nils-herrmann/clean_scirepeval huggingface dataset:
Convex Total Variation Denoising of Poisson Fluorescence Confocal Images With Anisotropic Filtering

Fluorescence confocal microscopy (FCM) is now one of the most important tools in biomedicine research. In fact, it makes it possible to accurately study the dynamic processes occurring inside the cell and its nucleus by following the motion of fluorescent molecules over time. Due to the small amount of acquired radiation and the huge optical and electronics amplification, the FCM images are usually corrupted by a severe type of Poisson noise. This noise may be even more damaging when very low intensity incident radiation is used to avoid phototoxicity. In this paper, a Bayesian algorithm is proposed to remove the Poisson intensity dependent noise corrupting the FCM image sequences. The observations are organized in a 3-D tensor where each plane is one of the images acquired along the time of a cell nucleus using the fluorescence loss in photobleaching (FLIP) technique. The method removes simultaneously the noise by considering different spatial and temporal correlations. This is accomplished by using an anisotropic 3-D filter that may be separately tuned in space and in time dimensions. Tests using synthetic and real data are described and presented to illustrate the application of the algorithm. A comparison with several state-of-the-art algorithms is also presented.
Robust brain activation detection in functional MRI

Functional Magnetic Resonance Imaging (MRI) is today one of the most important non-invasive tools to study the brain from a functional point of view. The blood-oxygenation-level-dependent (BOLD) signal is used to detect the activated regions based on the assumption that in these regions the metabolic activity increases. The normal procedure is the application of known sequences of stimulus and find out the brain regions whose activation sequence is correlated with the applied stimulus. This inference problem is difficult because the BOLD signal is very week and noisy. The underlying information is embedded in a large number of other signal related with the normal brain activity and in the noise introduced by the MRI scanner. Furthermore, the hemodynamic impulse response function (HRF), needed to know the expected BOLD response to a given stimulus, is usually unknown and is not constant across the whole brain. In this paper a robust Bayesian algorithm is proposed to detect regions where the activation patterns are correlated with the applied stimulus. The activation process is modeled by using binary explicative variables and the HRF is estimated at each location according to a physiological model proposed by the authors in . Monte Carlo tests using synthetic data are performed to evaluate the performance of the algorithm and results with real data are compared with the ones obtained by a neurologist with the commercial package BrainVoyager.
An Ultrasonographic Risk Score For Detecting Symptomatic Carotid Atherosclerotic Plaques

This paper proposes a risk score computed from ultrasound data that correlates to plaque activity. It has the twofold purpose of detecting symptomatic plaques and estimating the likelihood of the asymptomatic lesion to become symptomatic. The proposed ultrasonographic activity index (UAI) relies on the plaque active profile, which is a combination of the most discriminate ultrasound parameter associated with symptoms. These features are extracted by the automatic algorithm and also by the physician from the ultrasound images and from some transformations on it, such as monogenic decomposition, which is a novelty in this clinical problem. This information is used to compute a risk score from the conditional probabilities of either symptomatic or asymptomatic groups. Symptom detection performance is evaluated on a transversal dataset of 146 plaques, where UAI obtained 83.5% accuracy, 84.1% sensitivity, and 83.7% specificity. Performance is also assessed on a longitudinal study of 112 plaques, where UAI shows a significant improvement over the gold standard degree of stenosis, demonstrating higher power at predicting which asymptomatic plaques developed symptoms in an average follow-up of ten months. Results suggest that this score could have a positive impact on early stroke prevention and treatment planning.
Ultrasonographic plaque characterization using a rayleigh mixture model

A correct modelling of tissue morphology is determinant for the identification of vulnerable plaques. This paper aims at describing the plaque composition by means of a Rayleigh Mixture Model applied to ultrasonic data. The effectiveness of using a mixture of distributions is established through synthetic and real ultrasonic data samples. Furthermore, the proposed mixture model is used in a plaque classification problem in Intravascular Ultrasound (IVUS) images of coronary plaques. A classifier tested on a set of 67 in-vitro plaques, yields an overall accuracy of 86% and sensitivity of 92%, 94% and 82%, for fibrotic, calcified and lipidic tissues, respectively. These results strongly suggest that different plaques types can be distinguished by means of the coefficients and Rayleigh parameters of the mixture distribution.
A fast MAP algorithm using high order Gibbs priors

