nils-herrmann/clean_scirepeval huggingface dataset:
Temporal correlation compensation (whitening) of the fMRI data using Akaike Information Criterion

This work concerns the noise present in BOLD-fMRI (Blood-Oxygen-Level-Dependent: functional Magnetic Resonance Imaging, a technique that evaluates the levels of oxygen in the blood vessels of the brain. It is known that there is a temporal correlation present in the BOLD-fMRI's noise signal, complicating the estimation of the active regions of the brain due to an external stimulus. Using SPM-GLM methods (Statistical Parametric Mapping — General Linear Methods), denoised signal and response coefficients from all voxels (Volume Element) are estimated. The comparison between both signals gives an approximation of its noise signal. Using Akaike Information Criterion, this technique estimates the best model's order to decorrelate the noise and pre-whiten. Moreover, this algorithm recalculates new unknown parameters until a minimum threshold is achieved. Final results obtained were analyzed and concluded to have less false-positives, allowing a better definition of the real active regions.
Carotid Plaque 3D Compound Imaging and Echo-Morphology Analysis: a Bayesian Approach

This paper describes a method for volume reconstruction of the carotid plaque and presents a novel local characterization of its echo-morphology. The data is composed by a series of nearly parallel ultrasound images (3D Compound Imaging) and the acquisition is performed using traditional noninvasive ultrasound equipment available in most medical facilities, without need of a spatial locator device. The reconstruction algorithm uses the observed pixels inside the plaque, which were obtained in a pre-segmentation stage performed under medical guidance . The paper proposes a Bayesian algorithm which estimates the underlying volume inside the plaque, by filtering and interpolating the data in order to remove speckle noise and fill non- observed regions, respectively. This volume is further used in plaque echo-morphology analysis. The observation model is based on the Rayleigh distribution, commonly used to model speckle noise in ultrasound images. A prior model based on the edge preserving Total Variation Gibbs distribution is also used to fill the gaps on non-evenly spaced observations. An energy function is derived from these models and an iterative algorithm computes its minimizer. The estimated function, defined in a given volume of interest, is used in global and local plaque characterization, namely to estimate its average levels of stenosis, echo-morphology and to identify vulnerable foci inside the plaque. The goal is to make atherosclerosis diagnosis more accurate and complete than using traditional 2D ultrasound analysis.
Atheromatic™: Symptomatic vs. asymptomatic classification of carotid ultrasound plaque using a combination of HOS, DWT & texture

Quantitative characterization of carotid atherosclerosis and classification into either symptomatic or asymptomatic is crucial in terms of diagnosis and treatment planning for a range of cardiovascular diseases. This paper presents a computer-aided diagnosis (CAD) system (Atheromatic™, patented technology from Biomedical Technologies, Inc., CA, USA) which analyzes ultrasound images and classifies them into symptomatic and asymptomatic. The classification result is based on a combination of discrete wavelet transform, higher order spectra and textural features. In this study, we compare support vector machine (SVM) classifiers with different kernels. The classifier with a radial basis function (RBF) kernel achieved an accuracy of 91.7% as well as a sensitivity of 97%, and specificity of 80%. Encouraged by this result, we feel that these features can be used to identify the plaque tissue type. Therefore, we propose an integrated index, a unique number called symptomatic asymptomatic carotid index (SACI) to discriminate symptomatic and asymptomatic carotid ultrasound images. We hope this SACI can be used as an adjunct tool by the vascular surgeons for daily screening.
A 3-D Ultrasound-Based Framework to Characterize the Echo Morphology of Carotid Plaques

Carotid atherosclerosis is the main cause of brain stroke, which is the most common life-threatening neurological disease. Nearly all methods aiming at assessing the risk of plaque rupture are based on its characterization from 2-D ultrasound images, which depends on plaque geometry, degree of stenosis, and echo morphology (intensity and texture). The computation of these indicators is, however, usually affected by inaccuracy and subjectivity associated with data acquisition and operator-dependent image selection. To circumvent these limitations, a novel and simple method based on 3-D freehand ultrasound is proposed that does not require any expensive equipment except the common scanner. This method comprises the 3-D reconstruction of carotids and plaques to provide clinically meaningful parameters not available in 2-D ultrasound imaging, namely diagnostic views not usually accessible via conventional techniques and local 3-D characterization of plaque echo morphology. The labeling procedure, based on graph cuts, allows us to identify, locate, and quantify potentially vulnerable foci within the plaque. Validation of the characterization method was made with synthetic data. Results of plaque characterization with real data are encouraging and consistent with the results from conventional methods and after inspection of surgically removed plaques.
Spatial priors for perfusion and transit time estimation in PASL-MRI

