nils-herrmann/clean_scirepeval huggingface dataset:
Video-Based Measuring of Quality Parameters for Tricuspid Xenograft Heart Valve Implants

Defective heart valves are often replaced by implants in open-heart surgery. Both mechanical and biological implants are available. Among biological implants, xenograft ones - i.e., valves grafted from animals such as pigs, are widely used. Good implants should exhibit certain typical anatomical and functional characteristics to successfully replace the native tissue. Here, we describe a video-based system for measuring quality parameters of xenograft heart valve implants, including the area of the orifice and the fluttering of the valves' leaflets, i.e., their flaps (or cusps). Our system employs automatic methods that provide a precise and reproducible way to infer the quality of an implant. The automatic analysis of both a valve's orifice and the fluttering of its leaflets offers a more comprehensive quality assessment than current, mostly manual methods. We focus on valves with three leaflets, i.e., aortic, pulmonary, and tricuspid valves.
Lapped directional transform: a new transform for spectral image analysis

We propose a new real-valued lapped transform for 2D-signal and image processing. Lapped transforms are particularly useful in block-based processing, since their intrinsically overlapping basis functions reduce or prevent block artifacts. Our transform is derived from the modulated lapped transform (MLT), which, as a real-valued and separable transform like the discrete cosine transform, does not allow to unambiguously identify oriented structures from modulus spectra. This is in marked contrast to the (complex-valued) discrete Fourier transform (DFT). The new lapped transform is real-valued, and at the same time allows unambiguous detection of spatial orientation. Furthermore, a fast algorithm for this transform exists. As an application example, we investigate the transform's performance in spectral approaches to image restoration and enhancement in comparison to the DFT.
Gap detection in endoscopic video sequences using graphs

In minimal invasive surgery (MIS) a complete and seamless inspection of organs, e.g. the urinary bladder, using video endoscopes is often required for diagnostics. Since the endoscope is usually guided by free-hand, it is difficult to ensure a sequence of seamless frame transitions. Also 2-D panoramic images showing an extended field of view (FOV) do not provide always reliable results, since their interpretations are limited by potentially strong geometric distortions. To overcome these limitations and provide a direct verification method, we develop a gap detection algorithm using graphs. Exploiting the motion information of the applied zig-zag scan, we construct a graph representation of the video sequence. Without any explicit global image visualization our graph search algorithm identifies reliably frame discontinuities, which would lead to holes and slit artifacts in a panoramic view. The algorithm shows high detection rates and provides a fast method to verify frame discontinuities in the whole video sequence. Missed regions are highlighted by local image compositions which can be displayed during the intervention for assistance and inspection control.
High Dynamic Range Images as a Basis for Detection of Argyrophilic Nucleolar Organizer Regions Under Varying Stain Intensities

Silver staining of cytopathologic specimens offers advantages in cancer diagnostics. A difficulty with such stained cell specimens is the very high dynamic range needed by the imaging system to appropriately cover the varying stain intensities. Beside those images of cell nuclei that can be used for the diagnostic interpretation, there are nuclei that appear too dark to observe their relevant properties, the so-called argyrophilic nucleolar organizer regions (AgNORs), which appear as spot-like areas darker than their immediate surroundings. We therefore show how high dynamic range images of nuclei can help to correctly segment the AgNORs. To this end, we acquire a sequence of differently exposed images, which are then combined into a high dynamic range image. Based on the dynamic range of the image signal within the segmented cell area, we compute another image which provides optimal contrast over this area of interest. To further increase the contrast for dark objects, a suitable nonlinear point transform is simultaneously applied. We provide examples of the thus generated images and their corresponding segmentations.
Predicting the benefit of sample size extension in multiclass k-NN classification

In industrial quality inspection obtaining the training data needed for classification problems is still a very costly task. Nevertheless, the classifier quality is crucial for economic success. Thus, the question whether the influence of the training data on the classification error has been fully exploited and enough data has been obtained is very important. This paper introduces a method to answer this question for a specific problem. To be able to make a concrete statement and not only general recommendations, we focus on the k-NN classifier, since it is widely used in industrial implementations. The method is tested on four different multiclass problems: original data from an optical media inspection problem, the MNIST database, and two artificial problems with known probability densities
Segment-oriented coding of textured images based on successive approximation

The paper reports a region-based coding scheme for segmented textured images. The proposed approach uses basic principles of transform image coding which are generalized for the case of arbitrarily shaped image segments. The texture inside each region is described by successive approximation through a set of shape independent basis functions while the image partition is coded employing generalized chain codes. In contrast to other approaches aimed at segment-oriented image coding, the proposed concept does not couple texture description and contour coding. The computational load is kept low, especially for the decoding process.<<ETX>>
Segmentation and analysis of nerve fibers in histologic sections of the cerebral human cortex

