nils-herrmann/clean_scirepeval huggingface dataset:
Segmentation of gated Tl-SPECT images for automatic computation of myocardial volume and ejection fraction

The authors present an algorithm for automatic assessment of left ventricular ejection fraction (EF) and absolute volume from gated Thallium-SPECT myocardial perfusion images. The system consists of two independent blocs for segmentation and computation of function. The authors introduce a novel formulation for computation of EF based on the epi-cardial contour. The algorithm was validated against manual border tracing from two physicians on different modalities, and gave consistent linear agreement. PET manual vs. Automatic-avg. r=0.91, Tl-SPECT manual vs. auto-r=0.79. The new epicardial method was compared to performance at. High noise. Finally, preliminary comparison were made of EFs computed for PET, /sup 99m/Tc-SPECT and /sup 201/Tl-SPECT to those obtained from planar gated blood pool imaging (GBP) yielding good results. The proposed scheme is specifically designed for /sup 201/Tl-SPECT, but may be used on other image modalities as well. It permits EF computation on relatively noisy images without the need for additional image acquisitions.
On Regularized Reconstruction of Vector Fields

In this paper, we give a general characterization of regularization functionals for vector field reconstruction, based on the requirement that the said functionals satisfy certain geometric invariance properties with respect to transformations of the coordinate system. In preparation for our general result, we also address some commonalities of invariant regularization in scalar and vector settings, and give a complete account of invariant regularization for scalar fields, before focusing on their main points of difference, which lead to a distinct class of regularization operators in the vector case. Finally, as an illustration of potential, we formulate and compare quadratic (L2) and total-variation-type (L1) regularized denoising of vector fields in the proposed framework.
Myocardial motion analysis from B-mode echocardiograms

The quantitative assessment of cardiac motion is a fundamental concept to evaluate ventricular malfunction. We present a new optical-flow-based method for estimating heart motion from two-dimensional echocardiographic sequences. To account for typical heart motions, such as contraction/expansion and shear, we analyze the images locally by using a local-affine model for the velocity in space and a linear model in time. The regional motion parameters are estimated in the least-squares sense inside a sliding spatiotemporal B-spline window. Robustness and spatial adaptability is achieved by estimating the model parameters at multiple scales within a coarse-to-fine multiresolution framework. We use a wavelet-like algorithm for computing B-spline-weighted inner products and moments at dyadic scales to increase computational efficiency. In order to characterize myocardial contractility and to simplify the detection of myocardial dysfunction, the radial component of the velocity with respect to a reference point is color coded and visualized inside a time-varying region of interest. The algorithm was first validated on synthetic data sets that simulate a beating heart with a speckle-like appearance of echocardiograms. The ability to estimate motion from real ultrasound sequences was demonstrated by a rotating phantom experiment. The method was also applied to a set of in vivo echocardiograms from an animal study. Motion estimation results were in good agreement with the expert echocardiographic reading.
Myocardial border tracking in M-mode echocardiograms using joint process estimation

A sequential approach to the detection of myocardial borders in M-mode echocardiograms is introduced. Initial border estimates are obtained from a cross-correlation detector. They are then improved by an adaptive lattice-form joint process predictor. Alternatively, a physiological constraint is used to improve the detection of the endocardium during systole. A least squares algorithm is proposed to update recursively the correlation templates in order to track their temporal variations.<<ETX>>
A new hybrid Bayesian-variational particle filter with application to mitotic cell tracking

Tracking algorithms are traditionally based on either a variational approach or a Bayesian one. In the variational case, a cost function is established between two consecutive frames and minimized by standard optimization algorithms. In the Bayesian case, a stochastic motion model is used to maintain temporal consistency. Among the Bayesian methods we focus on the particle filter, which is especially suited for handling multimodal distributions. In this paper, we present a novel approach to fuse both methodologies in a single tracker where the importance sampling of the particle filter is given implicitly by the optimization algorithm of the variational method. Our technique is capable of outlying nuclei and tracking the lineage of biological cells using different motion models for mitotic and non-mitotic stages of the life of a cell. We validate its ability to track the lineage of HeLa cells in fluorescence microscopy.
A fast thresholded Landweber algorithm for general wavelet bases: Application to 3D deconvolution microscopy

Wavelet-domain lscr1-regularization is a promising approach to deconvolution. The corresponding variational problem can be solved using a "thresholded Landweber" (TL) algorithm. While this iterative procedure is simple to implement, it is known to converge slowly. In this paper, we give the principle of a modified algorithm that is substantially faster. The method is applicable to arbitrary wavelet representations, thus generalizing our previous work which was restricted to the or- thonormal Shannon wavelet basis. Numerical experiments show that we can obtain up to a 10-fold speed-up with respect to the existing TL algorithm, while providing the same restoration quality. We also present an example with real data that demonstrates the feasibility of wavelet-domain regularization for 3D deconvolution microscopy.
Multi-target tracking of packed yeast cells

