image-based spectral CT method. Its central formula expresses measured CT data as a spectral integration of the spectral attenuation coefficient multiplied by a LocalWeighting Function (LWF). The LWF represents the local energy weighting in the image domain, taking into account the system and reconstruction properties and the object self attenuation. A generalized image-based formulation of spectral CT algorithms is obtained, with no need for additional corrections of e.g. beam hardening. The iterative procedure called Local Spectral Reconstruction (LSR) yields both the mass attenuation coefficients of the object and a representation of the LWF. The quantitative accuracy and precision of the method is investigated in several applications, including beam hardening correction, attenuation correction for SPECT/CT and PET/CT and a direct identification of spectral attenuation functions using the LWF result is demonstrated. In all applications the ground truth of the objects is reproduced with a quantitative accuracy in the sub-percent to two percent range. An exponential convergence behavior of the iterative procedure is observed, with one to two iteration steps as a good compromise between quantitative accuracy and precision. We conclude that the method can be used to perform image-based spectral CT reconstructions with quantitative accuracy. Existing algorithms benefit from the intrinsic treatment of beam hardening and system properties. Novel algorithms are enabled to directly compare material model functions to spectral measurement data.

Look-up table-based simulation of scintillation detectors in computed tomography

The design of a CT detector requires a precise detector model, since building prototypes for many different proposed detector geometries is too costly. We introduce a look-up table-based simulation of scintillation detectors on X-ray photon level. It uses energy-resolved sinograms of incoming X-ray intensities as input data and generates photon counts for each channel and reading. The effects of X-ray- and optical cross-talk, temporal cross-talk between readings, Poisson noise and electronics effects are covered. The photon interaction data as well as optical cross-talk distribution are provided in the form of detector specific look-up tables. Unlike standard Monte-Carlo simulations of X-ray interaction processes, our approach is capable of simulating whole sinograms in a reasonable amount of time and still offers a very high precision of the detector model. This way the influence of detector effects can be investigated in the reconstructed image data. The simulation is verified against data measured with a CT scanner and data from a fully single photon-based Monte-Carlo simulation in terms of image modulation transfer function (MTF) and detector noise power spectrum (NPS).