nils-herrmann/clean_scirepeval huggingface dataset:
Explicit Incorporation of Prior Anatomical Information Into a Nonrigid Registration of Thoracic and Abdominal CT and 18-FDG Whole-Body Emission PET Images

The aim of this paper is to develop a registration methodology in order to combine anatomical and functional information provided by thoracic/abdominal computed tomography (CT) and whole-body positron emission tomography (PET) images. The proposed procedure is based on the incorporation of prior anatomical information in an intensity-based nonrigid registration algorithm. This incorporation is achieved in an explicit way, initializing the intensity-based registration stage with the solution obtained by a nonrigid registration of corresponding anatomical structures. A segmentation algorithm based on a hierarchically ordered set of anatomy-specific rules is used to obtain anatomical structures in CT and emission PET scans. Nonrigid deformations are modeled in both registration stages by means of free-form deformations, the optimization of the control points being achieved by means of an original vector field-based approach instead of the classical gradient-based techniques, considerably reducing the computational time of the structure registration stage. We have applied the proposed methodology to 38 sets of images (33 provided by standalone machines and five by hybrid systems) and an assessment protocol has been developed to furnish a qualitative evaluation of the algorithm performance
Detection of periodic signals in brain echo-planar functional images

In this paper, we propose to use signal autocorrelation values at specially selected delays in order to construct brain periodic activity images. In comparison to the usual approaches, our method requires very little a priori information about the real signal. We applied this method to a well-known vision paradigm. The images thus obtained revealed that the autocorrelation peak method discriminates better expected activated areas from background than the classical method based on correlation with an artificial waveform which may induce false detection.
Medial-based Bayesian tracking for vascular segmentation: Application to coronary arteries in 3D CT angiography

We propose a new Bayesian, stochastic tracking algorithm for the segmentation of blood vessels from 3D medical image data. Inspired by the recent developments in particle filtering, it relies on a constrained, medial-based geometric model and on an original sampling scheme for the selection of tracking hypotheses. A key property of this new sampling scheme is the ability to take into account a distribution of hypotheses broader than similar methods such as classical particle filters, while remaining computationally efficient. The proposed method was applied to the challenging and medically critical task of coronary artery segmentation from 3D cardiac computed tomography (CT) images. Prior knowledge, injected in the process, was learned from a manually segmented database of 19 cases. Qualitative and quantitative evaluation is presented on clinical data, including pathologies and local anomalies.
Base surface definition for morphotometry of the ear in phantoms of human heads

To assess ear thickness and other features of human heads, we defined a reference surface that interpolates the head shape in the region of interest. Laser-scan acquisitions were obtained from real human heads and processed for the construction of anthropomorphical phantoms, originally used for dosimetry studies of hand-held mobile phones. Raw data was converted to cylindrical range images and several image processing techniques were then applied for 3D-model construction. A triangulated surface was then obtained for rendering, and for CAD and finite-element simulations. The ear region was then isolated and replaced by a Coons patch surface that interpolates the skull, using the range-image information around the ear. A baseline surface was thus defined, useful for measuring features of human heads; in particular, an average thickness of the ear was estimated on 41 individuals. Head models "with" or "without" ears were then built for assessing dosimetry and anthropometry problems.
Fuzzy Relative Position Between Objects in Image Processing: A Morphological Approach

In order to cope with the ambiguity of spatial relative position concepts, we propose a new definition of the relative position between two objects in a fuzzy set framework. This definition is based on a morphological and fuzzy pattern-matching approach, and consists of comparing an object to a fuzzy landscape representing the degree of satisfaction of a directional relationship to a reference object. It has good formal properties, it is flexible, it fits the intuition, and it can be used for structural pattern recognition under imprecision. Moreover, it also applies in 3D and for fuzzy objects issued from images.
An automated evaluation of regional left ventricular function on cine magnetic resonance images

Routine evaluation of the regional ventricular function in cardiac magnetic resonance (CMR) remains mostly based on visual analysis of cine loops and subsequent interobserver variability remains a potential drawback for diagnosis and follow-up of coronary artery disease (CAD). In this paper we propose a method that ensures automated assessment of normalized mean contraction times and radial endocardial velocities from MR cine images, by combining a parametric imaging analysis to an automatic segmentation method of the left ventricle. Short-axis cine loops were obtained in 20 subjects (10 myocardial infarction (MI) and 10 control). Reference segmental wall motion was scored by an expert according to 4 classes (normokinesia, hypokinesia, akinesia and dyskinesia). Results showed increased values for normalized mean contraction times and decreased values for the radial velocity in segments with wall motion abnormalities (WMA) compared to segments with normal function. These functional parameters could help to detect regional WMA.
Morphological filtering of SAR interferometric images