Maps of perfusion and Arterial Transit Time (ATT) can be measured quantitatively using non-invasive Pulsed Arterial Spin Labeling (PASL) techniques. This can be achieved by fitting a kinetic model to magnetization difference data images acquired at multiple inversion times (TI). Here, spatial information is incorporated into a Bayesian estimation method, based on a maximum a posteriori (MAP) criterion, which also incorporates a priori knowledge regarding the model physiological parameters. Two types of spatial priors were tested. Monte Carlo simulations showed reduced parameter estimation errors when including spatial information using a Total Variation prior. Furthermore, the feasibility of the method proposed here was demonstrated through the application to empirical data.
Phase-locking factor in a motor imagery brain-computer interface

A Brain-Computer Interface (BCI) attempts to create a direct channel of communication between the brain and a computer. This is especially important for patients that are “locked in”, as they have limited motor function and thus require an alternative means of communication. In this scope, a BCI can be controlled through the imagination of motor tasks, i.e. Motor Imagery. This thinking of actions produce changes on the ongoing Electroencephalogram (EEG), such as the so called Event-Related Desynchronization (ERD), that can be detected and measured. Traditionally, ERD is measured through the estimation of EEG signal power in specific frequency bands. In this work, a new method based on the phase information from the EEG channels, through the Phase-Locking Factor (PLF), is proposed. Both feature types were tested in real data obtained from 6 voluntary subjects, who performed 7 motor tasks in an EEG session. The features were classified using Support Vector Machine (SVM) classifiers organized in a hierarchical structure. The results show that the PLF features are better, with an average accuracy of ≈ 86%, against an accuracy of ≈ 70% for the band power features. Although more research is still needed, the PLF measure shows promising results for use in a BCI system.
Neural physiological modeling towards a hemodynamic response function for fMRI

The BOLD signal provided by the functional MRI medical modality measures the ratio of oxy- to deoxy- haemoglobin at each location inside the brain. The detection of activated regions upon the application of an external stimulus, e.g., visual or auditive, is based on the comparison of the mentioned ratios of a rest condition (pre-stimulus) and of a stimulated condition (post-stimulus). Therefore, an accurate knowledge of the impulse response of the BOLD signal to neural stimulus in a given region is needed to design robust detectors that discriminate, with a high level of confidence activated from non activated regions. Usually, in the literature, the hemodynamic response has been modeled by known functions, e.g., gamma functions, fitting them, or not, to the experimental data. In this paper we present a different approach based on the physiologic behavior of the vascular and neural tissues. Here, a linear model based on reasonable physiological assumptions about oxygen consumption and vasodilatation processes are used to design a linear model from which a transfer function is derived. The estimation of the model parameters is performed by using the minimum square error (MSE) by forcing the adjustment of the stimulus response to the observations. Experimental results using real data have shown that the proposed model successfully explains the observations allowing to achieve small values for the fitting error.
Ultrasound imaging LV tracking with adaptive window size and automatic hyper-parameter estimation

The segmentation of the heart's left ventricle (LV) chamber in several medical imaging modalities, e.g. Ultrasound (US) said Magnetic Resonance (MRI), is important from a clinical point of view in the diagnosis of certain cardiopathies. Manual segmentation is difficult, not accurate and time consuming. Therefore, automatic segmentation and tracking during cardiac cycles is needed. In this paper an automatic algorithm to segment the LV boundary along a cardiac cycle from ultrasound image sequences is used and a Bayesian despeckling algorithm is proposed. The prior parameter of the Bayesian filter is automatically estimated and an automatic window size selection strategy in used to adapt its dimension to the statistical characteristics of the image in the vicinity of the deformable contour model which segments the LV boundary. Sequences of real ultrasound images are used to illustrate the effectiveness of the approach and a comparison with other state-of-the-art filtering algorithms is provided.
Adaptive colorization-based compression for stereoscopic images

Considerable research efforts have been devoted to stereo image compression. However, most of them focused on the luminance and ignored the chromatic information. Consequently, in this paper we propose a stereo color image coding method based on colorization. The main idea is to compress one view of the stereo pair using a standard coding method, while for the other view, only the luminance component is considered for compression. The chromatic information of this latter view are transmitted to the decoder for a few representative pixels (RPs) only. These RPs are defined using a novel proposed RP extraction method based on skeletonization. At decoder side, color values of all the remaining pixels are reconstructed by colorization methods. Experimental results showed that our coding method can achieve considerable bit-rate saving compared to conventional coding method.
Correction of over-exposure using color channel correlations

