nils-herrmann/clean_scirepeval huggingface dataset:
Markov-Gibbs model based registration of CT lung images using subsampling for the follow-up assessment of pleural thickenings

Examining the growth rate of pleural thickenings in consecutive 3D-CT images requires the matching of identical thickenings in lung images acquired at two different points in time. The thickenings can be subject to strong deformations caused by their growth. This implies that position information should play a major role in finding correspondences. Here, a MGRF approach is presented to determine a rigid transformation. It aligns the lung volumes by maximizing the probability of the regarded lung tissue to fit an offline trained model. To ensure a symmetrical matching of lung surfaces this probability is calculated reciprocally. Using precalculation, strong sub-sampling and a multiscale approach, the required time can be reduced by a factor of about 80, depending on the image resolution. Due to this speed-up, online follow-up assessment is feasible. We show that this approach results in precise registrations which can be used for a reliable matching of lung thickenings.
Multispectral Filter-Wheel Cameras: Geometric Distortion Model and Compensation Algorithms

Multispectral image acquisition considerably improves color accuracy in comparison to RGB technology. A common multispectral camera design concept features a filter-wheel consisting of six or more optical bandpass filters. By shifting the filters sequentially into the optical path, the electromagnetic spectrum is acquired through the channels, thus making an approximate reconstruction of the spectrum feasible. However, since the optical filters exhibit different thicknesses, refraction indices and may not be aligned in a perfectly coplanar manner, geometric distortions occur in each spectral channel: The reconstructed RGB images thus show rainbow-like color fringes. To compensate for these, we analyze the optical path and derive a mathematical model of the distortions. Based on this model we present two different algorithms for compensation and show that the color fringes vanish completely after application of our algorithms. We also evaluate our compensation algorithms in terms of accuracy and execution time.
Spatio-Temporal Defect Pixel Interpolation using 3-D Frequency Selective Extrapolation

Flat panel X-ray detectors allow the immediate availability of the acquired images for display. However, they provide images with defective areas amongst others due to manufacturing problems. In this contribution, we present a frequency selective extrapolation method in order to restore these defects by extending the surrounding signal into the defective area. In case of static radiographs, the spatial surrounding is evaluated by a 2-D approach. Defects in sequences acquired by cine-angiography or fluoroscopy are processed by 3-D extrapolation. The defects are replaced by extrapolating the signal from the spatial and at the same time temporal surrounding, taking previous and subsequent frames into account. Hence, inherent motion compensation is accomplished. The application of the spatial and where applicable spatio-temporal extrapolation approach allows to restore smooth areas, edges, patterns as well as noise. The ability to restore noise is especially important for medical images because it leads to a natural appearance of the concealed defective areas.
Vessel Segmentation in 2D-Projection Images Using a Supervised Linear Hysteresis Classifier

2D projection imaging is a widely used procedure for vessel visualization. For the subsequent analysis of the vasculature, precise measurements of e.g. vessel area, vessel length or the number of vessel segments are needed. To achieve these goals vessel enhancement and segmentation are required. While there are already many vasculature specific vessel segmentation algorithms, we describe in this contribution a more general supervised segmentation method which includes a feature extraction step followed by feature selection and segmentation based on the hysteresis classification paradigm. The method was tested on retina photographs. The rates of false positives and correct classifications were comparable with dedicated methods on similar data sets while it needed less time for both training and providing a segmentation result
CAMERA CALIBRATION FOR FISH-EYE LENSES IN ENDOSCOPYWITH AN APPLICATION TO 3D RECONSTRUCTION

Image analysis tasks such as 3D reconstruction from endoscopic images require compensation of geometric distortions introduced by the lens system. Appropriate camera calibration is thus necessary. Commonly used calibration algorithms rely on the well-known pinhole camera model, extended by parametric terms for radial distortions. In this paper, we demonstrate that these models are not appropriate if very strong distortions occur as is the case for endoscopic fish-eye lenses. As an alternative, we analyze a generic calibration algorithm published recently by Kannala and Brandt, which is based on more general projection equations. We show qualitatively and quantitatively that this algorithm is well suited to deal with significant distortions especially in the image's rim regions. Furthermore, we demonstrate how images of a colon phantom that were corrected in such a manner can be used to obtain a 3D reconstruction
Vessel segmentation and analysis in laboratory skin transplant micro-angiograms

