nils-herrmann/clean_scirepeval huggingface dataset:
An Evaluation Framework for the Accuracy of Camera Transfer Functions Estimated from Differently Exposed Images

Intensity values read from CCD- or CMOS-cameras are usually not proportional to the irradiance acquired by the sensor, but are mapped by a mostly nonlinear camera transfer function (CTF). This CTF can be measured using a test chart. This, however, is afflicted with the difficulty of ensuring uniform illumination of the chart. An alternative to chart-based measurements of the CTF is to use differently exposed images of the same scene. In this paper, we describe a radiometry-based experimental setup to directly measure the CTF. We furthermore show how to obtain image pairs of known exposure ratios from the same experiment, i.e., under identical environmental circumstances (light, temperature, camera settings). We use these images to estimate the CTF on differently exposed images, thus enabling a quantitative comparison of estimated and measured CTF
Simultaneous Reconstruction of Activity and Attenuation for PET/MR

Medical investigations targeting a quantitative analysis of the position emission tomography (PET) images require the incorporation of additional knowledge about the photon attenuation distribution in the patient. Today, energy range adapted attenuation maps derived from computer tomography (CT) scans are used to effectively compensate for image quality degrading effects, such as attenuation and scatter. Replacing CT by magnetic resonance (MR) is considered as the next evolutionary step in the field of hybrid imaging systems. However, unlike CT, MR does not measure the photon attenuation and thus does not provide an easy access to this valuable information. Hence, many research groups currently investigate different technologies for MR-based attenuation correction (MR-AC). Typically, these approaches are based on techniques such as special acquisition sequences (alone or in combination with subsequent image processing), anatomical atlas registration, or pattern recognition techniques using a data base of MR and corresponding CT images. We propose a generic iterative reconstruction approach to simultaneously estimate the local tracer concentration and the attenuation distribution using the segmented MR image as anatomical reference. Instead of applying predefined attenuation values to specific anatomical regions or tissue types, the gamma attenuation at 511 keV is determined from the PET emission data. In particular, our approach uses a maximum-likelihood estimation for the activity and a gradient-ascent based algorithm for the attenuation distribution. The adverse effects of scattered and accidental gamma coincidences on the quantitative accuracy of PET, as well as artifacts caused by the inherent crosstalk between activity and attenuation estimation are efficiently reduced using enhanced decay event localization provided by time-of-flight PET, accurate correction for accidental coincidences, and a reduced number of unknown attenuation coefficients. First results achieved with measured whole body PET data and reference segmentation from CT showed an absolute mean difference of 0.005 cm in the lungs, 0.0009 cm in case of fat, and 0.0015 cm for muscles and blood. The proposed method indicates a robust and reliable alternative to other MR-AC approaches targeting patient specific quantitative analysis in time-of-flight PET/MR.
Disparity-based segmentation of stereoscopic foreground/background image sequences

Describes a method for displacement estimation in stereoscopic images, which is closely coupled with a segmentation of the pictures into homogeneously displaced regions. The technique is driven by a statistical optimization criterion which assesses the quality of the disparity estimate and of the segmentation, thus improving both of these simultaneously. In addition, the optimization criterion explicitly takes occluded areas into consideration. With the additional help of two constraints, this enables the algorithm to locate regions corresponding to occlusions accurately.
Quantitative evaluation of virtual angiography for interventional X-ray acquisitions

Virtual angiography has been recently proposed [1] as a method to indirectly validate the accuracy of computational fluid dynamic (CFD) simulations. In doing so, X-ray angiograms are created from CFD results and compared with the acquired data. In this paper, we apply the extended virtual angiography method  which has been only tested on an imaged phantom to clinical data for the first time. To obtain realistic CFD simulations, the contrast agent injection (CA) curve and patient specific blood flow measured by transcranial colour coded Doppler ultrasound (TCCD) are used as boundary conditions. In a quantitative comparison, the spatial and temporal variation of CA concentration of both clinical and virtual angiograms showed good correspondence.
Volume-preserving correction for image registration using free-form deformations

Registration of an image non-rigidly to another one causes deformations, which generally do not preserve the initial volume. Volume preservation is however indispensable for observing tumors in medical images. This paper presents the correction of B-spline based registration to preserve the volume in observed regions. In contrast to other approaches, our solution is not obtained through energy minimization, but by calculating the correction parameters for the deformation directly. Especially for high resolution image data this strategy is very efficient in terms of computational expenses. We derive a closed form solution to optimize the registration with respect to the compression at a single point and then extend the problem to multiple points. Finally we prove also that the correction terms do not have any significant influence on the registration quality.
On Bounds of Shift Variance in Two-Channel Multirate Filter Banks

