nils-herrmann/clean_scirepeval huggingface dataset:
Gamma mixture classifier for plaque detection in intravascular ultrasonic images

Carotid and coronary vascular incidents are mostly caused by vulnerable plaques. Detection and characterization of vulnerable plaques are important for early disease diagnosis and treatment. For this purpose, the echomorphology and composition have been studied. Several distributions have been used to describe ultrasonic data depending on tissues, acquisition conditions, and equipment. Among them, the Rayleigh distribution is a one-parameter model used to describe the raw envelope RF ultrasound signal for its simplicity, whereas the Nakagami distribution (a generalization of the Rayleigh distribution) is the two-parameter model which is commonly accepted. However, it fails to describe B-mode images or Cartesian interpolated or subsampled RF images because linear filtering changes the statistics of the signal. In this work, a gamma mixture model (GMM) is proposed to describe the subsampled/interpolated RF images and it is shown that the parameters and coefficients of the mixture are useful descriptors of speckle pattern for different types of plaque tissues. This new model outperforms recently proposed probabilistic and textural methods with respect to plaque description and characterization of echogenic contents. Classification results provide an overall accuracy of 86.56% for four classes and 95.16% for three classes. These results evidence the classifier usefulness for plaque characterization. Additionally, the classifier provides probability maps according to each tissue type, which can be displayed for inspecting local tissue composition, or used for automatic filtering and segmentation.
Three-dimensional labeling of vulnerable regions in carotid plaques using Graph-Cuts

Carotid plaque morphology assessed through high resolution ultrasound is nowadays an important diagnostic approach in the evaluation of stroke risk. The literature describes several methods for the characterization of plaque echogenicity and texture by using overall statistics across the carotid plaques. However, this averaged indicators may not be enough for a correct diagnosis of plaque instability. Therefore, it has been suggested that a local characterization of plaque morphology, where the extension and location of vulnerable regions inside the plaques is taken into account, could lead to significant improvements in the assessment of stroke risk.
A CAD system for atherosclerotic plaque assessment

Recently, several atherosclerotic plaque characterization methods were proposed based on plaque morphology assessed through 2D ultrasound. It is of extreme importance to establish an objective quantification measure which allows the physicians to determine the risk of plaque rupture, and thus, of brain stroke. Having these, sometimes complex, measures easily and quickly assessed might prove invaluable for the physician an patient alike. This paper is a first attempt to incorporate such scores in a user-friendly software platform for Computer-aided Diagnosis. This tool provides a way to objectively and interactively characterize the atherosclerotic plaque, to store relevant patient data and to use several processing tools to outline the plaque and compute different echogenicity measures. Combinations of these features are used to provide two objective measure with clinical significance, known as activity index and enhanced activity index.
An actigraphy heterogeneous mixture model for sleep assessment

Wrist actigraphy is a well established procedure to monitor human activity. Among other areas, it has a special relevance in sleep studies where its lightweight and non-intrusive nature make it a valuable tool to access the circadian cycle. While there are several methods to extract information from the data, the differentiation between sleep and wakefulness states is still an open discussion. In this paper, the characteristics of the movements in the different states are assumed to be intrinsically different. These differences are not simply related with magnitude and movement counting, but due to real differences on the statistical distributions describing the actigraphy data. Thus it is possible to refine the discrimination level when detecting these states. The proposed methodology to characterize the actigraphy data is based on a mixture of three canonical distributions; i)Exponential, ii)Rayleigh and iii)Gaussian. It is shown that the weights and parameters estimated in each state are organized into almost separable clusters on the feature space. This suggests the ability of the method to discriminate these states based only on the movements recorded on actigraphy data.
Blind Inpainting Using  $\ell _{0}$  and Total Variation Regularization

In this paper, we address the problem of image reconstruction with missing pixels or corrupted with impulse noise, when the locations of the corrupted pixels are not known. A logarithmic transformation is applied to convert the multiplication between the image and binary mask into an additive problem. The image and mask terms are then estimated iteratively with total variation regularization applied on the image, and ℓ0 regularization on the mask term which imposes sparseness on the support set of the missing pixels. The resulting alternating minimization scheme simultaneously estimates the image and mask, in the same iterative process. The logarithmic transformation also allows the method to be extended to the Rayleigh multiplicative and Poisson observation models. The method can also be extended to impulse noise removal by relaxing the regularizer from the ℓ0 norm to the ℓ1 norm. Experimental results show that the proposed method can deal with a larger fraction of missing pixels than two phase methods, which first estimate the mask and then reconstruct the image.
Joint Bayesian detection of brain activated regions and local HRF estimation in functional MRI