This paper describes an image acquisition and processing chain toward the analysis of images of thin histologic sections of the cerebral human cortex. We are particularly interested in the spatial distributions of nerve fibers, the inhomogeneities of which indicate architectonic and functional changes between different cortical areas. We therefore focus on the development of a specific optical and algorithmic chain to acquire histologic images in an appropriate resolution, and to enhance, segment, skeletonize and separate nerve fibers in these images. The processing results prepare the images for subsequent local measurements of nerve fiber orientation, fiber lengths, fiber density, area fraction occupied by fibers, and others.
Imaging and analysis of angiogenesis for skin transplantation by microangiography

The success of skin transplantations depends on a proper revascularization of the transplanted tissue. Angiogenesis (vessel growth) in the transplanted dermal matrices can be stimulated by administering different drugs. To evaluate the effectiveness of different drug treatments in an experimental setting using laboratory animals, vessels in transplant samples are visualized by so-called micro-angiograms, i.e., X-ray images after contrast agent injection. We describe a framework for the acquisition of such micro-angiograms as well as for the subsequent semi-automatic analysis of angiogenesis. Central to our analysis is the segmentation of small fasciocutaneous vessels in the transplant sample.
Geometric Calibration of Lens and Filter Distortions for Multispectral Filter-Wheel Cameras

High-fidelity color image acquisition with a multispectral camera utilizes optical filters to separate the visible electromagnetic spectrum into several passbands. This is often realized with a computer-controlled filter wheel, where each position is equipped with an optical bandpass filter. For each filter wheel position, a grayscale image is acquired and the passbands are finally combined to a multispectral image. However, the different optical properties and non-coplanar alignment of the filters cause image aberrations since the optical path is slightly different for each filter wheel position. As in a normal camera system, the lens causes additional wavelength-dependent image distortions called chromatic aberrations. When transforming the multispectral image with these aberrations into an RGB image, color fringes appear, and the image exhibits a pincushion or barrel distortion. In this paper, we address both the distortions caused by the lens and by the filters. Based on a physical model of the bandpass filters, we show that the aberrations caused by the filters can be modeled by displaced image planes. The lens distortions are modeled by an extended pinhole camera model, which results in a remaining mean calibration error of only 0.07 pixels. Using an absolute calibration target, we then geometrically calibrate each passband and compensate for both lens and filter distortions simultaneously. We show that both types of aberrations can be compensated and present detailed results on the remaining calibration errors.
3D segmentation of keratin intermediate filaments in confocal laser scanning microscopy

In this paper, we propose and compare different methods for the 3D segmentation of keratin intermediate filaments (KFs) in images acquired using confocal laser scanning microscopy (CLSM). KFs are elastic cables forming a complex scaffolding within epithelial cells. They are involved in many basic cell functions. To understand the mechanisms of filament formation and network organisation under physiological and pathological conditions, quantitative measurements of dynamic network alterations are essential. Segmenting KFs is a key component for analyzing their dynamic and biomechanical properties. KFs were labeled with fluorescent keratins to allow high resolution imaging of network dynamics in native cells. Our segmentation methods follow the principle of ridge enhancement filtering and subsequent centerline extraction. The evaluation of the methods is two-fold: (i) We develop synthetic data that exhibit the characteristics of real CLSM data to evaluate the precision of the different methods in terms of centerline localisation and (ii) we perform a connected component analysis on the segmentation results of real KF data to assess whether the connectivity of highly complex networks is being preserved by the segmentation. Our evaluation shows that in the presence of strong noise and despite the highly anisotropic spatial resolution of CLSM images the proposed method is able to accurately localize the centerlines of the KFs and to preserve the KF networks' connectivity. Taken together this is a strong indicator that also the network topology is being preserved.
Fluorescence microscopic imaging and image analysis of the cytoskeleton

Cell stability and motility depends on a complex dynamic cytoplasmic scaffolding called the cytoskeleton. It is composed of actin filaments, intermediate filaments and microtubules, and interacts with neighbouring cells and the extracellular matrix via specialized adhesion sites - multimolecular complexes responsible for the transmission of mechanical force and regulatory signals. The dynamic behaviour of these subcellular structures in living cells can be analysed by fluorescence microscopy yielding series of 2D or 3D images. Towards a quantitative analysis, we present methods for the segmentation and motion estimation of cytoskeletal filaments as well as for the tracking of adhesion sites, allowing the quantification of cytoskeletal dynamics under different conditions.
Fast virtual destaining of immunocytological specimens

