nils-herrmann/clean_scirepeval huggingface dataset:
Tractography clustering for fiber selection in ROI-based diffusion tensor studies

Estimating white matter ber pathways from a diffusion tensor MRI dataset has many important applications in medical research. Even after the definition of white matter ROIs, the precise selection of appropriate fibers of interest for further analysis in population studies is often a time consuming and error prone task. Tractography segmentation methods based on pairwise distances between fibers are computationally expensive and have limitations addressing this problem. This paper proposes a new method to automatically perform fiber tract selection for ROI-based tractography analysis. The tractography segmentation is based on the representation of the fibers by means of their orientation at the closest point to the centroid of the associated ROI. Results and comparisons on two real-world fiber tract selection problems show the proposed method to constitute a simple yet effective tool for addressing this important problem.
A Comparative Study on Microcalcification Detection Methods with Posterior Probability Estimation based on Gaussian Mixture Models

Automatic detection of microcalcifications in mammograms constitutes a helpful tool in breast cancer diagnosis. Radiologist's confidence level on microcalcification detection would be improved if a probability estimate of its presence could be obtained from computer-aided diagnosis. In this paper we explore detection performance of a simple Bayesian classifier based on Gaussian mixture probability density functions (pdf). Posterior probability of microcalcification presence may be estimated from the probabilistic model. Two model selection algorithms have been tested, one based on the minimum message length criterion and the other on discriminative criteria obtained from the classifier performance. In addition, we propose a complementing model selection algorithm in order to improve the initial system performance obtained with these methods. Simulation results show that our model gets a good compromise between classification performance and probability estimation accuracy
Markov random fields and the disparity gradient constraint applied to stereo correspondence

We address the problem of stereo matching in the context of Markov random fields (MRFs) and the Bayesian approach. Although MRFs are used as a tool intended to formulate the problem in a way that allows the application of a powerful optimization technique called simulated annealing, the bases for the formulation of the stereo matching problem from a statistical and Markovian point of view are also stated.
A Markov Random Field Approach for Topology-Preserving Registration: Application to Object-Based Tomographic Image Interpolation

This paper proposes a topology-preserving multiresolution elastic registration method based on a discrete Markov random field of deformations and a block-matching procedure. The method is applied to the object-based interpolation of tomographic slices. For that purpose, the fidelity of a given deformation to the data is established by a block-matching strategy based on intensity- and gradient-related features, the smoothness of the transformation is favored by an appropriate prior on the field, and the deformation is guaranteed to maintain the topology by imposing some hard constraints on the local configurations of the field. The resulting deformation is defined as the maximum a posteriori configuration. Additionally, the relative influence of the fidelity and smoothness terms is weighted by the unsupervised estimation of the field parameters. In order to obtain an unbiased interpolation result, the registration is performed both in the forward and backward directions, and the resulting transformations are combined by using the local information content of the deformation. The method is applied to magnetic resonance and computed tomography acquisitions of the brain and the torso. Quantitative comparisons offer an overall improvement in performance with respect to related works in the literature. Additionally, the application of the interpolation method to cardiac magnetic resonance images has shown that the removal of any of the main components of the algorithm results in a decrease in performance which has proven to be statistically significant.
A variational method for scar segmentation with myocardial contour correction in DE-CMR images

Most automatic scar segmentation methods for cardiac DE-CMR images rely on an existing myocardial segmentation (from CINE-CMR) that is registered to the DE-CMR volume, step where alignment errors are usually introduced. We present a variational method that, with the same inputs, identifies the healthy and scarred tissue and selectively corrects the endocardial and epicardial contours. For this, we tailor an existing multiphase segmentation method to provide different regularization costs for each region, and model the data fidelity energy term with a Bayesian approach that unifies the prior tissue probabilities and the myocardial labels. Experimental results show better overlapping for the ground truth and segmented myocardium, and the segmented scar compares favorably with respect to state of the art methods.
Inference with fuzzy granules for computing with words: a practical viewpoint

In this paper we propose an alternative implementation of the concept of fuzzy granule. Granules are defined in terms of the degree of overlap with other granules, as opposed to by assigning (somehow arbitrarily) membership values to each and every point of the university of discourse in which the granule is defined. We believe this alternative definition is much closer to the human way of thinking. Two examples of real world applications illustrate this new definition.
Noise and Signal Estimation in Magnitude MRI and Rician Distributed Images: A LMMSE Approach