The blood-oxygenation-level-dependent (BOLD) signal, measured with the magnetic resonance imaging (MRI), is currently used to detect the activation of brain regions with a stimulus application, e.g., visual or auditive. In a block design approach, the stimuli (called paradigm in the fMRI scope) are designed to detect activated and non activated brain regions with maximized certainty. However, corrupting noise in MRI volumes acquisition, patient motion and the normal brain activity interference makes this detection a difficult task. In this paper a new Bayesian method, called SPM-MAP, is proposed where a joint detection of brain activated regions and estimation of the underlying hemodynamic impulse response function (HRF) is proposed. Monte Carlo tests on its error probability and HRF estimation with synthetic data are performed and presented.
Sampling strategy for perfusion quantification using PASL-MRI

Arterial spin labeling (ASL) techniques have been developed over the past decade for mapping blood perfusion using MRI. However, their unique potential for the absolute quantification of perfusion, in a completely non-invasive manner and with greater spatial and temporal resolution than alternative methods, has not yet been fully realized. The main limitations of ASL techniques are their intrinsically low SNR and the complexity of the underlying kinetics. In this study, we investigated different sampling strategies for perfusion quantification based on multi-time point pulsed ASL acquisitions. We found that, for the same number of acquisitions, sampling the kinetics curve over a larger number of different time points yields more stable results than averaging over a limited number of time points.
A Novel Metric for Bone Marrow Cells Chromosome Pairing

Karyotyping is a set of procedures, in the scope of the cytogenetics, that produces a visual representation of the 46 chromosomes observed during the metaphase step of the cellular division, called mitosis, paired and arranged in decreasing order of size. Automatic pairing of bone marrow cells is a difficult task because these chromosomes appear distorted, overlapped, and their images are usually blurred with undefined edges and low level of detail. In this paper, a new metric is proposed to compare this type of chromosome images toward the design of an automatic pairing algorithm for leukemia diagnostic purposes. Besides the features used in the traditional karyotyping procedures, a new feature, based on mutual information , is proposed to increase the discriminate power of the G-banding pattern dissimilarity between chromosomes and improve the performance of the classifier. The pairing algorithm is formulated as a combinatorial optimization problem where the distances between homologous chromosomes are minimized and the distances between nonhomologous ones are maximized. The optimization task is solved by using an integer programming approach. A new bone marrow chromosome dataset-Lisbon-K1 (LK1) chromosome dataset with 9200 chromosomes---was build for this study. These chromosomes have much lower quality than the classic Copenhagen, Edinburgh, and Philadelphia datasets, and its classification and pairing is therefore more difficult. Experiments using real images from the LK1 and Grisan et al. datasets based on a leave-one-out cross-validation strategy are performed to test and validate the pairing algorithm.
Total variation with automatic hyper-parameter estimation

Medical diagnosis is often hampered by the quality of the images. This happens in a wide range of image modalities. Image noise reduction is a crucial step, however difficult to be accomplished. Bayesian algorithms have been commonly used with success, namely with additive white Gaussian noise (AWGN) model. In fact, the noise corrupting some of the most used medical imaging modalities is not additive neither Gaussian but multiplicative described by Poisson or Rayleigh distributions. This paper proposes a unified framework with automatic hyper parameters estimation. The proposed framework deals with AWGN but also with both Poisson and Rayleigh distributions. The algorithm proposed herein, is based on a maximum a posteriori (MAP) criterion with the edge preserving prior based on the total variation (TV), which avoids the distortion of relevant anatomical details. The denoising technique is performed via single parametric iterative scheme parameterized for each noise model considered. Tests with real data from several medical imaging modalities testify the performance of the algorithm.
3D femur reconstruction using a robotized ultrasound probe

Three-dimensional reconstruction of images is a fundamental procedure in medical imaging because it allows to build virtual models of anatomical units of the human body, useful in several areas, such as in surgical navigation. This paper presents a non freehand Ultrasound acquisition system for human femur reconstruction where the probe displacement and position are accurately controlled by an anthropomorphic arm robot. The aim of this system is to acquire a sequence of 2D parallel cross-sections evenly spaced along the displacement direction in order to perform an accurate 3D reconstruction of the femur to be used in the scope of computer-assisted orthopedic surgery. Here, the system is described as well as the image processing and calibration procedures implemented. Results with real data are presented to illustrate the operation of the system.
Chromosome pairing for karyotyping purposes using mutual information

