nils-herrmann/clean_scirepeval huggingface dataset:
Morphological Component Analysis: An Adaptive Thresholding Strategy

In a recent paper, a method called morphological component analysis (MCA) has been proposed to separate the texture from the natural part in images. MCA relies on an iterative thresholding algorithm, using a threshold which decreases linearly towards zero along the iterations. This paper shows how the MCA convergence can be drastically improved using the mutual incoherence of the dictionaries associated to the different components. This modified MCA algorithm is then compared to basis pursuit, and experiments show that MCA and BP solutions are similar in terms of sparsity, as measured by the lscr1 norm, but MCA is much faster and gives us the possibility of handling large scale data sets.
Image segmentation via multiple active contour models and fuzzy clustering with biomedical applications

We address the problem of automatically segmenting cell nuclei or cluster of cell nuclei in image medical microscopy. We present a system of automatic segmentation combining fuzzy clustering and multiple active contour models. An automatic initialization algorithm based on fuzzy clustering is used to robustly identify and classify all possible seed regions in the image. These seeds are propagated outward simultaneously to localize the final contours of all objects. We present examples of quantitative segmentation on biomedical images: segmentation of lobules in color images of histology and segmentation of nuclei in cytological images.
Wavelet methods for characterising mono- and poly-fractal noise structures in shortish time series: an application to functional MRI

Functional magnetic resonance imaging (fMRI) time series generally demonstrate serial dependence. This endogenous auto-correlation typically exhibits long-range dependence described by a 1/f-like power law. We present a novel wavelet-based methodology for characterising the noise structure in short-medium length (shortish) fMRI time series. Mono-fractality is assessed in terms of the Hurst exponent and the noise variance. We then investigate potential local stationarity of the Hurst exponent in MM data and present a uniformly most powerful test for its time constancy. A novel bootstrap approach is presented as an alternative to the normal assumption and its advantages are discussed. From several datasets investigated, we specifically show that the 1/f model is particularly suited to describe color in MM nose. We also demonstrate that even if most of the brain voxels are mono-fractal, there are many locations in the brain where time constancy of the Hurst exponent is violated, ie, the noise structure is poly-fractal.
A multi-label front propagation approach for object segmentation

For effective image segmentation methods, speed, accuracy and smoothness of the result are essential. In this paper, an iterative object segmentation approach is proposed based on minimal path theory. Each iterative step includes one morphological dilatation and one multi-label front propagation. A narrow band is obtained by dilating the current contour with the known size. A new contour is again formed by multi-label front propagation, which is based on minimal path theory. Its propagation speed is decided by the local image mean values together with the edge function. The final boundary is obtained automatically through finite iterations. This algorithm is a global optimization method. It is simple and fast with complexity O(N). The initial contour may be chosen freely. The multi-label front propagation guarantees continuity and smooth contours with the capability to handle topology changes. Furthermore, it is easy to extend to the 3D case. Some experimental results are also presented.
EM algorithm for sparse representation-based image inpainting

We introduce an expectation-maximization (EM) algorithm for image inpainting based on a penalized likelihood formulated using linear sparse representations. Taking advantage of the sparsity of representations, a regularization through a prior penalty is imposed on the reconstructed coefficients. From a statistical point of view, the inpainting can be viewed as an estimation problem with missing data. The EM framework is a general iterative algorithm for ML estimation in such situations. The EM framework gives a principled way to establish formally the idea that missing samples can be recovered based on sparse representations. Furthermore, owing to its well known theoretical properties, the EM algorithm allows to investigate the convergence behavior of the inpainting algorithm.
Analytical form for a Bayesian wavelet estimator of images using the Bessel K form densities

A novel Bayesian nonparametric estimator in the wavelet domain is presented. In this approach, a prior model is imposed on the wavelet coefficients designed to capture the sparseness of the wavelet expansion. Seeking probability models for the marginal densities of the wavelet coefficients, the new family of Bessel K forms (BKF) densities are shown to fit very well to the observed histograms. Exploiting this prior, we designed a Bayesian nonlinear denoiser and we derived a closed form for its expression. We then compared it to other priors that have been introduced in the literature, such as the generalized Gaussian density (GGD) or the /spl alpha/-stable models, where no analytical form is available for the corresponding Bayesian denoisers. Specifically, the BKF model turns out to be a good compromise between these two extreme cases (hyperbolic tails for the /spl alpha/-stable and exponential tails for the GGD). Moreover, we demonstrate a high degree of match between observed and estimated prior densities using the BKF model. Finally, a comparative study is carried out to show the effectiveness of our denoiser which clearly outperforms the classical shrinkage or thresholding wavelet-based techniques.
DeQuantizing Compressed Sensing with non-Gaussian constraints

