nils-herrmann/clean_scirepeval huggingface dataset:
Time-evolution analysis of differential features on 3D surfaces of the heart walls

Differential feature computation such as curvature computation proves itself useful in 3D shape reconstruction, segmentation and is potentially interesting in matching and motion estimation. Several methods have been developed, generally dependent on the surface representation. The authors focus here on discrete voxel-based surface representations and they use the now classical algorithm proposed by Sander and Zucker (1990) for which the authors assess the accuracy. With the aim of analyzing and synthesizing the motion of 3D deformable models, the authors track the evolution of the curvatures through 2 different approaches: one based on global differential features calculation while the other one visualizes the amount of deformation locally. These methods are applied to synthetic data generated from parametric models, and to a sequence of 18 3D X-ray data of the left ventricle of the heart. The simulations try to assess the temporal stability and accuracy of the curvature measures of such deformable surfaces. The experiments on real data of the left ventricular walls exhibit highly deformed regions opposite to quasi-stationary regions that can be used advantageously in 3D motion estimation or in a matching process.<<ETX>>
Regularized autoregressive models for a spectral estimation scheme dedicated to medical ultrasonic radio-frequency images

The local spectral estimation from radio-frequency (RF) signals in medical echographic ultrasound images is not a trivial task due to the noisy nature of the data resulting from a stochastic and nonstationary process, Significant improvements may be obtained by proposing a spatial regularization scheme, smoothing the local spectral estimates while preserving the discontinuities. Based on AR models, the authors propose a 2D regularization scheme in a Bayesian framework. The a-priori knowledge is expressed by means of Markovian Random Fields (MRF) defined on the reflection coefficients. The use of nonquadratic functions allows to preserve discontinuities. First the authors applied their method on simulated data containing spatial discontinuities of spectral characteristics, which showed the efficiency of the regularization technique. Then the technique was used on cardiac RF data. This shows the improvements as well for Integrated Backscatter (IBS) images as for Mean Central Frequency (MCF) Images or whole spectral estimation.
Multi-modality image simulation with the Virtual Imaging Platform: Illustration on cardiac echography and MRI

Medical image simulation is useful for biological modeling, image analysis, and designing new imaging devices but it is not widely available due to the complexity of simulators, the scarcity of object models, and the heaviness of the associated computations. This paper presents the Virtual Imaging Platform, an openly-accessible web platform for multi-modality image simulation. The integration of simulators and models is described and exemplified on simulated cardiac MRIs and ultrasonic images.
Transverse oscillations beamformer design for sector scan using back-propagation

Echocardiographic acquisition based on point spread function (PSF) with transverse oscillations (TO) has been shown to be an efficient image formation technique for improving the accuracy of motion estimation in the transverse direction (direction perpendicular to the propagation axis). To date, beamforming yielding TO has mainly been developed for acquisitions performed in linear geometry. However, one of the most important applications of motion estimation is cardiac imaging, which implies acquisition done in sectorial geometry. In this framework, this paper describes a new beamformer design method as well as the corresponding implementation allowing for transverse oscillations for sector scan. The proposed method uses a back-propagation (BP) approach. The performance of this beamformer is quantitatively evaluated through numerical simulations. In particular, we show that the obtained PSF are closer to the expected ones as compared to PSF obtained using the conventional Fraunhofer approximation and the associated Fourier relation.
Classification of Contrast Ultrasound Images using Autoregressive Model Coupled to Gaussian Mixture Model

Contrast ultrasound images are not clear enough to be directly adopted in the diagnostic. In fact, the ultrasound agents enhance the vascular zones but unfortunately the signals backscattered from agent and tissues are still close. Therefore, it is necessary to implement image-processing techniques to enhance the contrast echo and thus have the capability of classification. In this article, we apply a new approach based on the autoregressive model coupled to the Gaussian mixture model to represent both agent and tissue behaviors. Then, we process the resultant image by a classification method based on a fixed window's size in order to obtain a satisfying differentiation of the ultrasound image into two classes. Finally, we adopt the agent to tissue ratio (ATR) factor and the Fisher criterion to compare the performance of this method with existing techniques as harmonic and B mode.
Computing Histogram of Tensor Images Using Orthogonal Series Density Estimation and Riemannian Metrics