Microscopy-based diagnosis of certain diseases or infections, e.g. with human papilloma viruses (HPV) for the identification of high risk patients for cervical cancer, relies more and more often on immunocytochemical marker stains. These markers stain cells that exhibit a particular protein. In addition to one or more marker stains, pathologists need to simultaneously assess the morphology of the cell. Therefore the specimens are commonly also stained with a counter stain.
Towards fully automated precise measurement of camera transfer functions

The mapping of incident irradiance to sensor output value, the so-called camera transfer function (CTF), of CCD or CMOS imaging devices is in general a non-linear function. Applications that utilize the camera as a measurement device for the incident irradiance, e.g DNA image cytometry for early cancer detection or HDR imaging, require a linear CTF. Thus, this non-linearity has to be corrected. This, in turn, requires the non-linear CTF to be estimated or measured. In this paper we present a radiometric measurement setup. We point out a particular problem, i.e., the calibration of the distance offset between sensor and light source. We then show a solution which integrates this calibration into the measurement process, enabling a fully automated measurement of the CTF.
Directional interpolation of sparsely sampled cone-beam CT sinogram data

To compensate for sparse angular sampling in cone-beam computed tomography causing characteristic streak artifacts, we propose to increase the number of projected views by means of nonlinear directional interpolation. For this purpose, a specific method for accurate interpolation of 3D sinogram data based on a structure tensor approach has been developed. Quantitative evaluation of the interpolation error shows that our method clearly outperforms conventional interpolation schemes. Using a set of simulated cone-beam CT projection data, it is demonstrated that inclusion of interpolated projections into the reconstruction process significantly reduces streak artifacts and noise while preserving spatial resolution to a high degree.
Bayesian illumination-invariant motion detection

We describe an algorithm for change detection which is insensitive to both slow and fast temporal variations of scene illumination. Our algorithm is based on statistical decision theory by using a Bayesian approach. The goal is to detect only temporal changes which are induced by true scene changes, like motion, but not changes due to varying illumination or noise. To this end, our algorithm uses a simple illumination model which is invariant to common camera nonlinearities like gamma-nonlinearity. This is combined with a model for the influence of noise as well as an a priori model for the expected properties of the sought change masks. Key ingredients of the resulting algorithm are a suitable test statistic and an adaptive threshold mechanism. As the algorithm can be applied in a noniterative manner, it is also computationally attractive.
Denoising fluorescence endoscopy - A motion compensated temporal recursive video filter with an optimal minimum mean square error parameterization

Fluorescence endoscopy is an emerging technique for the detection of bladder cancer. A marker substance is brought into the patient's bladder which accumulates at cancer tissue. If a suitable narrow band light source is used for illumination, a red fluorescence of the marker substance is observable. Because of the low fluorescence photon count and because of the narrow band light source, only a small amount of light is detected by the camera's CCD sensor. This, in turn, leads to strong noise in the recorded video sequence. To overcome this problem, we apply a temporal recursive filter to the video sequence. The derivation of a filter function is presented, which leads to an optimal filter in the minimum mean square error sense. The algorithm is implemented as plug-in for the real-time capable clinical demonstrator platform RealTimeFrame and it is capable to process color videos with a resolution of 768×576 pixels at 50 frames per second.
Estimation of multiple local orientations in image signals

Local orientation estimation can be posed as the problem of finding the minimum grey level variance axis within a local neighbourhood. In 2D image signals, this corresponds to the eigensystem analysis of a 2 /spl times/ 2-tensor, which yields valid results for single orientations. We describe extensions to multiple overlaid orientations, which may be caused by transparent objects, crossings, bifurcations, corners etc. Multiple orientation detection is based on the eigensystem analysis of an appropriately extended tensor, yielding so-called mixed orientation parameters. These mixed orientation parameters can be regarded as another tensor built from the sought individual orientation parameters. We show how the mixed orientation tensor can be decomposed into the individual orientations by finding the roots of a polynomial. Applications are, e.g., in directional filtering and interpolation, feature extraction for corners or crossings, and signal separation.
A stochastic model for image segmentation involving constrained least squares estimation

The aim of the paper is to outline a layered statistical image model suitable for unsupervised image segmentation. The segment internal texture signal is described based on its spatial frequency representation while the image partition is modelled as a sample of a Gibbs/Markov random field. The most likely segmentation is estimated using a maximum a posteriori (MAP) formulation with the unknown parameters being determined by constrained least squares (CLS) estimation.<<ETX>>
Blur identification using a spectral inertia tensor and spectral zeros