The tracking of cell populations in time-lapse microscopy images enables high-throughput spatiotemporal measurements of cell dynamics. In this paper, we present a new algorithm to simultaneously track many cells in crowded areas. The algorithm runs in real time and deals with thousands of cells. The main contribution of this paper is that the algorithm is able to maintain the spatiotemporal consistency of the tracks in crowded areas, even when the temporal resolution is coarse. We validate our approach in terms of its ability to track yeast cells.
Quantitative Fourier analysis of approximation techniques. II. Wavelets

For pt.I see ibid., vol.47, no.10, p.2783-95 (1999). In a previous paper, we proposed a general Fourier method that provides an accurate prediction of the approximation error, irrespective of the scaling properties of the approximating functions. Here, we apply our results when these functions satisfy the usual two-scale relation encountered in dyadic multiresolution analysis. As a consequence of this additional constraint, the quantities introduced in our previous paper can be computed explicitly as a function of the refinement filter. This is, in particular, true for the asymptotic expansion of the approximation error for biorthonormal wavelets as the scale tends to zero. One of the contributions of this paper is the computation of sharp, asymptotically optimal upper bounds for the least-squares approximation error. Another contribution is the application of these results to B-splines and Daubechies (1988, 1992) scaling functions, which yields explicit asymptotic developments and upper bounds. Thanks to these explicit expressions, we can quantify the improvement that can be obtained by using B-splines instead of Daubechies wavelets. In other words, we can use a coarser spline sampling and achieve the same reconstruction accuracy as Daubechies. Specifically, we show that this sampling gain converges to /spl pi/ as the order tends to infinity.
Convolutional Neural Networks for Inverse Problems in Imaging: A Review

In this article, we review recent uses of convolutional neural networks (CNNs) to solve inverse problems in imaging. It has recently become feasible to train deep CNNs on large databases of images, and they have shown outstanding performance on object classification and segmentation tasks. Motivated by these successes, researchers have begun to apply CNNs to the resolution of inverse problems such as denoising, deconvolution, superresolution, and medical image reconstruction, and they have started to report improvements over state-of-the-art methods, including sparsity-based techniques such as compressed sensing. Here, we review the recent experimental work in these areas, with a focus on the critical design decisions.
SURE-LET for Orthonormal Wavelet-Domain Video Denoising

We propose an efficient orthonormal wavelet-domain video denoising algorithm based on an appropriate integration of motion compensation into an adapted version of our recently devised Stein's unbiased risk estimator-linear expansion of thresholds (SURE-LET) approach. To take full advantage of the strong spatio-temporal correlations of neighboring frames, a global motion compensation followed by a selective block-matching is first applied to adjacent frames, which increases their temporal correlations without distorting the interframe noise statistics. Then, a multiframe interscale wavelet thresholding is performed to denoise the current central frame. The simulations we made on standard grayscale video sequences for various noise levels demonstrate the efficiency of the proposed solution in reducing additive white Gaussian noise. Obtained at a lighter computational load, our results are even competitive with most state-of-the-art redundant wavelet-based techniques. By using a cycle-spinning strategy, our algorithm is in fact able to outperform these methods.
Robust real-time segmentation of images and videos using a smooth-spline snake-based algorithm

This paper deals with fast image and video segmentation using active contours. Region-based active contours using level sets are powerful techniques for video segmentation, but they suffer from large computational cost. A parametric active contour method based on B-Spline interpolation has been proposed in to highly reduce the computational cost, but this method is sensitive to noise. Here, we choose to relax the rigid interpolation constraint in order to robustify our method in the presence of noise: by using smoothing splines, we trade a tunable amount of interpolation error for a smoother spline curve. We show by experiments on natural sequences that this new flexibility yields segmentation results of higher quality at no additional computational cost. Hence, real-time processing for moving objects segmentation is preserved.
A Unified Formulation of Gaussian Versus Sparse Stochastic Processes—Part I: Continuous-Domain Theory