This paper deals with the computation of the histogram of tensor images, that is, images where at each pixel is given a n n × n positive definite symmetric matrix, SPD(n). An approach based on orthogonal series density estimation is introduced, which is particularly useful for the case of measures based on Riemannian metrics. By considering SPD(n) as the space of the covariance matrices of multivariate gaussian distributions, we obtain the corresponding density estimation for the measure of both the Fisher metric and the Wasserstein metric. Experimental results on the application of such histogram estimation to DTI image segmentation, texture segmentation and texture recognition are included.
Filtering, segmentation and region classification by hyperspectral mathematical morphology of DCE-MRI series for angiogenesis imaging

Segmenting dynamic contrast enhanced-MRI series of small animal, which are intrinsically noisy and low contrasted images with low resolution, is the aim of this paper. To do this, a segmentation method taking into account the temporal (spectral) and spatial information is presented on several series. The idea is to start from a temporal classification, and to build a probability density function of contours conditionally to this classification. Then, this function is segmented to find potentially tumorous areas. The method is presented on several series after a range normalization histogram in order to compare the series.
Automatic Detection of Optic Disc Based on PCA and Mathematical Morphology

The algorithm proposed in this paper allows to automatically segment the optic disc from a fundus image. The goal is to facilitate the early detection of certain pathologies and to fully automate the process so as to avoid specialist intervention. The method proposed for the extraction of the optic disc contour is mainly based on mathematical morphology along with principal component analysis (PCA). It makes use of different operations such as generalized distance function (GDF), a variant of the watershed transformation, the stochastic watershed, and geodesic transformations. The input of the segmentation method is obtained through PCA. The purpose of using PCA is to achieve the grey-scale image that better represents the original RGB image. The implemented algorithm has been validated on five public databases obtaining promising results. The average values obtained (a Jaccard's and Dice's coefficients of 0.8200 and 0.8932, respectively, an accuracy of 0.9947, and a true positive and false positive fractions of 0.9275 and 0.0036) demonstrate that this method is a robust tool for the automatic segmentation of the optic disc. Moreover, it is fairly reliable since it works properly on databases with a large degree of variability and improves the results of other state-of-the-art methods.
Spatial structures detection in hyperspectral images using mathematical morphology

The aim of this paper is to apply genuine hyperspectral mathematical morphology to extract spatial structures according to their spectral nature. To achieve this objective, a novel approach for vectorial ordering is introduced in this paper. The proposed ordering is based on a supervised framework which requires a reference spectrum for the image background and, at least, another reference spectrum for the image target. This supervised ordering may then used for the extension of mathematical morphology to vectorial images and in particular, we focus here on the application of morphological processing to hyperspectral images, illustrating the performance with real examples.
Anisotropic Morphological Filters With Spatially-Variant Structuring Elements Based on Image-Dependent Gradient Fields

This paper deals with the theory and applications of spatially-variant discrete mathematical morphology. We review and formalize the definition of spatially variant dilation/erosion and opening/closing for binary and gray-level images using exclusively the structuring function, without resorting to complement. This theoretical framework allows to build morphological operators whose structuring elements can locally adapt their shape and orientation across the dominant direction of the structures in the image. The shape and orientation of the structuring element at each pixel are extracted from the image under study: the orientation is given by means of a diffusion process of the average square gradient field, which regularizes and extends the orientation information from the edges of the objects to the homogeneous areas of the image; and the shape of the orientated structuring elements can be linear or it can be given by the distance to relevant edges of the objects. The proposed filters are used on binary and gray-level images for enhancement of anisotropic features such as coherent, flow-like structures. Results of spatially-variant erosions/dilations and openings/closings-based filters prove the validity of this theoretical sound and novel approach.
Morphological processing of hyperspectral images using kriging-based supervised ordering

A novel approach for vectorial ordering is introduced in this paper. The generic framework is based on a supervised learning formulation which leads to reduced orderings. A training set for the background and another training set for the foreground are needed as well as a supervised method to construct the ordering mapping. In particular, we consider here a kriging-based vectorial ordering. This supervised ordering may then used for the extension of mathematical morphology to vectorial images. Application of morphological processing to hyperspectral image illustrates the performance of proposal operators.
Advanced statistical matrices for texture characterization: Application to DNA chromatin and microtubule network classification

This paper presents significant improvements of Gray Level Size Zone Matrix (GLSZM) which is a bivariate statistical representation of texture, based on the co-occurrences of size/intensity of each flat zone (connected pixels of the same gray level). The first improvement is a multi-scale extension of the matrix which merges various quantizations of gray levels. A second alternative is proposed to take into account radial distribution of zone intensities. The third variant is a generalization of the matrix structure which allows to analyze fibrous textures, by changing the pair intensity/size for the pair length/orientation of each region. The interest of these improved descriptors is illustrated by texture classification problems arising from quantitative cell biology.
Colour normalization of fundus images based on geometric transformations applied to their chromatic histogram