The MAP method has the ability to deal with ill-posed problems, involving noisy data, by reducing the number of admissible solutions. However, this method is usually iterative and time consuming and not appropriate to work in a real time basis. In the context of image reconstruction/restoration, Gibbs priors with quadratic potential functions have been used because they allow simple mathematical formulations. However, these priors lead to smooth solutions with a poor representation of transitions. To overcome this problem, several authors have proposed other potential functions, non quadratic, that deal better with the transitions, but lead to an increase of complexity. In this paper, we come back to the Gibbs priors with quadratic potential functions to show that MAP algorithms can be formulated as a linear filtering problem. The recursive nature of the filtering methods allows us to obtain very fast and efficient restoration/reconstruction MAP algorithms. Furthermore, we show that by using quadratic potential functions involving high order differences between neighbors it is possible to significantly improve the solution at the transitions. In fact, with this strategy we are increasing the order of the filter that models the original MAP problem. The approach presented in this paper can be used as a method to design Gibbs priors based on the filter design theory.
FMRI brain activity and underlying hemodynamics estimation in a new Bayesian framework

The emerging functional MRI (magnetic resonance imaging), fMRI, imaging modality was developed to obtain non-invasive information regarding the neural processes behind pre-determined task. The data is gathered in such a way that the extraction certainty of the desired information is maximized. Still this is a difficult task due to low Signal-to-Noise Ratio (SNR), corrupting noise and artifacts from several sources. The most prevalent method, here called SPM-GLM uses a conventional statistical inference methodology based on the t-statistics, where it assumes a rather rigid shape on the BOLD hemodynamic response function (HRF), constant for the whole region of interest (ROI). A new algorithm, designed in a Bayesian framework, is presented in this paper, called SPM-MAP. The algorithm jointly detects the brain activated regions and the underlying HRF in an adaptative and local basis. This approach presents two main advantages: (1) the activity detection benefits from the method's high flexibility toward the HRF shape; (2) it provides local estimations for the HRF. The SPM-MAP algorithm is validated by using Monte Carlo tests with synthetic data and comparisons with the SPM-GLM are also performed. Tests using real data are also performed and results are compared with the ones provided by the SPM-GLM method tuned by the medical doctor.
Photoblinking/photobleaching differential equation model for intensity decay of fluorescence microscopy images

Fluorescence confocal microscopy images are affected by fading effects, such as photoblinking and photobleaching, that leads to image intensity decreasing along the time, preventing long exposure time experiments. These fading effects increase with the amount of radiation that illuminates the speciem. Additionally, these type of images present a low signal to noise ratio and are corrupted by a type of multiplicative noise with Poisson distribution due to the strong amplification needed to observe the small fluorescent radiation emitted by the fluorosphore. Therefore a trade-off exists between the needs of increasing the incident radiation to improve the signal to noise ratio and the needs of decreasing that radiation to minimize the fading effects. The main goal of this paper is to obtain an intensity decay law to describe the photoblinking/photobleaching effect based on a theoretical model derived from the quantic phenomena described in the literature, involved in the process. The decay law, derived here from the theoretical model, matches the intensity decay law that is usually referred in the literature, obtained from experimental data. The model is plugged in a Bayesian denoising algorithm that takes into account the temporal correlation among consecutive images to improve its visualization, mainly in the last ones where the intensity is very small. Results with real data is presented to illustrate the validity of the model.
Rayleigh Mixture Model for Plaque Characterization in Intravascular Ultrasound

Vulnerable plaques are the major cause of carotid and coronary vascular problems, such as heart attack or stroke. A correct modeling of plaque echomorphology and composition can help the identification of such lesions. The Rayleigh distribution is widely used to describe (nearly) homogeneous areas in ultrasound images. Since plaques may contain tissues with heterogeneous regions, more complex distributions depending on multiple parameters are usually needed, such as Rice, K or Nakagami distributions. In such cases, the problem formulation becomes more complex, and the optimization procedure to estimate the plaque echomorphology is more difficult. Here, we propose to model the tissue echomorphology by means of a mixture of Rayleigh distributions, known as the Rayleigh mixture model (RMM). The problem formulation is still simple, but its ability to describe complex textural patterns is very powerful. In this paper, we present a method for the automatic estimation of the RMM mixture parameters by means of the expectation maximization algorithm, which aims at characterizing tissue echomorphology in ultrasound (US). The performance of the proposed model is evaluated with a database of in vitro intravascular US cases. We show that the mixture coefficients and Rayleigh parameters explicitly derived from the mixture model are able to accurately describe different plaque types and to significantly improve the characterization performance of an already existing methodology.
Temporal 2D reconstruction of cell nucleus from Fluorescence Confocal Microscopy images with anisotropic filtering