Restoration of global motion blur requires the blur parameters (direction and extent) to be known. To this end, spectral approaches based on finding zeros in power spectrum or bispectrum estimates are widely reported. In noisy conditions, however, finding spectral zeros is hardly possible as they are covered by the noise power spectrum. In this paper, we separate the determination of blur direction and blur extent. The advantage is that, once the blur direction is known, it suffices to estimate a slice of the power spectrum or bispectrum in this direction. These one-dimensional estimates are considerably less sensitive to noise than higher-dimensional ones. The blur direction is determined by an inertia-like tensor, which assesses spectral anisotropy caused by the motion blur. This approach is applied to noisy low-dose X-ray images, which are particularly hard to process since the blur degrades the signal but not the relatively strong noise.
Texture Classification by Modeling Joint Distributions of Local Patterns With Gaussian Mixtures

Texture classification generally requires the analysis of patterns in local pixel neighborhoods. Statistically, the underlying processes are comprehensively described by their joint probability density functions (jPDFs). Even for small neighborhoods, however, stable estimation of jPDFs by joint histograms (jHSTs) is often infeasible, since the number of entries in the jHST exceeds by far the number of pixels in a typical texture region. Moreover, evaluation of distance functions between jHSTs is often computationally prohibitive. Practically, the number of entries in a jHST is therefore reduced by considering only two-pixel patterns, leading to 2D-jHSTs known as cooccurrence matrices, or by quantization of the gray levels in local patterns to only two gray levels, yielding local binary patterns (LBPs). Both approaches result in a loss of information. We introduce here a framework for supervised texture classification which reduces or avoids this information loss. Local texture neighborhoods are first filtered by a filter bank. Without further quantization, the jPDF of the filter responses is then described parametrically by Gaussian mixture models (GMMs). We show that the parameters of the GMMs can be reliably estimated from small image regions. Moreover, distances between the thus modelled jPDFs of different texture patterns can be computed efficiently in closed form from their model parameters. We furthermore extend this texture descriptor to achieve full invariance to rotation. We evaluate the framework for different filter banks on the Brodatz texture set. We first show that combining the LBP difference filters with the GMM-based density estimator outperforms the classical LBP approach and its codebook extensions. When replacing these-rather elementary-difference filters by the wavelet frame transform (WFT), the performance of the framework on all 111 Brodatz textures exceeds the one obtained more recently by spin image and RIFT descriptors by Lazebnik et al.
Combined displacement estimation and segmentation of stereo image pairs based on Gibbs random fields

A technique for displacement estimation in stereo image pairs, which is closely coupled with a segmentation of the images into homogeneously displaced regions, is described. An arbitrary initial displacement field is modified by a displacement vector relaxation, evaluating a statistical optimization criterion. Since the displacements are considered vector by vector during the relaxation, the method provides accurate locations of the boundaries between differently displaced regions. The proposed technique derives its power from the fact that the spatial array of displacements-and hence the resulting segmentation-is modeled by a Gibbs-Markov random field.<<ETX>>
Transformation of adaptive thresholds by significance invariance for change detection

The detection of changes in image sequences often is the first essential step to video analysis, e.g. for the detection, classification and tracking of moving objects. As a binary classification problem, change detection is afflicted by the trade-off between two class error probabilities, viz. the rates of false positives and false negatives. In this contribution, we derive an adaptive two-threshold scheme to improve on this trade-off. The threshold selection for each pixel in the current frame is controlled by the previous detection result for this pixel. Since the test statistics are calculated from samples comprising several pixels within a local sliding window, a transformation of the thresholds from the single-pixel observations to decisions based on larger samples is required. Based on the fact that we can only model the null hypothesis, i.e. absence of motion, realistically, we suggest transforming the threshold under the constraint of a constant false-positive rate, or significance invariance the resulting detection algorithm is only marginally more complex than a straightforward global thresholding procedure, while providing visibly improved results
Wavelet based denoising by correlation analysis for high dynamic range imaging

High dynamic range (HDR) imaging is used to acquire the full dynamic range of a scene with a camera of limited dynamic range. To this end, an exposure set of the scene is acquired, followed by the linearization of each image with the inverse camera transfer function (CTF), which needs to be measured or estimated. Subsequently, the images are combined into one HDR image. Several weighting functions have been proposed for this combination. Naturally, each individual image is afflicted with noise from the acquisition process. The resulting HDR image features a higher SNR than the acquired images as a consequence of the weighted averaging during reconstruction. We show that the fact that the individual images partially show the same structures with independent noise can be utilized to further improve the SNR. Thus, we propose a wavelet-based denoising using correlation analysis between different images from the exposure set that outperforms the denoising properties of commonly applied weighted averages.