Critically sampled multirate FIR filter banks exhibit periodically shift variant behavior caused by nonideal antialiasing filtering in the decimation stage. We assess their shift variance quantitatively by analysing changes in the output signal when the filter bank operator and shift operator are interchanged. We express these changes by a so-called commutator. We then derive a sharp upper bound for shift variance via the operator norm of the commutator, which is independent of the input signal. Its core is an eigensystem analysis carried out within a frequency domain formulation of the commutator, leading to a matrix norm which depends on frequency. This bound can be regarded as a worst case instance holding for all input signals. For two channel FIR filter banks with perfect reconstruction (PR), we show that the bound is predominantly determined by the structure of the filter bank rather than by the type of filters used. Moreover, the framework allows to identify the signals for which the upper bound is almost reached as so-called near maximizers of the frequency-dependent matrix norm. For unitary PR filter banks, these near maximizers are shown to be narrow-band signals. To complement this worst-case bound, we derive an additional bound on shift variance for input signals with given amplitude spectra, where we use wide-band model spectra instead of narrow-band signals. Like the operator norm, this additional bound is based on the above frequency-dependent matrix norm. We provide results for various critically sampled two-channel filter banks, such as quadrature mirror filters, PR conjugated quadrature filters, wavelets, and biorthogonal filters banks.
Toward quantitative virtual angiography: Evaluation with in vitro studies

Medical imaging and computational fluid dynamics (CFD) modeling have been combined to obtain detailed knowledge of local hemodynamics, known to play an important role in cardiovascular diseases. Given the absence of a "gold standard" to measure the blood velocities in vivo, simulation of X-ray angiograms was proposed as a method to indirectly validate the accuracy of image-based CFD analysis. This paper presents a method to simulate the contrast agent transport and the creation of virtual angiograms which takes into account the physics of the contrast agent injection and X-ray transmission. The simulated and acquired angiograms are compared by analyzing the spatial and temporal development of the contrast agent concentration represented by a flow map. This approach was tested with in vitro experiments.
Efficient application of 3D morphological operations in the framework of a computer-assisted diagnosis system

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 identifies the pleural contours and detects pleural thickenings. In two algorithm steps, namely the detection of the thorax and the removal of air and trachea, 3D morphological operations were implemented and tested, in order to find an optimum of its application in the aspect of calculation expense and accuracy. After an evaluation, we obtained an experimental optimum for both algorithm steps.
Local and global panoramic imaging for fluorescence bladder endoscopy

Endoscopic treatment of bladder cancer is more and more often based on photodynamic diagnostics (PDD), a specialized endoscopic technique where a narrow-band bluish illumination causes tumors to fluoresce reddish. Contrast between tumors and healthy bladder tissue is thus noticeably increased compared to white light endoscopy. A downside of PDD is the low illumination power, which requires that the distance between endoscope and bladder wall be kept low, thus resulting in a small field of view (FOV). We therefore describe an approach to combine several successive frames into a local PDD panorama, which provides a larger and sufficiently bright FOV for treatment. Furthermore, the endoscopic cancer treatment generally starts with a complete scan of the bladder to detect the tumors. For diagnosis, navigation and reporting, a global overview image of the bladder wall is often desired. While construction of such a global panorama can be based on the same algorithm as the local panorama, direct planar visualization of the sphere-shaped bladder may cause severe distortions. Apart from the global panorama computation itself, we therefore analyze these distortions, and provide an alternative visualization which is based on bladder depictions used in standard reporting forms and anatomy textbooks.
A priori knowledge-based recognition and inspection in carbide insert production

In processes of the production chain of carbide inserts, such as unloading or packaging, the conformity test of the insert type is performed manually, which causes a statistic increase of errors due to monotony and fatigue of workers and the wide variety of insert types. A machine vision system is introduced that automatically measures and inspects the chip-former geometry of inserts, the most significant insert quality feature, in the production line. The proposed recognition approach is developed with utilisation of a priori knowledge of carbide inserts and of production environments. This new method has been tested on several inserts of different types. Test results show that prevalent insert types can be inspected and robustly classified in a real production environment and therefore the manufacturing automation can be improved.
Attenuation Corrected Cardiac SPECT Imaging Using Simultaneous Reconstruction and a Priori Information

Attenuation correction in SPECT typically performed using CT or Gadolinium-source based transmission scans, is more and more becoming established practice in many medical applications. For cases where no further anatomical information from a transmission scan is available, different approaches have been proposed in the past, attempting to recover the attenuation information directly from SPECT projection data. However, these methods often result in noticeable image artifacts caused by an inherent cross-dependency between the estimated attenuation and activity distribution.
High Dynamic Range Microscopy for Cytopathological Cancer Diagnosis

