nils-herrmann/clean_scirepeval huggingface dataset:
Some Extensions of the Converging Squares Algorithm for Image Feature Analysis

In , the converging squares algorithm was introduced as a method designed to effectively and efficiently locate peaks in data of two dimensions or higher. In this correspondence, the performance of the algorithm on a signal in noise is examined, and some extensions of the algorithm-beyond peak-picking-are introduced. The minimum-area enclosing square is one extension, which locates an image region in a uniform background, and finds the smallest square which entirely encloses it. The maximum-difference enclosing square is another extension by which a global feature of the image is found which separates it into a foreground square region and background region, based on the maximum statistical difference between the two. Some applications of these extensions are shown, including object location, tracking of a moving object, and adaptive binarization.
Image and document processing techniques for the RightPages electronic library system

Describes some of the document processing techniques used in the RightPages electronic library system. Since the system deals with scanned images of document pages, these techniques are critical to the use and appearance of the system. The author describes three techniques: (1) for noise reduction from binary document pages to improve page appearance and subsequent optical character recognition and compression; (2) for subsampling the text image to fit on the computer screen white maintaining readability; and (3) a document layout analysis technique to determine text blocks.<<ETX>>
An ASIC architecture for contour line filtering

Proposes an application-specific IC architecture for implementing a contour line filter for a real-time fingerprint recognition system. The architecture demonstrates the fact that a library-based set of data-path modules which are steered by powerful, synchronized controllers can result in real-time performance on a single chip, even for a complex image processing application. Moreover, the external frame memories required are minimized in the design by working in-place and by appropriate scheduling at the cost of little extra hardware and few additional evaluation cycles.<<ETX>>
The RightPages image-based electronic library for alerting and browsing

The RightPages electronic library prototype system, which gives users full online library services, is described. The prototype takes advantage of fast hardware, multimedia workstations, and broadband networks to process scientific and technical journals for users and to offer a service that: alerts them to the arrival of new journal articles matching their interest profiles; lets them immediately examine images of pages in the alerted articles and browse through other articles in the database; and enables them to order paper copies of any articles in the database. The system runs on a local area network that connects one or more scanning stations, a centralized document database server and multiple user stations running X Windows servers. The RightPages interface runs as an X Windows application on Sun workstations or X terminals. The system's image and document processing, including noise reduction, document layout analysis, text processing, and display processing are discussed.<<ETX>>
The Document Spectrum for Page Layout Analysis

Page layout analysis is a document processing technique used to determine the format of a page. This paper describes the document spectrum (or docstrum), which is a method for structural page layout analysis based on bottom-up, nearest-neighbor clustering of page components. The method yields an accurate measure of skew, within-line, and between-line spacings and locates text lines and text blocks. It is advantageous over many other methods in three main ways: independence from skew angle, independence from different text spacings, and the ability to process local regions of different text orientations within the same image. Results of the method shown for several different page formats and for randomly oriented subpages on the same image illustrate the versatility of the method. We also discuss the differences, advantages, and disadvantages of the docstrum with respect to other lay-out methods.
Experimental comparisons of binarization and multi-thresholding methods on document images

Thresholding methods are applied here to document images and their experimental results compared. In one set of tests, different thresholding methods are used to binarize document images, then optical character recognition (OCR) is performed on the resulting text and the recognition results are compared. In the other set of tests, multi-thresholding is performed on document images-to obtain three or more levels for images with more than binary levels-and the results are compared. Four thresholding methods are compared in the experiments: a discriminant analysis method, a maximum entropy method, a moment-preserving method, and a connectivity-preserving method. A method using a minimum-error criterion is also commented upon. The moment-preserving and connectivity-preserving methods are found to yield the best OCR results from the binarized images, and the connectivity-preserving method yields the fewest binarization and multi-thresholding failures.
Multiresolution reconstruction of real-time MRI with motion compensated compressed sensing: Application to 2D free-breathing cardiac MRI

Real-time MRI is a novel noninvasive imaging technique that allows the visualization of physiological processes with both good spatial and temporal resolutions. However, the reconstruction of images from highly undersampled data, needed to perform real-time imaging, remains challenging. Recently, the combination of Compressed Sensing theory with motion compensation techniques has shown to achieve better results than previous methods. In this work we describe a real-time MRI algorithm based on the acquisition of the k-space data following a Golden Radial trajectory, Compressed Sensing reconstruction and a groupwise temporal registration algorithm for the estimation and compensation of the motion in the image, all this embedded within a temporal multiresolution scheme. We have applied the proposed method to the reconstruction of free-breathing acquisition of short axis views of the heart, achieving a temporal resolution of 25ms, corresponding to an acceleration factor of 28 with respect to fully sampled Cartesian acquisitions.
Anisotropic Diffusion Filter With Memory Based on Speckle Statistics for Ultrasound Images