We present a new method for correcting over-exposed areas in images. The method takes advantage of the strong correlation between the RGB color channels and relies on the observation that in images the RGB components are often not over-exposed at the same position. Our solution operates on line profiles, making it ideally suited for hardware implementations. In addition to its low computational complexity, our method can accurately recover information in areas where one or two channels are over-exposed, while it reconstructs information in areas that are fully clipped. We show that our method outperforms previous algorithms through quantitative analysis and we demonstrate an important application of this type of solution in the context of high dynamic range image reconstruction.
Stereo image coding based on binocular energy modeling

Stereoscopic imaging technologies are seen as the next generation of visual presentation, improving the quality of experience of the viewer. It uses two different sequences acquired from two regular cameras or from a regular camera with an additional specific depth camera. This means that the size of data is at least doubled. Thus, the coding process becomes very crucial. In this framework, we propose a stereoscopic coder based on visual properties. The matching of two images is computed by a binocular energy model based on the simple and complex cells functions allowing the fusion of both retinal images in the visual cortex. Mathematical functions were used to reproduce the behavior of these cells particularly complex wavelet transform (CWT) and bandelet transform. Our coder output is a disparity map, a residual image and the reference image. The innovative part of this work lies in a matching technique based on the binocular energy. The results are presented in comparative curves with one of the most known coder in literature.
A block level adaptive MV resolution for video coding

The latest video coding standard, HEVC, uses quarter pixel motion vector (MV) resolution for motion compensation. The adaptation of MV resolution supported by PMVR (progressive MV resolution) brings further improvement of the performance, by progressively adjusting the resolution according to the distance between the MV and the MV predictor (MVP). In this work, we propose to improve PMVR by adapting MV resolution at the prediction unit (PU) level relying on its size and its average absolute gradient. We additionally perform a smarter motion estimation around multiple MV predictors to fully take advantage of the proposed scheme. Compared to HEVC reference software (HM-16.6), the proposed method provides 1.2%, 3.2% and 1.2% average BD rate savings respectively for random access (RA), low-delay P (LP) and low-delay (LD) configurations.
Universal blind image quality assessment for stereoscopic images

Quality assessment of stereoscopic 3D images is a challenging field and represents a key factor in the success of 3D multimedia applications. Despite the important research effort in the last few years, there is no commonly accepted metric ensuring a reliable 3D quality evaluation. This statement becomes even worse when it comes to asymmetrically distorted stereoscopic content. In this paper, we propose a universal blind quality assessment metric for stereoscopic images relying on: 1) distortion type detection and 2) asymmetry nature determination. Based of the latter key information, the 3D image quality is appropriately estimated using a binocular combination strategy. Experimental results showed that the proposed metric reached a significant prediction consistency and accuracy when compared with state-of-the-art metrics.
Towards a perceptual quality metric for color stereo images

In this paper, we propose a quality metric for color stereo images. The concept of our metric is inspired by the behavior of simple and complex cells located in the primary visual cortex. These cells are responsible for merging left and right retinal images. To replicate the task performed by these cells, we adopted an approach based on spatial-frequency transform with the processing of selective orientations. From that, a model that calculates the binocular energy contained in the left and right retinal images has been proposed. The amplitude variation of the binocular energy defines the quality criterion of the reconstructed depth within the Human Visual System (HVS). Finally, from the experimental results, the used criterion seems to be correlated to human judgment obtained by psychophysical tests.
Bandelet-based stereo image coding

In this work, a bandelet-based coding scheme for stereo images is presented. The proposed scheme efficiently integrates the coding of the disparity map with the reference image. The disparity map is obtained via disparity estimation in a geometric similarity framework. The scheme first computes the bandelet transform of both left and right images. Consequently, each image is segmented into a quadtree where each dyadic square regroups pixels sharing the same geometric flow direction. Then, the disparity map is obtained by studying the geometric similarities between the dyadic squares of both images quadtrees. This is accomplished by the minimization of a cost measure function that is defined on the geometric properties of the quadtrees. Finally, the bandelet transform coefficients of the reference and residual images with the disparity map are encoded and transmitted in partitions (squares) which leads to lower entropy. The experimental evaluation of the proposed scheme shows beneficial performance over other stereoscopic coders in the literature.
A Novel Approach for Constructing an Achromatic Contrast Sensitivity Function by Matching