Cancer is one of the most common causes of death. Cytopathological, i.e., cell-based, diagnosis of cancer can be applied in screening scenarios and allows an early and highly sensitive detection of cancer, thus increasing the chance for cure. The detection of cancer on cells addressed in this paper is based on bright field light microscopy. The cells are imaged with a camera mounted on a microscope, allowing to measure cell properties. However, these cameras exhibit only a limited dynamic range, which often makes the quantification of properties difficult or even impossible. Consequently, to allow a computer-assisted analysis of microscopy images, the imaging has to be improved. To this end, we show how the dynamic range can be increased by acquiring a set of differently exposed cell images. These high dynamic range (HDR) images allow to measure cellular features that are otherwise difficult to capture, if at all. We show that HDR microscopy not only increases the dynamic range, but furthermore reduces noise and improves the acquisition of colors. We develop HDR microscopy-based algorithms, which are essential for cytopathological oncology and early cancer detection and only possible with HDR microscopy imaging. We show the detection of certain subcellular features, so-called AgNORs, in silver (Ag) stained specimens. Furthermore, we give examples of two further applications, namely: 1) the detection of stained cells in immunocytochemical preparations and 2) color separation for nuclear segmentation of specimens stained with low contrast.
Texture classification of gray-level images by multiscale cross co-occurrence matrices

Local gray level dependencies of natural images can be modelled by means of co-occurrence matrices containing joint probabilities of gray-level pairs. Texture, however, is a resolution-dependent phenomenon and hence, classification depends on the chosen scale. Since there is no optimal scale for all textures we employ a multiscale approach that acquires textural features at several scales. Thus linear and nonlinear scale-spaces are analyzed by multiscale co-occurrence matrices that describe the statistical behavior of a texture in scale-space. Classification is then performed on the basis of texture features taken from the individual scale with the highest discriminatory power. By considering cross-scale occurrences of gray level pairs, the impact of filters on the feature is described and used for classification of natural textures. This novel method was found to improve classification rates of the common co-occurrence matrix approach on standard textures significantly.
Estimation of multiple orientations in multidimensional signals

We present a solution to the general problem of estimating multiple orientations in multidimensional signals. The solution is divided in a linear part that provides the mixed-orientation space (MOS) and a nonlinear part that gives the actual orientation spaces. We show that the angle between two overlaid orientations is an invariant that can be derived from the MOS without solving the nonlinear part and that all other invariants are generated by this angle. Results obtained for synthetic images illustrate that the above invariant is a useful image feature for various applications such as pattern recognition and texture segmentation.
Anisotropic spectral magnitude estimation filters for noise reduction and image enhancement

Describes an algorithm for noise reduction and enhancement of images which is able to take into account anisotropies of signal as well as of noise. Processing is based on subjecting each image to a block DFT, followed by comparing each observed magnitude coefficient to the expected noise standard deviation for it. Depending on this comparison, each coefficient is attenuated the more, the more likely it is that it contains only noise. In addition, the attenuation is made dependent on whether or not the observed coefficient contributes to an oriented prominent structure within the processed image block. Orientation as well as the distinctness with which it occurs are detected in the spectral domain by an inertia-like matrix. Orientation information is additionally exploited to selectively enhance oriented structures, thus only marginally increasing noise as compared to isotropic enhancement.
Modeling and Compensation of Ghosting in Multispectral Filter Wheel Cameras

Multispectral filter wheel cameras divide the visible electromagnetic spectrum by using several optical bandpass filters mounted on a filter wheel and acquire one color component for each filter wheel position. Afterwards, the single images are combined into one multispectral image. While the color accuracy of this approach and the stop-band attenuation of the bandpass filters is superior to other technologies, ghosting images are produced by reflections between the image sensor and the filter surface: The original image is duplicated in a displaced, weaker and softened form and added to the original image, thus compromising the original. We analyze the path of rays in this specific optical setup and derive a physical model for the ghosting reflections. By linking the physical model to the image content, we derive a calibration and compensation algorithm, whose parameters are estimated from a test image. Application of our correction algorithm makes the ghosting virtually vanish.
Detection of moving shadows using mean shift clustering and a significance test