The success of skin transplantations depends on the adequate revascularization of the transplanted dermal matrix. To induce vessel growth or angiogenesis, pharmacological substances may be applied to the dermal matrix. The effectiveness of different such substances has been evaluated in laboratory experiments. For this purpose, the surface and length of newly grown vessels have to be measured in micro-angiograms (x-ray images of the blood vessels recorded after the injection of a radiopaque substance) of tissue transplanted on the back of laboratory animals. To this end we describe in this contribution a vessel analysis environment central to which is a vessel segmentation tool for surface quantification in fasciocutaneous skin transplant micro-angiograms.
Design of Multi-Steerable Filters and their Application for the Detection of Corners and Junctions

The theory of steerable filters (introduced by Freeman and Adelson in 1991) offers a convenient and computationally efficient method to represent the rotation of a specific class of 2D filters as a linear combination of base filters. In this paper, we generalize this theory to filters which are modeled as point-by-point products of multiple (in our examples: two) base filters which each are rotated by individual rotation angles, thus introducing the term "multi-steerability". While standard steerable filters are useful for the detection of edges or lines (oriented in some unknown direction), our novel extended theory can be applied to the detection of features which are characterized by multiple orientations, for instance corners or line crossings, which greatly increases the applicability of the theory. We conclude our paper with an example for detecting the crossings in a checkerboard image using double-steerable filters.
User-defined ROI tracking for estimation of the myocardial blush grade

For a proper diagnosis and post treatment evaluation of acute myocardial infarction, angiographic measures of the myocardial perfusion such as the myocardial blush grade are pivotal. The presence of blood with contrast agent in heart tissue can be observed as a darkening of the target region. Currently, the myocardial blush grade is established by a visual analysis of this darkening. We present methods which allow the automatization this important procedure thereby improving from a what in clinical routine is termed a "semi-quantitative" analysis to a "quantitative" analysis with less inter- and intra- observer variability
Directional View Interpolation for Compensation of Sparse Angular Sampling in Cone-Beam CT

In flat detector cone-beam computed tomography and related applications, sparse angular sampling frequently leads to characteristic streak artifacts. To overcome this problem, it has been suggested to generate additional views by means of interpolation. The practicality of this approach is investigated in combination with a dedicated method for angular interpolation of 3-D sinogram data. For this purpose, a novel dedicated shape-driven directional interpolation algorithm based on a structure tensor approach is developed. Quantitative evaluation shows that this method clearly outperforms conventional scene-based interpolation schemes. Furthermore, the image quality trade-offs associated with the use of interpolated intermediate views are systematically evaluated for simulated and clinical cone-beam computed tomography data sets of the human head. It is found that utilization of directionally interpolated views significantly reduces streak artifacts and noise, at the expense of small introduced image blur.
Improved minimum distance classification with Gaussian outlier detection for industrial inspection

A pattern recognition system used for industrial inspection has to be highly reliable and fast. The reliability is essential for reducing the cost caused by incorrect decisions, while speed is necessary for real-time operation. We address the problem of inspecting optical media like compact disks and digital versatile disks. As the disks are checked during production and the output of the production line has to be sufficiently high, the time available for the whole examination is very short, ie, about 1 sec per disk. In such real-time applications, the well-known minimum distance algorithm is often used as classifier. However, its main drawback is the unreliability when the training data are not well clustered in feature-space. Here we describe a method for off-line outlier detection, which cleans the training data set and yields substantially better classification results. It works on a statistical test basis. In addition, two improved versions of the minimum distance classifier, which both yield higher rates of correct classification with practically no speed-loss are presented. To evaluate the results, we compare them to the results obtained using a standard minimum distance classifier, a k-nearest neighbor classifier, and a fuzzy k-nearest neighbor classifier.
Experimental validation and sensitivity analysis for CFD simulations of cerebral aneurysms

In this work, by exploiting a phantom aneurysm model, we illustrate the correlation between experimental data and computational fluid dynamics (CFD) simulation results under well controlled conditions. This is difficult to achieve with clinical patient cases where several uncertainties are present. Quantitative measures are defined for CFD validation by virtual angiography. In addition, a parametric study has been carried out to systematically investigate the sensitivity of current measuring technique on the flow pattern.
Shift Variance Measures for Multirate LPSV Filter Banks With Random Input Signals

The linear periodically shift variant (LPSV) properties within critically sampled multirate FIR filter banks are generally analysed using deterministic signals. Periodic shift variance is, however, closely related to cyclostationarities introduced by the LPSV system into originally wide sense stationary (WSS) random signals passing through the system. We provide first a unified framework to measure both shift variance of the LPSV system and the amount of cyclostationarity it generates. In this respect, the key concept is the covariance operator associated to a random variable. Cyclostationarity of the variable translates to LPSV properties of the operators, and vice versa. We study several related concepts for the quantification of shift variance in operators and their interpretation in the stochastic setting. We then introduce a new concept called expected shift variance. Fourier-analytic expressions for the various measures are given, and subsequently used to derive explicit formulae for the case of critically sampled two-channel filter banks, as well as sharp upper bounds for unitary two-channel filter banks. Numerical evaluations of the measures show that they provide largely consistent rankings between various critically sampled two-channel filter banks.
Locally adaptive fuzzy pulmonary vessel segmentation in contrast enhanced CT data