Cytogenetics is the preferred tool in the diagnosis of genetic diseases such as leukemia and detection of aquired chromosomal abnormalities, such as translocations, deletions, monosomies or trisomies, etc. The karyotyping is a set of procedures, in the scope of the cytogenetics, that produces a visual representation of the 46 chromosomes, paired and arranged in decreasing order of size, observed during the metaphase step of the cellular division (meiosis). The pairing is the procedure in the karyotyping process where the homologous chromosomes are paired according to dimensional, morphological and textural similarity criteria. This process is time consuming and is usually performed manually by experts. An automatic pairing algorithm is still an open problem. In this paper we present new contributions to solve the automatic pairing problem in the scope of the karyotyping process for leukemia diagnostic purposes. Besides the traditional features used to compute the similarity between chromosomes, such as, normalized area, ellipsis axis length and banding profiles, we introduce the Mutual Information (MI) measure to assess the textural similarity between two chromosomes. A supervised linear classifier is trained to combine the different features computed from each pair, aiming at the correct pairing (as given by experts). The resulting classifier is then employed, together with a combinatorial optimization algorithm based on A*, to compute the pairing for any given image. Simulations using real images, obtained with a Leicatrade Optical Microscope DM 2500, were performed. These images were manually paired by experts and used as a ground truth for the pairing process to assess the performance of the proposed classifier. Furthermore, qualitative comparisons with the results obtained with a Leicatrade CW 4000 Karyo software were also performed.
Geometric correction of deformed chromosomes for automatic Karyotyping

Automatic Karyotyping is the process of classifying chromosomes from an unordered karyogram into their respective classes to create an ordered karyogram. Automatic karyotyping algorithms typically perform geometrical correction of deformed chromosomes for feature extraction; these features are used by classifier algorithms for classifying the chromosomes. Karyograms of bone marrow cells are known to have poor image quality. An example of such karyograms is the Lisbon-K1 (LK1) dataset that is used in our work. Thus, to correct the geometrical deformation of chromosomes from LK1, a robust method to obtain the medial axis of the chromosome was necessary. To address this problem, we developed an algorithm that uses the seed points to make a primary prediction. Subsequently, the algorithm computes the distance of boundary from the predicted point, and the gradients at algorithm-specified points on the boundary to compute two auxiliary predictions. Primary prediction is then corrected using auxiliary predictions, and a final prediction is obtained to be included in the seed region. A medial axis is obtained this way, which is further used for geometrical correction of the chromosomes. This algorithm was found capable of correcting geometrical deformations in even highly distorted chromosomes with forked ends.
Bayesian optimization of perfusion and transit time estimation in PASL-MRI

Pulsed Arterial Spin Labeling (PASL) techniques potentially allow the absolute, non-invasive quantification of brain perfusion and arterial transit time. This can be achieved by fitting a kinetic model to the data acquired at a number of inversion time points (TI). The intrinsically low SNR of PASL data, together with the uncertainty in the model parameters, can hinder the estimation of the parameters of interest. Here, a two-compartment kinetic model is used to estimate perfusion and transit time, based on a Maximum a Posteriori (MAP) criterion. A priori information concerning the physiological variation of the multiple model parameters is used to guide the solution. Monte Carlo simulations are performed to compare the accuracy of our proposed Bayesian estimation method with a conventional Least Squares (LS) approach, using four different sets of TI points. Each set is obtained either with a uniform distribution or an optimal sampling strategy designed based on the same MAP criterion. We show that the estimation errors are minimized when our proposed Bayesian estimation method is employed in combination with an optimal set of sampling points. In conclusion, our results indicate that PASL perfusion and transit time measurements would benefit from a Bayesian approach for the optimization of both the sampling strategy and the estimation algorithm, whereby prior information on the parameters is used.
Classification and Staging of Chronic Liver Disease From Multimodal Data

Chronic liver disease (CLD) is most of the time an asymptomatic, progressive, and ultimately potentially fatal disease. In this study, an automatic hierarchical procedure to stage CLD using ultrasound images, laboratory tests, and clinical records are described. The first stage of the proposed method, called clinical based classifier (CBC), discriminates healthy from pathologic conditions. When nonhealthy conditions are detected, the method refines the results in three exclusive pathologies in a hierarchical basis: 1) chronic hepatitis; 2) compensated cirrhosis; and 3) decompensated cirrhosis. The features used as well as the classifiers (Bayes, Parzen, support vector machine, and k-nearest neighbor) are optimally selected for each stage. A large multimodal feature database was specifically built for this study containing 30 chronic hepatitis cases, 34 compensated cirrhosis cases, and 36 decompensated cirrhosis cases, all validated after histopathologic analysis by liver biopsy. The CBC classification scheme outperformed the nonhierachical one against all scheme, achieving an overall accuracy of 98.67% for the normal detector, 87.45% for the chronic hepatitis detector, and 95.71% for the cirrhosis detector.
Medical Image Noise Reduction Using the Sylvester–Lyapunov Equation