An algorithm that discriminates moving objects from their shadows is presented. Starting from the change mask of an image sequence, first of all the changed area is divided into subregions consisting of pixels with similar colour properties. This is done using the mean shift algorithm, which is very powerful in non-parametric clustering of data. In a second step a significance test is performed to classify each image pixel inside the change mask into one of the classes foreground or shadow. To do this a straightforward image model is used where the grey level of a foreground pixel covered by a shadow is given by the product of the corresponding background pixels' grey-level and a constant value. Assuming that fore- and background images are corrupted by Gaussian white noise, a significance test is derived which classifies all pixels inside the change mask. In the third step global and local information from the first and second steps are combined. For each region inside the change mask it is examined if the majority of pixels survived the second step. If this is the case, the whole region is kept for the final moving object mask, if not the region is set to zero.
Enhancement of visual contrast in fluorescence endoscopy

Bladder tumors are difficult to recognize during an endoscopy of the bladder (cystoscopy) using a conventional white light source. Therefore, a technique called photo dynamic diagnosis (PDD) or fluorescence endoscopy is used to enhance both sensitivity and specificity of tumor detection. As preparation for a PDD cystoscopy, a marker substance is brought into the patient's bladder which leads to a fluorescence of cancerous tissue if a suitable light source is used. Even though tumors are better distinguishable from healthy bladder tissue, the contrast between both types of tissue can still be low and it further decreases with the decay of the marker substance. To overcome these difficulties, we enhance the contrast between fluorescence and background. To this end, we perform a color space transformation which separates fluorescence information from the remaining information. In this new color space, we apply a non-linear transformation to the fluorescence component which leads to a better separation of fluorescing and non-fluorescing pixels. Finally, the image is transferred back to the RGB color space. For clinical evaluation, we implemented the proposed algorithm as plug-in for the real-time capable software framework RealTimeFrame.
3D modeling of detected pleural thickenings through thin plate spline interpolation

Pleuramesothelioma is a malignant tumor. A computer-assisted diagnosis system shall provide physicians with the assessment of the detected pleural thickening from the 3D CT data. This paper describes a new, improved method to assess the size of detected thickening using both the 2D and 3D thin plate spline. First, a coordinate transformation was applied. Next, set of landmarks was selected as input for the thin plate spline. The final step is the numerical area integration. The results show that the calculated area was more accurate than the former pixel counting technique, and promise further automatic development.
Coding of segmented images using shape-independent basis functions

An important issue in object-based image coding is the efficient description of image segments having an arbitrary shape. This paper outlines a solution to this task using basic principles of transform image coding that are generalized for the case of arbitrarily shaped image segments. The texture inside each region is successively approximated using two-dimensional (2-D) shape-independent basis functions defined on a rectangle circumscribing the given image segment. The resulting texture description exhibits a high energy compactness and is well suited for low bit rate image coding. Unlike other approaches aimed at segment-oriented image coding, the proposed concept does not couple texture description with contour coding. The computational load is kept low, especially for the decoding process.
Enhanced B-spline based compression performance for images

The B-spline mathematical functions have long been utilized for signal representation, zooming and interpolation. However they have not been investigated for different image coding applications. In spite of the fact that B-splines are a semi-orthogonal basis (not full orthogonal) they still can be utilized for redundancy removal and decorrelation maximization prior to any compression scheme. This is due to their distinctive feature of preserving energy in low frequencies. In this paper we propose a novel technique for preprocessing signals/images prior to the decomposition stage in different image coders based on the B-spline decomposition. Mathematical explanation and derivation for the proposed B-spline decomposition basis is presented and analyzed. We derive our theoretic/mathematical justification, through some Eigen analysis calculations, for the enhancement in compression performance achieved with our B-spline based compression approach. We also present a straightforward approach for calculating the B-spline basis in a fast and efficient manner. Extensive simulations have been carried out on the well known SPIHT image coder with and without the proposed correlation removal methodology. Simulation results that demonstrate the effectiveness of the proposed technique are presented.
Multiscale linear/median hybrid filters for noise reduction in low dose X-ray images

This contribution describes a new filtering technique for the reduction of noise in medical X-ray images acquired with very low doses, like fluoroscopy images. In such images, only between 10 and 200 X-ray quanta contribute to each pixel. The resulting Poisson statistic causes a strong deterioration of image quality by quantum noise, which, in the observed images, is signal-dependent and exhibits a lowpass-shaped power spectrum. The central part of our approach is a linear/median hybrid filter, which is well-known for its good detail preservation properties. Noise reduction by this filter, however, depends on the presence or absence of underlying signal slopes, and is limited to a small range of spatial frequencies. Also, like median filters this filter tends to generate streaking artifacts. We show how a combination of linear/median hybrid filtering with a multiscale pyramid avoids these shortcomings, while simultaneously improving noise reduction performance substantially.