Ultrasound (US) imaging exhibits considerable difficulties for medical visual inspection and for development of automatic analysis methods due to speckle, which negatively affects the perception of tissue boundaries and the performance of automatic segmentation methods. With the aim of alleviating the effect of speckle, many filtering techniques are usually considered as a preprocessing step prior to automatic analysis methods or visual inspection. Most of the state-of-the-art filters try to reduce the speckle effect without considering its relevance for the characterization of tissue nature. However, the speckle phenomenon is the inherent response of echo signals in tissues and can provide important features for clinical purposes. This loss of information is even magnified due to the iterative process of some speckle filters, e.g., diffusion filters, which tend to produce over-filtering because of the progressive loss of relevant information for diagnostic purposes during the diffusion process. In this paper, we propose an anisotropic diffusion filter with a probabilistic-driven memory mechanism to overcome the over-filtering problem by following a tissue selective philosophy. In particular, we formulate the memory mechanism as a delay differential equation for the diffusion tensor whose behavior depends on the statistics of the tissues, by accelerating the diffusion process in meaningless regions and including the memory effect in regions where relevant details should be preserved. Results both in synthetic and real US images support the inclusion of the probabilistic memory mechanism for maintaining clinical relevant structures, which are removed by the state-of-the-art filters.
Fuzzy anisotropic diffusion for speckle filtering

An anisotropic diffusion filter controlled by fuzzy rules is presented. The proposed filter is based in the Perona-Malik technique, using fuzzy reasoning to calculate the diffusion coefficient which controls the whole diffusion. The method has the advantage that it can be used for both smoothing and noise cleaning, as well as edge enhancement. This new approach also allows us to model the diffusion process through a rule base to have a better performance. Some examples are given to illustrate the effectiveness of the proposed technique.
3D-5 Parameter Estimation of the Homodyned K Distribution Based on Signal to Noise Ratio

The Homodyned K (H-K) distribution is very important in Ultrasound (US) imaging because it models the speckle in a general way, even if the number of scatterers per resolution cell is low and if a coherent component exists. Due to the analytical complexity of this distribution, only a few works based on the H-K have been reported and other simpler distributions have been used instead. Here we provide evidence that, once we have the signal to noise ratio (SNR) from a set of parameters of the H-K distribution, we can estimate the parameters of any given sample set in an easy computational way. In addition, we present a validation study for our estimation procedure.
Exact Calculation of Noise Maps and ${g}$ -Factor in GRAPPA Using a ${k}$ -Space Analysis

Characterization of the noise distribution in magnetic resonance images has multiple applications, including quality assurance and protocol optimization. Noise characterization is particularly important in the presence of parallel imaging acceleration with multi-coil acquisitions, where the noise distribution can contain severe spatial heterogeneities. If the parallel imaging reconstruction is a linear process, an accurate noise analysis can be carried out by taking into account the correlations between all the samples involved. However, for  ${k}$ -space-based techniques such as generalized autocalibrating partially parallel acquisition (GRAPPA), the exact analysis has been considered computationally prohibitive due to the very large size of the noise covariance matrices required to characterize the noise propagation from  ${k}$ -space to image space. Previously proposed methods avoid this computational burden by formulating the GRAPPA reconstruction as a pixel-wise linear operation performed in the image space. However, these methods are not exact in the presence of non-uniform sampling of  ${k}$ -space (e.g., containing a calibration region). For this reason, in this paper, we develop an accurate characterization of the noise distribution for self-calibrated parallel imaging in the presence of arbitrary Cartesian sampling patterns. By exploiting the symmetries and separability in the noise propagation process, the proposed method is computationally efficient and does not require large matrices. Under the assumption of a fixed reconstruction kernel, this method provides the precise distribution of the noise variance for each coil’s image. These coil-by-coil noise maps are subsequently combined according to the coil combination approach used in image reconstruction, and therefore can be applied with both complex coil combination and root-sum-of-squares approaches. In this paper, we present the proposed noise characterization method and compare it to previous techniques using Monte Carlo simulations as well as phantom acquisitions.
Fast inference in SAM fuzzy systems using transition matrices