In this paper, following the Compressed Sensing (CS) paradigm, we study the problem of recovering sparse or compressible signals from uniformly quantized measurements. We present a new class of convex optimization programs, or decoders, coined Basis Pursuit DeQuantizer of moment p (BPDQp), that model the quantization distortion more faithfully than the commonly used Basis Pursuit DeNoise (BPDN) program. Our decoders proceed by minimizing the sparsity of the signal to be reconstructed while enforcing a data fidelity term of bounded ℓp-norm, for 2 ≪ p ⩽ ∞. We show that in oversampled situations, i.e. when the number of measurements is higher than the minimal value required by CS, the performance of the BPDQp decoders outperforms that of BPDN, with reconstruction error due to quantization divided by. This reduction relies on a modified Restricted Isometry Property of the sensing matrix expressed in the ℓp-norm (RIPp); a property satisfied by Gaussian random matrices with high probability. We conclude with numerical experiments comparing BPDQp and BPDN for signal and image reconstruction problems.
Region-Based Active Contour with Noise and Shape Priors

In this paper, we propose to combine formally noise and shape priors in region-based active contours. On the one hand, we use the general framework of exponential family as a prior model for noise. On the other hand, translation and scale invariant Legendre moments are considered to incorporate the shape prior (e.g. fidelity to a reference shape). The combination of the two prior terms in the active contour functional yields the final evolution equation whose evolution speed is rigorously derived using shape derivative tools. Experimental results on both synthetic images and real life cardiac echography data clearly demonstrate the robustness to initialization and noise, flexibility and large potential applicability of our segmentation algorithm.
Sparsity and morphological diversity for hyperspectral data analysis

Recently morphological diversity and sparsity have emerged as new and effective sources of diversity for Blind Source Separation. Based on these new concepts, novelmethods such as Generalized Morphological Component Analysis have been put forward. The latter takes advantage of the very sparse representation of structured data in large overcomplete dictionaries, to separate sources based on their morphology. Building on GMCA, the purpose of this contribution is to describe a new algorithm for hyperspectral data processing. Large-scale hyperspectral data refers to collected data that exhibit sparse spectral signatures in addition to sparse spatial morphologies, in specified dictionaries of spectral and spatial waveforms. Numerical experiments are reported which demonstrate the validity of the proposed extension for solving source separation problems involving hyperspectral data.
Segmentation of magnetic resonance images using fuzzy Markov random fields

We present a fuzzy Markovian method for brain tissue segmentation from magnetic resonance images. Generally, there are three principal brain tissues in a brain dataset: gray matter, white matter and cerebrospinal fluid. However, due to the limited resolution of the acquisition system, many voxels may be composed of multiple tissue types (partial volume effects). The proposed method aims to calculate the fuzzy membership of each voxel to indicate the partial volume degree using a fuzz, Markovian segmentation. Since our method is unsupervised, it first estimates the fuzzy Markovian random field model parameters using a stochastic gradient algorithm. The efficiency of the proposed method is quantified on a digital phantom using an absolute average error, and qualitatively tested on real MRI brain data.
Wavelets, Ridgelets, and Curvelets for Poisson Noise Removal

In order to denoise Poisson count data, we introduce a variance stabilizing transform (VST) applied on a filtered discrete Poisson process, yielding a near Gaussian process with asymptotic constant variance. This new transform, which can be deemed as an extension of the Anscombe transform to filtered data, is simple, fast, and efficient in (very) low-count situations. We combine this VST with the filter banks of wavelets, ridgelets and curvelets, leading to multiscale VSTs (MS-VSTs) and nonlinear decomposition schemes. By doing so, the noise-contaminated coefficients of these MS-VST-modified transforms are asymptotically normally distributed with known variances. A classical hypothesis-testing framework is adopted to detect the significant coefficients, and a sparsity-driven iterative scheme reconstructs properly the final estimate. A range of examples show the power of this MS-VST approach for recovering important structures of various morphologies in (very) low-count images. These results also demonstrate that the MS-VST approach is competitive relative to many existing denoising methods.
Bayesian denoising based on the MAP estimation in wavelet-domain using Bessel K form prior

In this paper, a nonparametric Bayesian estimator in the wavelet domain using the Bessel K form (BKF) distribution will be presented. Our first contribution is to show how the BKF prior is suited to characterize images belonging to Besov spaces. Exploiting this prior, our second contribution is to design a Bayesian L/sub 1/-loss maximum a posteriori estimator nonlinear denoiser, for which we formally establish the mathematical properties. Finally, a comparative study is carried to show the effectiveness of our Bayesian denoiser compared to other denoising approaches.
Region-based active contours and sparse representations for texture segmentation