This paper proposes a new morphological filter for SAR interferograms. It is based on a modified version of alternate sequential filters with reconstruction (MASF), in which the structuring elements are adaptively defined according to the fringe directions. This provides a good fidelity to the fringe information while efficiently removing noise. Another feature of the proposed approach is to apply the filter on the original interferogram and on shifted version, to overcome the wrapping of the phase, and to combine the two results. The proposed filtering technique is then tested on both simulated and real data with different levels of noise. It is also compared to previous techniques according to simplicity and noise reduction.
A unified unsupervised clustering algorithm and its first application to landcover classification

The problem of classification is so fundamental that it has been intensively investigated by many researchers from different domains. In this paper, we present a novel unsupervised clustering algorithm derived from the techniques of probabilistic modeling which is implemented by a stochastic gradient algorithm. Then its application to challenging landcover classification based on Daedalus data of the SMART project is explored by combining both spectral feature and spatial contextual information. Our first experiments show its potential usefulness in remote sensing.
Improvement of the SNR-resolution compromise in MRA using spatial segmented k-space acquisitions

A method is proposed to reconstruct Magnetic Resonance Angiography (MRA) volumes from successive acquisitions. A specific set up of the MR system is used to acquire highly anisotropic MRA volumes with reduced k-space coverage. These partial k-spaces are then combined to obtain a 3D k-space fitted to the frequency support of the angiographic image. This allows us to improve the SNR-resolution compromise of MRA by focusing the acquisition on the meaningful k-space regions. The reconstruction is performed in the image domain by minimizing a convex energy function using a gradient like method. Introduction of priors still further improves this compromise. The method is illustrated on a lucite stenosis phantom and a carotid MRA examination.
Classification of MRI data using deep learning and Gaussian process-based model selection

The classification of MRI images according to the anatomical field of view is a necessary task to solve when faced with the increasing quantity of medical images. In parallel, advances in deep learning makes it a suitable tool for computer vision problems. Using a common architecture (such as AlexNet) provides quite good results, but not sufficient for clinical use. Improving the model is not an easy task, due to the large number of hyper-parameters governing both the architecture and the training of the network, and to the limited understanding of their relevance. Since an exhaustive search is not tractable, we propose to optimize the network first by random search, and then by an adaptive search based on Gaussian Processes and Probability of Improvement. Applying this method on a large and varied MRI dataset, we show a substantial improvement between the baseline network and the final one (up to 20% for the most difficult classes).
Segmentation of retinal vessels in adaptive optics images for assessment of vasculitis

In this paper we propose a new method for segmenting retinal vessels in adaptive optics images. This method is particularly dedicated for segmenting vessels with significant morphological alterations due to vasculitis, but it is also accurate for vessels with moderate or without alteration. It relies on a pre-segmentation step which is crucial for the robustness and accuracy of the results. This step is based on a specific morphological processing of isolines of the original image: they constitute of good basis for the segmentation because they are disposed along the wall borders of the vessels. Regularization is then performed using active contour model embedding a parallelism constraint. This novel model allows precise segmenting inner and outer walls of the vessel. In particular it is more accurate in the case of vasculitis than the existing methods. This is the only method that allows quantification. The results and the runtime make it suitable for clinical use.
Attributed Graphs for Tracking Multiple Objects in Structured Sports Videos

In this paper we propose a novel approach for tracking multiple object in structured sports videos using graphs. The objects are tracked by combining particle filter and frame description with Attributed Relational Graphs. We start by learning a probabilistic structural model graph from annotated images and then use it to evaluate and correct the current tracking state. Different from previous studies, our approach is also capable of using the learned model to generate new hypotheses of where the object is likely to be found after situations of occlusion or abrupt motion. We test the proposed method on two datasets: videos of table tennis matches extracted from YouTube and badminton matches from the ACASVA dataset. We show that all the players are successfully tracked even after they occlude each other or when there is a camera cut.
Fuzzy relaxation labeling reconsidered