A new method for noise filtering in images that follow a Rician model-with particular attention to magnetic resonance imaging-is proposed. To that end, we have derived a (novel) closed-form solution of the linear minimum mean square error (LMMSE) estimator for this distribution. Additionally, a set of methods that automatically estimate the noise power are developed. These methods use information of the sample distribution of local statistics of the image, such as the local variance, the local mean, and the local mean square value. Accordingly, the dynamic estimation of noise leads to a recursive version of the LMMSE, which shows a good performance in both noise cleaning and feature preservation. This paper also includes the derivation of the probability density function of several local sample statistics for the Rayleigh and Rician model, upon which the estimators are built.
Hypothesis testing for coarse region estimation and stable point determination applied to Markovian texture segmentation

In this paper we show the benefits of applying hypothesis testing to the problem of texture segmentation. In our approach, hypothesis testing is used at two different stages that help to reduce the computational burden associated to iterative methods commonly used in image processing. Specifically, hypothesis testing is used to initially estimate the number of regions the image must be divided into, and to determine a set of points that will remain unchanged after the Markovian postprocessing scheme. These fixed points will contribute to reduce the number of iterations required by the Markovian stage and introduce geometry constraints that will reduce the boundary distortion caused by the stochastic procedure.
Endocardium and Epicardium Contour Modeling Based on Markov Random Fields and Active Contours

A segmentation application prototype of the volume of the left ventricle for Magnetic Resonance Imaging is being developed. The foundation for this work is given by modeling possible radial deformations of the epicardium and endocardium contours by means of a Markov Random Field over which the most probable configuration is estimated. The field makes use of a Bayesian approach based on a priori terms which impose smoothness along the coupled contours and likelihood terms which gather information provided by the images about the areas where the contours are supposed to be. The parameters of the field are estimated on a supervised basis
Object CFAR detection in gamma-distributed textured-background images

The authors introduce a new non-Gaussian CFAR detector, specifically a CFAR detector for gamma-distributed textured backgrounds. The design and analysis of the detector is carried out in several steps: basic design, analysis in only noise conditions, bias removal and analysis in the presence of correlation. The authors also discuss the possibility of applying a data prewhitening technique to control the false alarm rate in correlated textured patterns. In terms of detection, they analyse the performance of their detector for a certain model of target. They compare its performance to that of the ideal detector, and quantify in which conditions the former behaves closely enough to the latter.
A general approach to nonrigid registration: decoupled optimization

To a great extent, the success or advanced image-guided medical procedures hinges on non-rigid volume registration. For example, non-rigid registration must be applied in interventional approaches where intra-operative information is used to update high-quality preoperative data; in follow-up studies in order to assess time-evolution of development; aging, pathology or treatment; and in many other applications including inter-subject variability and population-based atlas construction. In this paper we examine several computational schemes that warp one volumetric dataset onto another. We also explore the inevitable trade-off between the computational load and the incorporation of sophisticated similarity measures necessary for multimodal volumes. Estimated deformation fields are based on the variational formulation of partial derivative equations (PDEs), which includes a similarity and a regularization term. We compare numerical solutions to this problem using the Euler-Lagrange equations (EL), the finite element discretization (FE), and decoupled optimization over the possible deformations (DO).
A novel Markovian formulation of the correspondence problem in stereo vision

This paper addresses the problem of finding matching points in stereo image pairs, i.e., the problem of correspondence. Even though this topic is well-known, a complete probabilistic formulation of it using psychovisual cues is still missing. We propose a novel Bayesian model based on Markov Random Fields (MRFs); the prior energy function is built in terms of the probability density function (pdf) of the disparity gradient. This pdf has never been reported in the past. The likelihood energy function is defined in terms of the pdf of the square normalized cross covariance between any two matching points. The stereo correspondence map is then obtained as the MAP estimator of the posterior field. Comparative results with methods previously reported, show the adequacy of the general model here proposed, and a good compromise between deterministic and stochastic images is attained.
Human hand postures and gestures recognition: towards a human-gesture communication interface

In this paper we present a novel framework for posture and gesture recognition performed by human articulated limbs, focusing the exposition on a notable case study: the human hand. The method relies on visual cues located on the surface of the body which are to be 3D reconstructed by means of stereo. A simple hand geometrical model and a visibility ordering scheme provide the regularization information needed to solve the posture recognition problem (the instantaneous hand configuration) in the presence of occlusions. These recognized postures feed a gesture (sequence of postures with semantic content) recognition system based on a clustering of postures paths and on a Viterbi decoder which chooses the one with the maximum likelihood. Some results are presented for a simple vocabulary consisting of sequences of signs taken from the Spanish hearing-impaired alphabet.
Groupwise non-rigid registration on multiparametric abdominal DWI acquisitions for robust ADC estimation: Comparison with pairwise approaches and different multimodal metrics