Pulmonary vascular tree segmentation is the fundamental basis for different applications, such as the detection and visualization of pulmonary emboli (PE). Such an application requires an accurate and reliable segmentation of pulmonary vessels with varying diameters. We present a novel fuzzy approach to pulmonary vessel segmentation in contrast enhanced computed tomography (CT) data that considers a radius estimate of the current vessel to adapt the segmentation parameters. Hence, our method allows to capture even vessels with small diameters while suppressing leakage into surrounding structures in close proximity of vessels with large diameters. The method has been evaluated on different chest CT scans of patients referred for PE and demonstrates promising results. For quantitative validation, randomly selected sub-volumes that have been semi-automatically segmented by a medical expert have been used as reference to compare the locally adaptive method against the same method with global parameters.
Illumination-invariant change detection

Moving objects in image sequences acquired by a static camera can be detected by analyzing the grey-level difference between successive frames. Direct motion detection, however, will also detect fast variations of scene illumination. This paper describes a method for motion detection that is considerably less sensitive to time-varying illumination. It is based on combining a motion detection algorithm with an homomorphic filter which effectively suppresses variable scene illumination. To this end, the acquired image sequence is modelled as being generated by an illumination and a reflectance component that are approximately separated by the filter. Detection of changes in the reflectance component is directly related to scene changes, i.e., object motion. Real video data are used to illustrate the system's performance.
Flux-based 3D segmentation of keratin intermediate filaments in confocal laser scanning microscopy

We propose an optimally oriented flux-based method for the 3D segmentation of keratin intermediate filaments (KFs) in images acquired using fluorescence confocal laser scanning microscopy (CLSM). KFs are elastic cables forming a complex scaffolding within epithelial cells that is involved in many basic cell functions. Segmentation of KFs to generate a graph representation of the KF network therefore is a key component for analyzing the KFs' dynamic and biomechanical properties. Our segmentation method follows the principle of line enhancement, thresholding and centerline extraction to yield a one-voxel-wide centerline as result. The method is quantitatively evaluated using synthetic data generated by simulating the imaging process in CLSM. It is compared to segmentation results achieved with well-known vesselness measures. The results show that despite limited image quality in CLSM a high degree of precision is achieved.
Linear and regularized solutions for multiple motions

We extend a novel framework for the estimation of multiple transparent motions to include regularization. We use mixed-motion parameters to obtain linear Euler-Lagrange equations with a regularization term. The equations are solved iteratively for the mixed-motion parameters based on an update rule that is similar to the case of only one motion. The motion parameters are then obtained as the roots of a complex polynomial of a degree that is equal to the number of overlaid motions. An experimental error analysis is performed and reported.
Segmentation and Detection of Nuclei in Silver Stained Cell Specimens for Early Cancer Diagnosis

For successful cure, cancer has to be detected as early as possible. Since cancer starts from a single cell, this can best be done using cytopathological methods. One important diagnostically relevant measure is the proliferation rate of the cells, which can be estimated from segmented silver stained nuclei. However, the microscopy images of silver stained specimens vary strongly in intensity and contrast and are furthermore compromised by an overall texture. We show that a precise segmentation of the nuclei is possible using a two-step approach. First, an oversegmentation with the mean shift algorithm is obtained. In a second step, these regions are merged to objects, guided by a suitable shape model, viz an ellipse, but simultaneously allowing deviations from this shape model. The segmentation results are compared to a gold standard of 8617 nuclei from 23 specimens of the thyroid gland, achieving a mean areal segmentation error of DeltaA macrnucleus = 12mum2 per nucleus.
Intensity based multi-scale blending for panoramic images in fluorescence endoscopy

Panoramic images providing a larger local overview of the internal urinary bladder wall can be used for documentation and surgery planning, as well as assist the re-identification of multi-focal tumors during a cystoscopy. In contrast to white light illumination, the photodynamic diagnosis (PDD) leads to an enhanced tissue contrast, resulting in a reddish fluorescence of malignant tissue excited by a bluish small-band illumination. Due to the low illumination power, the fluorescence intensity and the contrast of vasculature are heavily dependent on the distance between the endoscope and the bladder wall. Thus, images with varying illumination and different resolved structures are combined during the iterative panorama composition. In this case, common linear blending methods applied by mosaicking algorithm for endoscopic images, generate strong visual interpolation artifacts in the output image. Instead, the developed intensity based multi-scale blending method, tailored to fluorescence endoscope images, provides a better visual panoramic image quality. Based on a highest intensity decision, a non-linear weighting function is applied on several sub-bands of a Laplacian pyramid image representation. Thus, bright fluorescence information and small vessel texture are preserved in the image composition at the same time. Besides an image quality improvement, the blending algorithm permits a more comfortable and unrestricted endoscope movement for the physician during the bladder scan.
Detection and follow-up assessment of pleural thickenings from 3D CT data