Models of the human visual system are particularly interesting to quantify the quality of the display systems and to predict if visual information will be perceptible or not. One of these models is the contrast sensitivity function (CSF) which characterizes the sensitivity of the visual system to the spatial and temporal frequencies. The achromatic CSF can be measured, relatively, by a method of pairing which consists in matching the contrast of a test grid with that of a reference grid. To determine the reproducible grids on a screen, it is practical to use a frequency/observation distance diagram. The tests of this study are carried out under the conditions of medical diagnosis for radiographies with sinusoidal stimuli. The obtained results were approximated by a model in order to facilitate their integration in other models.
Asymmetric coding using Binocular Just Noticeable Difference and depth information for stereoscopic 3D

The problem of determining the best level of asymmetry has been addressed by several recent works with the aim to guarantee an optimal binocular perception while keeping the minimum required information. To do so, subjective experiments have been conducted for the definition of an appropriate threshold. However, such an approach is lacking in terms of generalization because of the content variability. Moreover, using a fixed threshold does not allow an adaptation to the content and to the images' quality. The traditional asymmetric stereoscopic coding methods apply a uniform asymmetry by considering that all regions of an image have the same perceptual relevance which is not in compliance with the characteristics of human visual system (HVS). Consequently, this paper describes a fully automated model that dynamically determines the best bounds of asymmetry for each region of the image. Based on the Binocular Just Noticeable Difference (BJND) and the depth level in the scene, the proposed method achieves non-uniform reduction of spatial resolution of one view of the stereo pair with the aim to reduce bandwidth requirement. Experimental results show that the proposed method results in up to 43% of bitrate saving while outperforming the widely used asymmetric coding approaches in terms of 3D visual quality.
Full-reference stereoscopic image quality assessment accounting for binocular combination and disparity information

One of the most challenging issues in stereoscopic image quality assessment (SIQA) is how to effectively model the binocular behavior of the human visual system (HVS). The latter has a great impact on the perceptual 3D quality. In this paper, we propose a SIQA metric accounting for binocular combination properties and disparity information. Instead of computing the quality of the left and the right views separately, the proposed metric predicts the quality of a cyclopean image so as to have a good consistency with 3D human perception. The cyclopean image is synthesized based on the local entropy and the visual saliency of each view with the aim to simulate the phenomena of binocular fusion/rivalry. A 2D IQA metric is employed to assess the quality of both the cyclopean image and the disparity map. The obtained scores are used to derive the 3D quality score thanks to a pooling stage. Experimental results on three public 3D IQA databases show that the proposed method outperforms many other state-of-the-art SIQA methods, and achieves high prediction accuracy on these databases.
Annoyance Maximization for Digital Cinema Anti-piracy Applications

Data security is a major issue for digital cinema industry. One common way to pirate a movie called "screening" is to use a camcorder of a good quality during the projection, to record the movie and then share it on the Internet. Different strategies have been explored for resolving the problem of securing digital cinema content, some are based on temporal effects, others on colorimetric aspects. . The main idea is to insert a mark on the projected data that is invisible to a "normal" human observer but visible on the recorded data allowing to impair the content. Starting from this concept, it is very important to find a way for maximizing annoyance in order to make the pirate copy useless. This paper deals with a strategy for defining insertion maps that increase the annoyance and determining which type of content (text, graphics) is most suitable. So, we developed a method that takes into account the specificities of images and their content to determine such a map. We then managed series of psychophysical tests with a group of normal observers in order to provide recommendations to anti-piracy technology developers to maximize the annoyance dependently on the content of images.
Image Rendering Based on a Spatial Extension of the CIECAM02

With the multiplicity of imaging devices, the color quality and portability have become a very challenging problem. Moreover, a color is perceived with regards to its environment. So, if this environment changes it implies a change in the perceived color. In order to address this influence, the CIE (Commission Internationale de I'eclairage) has standardized a tool named color appearance model (CIECAM97*, CIECAM02). These models are able to take into account many phenomena related to human vision of color and can predict the color of a stimulus, function of its observations conditions. However, these models do not deal with the influence of spatial frequencies which can have a big impact on our perception. In this paper, we present an extended version of the CIECAM02 that integrates a spatial model correcting the color in relation to its spatial frequency. Moreover, the previous model has been modified to deal with images and not only single stimulus. The main difference with the rendering models (e.g. iCAM) lies in the fact that the proposed model, takes into account the spatial repartition of a pixel in addition to its environment. The obtained results are sound and demonstrate the efficiency of the proposed extension. This has been checked thanks to a psychophysical study where observers were assigned the task of assessing the quality of the improved version in comparison to the original.