Registration of diffusion weighted datasets remains a challenging task in the process of quantifying diffusion indexes. Respiratory and cardiac motion, as well as echo-planar characteristic geometric distortions, may greatly limit accuracy on parameter estimation, specially for the liver. This work proposes a methodology for the non-rigid registration of multiparametric abdominal diffusion weighted imaging by using different well-known metrics under the groupwise paradigm. A three-stage validation of the methodology is carried out on a computational diffusion phantom, a watery solution phantom and a set of voluntary patients. Diffusion estimation accuracy has been directly calculated on the computational phantom and indirectly by means of a residual analysis on the real data. On the other hand, effectiveness in distortion correction has been measured on the phantom. Results have shown statistical significant improvements compared to pairwise registration being able to cope with elastic deformations.
A 3-D Collision Handling Algorithm for Surgery Simulation Based on Feedback Fuzzy Logic

In this paper, we propose a new method for collision handling between 3-D deformable (organs) and rigid (surgical tools) objects valid for nonstructured interaction scenes and, specifically, for laparoscopic surgery simulators. During the simulation step, vertices of the organ detected as collided must be accurately shifted out of the tool to elude a visual interpenetration. The proposed approach obtains the new position of each collided vertex of the organ taking into account both kinematic information of the surgical tool and geometric information of the organ surface that surrounds the vertex under analysis. Three parameters inferred from a fuzzy feedback system weigh the nature of the tool motion with respect to the organ. Experimental results show that the solution proposed in this paper is able to avoid the interpenetration among the multiple colliding points efficiently with physically and spatially coherent results.
Strain Rate Tensor estimation in cine cardiac MRI based on elastic image registration

In this paper we propose an alternative method to estimate and visualize the strain rate tensor (ST) in magnetic resonance images (MRI) when phase contrast MRI (PCMRI) and tagged MRI (TMRI) are not available. This alternative is based on image processing techniques. Concretely, an elastic image registration algorithm is used to estimate the movement of the myocardium at each point. Our experiments with real data prove that the registration algorithm provides a useful deformation field to estimate the ST fields. A classification between regional normal and dysfunctional contraction patterns, as compared with professional diagnosis, points out that the parameters extracted from the estimated ST can represent these patterns.
Automatic diagnosis of ADHD based on multichannel nonlinear analysis of actimetry registries

Attention-Deficit Hyperactivity Disorder (ADHD) is the most common mental health problem in childhood and adolescence. It is commonly diagnosed by means of subjective methods which tend to overestimate the severity of the pathology. A number of objective methods also exist, but they are either expensive or time-consuming. Some recent proposals based on nonlinear processing of activity registries have deserved special attention. Since they rely on actigraphy measurements, they are both inexpensive and non-invasive. Among these methods, those shown to have higher reliability are based on single-channel complexity assessment of the activity patterns. This way, potentially useful information related to the interaction between the different channels is discarded. In this paper we propose a new methodology for ADHD diagnosis based on joint complexity assessment of multichannel activity registries. Results on real data show that the proposed method constitute a useful diagnostic aid tool reaching 87:10% sensitivity and 84.38% specificity. The combination of ADHD indicators extracted with the proposed method with single-channel complexity-based indices previously proposed lead to sensitivity and specifity values above 90%.
Spatial and spectral anisotropy in HARP images: An automated approach

Strain and related tensors play a major role in cardiac function characterization, so correct estimation of the local phase in tagged images is crucial for quantitative myocardial motion studies. We propose an Harmonic Phase related procedure that is adaptive in the spatial and the spectral domains: as for the former, we use an angled-steered analysis window prior to the Fourier Transform; as for the latter, the bandpass filter is also angle-adaptive. Both of them are narrow in the modulation direction and wide in the orthogonal direction. Moreover, no parameters are manually set since their values are partially based on the information available at the DICOM headers and additional information is estimated from data. The procedure is tested in terms of accuracy (on synthetic data) and reproducibility (on real data) of the deformation gradient tensor, measured by means of the distribution of the Frobenius norm differences between two tensor datasets.
Matrix Modeling of Hierarchical Fuzzy Systems