Multiplicative noise is often present in medical and biological imaging, such as magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET), single photon emission computed tomography (SPECT), and fluorescence microscopy. Noise reduction in medical images is a difficult task in which linear filtering algorithms usually fail. Bayesian algorithms have been used with success but they are time consuming and computationally demanding. In addition, the increasing importance of the 3-D and 4-D medical image analysis in medical diagnosis procedures increases the amount of data that must be efficiently processed. This paper presents a Bayesian denoising algorithm which copes with additive white Gaussian and multiplicative noise described by Poisson and Rayleigh distributions. The algorithm is based on the maximum a posteriori (MAP) criterion, and edge preserving priors which avoid the distortion of relevant anatomical details. The main contribution of the paper is the unification of a set of Bayesian denoising algorithms for additive and multiplicative noise using a well-known mathematical framework, the Sylvester-Lyapunov equation, developed in the context of the control theory.
Topographic EEG brain mapping before, during and after Obstructive Sleep Apnea Episodes

Obstructive Sleep Apnea Syndrome (OSAS) is a very common sleep disorder that is associated with several neurocognitive impairments. The present study aims to assess the electroencephalographic (EEG) power before, during and after obstructive apnea episodes, in four frequency bands: delta (δ), theta (θ), alpha α and beta (β). For that propose, continuous wavelet transform was applied to the EEG signals obtained with polysomnography, and topographic EEG brain mapping to visualize the power differences across the whole brain. The results demonstrate that there is a significant decrease in the EEG δ power during OSAS that does not totally recover immediately after the episode. Since δ waves are linked to learning and plasticity processes, it is hypothesized that decreased δ power during the episode may contribute to the cognitive deterioration in patients with OSAS.
Statistical characterization of actigraphy data during sleep and wakefulness states

Human activity can be measured with actimetry sensors used by the subjects in several locations such as the wrists or legs. Actigraphy data is used in different contexts such as sports training or tele-medicine monitoring. In the diagnosis of sleep disorders, the actimetry sensor, which is basically a 3D axis accelerometer, is used by the patient in the non dominant wrist typically during an entire week. In this paper the actigraphy data is described by a weighted mixture of two distributions where the weight evolves along the day according to the patient circadian cycle. Thus, one of the distributions is mainly associated with the wakefulness state while the other is associated with the sleep state. Actigraphy data, acquired from 20 healthy patients and manually segmented by trained technicians, is used to characterize the acceleration magnitude during sleep and wakefulness states. Several mixture combinations are tested and statistically validated with conformity measures. It is shown that both distributions can co-exist at a certain time with varying importance along the circadian cycle.
Optimal Sampling and Estimation in PASL Perfusion Imaging

Pulsed arterial spin labeling (PASL) techniques potentially allow the absolute, noninvasive quantification of brain perfusion using MRI. This can be achieved by fitting a kinetic model to the data acquired at a number of sampling times. However, the intrinsically low signal-to-noise ratio of PASL measurements usually requires substantial signal averaging, which may result in undesirably long scanning times. A judicious choice of the sampling points is, therefore, crucial in order to minimize scanning time, while optimizing estimation accuracy. On the other hand, a priori information regarding the model parameters may improve estimation performance. Here, we propose a Bayesian framework to determine an optimal sampling strategy and estimation method for the measurement of brain perfusion and arterial transit time (ATT). A Bayesian Fisher information criterion is used to determine the optimal sampling points and a MAP criterion is employed for the estimation of the model parameters, both taking into account the uncertainty in the model parameters as well as the amount of noise in the data. By Monte Carlo simulations, we show that using optimal compared to uniform sampling strategies, as well as the Bayesian estimator relative to a standard least squares approach, improves the accuracy of perfusion and ATT measurements. Moreover, we also demonstrate the applicability of the proposed approach to real data, with the advantage of reduced intersubject variability relative to conventional sampling and estimation approaches.
Fluorescence microscopy imaging denoising with log-Euclidean priors and photobleaching compensation

Fluorescent protein microscopy imaging is nowadays one of the most important tools in biomedical research. However, the resulting images present a low signal to noise ratio and a time intensity decay due to the photobleaching effect. This phenomenon is a consequence of the decreasing on the radiation emission efficiency of the tagging protein. This occurs because the fluorophore permanently loses its ability to fluoresce, due to photochemical reactions induced by the incident light. The Poisson multiplicative noise that corrupts these images, in addition with its quality degradation due to photobleaching, make long time biological observation processes very difficult. In this paper a denoising algorithm for Poisson data, where the photobleaching effect is explicitly taken into account, is described. The algorithm is designed in a Bayesian framework where the data fidelity term models the Poisson noise generation process as well as the exponential intensity decay caused by the photobleaching. The prior term is conceived with Gibbs priors and log-Euclidean potential functions, suitable to cope with the positivity constrained nature of the parameters to be estimated. Monte Carlo tests with synthetic data are presented to characterize the performance of the algorithm. One example with real data is included to illustrate its application.