Fluorescence Confocal Microscopy(FCM) is nowa-days one of the most important tools in biomedicine research. In fact, it makes possible to accurately study the dynamic processes occurring inside the cell and its nucleus by following the motion of fluorescent molecules along the time. Due to the small amount of acquired radiation and the huge optical and electronics amplification, the FCMimages are usually corrupted by a severe type of Poisson noise. This noise may be even more damaging when very low intensity incident radiation is used to avoid phototoxicity.
Estimation of cardiac phases in echographic images using multiple models

This paper presents an algorithm for tracking the left ventricle in echocardiographic sequences, using multiple models. The use of multiple dynamic models is appropriate since the heart motion presents two phases (diastole and systole) with different dynamics. The main difficulty concerns the low contrast and speckle noise present in ultrasound images. To overcome this problem a robust multiple model tracker is used, based on a bank of nonlinear filters, organized in a tree structure. This algorithm determines which model is active at each instant of time and updates its state by propagating the probability distribution, using robust estimation techniques. It is shown in the paper that the proposed algorithm simultaneously copes with several dynamic models and with outliers. Furthermore the proposed algorithm provides high level information that is not available when a single model is used.
Neuronal electrical activity, energy consumption and mitochondrial ATP restoration dynamics: A physiological based model for FMRI

fMRI is a widely used method to detect the activated brain regions due to a stimulus application. Commonly, it employs the BOLD contrast, which is based on the correlation between physiological function, energy metabolism and haemodynamics. However, the BOLD signal is weak and noisy, so that, an accurate mathematical model to describe the Haemodynamic Response Function (HRF) to activation is needed.
The usefulness of ultrasound in the classification of chronic liver disease

Chronic Liver Disease is a progressive, most of the time asymptomatic, and potentially fatal disease. In this paper, a semi-automatic procedure to stage this disease is proposed based on ultrasound liver images, clinical and laboratorial data. In the core of the algorithm two classifiers are used: a k nearest neighbor and a Support Vector Machine, with different kernels. The classifiers were trained with the proposed multi-modal feature set and the results obtained were compared with the laboratorial and clinical feature set. The results showed that using ultrasound based features, in association with laboratorial and clinical features, improve the classification accuracy. The support vector machine, polynomial kernel, outperformed the others classifiers in every class studied. For the Normal class we achieved 100% accuracy, for the chronic hepatitis with cirrhosis 73.08%, for compensated cirrhosis 59.26% and for decompensated cirrhosis 91.67%.
On estimating de-speckled and speckle components from B-mode ultrasound images

The information encoded in ultrasound speckle is often discarded but it is widely recognized that this phenomenon is dependent of the intrinsic acoustic properties of tissues. In this paper we propose a robust method to estimate the de-speckled and speckle components from the ultrasound data with the purpose of tissue characterization. A de-speckling method, which can conveniently work with either Radio Frequency (RF) or B-mode data, contributes to an improvement on the visualization of anatomical details, while providing useful fields from where echogenicity and texture features can be extracted. The adequacy of the RF image retrieval and despeckling methods are tackled using both synthetic and real ultrasonic data.
3D reconstruction from log-compressed Rayleigh images

This paper addresses 3D reconstruction in the presence of multiplicative noise and non-linear compression of the ultrasound data. Ultrasound images are often considered as being corrupted by multiplicative noise with Rayleigh distribution. However, commercial ultrasound equipment also performs a non-linear image compression which reduces the dynamic range of the ultrasound signal, for visualization purposes. In this paper the non-linear compression is explicitly modelled and considered for 3D reconstruction of medical data. The proposed algorithm is tested with synthetic and real data and the results are discussed.
Automatic annotation of actigraphy data for Sleep disorders diagnosis purposes

The diagnosis of Sleep disorders, highly prevalent in the western countries, typically involves sophisticated procedures and equipments that are intrusive to the patient. Wrist actigraphy, on the contrary, is a non-invasive and low cost solution to gather data which can provide valuable information in the diagnosis of these disorders. The acquired data may be used to infer the Sleep/Wakefulness (SW) state of the patient during the circadian cycle and detect abnormal behavioral patterns associated with these disorders. In this paper a classifier based on Autoregressive (AR) model coefficients, among other features, is proposed to estimate the SW state. The real data, acquired from 23 healthy subjects during fourteen days each, was segmented by expert medical personal with the help of complementary information such as light intensity and Sleep e-Diary information. Monte Carlo tests with a Leave-One-Out Cross Validation (LOOCV) strategy were used to assess the performance of the classifier which achieves an accuracy of 96%.
Bayesian fisher information criterion for sampling optimization in ASL-MRI