The high variability in fundus image databases is an important limiting drawback for detecting some retinal pathologies automatically. Age, human retinal pigmentation or lighting conditions affects in the colour of the acquired images. In this paper a colour-normalization method is presented as an initial pre-processing step in order to reduce the heterogeneity of retinal databases. The proposed method is based on geometric transformations applied to the chromaticity diagram of a target image taking into account a reference image. With the aim of quantifying the effect of the proposed colour normalization, a bright lesion detection from pathological images is carried out. A home-made system based on texture analysis and Support Vector Machine classification is used for this purpose. An improvement around a three percent in the detection accuracy demonstrates the importance of a retinal image colour pre-processing before any specific analysis.
Morphological-based adaptive segmentation and quantification of cell assays in high content screening

In fluorescence-labelled cell assays for high content screening applications, image processing software is necessary to have automatic algorithms for segmenting the cells individually and for quantifying their intensities, size/shape parameters, etc. Mathematical morphology is a non-linear image processing technique which is proven to be a very powerful tool in biomedical microscopy image analysis. This paper presents a morphological methodology based on connected filters, watershed transformation and granulometries for segmenting cells of different size, contrast, etc. In particular, the performance of the algorithms is illustrated with cell images from a toxicity assay in three-labels (Hoechst, EGFP, Phalloi'din) on nanodrops cell-on-chip format.
Robust RX anomaly detector without covariance matrix estimation

We explore the problem of anomaly detection based on several one-dimensional projections. The main advantage of the proposed approach is that it does not require any covariance matrix estimation, allowing to compute spatial adaptive anomaly detection in small neighborhoods. Although this is contrary to common sense, theoretical results support the consistence of our approach when a large number of univariate random projection is considered. The theoretical convergence to the popular RX anomaly detector is derived.
Hyperspectral imaging as an analytical tool for thin single and multilayer oxides characterization: A laboratory study

Characterization of deposited thin oxide layers using conventional laboratory techniques such as ellipsometry and Fourier transform infrared spectroscopy (FTIR) requires long sampling time. The hyperspectral technology with a high acquisition rate appears to be an interesting solution for high throughput quantification. To check its capability for this purpose, we develop an analysis protocol in the laboratory using this device to measure thin oxide layers. In this paper we study the acquisition setup regarding the incidence angle in reflection mode, the relation of layer thickness and measured signal to enable the quantification and identification of deposited Al2O3 and SiO2 thin films. Analysis results from FTIR, hyperspectral camera and ellipsometry are presented along with calculations based on Fresnel equations. Measured results correspond with the expected theoretical spectra. The study of the detection sensitivity also extends to multilayer oxide. Overall, the capability of the hyperspectral camera to detect nanometric oxide layers is demonstrated. With this conclusion and in combination with its many advantages, the hyperspectral camera could be used for dynamic analysis of oxide layer.
Random Projection Depth for Multivariate Mathematical Morphology

The open problem of the generalization of mathematical morphology to vector images is handled in this paper using the paradigm of depth functions. Statistical depth functions provide from the “deepest” point a “center-outward ordering” of a multidimensional data distribution and they can be therefore used to construct morphological operators. The fundamental assumption of this data-driven approach is the existence of “background/foreground” image representation. Examples in real color and hyperspectral images illustrate the results.
Cyclic Mathematical Morphology in Polar-Logarithmic Representation

We propose in this paper to perform mathematical morphology operators in a geometric transformation of an image. As a result of this procedure, processing images with regular structuring elements in the transformed domain is equivalent to working with deformed structuring elements in the original representation. More specifically, the conversion into polar-logarithmic coordinates provides satisfying results in image analysis applied to round objects, if they are roughly origin-centered. We have illustrated the interest of the derived cyclic morphology with two pattern recognition examples: erythrocyte shape analysis and multiscale description of iris textures.
Color segmentation by ordered mergings

The paper deals with the use of the various color pieces of information for segmenting color images and sequences with mathematical morphology operators. It is divided in four parts. The first one is concerning the choice of the color space suitable for morphological processing. The choice of a connection which induces a specific segmentation is discussed. The authors then present the color segmentation approach which is based on a nonparametric pyramid of watersheds, with a comparative study of different color gradients. Another multiscale color segmentation algorithm is then introduced, relying on the merging of chromatic-achromatic partitions ordered by the saturation component.
Image adapted total ordering for mathematical morphology on multivariate images