Pleural thickenings as biomarker of exposure to asbestos may evolve into malignant pleural mesothelioma. The diagnosis is based on a visual investigation of CT images, which is a time consuming and subjective procedure. Our image processing system segments the lung areas, and identifies the pleural contours using thresholding and contour relaxation. A convexity model is then used together with a Hounsfield unit threshold to detect pleural thickenings. The assessment of the detected pleural thickenings is based on a spline-based model of the healthy pleura. Follow-up study between two consecutive data, carried out by normalizing the coordinate system, leads to a diagnosis supporting tool to detect pleural mesothelioma in its early stage.
Image quality assessment of endoscopic panorama images

Endoscopic panorama images provide a wide field of view and assist in documentation and navigation. Their performance, registration errors, and blending artifacts have been evaluated. However, the similarity between local panorama regions and their input images based on a quality assessment regarding the human visual system (HVS) has not been addressed. Particularly, panorama regions composed by freehand sequences with varying image quality can result in less contrast and illumination. Thus, we develop a new fidelity score for quantitative image quality assessment based on structural similarity maps adopted to the HVS. The measure indicates how much structural information of relevant structures is preserved in the panorama. For evaluation, panoramas are generated from cystoscopic fluorescence videos, and three blending algorithms are evaluated. Our results show that the fidelity scores are consistent with visual assessments, and can be used to assess blending methods for panorama images specific to the given video sequence.
Efficient prediction of uncovered background in interframe coding using spatial extrapolation

The focus of this paper is on prediction of uncovered background in moving video coding. A new algorithm for estimating the background texture of uncovered areas is proposed which exploits spatial correlation in addition to temporal correlation and motion information. The surrounding known background area is analysed and spatially extrapolated using a highly efficient spectral domain extrapolation algorithm. The method avoids inherent insufficiencies of conventional static background memories such as strong sensitivity to camera zoom and pan and it can be incorporated into any existing motion compensating coding concept. Several prediction results for typical moving image sequences demonstrate the applicability of the proposed concept.<<ETX>>
Longitudinal aberrations caused by optical filters and their compensation in multispectral imaging

Multispectral cameras enable high-fidelity color image acquisition by separation of the electromagnetic spectrum with optical bandpass filters mounted on a filter wheel between optics and imaging sensor. A multispectral image is acquired by capturing one grayscale image per wheel position and combining the images afterwards. Since the optical filters differ in their thicknesses and refraction indices, both transversal (geometric distortions) and longitudinal aberrations (blurring) occur. We present a physical model for the longitudinal aberrations and describe the resulting distortion effects. This knowledge serves as a basis for our compensation algorithm, which performs a shift-variant deconvolution of the image, significantly improving the image quality. We furthermore describe a practicable calibration procedure for our algorithm.
A vision based system for high precision online fabric defect detection

A prototype system for automatic in-line flaw detection in industrial woven fabrics is presented. Where state of the art systems operate on low-resolved (≈ 200 ppi) image data, we describe here the process flow to segment single yarns in high-resolved (≈ 1000 ppi) textile images. This work is partitioned into two parts: First, mechanics, machine integration, vibration cancelling and illumination scenarios are discussed based on the integration into a real loom. Subsequently, the software framework for high precision fabric defect detection is presented. The system is evaluated on a database of 54 industrial fabric images, achieving a detection rate of 100% with minimal false alarm rate and very high defect segmentation quality.
Analysis of Multiple Orientations

Estimation of local orientations in multivariate signals is an important problem in image processing and computer vision. This general problem formulation also covers optical flow estimation, which can be regarded as orientation estimation in space-time-volumes. Modelling a signal using only a single orientation, however, is often too restrictive, since occlusions and transparencies occur frequently, thus necessitating the modelling and analysis of multiple orientations. We, therefore, develop a unifying mathematical model for multiple orientations: Beyond describing an arbitrary number of orientations in scalar- and vector-valued image data such as color image sequences, it allows the unified treatment of additively and occludingly superimposed oriented structures as well as of combinations of these. Based on this model, we describe estimation schemes for an arbitrary number of additively or occludingly superimposed orientations in images. We confirm the performance of our framework on both synthetic and real image data.