We introduce a general distributional framework that results in a unifying description and characterization of a rich variety of continuous-time stochastic processes. The cornerstone of our approach is an innovation model that is driven by some generalized white noise process, which may be Gaussian or not (e.g., Laplace, impulsive Poisson, or alpha stable). This allows for a conceptual decoupling between the correlation properties of the process, which are imposed by the whitening operator L, and its sparsity pattern, which is determined by the type of noise excitation. The latter is fully specified by a Lévy measure. We show that the range of admissible innovation behavior varies between the purely Gaussian and super-sparse extremes. We prove that the corresponding generalized stochastic processes are well-defined mathematically provided that the (adjoint) inverse of the whitening operator satisfies some Lp bound for p ≥ 1. We present a novel operator-based method that yields an explicit characterization of all Lévy-driven processes that are solutions of constant-coefficient stochastic differential equations. When the underlying system is stable, we recover the family of stationary continuous-time autoregressive moving average processes (CARMA), including the Gaussian ones. The approach remains valid when the system is unstable and leads to the identification of potentially useful generalizations of the Lévy processes, which are sparse and non-stationary. Finally, we show that these processes admit a sparse representation in some matched wavelet domain and provide a full characterization of their transform-domain statistics.
Approximation power of biorthogonal wavelet expansions

This paper looks at the effect of the number of vanishing moments on the approximation power of wavelet expansions. The Strang-Fix conditions imply that the error for an orthogonal wavelet approximation at scale a=2/sup -i/ globally decays as a/sup N/, where N is the order of the transform. This is why, for a given number of scales, higher order wavelet transforms usually result in better signal approximations. We prove that this result carries over for the general biorthogonal case and that the rate of decay of the error is determined by the order properties of the synthesis scaling function alone. We also derive asymptotic error formulas and show that biorthogonal wavelet transforms are equivalent to their corresponding orthogonal projector as the scale goes to zero. These results strengthen Sweldens earlier analysis and confirm that the approximation power of biorthogonal and (semi-)orthogonal wavelet expansions is essentially the same. Finally, we compare the asymptotic performance of various wavelet transforms and briefly discuss the advantages of splines. We also indicate how the smoothness of the basis functions is beneficial in reducing the approximation error.
Deconvolution of 3D fluorescence micrographs with automatic risk minimization

We investigate the problem of automatic tuning of a deconvolution algorithm for three-dimensional (3D) fluorescence microscopy; specifically, the selection of the regularization parameter lambda. For this, we consider a realistic noise model for data obtained from a CCD detector: Poisson photon-counting noise plus Gaussian read-out noise. Based on this model, we develop a new risk measure which unbiasedly estimates the original mean-squared-error of the deconvolved signal estimate. We then show how to use this risk estimate to optimize the regularization parameter for Tikhonov-type deconvolution algorithms. We present experimental results on simulated data and numerically demonstrate the validity of the proposed risk measure. We also present results for real 3D microscopy data.
Variational enhancement and denoising of flow field images

In this work we propose a variational reconstruction algorithm for enhancement and denoising of flow fields that is reminiscent of total-variation (TV) regularization used in image processing, but which also takes into account physical properties of flow such as curl and divergence. We point out the invariance properties of the scheme with respect to transformations of the coordinate system such as shifts, rotations, and changes of scale. To demonstrate the utility of the reconstruction method, we use it first to denoise a simulated phantom where the scheme is found to be superior to its quadratic (L2) variant both in terms of SNR and in preservation of discontinuities. We then use the scheme to enhance the quality of pathline visualizations in an application to 4D (3D+time) flow-sensitive magnetic resonance imaging of blood flow in the aorta.
Image Denoising in Mixed Poisson–Gaussian Noise

We propose a general methodology (PURE-LET) to design and optimize a wide class of transform-domain thresholding algorithms for denoising images corrupted by mixed Poisson-Gaussian noise. We express the denoising process as a linear expansion of thresholds (LET) that we optimize by relying on a purely data-adaptive unbiased estimate of the mean-squared error (MSE), derived in a non-Bayesian framework (PURE: Poisson-Gaussian unbiased risk estimate). We provide a practical approximation of this theoretical MSE estimate for the tractable optimization of arbitrary transform-domain thresholding. We then propose a pointwise estimator for undecimated filterbank transforms, which consists of subband-adaptive thresholding functions with signal-dependent thresholds that are globally optimized in the image domain. We finally demonstrate the potential of the proposed approach through extensive comparisons with state-of-the-art techniques that are specifically tailored to the estimation of Poisson intensities. We also present denoising results obtained on real images of low-count fluorescence microscopy.
Prognostic Analysis of Polypoidal Choroidal Vasculopathy Using an Image-Based Approach

In this paper, we rstly propose to perform prognostic analysis of polypoidal choroidal vasculopathy (PCV) using indocyanine green angiography (ICGA) sequence. Our goal is to develop a computer-aided diagnostic system which can predict the likely treatment outcome of patients with PCV based on their before-treatment ICGA sequences. In order to create a prognostic model for PCV, we utilize both the before-treatment and the aftertreatment ICGA sequences collected in the EVEREST study. By comparing the before-treatment and the after-treatment PCV region in ICGA sequences, we can generate positive and negative samples for training our prognostic model. Here, we design an 8-layer convolution neural network (CNN) and use it to serve as the prognostic model. We have conducted experiments using 17 patients cases. In particular, we perform leave-one-out cross validation so that each patient can be utilized as testing case once. Our proposed method achieves promising results on the EVEREST dataset.
Human detection using non-negative matrix factorization