In this paper, we discuss the problem of total ordering for mathematical morphology. We restrain the study to color images, but results are valid in any metric space. The discontinuities issue of total orders has already been evoked in mathematical morphology, but remains rarely studied. This phenomenon is highlighted and formalised in this paper. We propose a new approach to avoid discontinuities based on a recursive algorithm. The key point of the proposed method is to adapt the order to the studied image. The proposed framework presents invariance to isometric transformations of the color space. Promising results are presented.
Multiclass ordering for filtering and classification of hyperspectral images

The aim of this paper is to apply genuine hyperspectral mathematical morphology to extract spatial structures according to a set of reference spectra. To achieve this objective, a vector multiclass ordering is introduced in this paper. The proposed ordering is based on a supervised framework which requires a training set. This multiclass supervised ordering may then used for the extension of mathematical morphology to vector images and in particular, we focus here on the application of morphological processing to hyperspectral images, illustrating the performance with real examples. Granulometry for feature description and multi-scale classification are considered as practical application of the proposed approach.
Comparative study on morphological principal component analysis of hyperspectral images

This paper deals with a problem of dimensionality reduction for hyperspectral images using principal component analysis. Hyperspectral image reduction is improved by adding structural/spatial information to the spectral information, by means of mathematical morphology tools. It can be then useful for instance in supervised classification. The key element of the approach is the computation of a covariance matrix which integrates simultaneously both spatial and spectral information.
Morphological coding of color images by vector connected filters

This paper deals with the use of the vector levelings for coding color images. This class of morphological connected filters suppresses details but preserves the contours of the remaining objects. If the color images are filtered by independently leveling each color component, new colors may be introduced. In order to avoid this drawback, a total order must be imposed on the color vectors. A comparative study has been drawn for various lexicographical orders in the RGB and the HLS color systems. These filters can be especially useful as a preprocessing step for improving the compression of color images.
Aorta segmentation using the watershed algorithm for an augmented reality system in laparoscopic surgery

This paper presents an algorithm for a 3D segmentation of the aorta artery in magnetic resonance images (MRI). The purpose is to project the 3D segmented aorta in the patient's abdomen with an augmented reality (AR) system to help the surgeon in laparoscopic interventions. In order to obtain accurate results in the segmentation process a marker-controlled watershed algorithm is used. Since this method requires a robust gradient image and two marker sets, a preprocessing step is carried out in each image. The algorithm is automatic and the results are promising with a Jaccard coefficient (JC) of 0.8107 ± 0.0228.
Comparison of orientated and spatially variant morphological filters vs mean/median filters for adaptive image denoising

This paper shows a comparison of spatially-variant discrete operators for denoising gray-level images. These non-iterative operators use a neighborhood that varies over space, adapting their shape and orientation according to the data of the image under study. The orientation of the neighborhood is computed by means of a diffusion process of the average square gradient field, which regularizes and extends the orientation information from the edges of the objects to the homogeneous areas of the image; and the shape of the orientated neighborhood can be either a linear segment or a rectangle of anisotropy given by the distance to relevant edges of the objects. Results on gray-level images show the ability of spatially-variant morphological operators for adaptively preserving the main structures in the image while reducing the noise.
Advanced Statistical Matrices for Texture Characterization: Application to Cell Classification

This paper presents new structural statistical matrices which are gray level size zone matrix (SZM) texture descriptor variants. The SZM is based on the cooccurrences of size/intensity of each flat zone (connected pixels with the same gray level). The first improvement increases the information processed by merging multiple gray-level quantizations and reduces the required parameter numbers. New improved descriptors were especially designed for supervised cell texture classification. They are illustrated thanks to two different databases built from quantitative cell biology. The second alternative characterizes the DNA organization during the mitosis, according to zone intensities radial distribution. The third variant is a matrix structure generalization for the fibrous texture analysis, by changing the intensity/size pair into the length/orientation pair of each region.
Morphological scale-space for hyperspectral images and dimensionality exploration using tensor modeling

This paper proposes a framework to integrate spatial information into unsupervised feature extraction for hyperspectral images. In this approach a nonlinear scale-space representation using morphological levelings is formulated. In order to apply feature extraction, Tensor Principal Components are computed involving spatial and spectral information. The proposed method has shown significant gain over the conventional schemes used with real hyperspectral images. In addition, the proposed framework opens a wide field for future developments in which spatial information can be easily integrated into the feature extraction stage. Examples using real hyperspectral images with high spatial resolution showed excellent performance even with a low number of training samples.