Pulsed Arterial Spin Labeling (PASL) techniques potentially allow the absolute, non-invasive quantification of brain perfusion using Magnetic Resonance Imaging (MRI). This can be achieved by fitting a kinetic model to the data acquired at a number of inversion times (TI). Some model parameters such as the arterial transit time need to be estimated together with perfusion, while others are usually assumed to be known. The accuracy of the model estimation strongly depends on the distribution of the TI sampling points. Here, we propose a Bayesian framework for PASL perfusion estimation based on the Fisher information criterion, whereby the optimal sampling points can be determined taking into account the uncertainty of the model parameters as well as the amount of noise in the data. We show that the optimal sampling strategy for PASL depends on the a priori knowledge of the model parameters and this should therefore be taken into account.
A Method for the Dynamic Analysis of the Heart Using a Lyapounov Based Denoising Algorithm

Heart tracking in ultrasound sequences is a difficult task due to speckle noise, low SNR and lack of contrast. Therefore it is usually difficult to obtain robust estimates of the heart cavities since feature detectors produce a large number of outliers. This paper presents an algorithm which combines two main operations: i) a novel denoising algorithm based on the Lyapounov equation and ii) a robust tracker, recently proposed by the authors, based on a model of the outlier features. Experimental results are provided, showing that the proposed algorithm is computationally efficient and leads to accurate estimates of the left ventricle during the cardiac cycle
An Unified Framework for Bayesian Denoising for Several Medical and Biological Imaging modalities

Multiplicative noise is often present in several medical and biological imaging modalities, such as MRI, Ultrasound, PET/SPECT and Fluorescence Microscopy. Noise removal and preserving the details is not a trivial task. Bayesian algorithms have been used to tackle this problem. They succeed to accomplish this task, however they lead to a computational burden as we increase the image dimensionality. Therefore, a significant effort has been made to accomplish this tradeoff, i.e., to develop fast and reliable algorithms to remove noise without distorting relevant clinical information. This paper provides a new unified framework for Bayesian denoising of images corrupted with additive and multiplicative multiplicative noise. This allows to deal with additive white Gaussian and multiplicative noise described by Poisson and Rayleigh distributions respectively. The proposed algorithm is based on the maximum a posteriori (MAP) criterion, and an edge preserving priors are used to avoid the distortion of the relevant image details. The denoising task is performed by an iterative scheme based on Sylvester/Lyapunov equation. This approach allows to use fast and efficient algorithms described in the literature to solve the Sylvester/Lyapunov equation developed in the context of the Control theory. Experimental results with synthetic and real data testify the performance of the proposed technique, and competitive results are achieved when comparing to the of the state-of-the-art methods.
Global and local detection of liver steatosis from ultrasound

Liver steatosis is a common disease usually associated with social and genetic factors. Early detection and quantification is important since it can evolve to cirrhosis. Steatosis is usually a diffuse liver disease, since it is globally affected. However, steatosis can also be focal affecting only some foci difficult to discriminate. In both cases, steatosis is detected by laboratorial analysis and visual inspection of ultrasound images of the hepatic parenchyma. Liver biopsy is the most accurate diagnostic method but its invasive nature suggest the use of other non-invasive methods, while visual inspection of the ultrasound images is subjective and prone to error. In this paper a new Computer Aided Diagnosis (CAD) system for steatosis classification and analysis is presented, where the Bayes Factor, obatined from objective intensity and textural features extracted from US images of the liver, is computed in a local or global basis. The main goal is to provide the physician with an application to make it faster and accurate the diagnosis and quantification of steatosis, namely in a screening approach. The results showed an overall accuracy of 93.54% with a sensibility of 95.83% and 85.71% for normal and steatosis class, respectively. The proposed CAD system seemed suitable as a graphical display for steatosis classification and comparison with some of the most recent works in the literature is also presented.
Robust Band Profile Extraction Using Constrained Nonparametric Machine-Learning Technique

A typical characteristic of images of bone marrow cells taken during mitosis is poor quality. This renders the task of extraction of accurate band profile, representative of intensity distribution over each chromosome, more challenging. A robust method is hence required to tackle this problem. An algorithm was thus developed, which estimates a single-line medial axis, the basis for computation of band profile. Medial axis was generated by computing a final prediction, using primary and secondary predictions obtained by a nonparametric machine learning algorithm trained with data from chromosome's skeleton, and geometrical properties of medial axis, respectively. Experiments were performed using the LK1 dataset. The algorithm was found capable of estimating a satisfactory single-line medial axis. Band profile obtained was found to be a good representation of intensity levels in different regions of chromosomes. Additionally, this algorithm is robust in terms of growing a very small seed region into desired medial axis and also handling highly irregular chromosomes.