In this paper we propose a rigorous framework for texture image segmentation relying on region-based active contours (RBAC) and sparse texture representation. Such representations allow to efficiently describe a texture by transforming it in a dictionary of appropriate waveforms (atoms) where the texture representation coefficients are concentrated on a small set. For segmentation purposes. these atoms have to be multiscale and localized both in space and frequency, e.g. the wavelet transform. To discriminate different textures, we measure a ldquodistancerdquo between the non-parametric Parzen estimates of their respective sparse-representation coefficients probability density functions (pdfs). These distance measures are then used within RBAC, and we take benefit from shape derivative tools to derive the evolution speed expression of the RBAC. Our framework is applied to both supervised (with reference textures), and unsupervised texture segmentation. A series of experiments on synthetic textures illustrate the potential applicability of our method.
Unsupervised segmentation of three-dimensional brain images

This paper presents an unsupervised segmentation method applied to classify brain tissues in 3D for magnetic resonance (MR) images. An MR image volume may be composed of a mixture of several tissue types due to partial volume effects. The statistical model of the mixtures is proposed and studied by means of simulations. It is shown that it can be approximated by a Gaussian function under some conditions. The D'Agostino-Pearson normality test is used to calculate the risk /spl alpha/ of the approximation. In order to classify a brain into three brain tissues and deal with the problem of partial volume effects, the proposed algorithm classifies firstly the brain into pure classes and mix-classes, it then re-classifies the mix-classes into pure classes by adding the knowledge about the topology of the brain, based on the multifractal dimension. Both steps use Markov random field models. The algorithm is evaluated using both simulated images and real MR images.
Brain tissue classification of magnetic resonance images using partial volume modeling

Presents a fully automatic three-dimensional classification of brain tissues for Magnetic Resonance (MR) images. An MR image volume may be composed of a mixture of several tissue types due to partial volume effects. Therefore, the authors consider that in a brain dataset there are not only the three main types of brain tissue: gray matter, white matter, and cerebro spinal fluid, called pure classes, but also mixtures, called mixclasses. A statistical model of the mixtures is proposed and studied by means of simulations. It is shown that it can be approximated by a Gaussian function under some conditions. The D'Agostino-Pearson normality test is used to assess the risk or of the approximation. In order to classify a brain into three types of brain tissue and deal with the problem of partial volume effects, the proposed algorithm uses two steps: (1) segmentation of the brain into pure and mixclasses using the mixture model; (2) reclassification of the mixclasses into the pure classes using knowledge about the obtained pure classes. Both steps use Markov random field (MRF) models. The multifractal dimension, describing the topology of the brain, is added to the MRFs to improve discrimination of the mixclasses. The algorithm is evaluated using both simulated images and real MR images with different T1-weighted acquisition sequences.
Deconvolution of confocal microscopy images using proximal iteration and sparse representations

We propose a deconvolution algorithm for images blurred and degraded by a Poisson noise. The algorithm uses a fast proximal backward-forward splitting iteration. This iteration minimizes an energy which combines a non-linear data fidelity term, adapted to Poisson noise, and a non- smooth sparsity-promoting regularization (e.g lscr1-norm) over the image representation coefficients in some dictionary of transforms (e.g. wavelets, curvelets). Our results on simulated microscopy images of neurons and cells are confronted to some state-of-the-art algorithms. They show that our approach is very competitive, and as expected, the importance of the non-linearity due to Poisson noise is more salient at low and medium intensities. Finally an experiment on real fluorescent confocal microscopy data is reported.
Unsupervised fuzzy clustering analysis of fMRI series

The potential of an fMRI data analysis strategy using a paradigm independent unsupervised fuzzy clustering is presented. The power of the method is demonstrated to discriminate different types of responses without prior knowledge relative to the paradigm in contrast with classic statistical methods which require a priori knowledge about the stimulus. Different performance measure functions are proposed to solve the cluster validity problem and to detect the number of substructures present in the data. The results are presented on both simulated data (with Contrast to Noise Ratios similar to those observed in fMRT), and in vivo EPI data for a motor paradigm. This new data analysis procedure may be very useful for optimization of data analysis and quality in fMRI.
Sparsity and Morphological Diversity in Blind Source Separation