Fast inference using transition matrices (FITM) is a new fast algorithm for performing inferences in fuzzy systems. It is based on the assumption that fuzzy inputs can be expressed as a linear composition of the fuzzy sets used in the rule base. This representation let us interpret a fuzzy set as a vector, so we can just work with the coordinates of it instead of working with the whole set. The inference is made using transition matrices. The key of the method is the fact that a lot of operations can be precomputed offline to obtain the transition matrices, so actual inferences are reduced to a few online matrix additions and multiplications. The algorithm is designed for the standard additive model using the sum-product inference composition.
On the estimation of the coefficient of variation for anisotropic diffusion speckle filtering

In this paper, we focus on the problem of speckle removal by means of anisotropic diffusion and, specifically, on the importance of the correct estimation of the statistics involved. First, we derive an anisotropic diffusion filter that does not depend on a linear approximation of the speckle model assumed, which is the case of a previously reported filter, namely, SRAD. Then, we focus on the problem of estimation of the coefficient of variation of both signal and noise and of noise itself. Our experiments indicate that neighborhoods used for parameter estimation do not need to coincide with those used in the diffusion equations. Then, we show that, as long as the estimates are good enough, the filter proposed here and the SRAD perform fairly closely, a fact that emphasizes the importance of the correct estimation of the coefficients of variation
P2F-1 A Speckle Removal Filter Based on Anisotropic Wiener Filtering and the Rice Distribution

In this paper we will focus on the problem of removing speckle in 3D ultrasound (US) images while maintaining the structure information unaltered. It is intended as a preprocessing step for further image analysis modules. Optimum Wiener filter technique will be used but modified in two senses: first, Rice distribution will be used prior to applying the Wiener filter to correct for the bias which may show up for low SNR regions in the image; secondly, a boundary model will be embedded within the estimation framework in order to maintain the signal structure and to avoid blurring the image. Some experimental results are also presented giving rise to very promising outcomes
A stochastic modelling framework for the reconstruction of cardiovascular signals

This paper presents a common stochastic modelling framework for physiological signals which allows patient simulation following a synthesis-by-analysis approach. Within this framework, we propose a general model-based methodology able to reconstruct missing or artifacted signal intervals in cardiovascular monitoring applications. The proposed model consists of independent stages which provide high flexibility to incorporate signals of different nature in terms of shape, cross-correlation and variability. The reconstruction methodology is based on model sampling and selection based on a wide range of boundary conditions, which include prior information. Results on real data show how the proposed methodology fits the particular approaches presented so far for electrocardiogram (ECG) reconstruction and how a simple extension within the framework can significantly improve their performance.
Improving Harmonic Phase Imaging by the Windowed Fourier Transform

This work proposes a new methodology for the extraction of Harmonic Phase images in cardiac tagged Magnetic Resonance Imaging. The procedure draws upon the use of the Windowed Fourier Transform, which provides a spatially varying representation of the signal spectra. The spectral peaks of the local Fourier domain are then extracted by a conventional Harmonic Phase recovering technique in such a way that the peak position is separately estimated for each spatial location. Finally, the phase of the complex image is reconstructed by inverting the transformation. It turns out that the proposed methodology substantially outperforms the classical Harmonic Phase analysis on a simulated dataset and seems to be more accurate for determining the tag intersections in a real dataset.
Efficient multispectral texture segmentation using multivariate statistics

A complete, low computational cost method is presented for multispectral textured image segmentation. The procedure performs a tesselation of the image into non-overlapped rectangular regions and decides about the homogeneity of each region, using statistical hypothesis testing. Regions labelled as homogeneous are used to estimate the parameters that are necessary to classify the pixels of the heterogeneous regions. The proposed scheme can also be used to estimate the number of different textures in the image. This represents an efficient alternative to other computationally expensive methods, such as those that employ clustering techniques.
Groupwise Elastic Registration by a New Sparsity-Promoting Metric: Application to the Alignment of Cardiac Magnetic Resonance Perfusion Images

This paper proposes a methodology for the joint alignment of a sequence of images based on a groupwise registration procedure by using a new family of metrics that exploit the expected sparseness of the temporal intensity curves corresponding to the aligned points. Therefore, this methodology is able to tackle the alignment of temporal sequences of images in which the represented phenomenon varies in time. Specifically, we have applied it to the correction of motion in contrast-enhanced first-pass perfusion cardiac magnetic resonance images. The time sequence is elastically registered as a whole by using the aforementioned family of multi-image metrics and jointly optimizing the parameters of the transformations involved. The proposed metrics are able to cope with dynamic changes in the intensity content of corresponding points in the sequence guided by the assumption that these changes allow for a sparse representation in a properly selected frame. Results have shown the statistically significant improvement in the performance of the proposed metric with respect to previous groupwise registration metrics for the problem at hand, which is especially relevant to correct for elastic deformations.
Robust estimation of MRI myocardial perfusion parameters