A matrix procedure to obtain a hierarchical decomposition of a multiple-input-single-output (MISO) fuzzy system is proposed. It is based on the fast inference using transition matrices (FITM) framework recently described. The resulting hierarchical system is totally equivalent to the original one in terms of input-output relation. A study of the rule-reduction capability of this system is carried out. Additionally, this paper describes a rule-base definition procedure of each of the systems resulting within the hierarchy. This procedure is based on closed-form analytical expressions and it does not need any manual interaction.
Weaning from mechanical ventilation: a retrospective analysis leading to a multimodal perspective

Practitioners' decision for mechanical aid discontinuation is a challenging task that involves a complete knowledge of a great number of clinical parameters, as well as its evolution in time. Recently, an increasing interest on respiratory pattern variability as an extubation readiness indicator has appeared. Reliable assessment of this variability involves a set of signal processing and pattern recognition techniques. This paper presents a suitability analysis of different methods used for breathing pattern complexity assessment. The contribution of this analysis is threefold: 1) to serve as a review of the state of the art on the so-called weaning problem from a signal processing point of view; 2) to provide insight into the applied processing techniques and how they fit into the problem; 3) to propose additional methods and further processing in order to improve breathing pattern regularity assessment and weaning readiness decision. Results on experimental data show that sample entropy outperforms other complexity assessment methods and that multidimensional classification does improve weaning prediction. However, the obtained performance may be objectionable for real clinical practice, a fact that paves the way for a multimodal signal processing framework, including additional high-quality signals and more reliable statistical methods.
Maximum likelihood contour estimation using beta-statistics in ultrasound images

In this paper, we address the problem of detecting the contour of objects in speckled ultrasound images. A prior hypothesis is made on the first order statistics of the image intensity that is related to the shape of the objects: the mean and variance of the images can he non-stationary, but they should be invariant to the normalized distance from a particular point in the image to the contour center. Furthermore, the first order statistics are modeled with a beta distribution. The choice for that distribution is also validated with some experiments and seems to be more realistic than the log-compressed Rayleigh distribution reported in the literature for ultrasound images. These hypotheses can be validated for particular images by means of the hypotheses tests that we also propose in the paper. The algorithm is initially tuned to a particular image class by estimating the model parameters from a prototypical image of that class. Once our model is set, the contour is estimated in test images using the maximum likelihood (ML) criterion. The ML estimator is adjusted so as to give some means of contour regularization.
Tissue Identification in Ultrasound Images using Rayleigh Local Parameter Estimation

A method to identify different tissues in ultrasound images is presented. Assuming a Rayleigh model for speckle, the different tissues present in the image will be related to the Rayleigh sigma parameter, with different values for each different tissue. The parameter is locally estimated using well known estimation methods for the Rayleigh distribution, such as the maximum likelihood estimator. A significant increase in the separability of the different tissues present in the image is then achieved. A subsequent clustering process based on the position of the maxima of the estimator distribution allows the classification of each pixel in a tissue class.
Multi-Stencil Streamline Fast Marching: A General 3-D Framework to Determine Myocardial Thickness and Transmurality in Late Enhancement Images

We propose a fully 3-D methodology for the computation of myocardial nonviable tissue transmurality in contrast enhanced magnetic resonance images. The outcome is a continuous map defined within the myocardium where not only current state-of-the-art measures of transmurality can be calculated, but also information on the location of nonviable tissue is preserved. The computation is done by means of a partial differential equation framework we have called multi-stencil streamline fast marching. Using it, the myocardial and scarred tissue thickness is simultaneously computed. Experimental results show that the proposed 3-D method allows for the computation of transmurality in myocardial regions where current 2-D methods are not able to as conceived, and it also provides more robust and accurate results in situations where the assumptions on which current 2-D methods are based-i.e., there is a visible endocardial contour and its corresponding epicardial points lie on the same slice-, are not met.
P6D-4 Analysis of Ultrasound Images Based on Local Statistics. Application to the Diagnosis of Developmental Dysplasia of the Hip

This paper presents an analysis method for ultrasound images based on modeling the speckle noise with a Rayleigh distribution. This distribution is parametrized by the parameter sigma, related to the characteristics of the biological tissue. Since some parameters of the Rayleigh distribution are proportional to the value of sigma, the local estimation of such statistics allows the identification of regions corresponding to different tissue types. This property can be employed to improve the automatic analysis of ultrasound images. We have considered the segmentation of the hip joint from ultrasound images for the automatic diagnosis of Developmental Dysplasia of the Hip, a difficult problem that requires an accurate segmentation of different anatomical structures from the ultrasound image.