Over the last few years, the development of multichannel sensors motivated interest in methods for the coherent processing of multivariate data. Some specific issues have already been addressed as testified by the wide literature on the so-called blind source separation (BSS) problem. In this context, as clearly emphasized by previous work, it is fundamental that the sources to be retrieved present some quantitatively measurable diversity. Recently, sparsity and morphological diversity have emerged as a novel and effective source of diversity for BSS. Here, we give some new and essential insights into the use of sparsity in source separation, and we outline the essential role of morphological diversity as being a source of diversity or contrast between the sources. This paper introduces a new BSS method coined generalized morphological component analysis (GMCA) that takes advantages of both morphological diversity and sparsity, using recent sparse overcomplete or redundant signal representations. GMCA is a fast and efficient BSS method. We present arguments and a discussion supporting the convergence of the GMCA algorithm. Numerical results in multivariate image and signal processing are given illustrating the good performance of GMCA and its robustness to noise.
Penalized partially linear models using orthonormal wavelet bases with an application to fMRI time series

In this paper, we consider modeling the nonparametric component in partially linear models (PLM) using orthogonal wavelet expansions. We introduce a regularized estimator of the nonparametric part in the wavelet domain. The key innovation here is that the nonparametric part can be efficiently estimated by choosing an appropriate penalty function for which the hard and soft thresholding estimators are particular cases. This avoids excessive bias in estimating the parametric component. We give an efficient estimation algorithm. A large scale simulation study is also conducted to illustrate the finite sample properties of the estimator. The estimator is finally applied to real neurophysiological functional MRI data sets that are suspected to contain both smooth and transient drift features.
3D MRI segmentation of brain structures

Some complex applications in medical imaging require to combine results coming from different numerical operators referred as to edges-and regions-detectors. Combining "static" contours and regions methods, named cooperative methods, takes advantage of these two compatible segmentation results. To improve success of segmentation, we propose the idea of combining region and active contour segmentations. In a first step, we use tools from mathematical morphology and region growing algorithm for the region segmentation in order to get the automated initialisation of the active contour model. In the second step, the physically-based active model considers the contour undergoing an elastic deformation as a set of masses linked by springs and converging to an equilibrium state. Segmentation results are shown on cerebellum, brain stem and hemispheres on 3D MRI data sets.
Penalized partially linear models using sparse representations with an application to fMRI time series

In this paper, we consider modeling the nonparametric component in partially linear models (PLMs) using linear sparse representations, e.g., wavelet expansions. Two types of representations are investigated, namely, orthogonal bases (complete) and redundant overcomplete expansions. For bases, we introduce a regularized estimator of the nonparametric part. The important contribution here is that the nonparametric part can be parsimoniously estimated by choosing an appropriate penalty function for which the hard and soft thresholding estimators are special cases. This allows us to represent in an effective manner a broad class of signals, including stationary and/or nonstationary signals and avoids excessive bias in estimating the parametric component. We also give a fast estimation algorithm. The method is then generalized to handle the case of overcomplete representations. A large-scale simulation study is conducted to illustrate the finite sample properties of the estimator. The estimator is finally applied to real neurophysiological functional magnetic resonance imaging (MRI) data sets that are suspected to contain both smooth and transient drift features.
Multi-Scale Variance Stabilizing Transform for Multi-Dimensional Poisson Count Image Denoising

We propose in this paper a multi-scale variance stabilizing transform (MSVST) for approximately Gaussianizing and stabilizing the variance of a sequence of independent Poisson random variables (RVs) filtered by a low-pass linear filter. This approach is shown to be fast, very well adapted to extremely low-count situations and easily applicable to any dimensional data. It is shown that the RV transformed using Anscombe VST can be reasonably considered as stabilized for an intensity lambda gsim 10, using Fisz VST for lambda gsim 1 and using our VST (after low-pass filtering) for lambda gsim 0.1. We then use the MSVST technique to stabilize the detail coefficients of the isotropic undecimated wavelet transform (IUWT) of multi-dimensional Poisson count data. We use a hypothesis testing framework in the wavelet domain to denoise the Gaussianized and stabilized coefficients, and then apply the inverse MSVST-IUWT to get the estimated intensity image underlying the Poisson data. Finally, potential applicability of our approach is illustrated on an astronomical example where isotropic structures must be recovered
Bayesian denoising in the wavelet-domain using an analytical approximate /spl alpha/-stable prior

A nonparametric Bayesian estimator in the wavelet domain is presented. In this approach, we propose a prior model based on the /spl alpha/-stable densities to capture the sparseness of the wavelet coefficients. An attempt to apply this model image wavelet-denoising have been already proposed in A.Achim et al. (2001). However, despite its efficacy in modeling the heavy-tail behaviour of the empirical detail coefficients densities, their denoiser proves very poor in practice and suffers from many drawbacks such as the weakness of the hyperparameters estimator associated with the /spl alpha/-stable prior. Here, we propose to overcome these limitations using the scale-mixture of Gaussians as an analytical approximation for /spl alpha/-stable densities. Exploiting this prior, we design a Bayesian L/sub 2/-loss nonlinear denoiser.