Myocardial perfusion imaging by first-pass contrast-enhanced magnetic resonance allows to asses the viability of a tissue by the study of the contrast agent transit through the cardiac chambers and myocardium. Since visual inspection is difficult and may left aside critical temporal information, the need of automatic quantitative analysis arises. We propose two robust estimators of the parameters that quantify the perfusion according to a prior pharmacokinetic model. The estimators are based on the concentration of the contrast agent inside the tissue and the blood.
A Multichannel Model-Based Methodology for Extubation Readiness Decision of Patients on Weaning Trials

Discontinuation of mechanical ventilation is a challenging task that involves a number of subtle clinical issues. The gradual removal of the respiratory support (referred to as weaning) should be performed as soon as autonomous respiration can be sustained. However, the prediction rate of successful extubation is still below 25% based on previous studies. Construction of an automatic system that provides information on extubation readiness is thus desirable. Recent works have demonstrated that the breathing pattern variability is a useful extubation readiness indicator, with improving performance when multiple respiratory signals are jointly processed. However, the existing methods for predictor extraction present several drawbacks when length-limited time series are to be processed in heterogeneous groups of patients. In this paper, we propose a model-based methodology for automatic readiness prediction. It is intended to deal with multichannel, nonstationary, short records of the breathing pattern. Results on experimental data yield an 87.27% of successful readiness prediction, which is in line with the best figures reported in the literature. A comparative analysis shows that our methodology overcomes the shortcomings of so far proposed methods when applied to length-limited records on heterogeneous groups of patients.
Energy functions for the segmentation of ultrasound volume data using active rays

In this paper we address the problem of ultrasound volume data segmentation by means of active contours. The segmentation is performed on an slice-by-slice basis but exploiting the high correlation that exists across nearby slices. We have used active rays to alleviate the computational load of the optimization process. Classical energy functions have trouble finding the optimal solution in speckle imagery. We therefore propose new energy functions that work well under this type of images. Moreover, we propose both a probabilistic relaxation scheme and a heuristic search of the minimal energy configuration to end up making a hybrid use of both. We compare two rendered surfaces to illustrate the smoothness of our reconstruction.
Parametric 3D Hip Joint Segmentation for the Diagnosis of Developmental Dysplasia

In this paper, we present a segmentation method for the hip joint from 3D ultrasound data. The proposed approach starts from a well-known energy formulation of the segmentation problem, and employs the extended local structure tensor as image feature in order to incorporate gray level and texture information in a common framework. Using the Kullback-Leibler distance as an intrinsic dissimilarity measure, the energy minimization is performed by estimating the optimal parameters of the sphere and paraboloid that best approximate the femoral head and acetabulum, respectively. A 2D level set segmentation of the iliac bone and an easy user interaction step allow for the introduction of the necessary constraints to make the energy minimization feasible. Experimental results over several data volumes show this approach to be capable of successfully approximating the anatomy of the hip joint by simple geometrical surfaces
A fuzzy MHT algorithm applied to text-based information tracking

We carry out a detailed analysis of a fuzzy version of Reid's classical multiple hypothesis tracking (MHT) algorithm. Our fuzzy version is based on well-known fuzzy feedback systems, but the fact that the system we describe is specialized for likelihood discrimination makes this study particularly novel. We discuss several techniques for rule activation. One of them, namely the sum-product, seems particularly useful for likelihood management and its linearity makes it tractable for further analysis. Our analysis is performed in two stages: 1) we demonstrate that, with appropriately chosen rules, our system can discriminate the correct hypothesis; and 2) the steady-state behavior with a constant input is characterized analytically. This enables us to establish the optimality of the sum-product method and it also gives a simple procedure to predict the system's behavior as a function of the rule base. We believe this fact can be used to devise a simple procedure for fine-tuning the rule base according to the system designer's needs. The application driving our fuzzy MHT implementation and analysis is the tracking of natural language text-based messages. This application is used as an example throughout the paper.
Fast inference using transition matrices: an extension to nonlinear operators

Fast inference using transition matrices (FITMs) was recently presented as a new fast algorithm for performing inferences in fuzzy systems. However, this algorithm only applies to the standard additive model (SAM) using the sum-product inference composition. In this paper, we show how this methodology carries over to more general Mamdani fuzzy systems, i.e., those using t-norms and t-conorms that satisfy a requirement of distributivity. In addition, FITM, as originally described, requires inputs to be fuzzy sets. We here extend this methodology to handle crisp inputs as well.