Currently, most of the human detection methods are based on low-level features. In this paper, we proposed a middle-level feature generation method based on non-negative matrix factorization (NMF) for human detection. We also proposed an improvement scheme to guarantee that a better middle-level feature can be achieved. The proposed scheme can be applied to a complex background and the experimental results are better than those when only the low-level feature is involved.
Human action classification using histogram-based discriminative embedding

In order to have a rich representation for human action, we propose to combine two complementary features so that a human posture can be characterized in more details. In particular, the distance signal feature and the width feature are combined in an effective way to enhance each other's discriminating capability. The resulting feature vector is quantized into mid-level features using k-means clustering. In the mid-level feature space, we apply the nonparametric embedding method to construct a compact yet discriminative subspace model. We have conducted a series of experiments on the Weizmann dataset to validate the proposed scheme. Compared with the existing approaches, our method can achieve high recognition accuracy while having a reduced computational complexity in classification stage.
Human-oriented interaction with a TOF sensor

As human-oriented computing technologies gain greater importance, people will expect natural interfaces that communicate with various systems. Many researchers have focused on various possibilities. This study used camera interaction as an avenue for improving user experience during presentations. The study was divided into two stages. The first stage analyzed the design principles of human-oriented systems, and implemented a rationale based on related principles. The system can be adopted as the central mechanism in sensing the motion of visitors using a time-of-flight (TOF) sensor, linking the camera space to media content. We demonstrate that the proposed system supplies interactive power for natural interaction and improved usability. The second stage investigated user experience of such an interaction pattern. The results show that the system can be applied as a natural interface between visitors and presentations, providing the audience with a satisfying and pleasurable experience.
Fusion of Summation Invariants in 3D Human Face Recognition

A novel family of 2D and 3D geometrically invariant features, called summation invariants is proposed for the recognition of the 3D surface of human faces. Focusing on a rectangular region surrounding the nose of a 3D facial depth map, a subset of the so called semi-local summation invariant features is extracted. Then the similarity between a pair of 3D facial depth maps is computed to determine whether they belong to the same person. Out of many possible combinations of these set of features, we select, through careful experimentation, a subset of features that yields best combined performance. Tested with the 3D facial data from the on-going Face Recognition Grand Challenge v1.0 dataset, the proposed new features exhibit significant performance improvement over the baseline algorithm distributed with the datase
Bidirectional Transmission Using Tunable Fiber Lasers and Injection-Locked Fabry–PÉrot Laser Diodes for WDM Access Networks

We propose and demonstrate the use of fiber ring lasers and Fabry-Perot laser diodes (FP-LDs) for wavelength-division-multiplexing access networks. The fiber ring laser not only generates downstream data traffic but also serves as the wavelength-selecting injection light source for the FP-LD located at the subscriber site. Moreover, it is wavelength tunable and can be applied to dynamic wavelength assignment networks. The ring laser has a tunable range of 30 nm in the C-band and a power fluctuation smaller than 0.6 dB. For 10-Gb/s downstream and 1.25-Gb/s upstream transmissions over 10-km single-mode fiber, power penalties less than 0.9 and 0.5 dB are demonstrated, respectively. A 40-dB sidemode suppression ratio is obtained for the FP-LD injection-locking at 1544.8 nm.
3D Human Face Recognition Using Summation Invariants

A novel family of geometrically invariant features, called summation invariants are proposed for the recognition of the 3D surface of human faces. In particular, a 2D semi-local summation invariant feature is extracted from each column and each row of a rectangular region surrounding the nose of a 3D facial depth map. Through extensive experimentation, we empirically identify the most efficient 2D summation invariant features. We also investigate the proper pre-processing method for the 2D summation invariant features. Tested with the 3D facial data from the Face Recognition Grand Challenge v1.0 dataset, the proposed new features exhibit significant performance improvement over the baseline algorithm distributed with the dataset
Real-time marker tracking for MV treatment beam imaging

A new approach to obtain real-time positions of fiducial markers in MV treatment beam images is proposed. The MV images are firstly preprocessed to enhance contrast in the treatment field. To deal with large variations in projected marker shape, we propose a learning-based approach to detect marker locations in the enhanced MV images. We also show that marker tracking can be accomplished much more efficiently and reliably by exploiting temporal correlation between consecutive MV images. Thus, the proposed framework can accurately localize multiple markers in low-contrast MV images while satisfying the real-time constraints imposed by the IGRT. Our method has been validated using manual marker annotations as ground-truth. The marker detection rate on patient images was at least 96% for the cases collected from multiple treatment fractions.