Relaxation labeling is an iterative scheme for parallel information fusion forcing neighborhood constraint satisfaction. Rosenfeld et al. (1976) discussed various models for relaxation labeling: discrete, probabilistic, nonlinear probabilistic and fuzzy. Since then many new relaxation labeling schemes have been proposed only a few of which are incorporating methodologies from fuzzy set theory. It is shown that this is partly due to the traditional, but questionable classification of relaxation labeling schemes. A new classification is introduced which clarifies the role of fuzzy relaxation labeling. The new classification scheme is complemented by the introduction of a whole range of new fuzzy relaxation operators.
Evaluation of a thoracic elastic registration method using anatomical constraints in oncology

The aim of our work is to improve data analysis in thoracic oncology applications by means of non-rigid registration of CT and PET images. A mutual information-driven transformation using B-spline free-form deformations performs the alignment of the image volumes. Special constraints relying on hierarchically identifying corresponding structures in both modalities have been added to guarantee the convergence towards an optimal registration.
Time-frequency selection in two bipolar channels for improving the classification of motor imagery EEG

Time and frequency information is essential to feature extraction in a motor imagery BCI, in particular for systems based on a few channels. In this paper, we propose a novel time-frequency selection method based on a criterion called Time-frequency Discrimination Factor (TFDF) to extract discriminative event-related desynchronization (ERD) features for BCI data classification. Compared to existing methods, the proposed approach generates better classification performances (mean kappa coefficient= 0.62) on experimental data from the BCI competition IV dataset IIb, with only two bipolar channels.
Accurate segmentation of blood vessels from 3D medical images

The authors' work contributes to the accurate segmentation of blood vessels from 3D medical images. The blood vessel axis and surface are optimized in an alternating way. Starting from an initial blood vessel axis estimate, slices are resampled in the 3D data volume perpendicular to this axis. In these slices, blood vessel contour candidate points are extracted at maximum gradient positions on a star pattern. The selection of a closed contour among these candidates is optimized with respect to a cost function by dynamic programming. The blood vessel axis is re-estimated at the center of the extracted contours and the process is repeated until convergence. Results are shown on both synthetic and real spiral CT angiographic images.
Data fusion in 2D and 3D image processing: an overview

This paper presents an overview of the current state of the art in image fusion, with an emphasis on the emergence of new techniques, often issued from other domains like artificial intelligence and uncertainty modeling. We address the two following points: firstly the aim of data fusion and its specificity when image information has to be combined, with emphasis on the respective roles of numerical and symbolic information, vs. numerical and symbolic types of treatment, secondly the theoretical frameworks for modeling imprecision and uncertainty (probability, fuzzy sets, belief functions). The main steps of image fusion are illustrated in a simple example in 3D medical image fusion.
Estimation of partial volume effect using spatial context. Application to morphometry in cerebral imaging [MRI]

The purpose of the work presented in this article is the estimation of the partial volume effect very often observed in medical image processing. The authors' working hypothesis is that such partial volume effects will occur in tissue interfaces, the proposed method uses a tissue-labeled distance map to detect tissue interfaces as being those pixels closer to two particular tissues than to any others. The tissue labeled distance map is also used to detect pure tissue means as being the gray-level means of pixels far from tissue interfaces. Knowing the positions of the different interfaces as well as the pure tissue means, the authors are able to compute the proportion of the two tissues mixed in each pixel of the interface.
Blotch Detection for Digital Archives Restoration based on the Fusion of Spatial and Temporal Detectors

This paper proposes a method based on the Dempster-Shafer evidence theory for the detection of blotches in digitized archive film sequences. The detection scheme relies on the fusion of two uncorrelated fast, no motion compensated, spatio-temporal blotch detectors. The imprecision and uncertainty of both detectors are modeled using Dempster-Shafer evidence theory, which improves the decision, by taking into account the ignorance and the conflict between detectors. We found that this combination scheme improves the global performance, and compares favorably to the motion compensated, complex and time consuming blotch detection methods, for real archive film sequences
Segmentation of the fetal envelope on ante-natal MRI

Recent improvements in MRI scanners have enabled the acquisition of spatially consistent 3D images of the fetus. While recent works focused on the fetal brain segmentation, important outcome could be obtained from the segmentation of the whole fetal body envelope, such as accurate fetal weight estimation. In this paper, we propose to segment this envelope using a semi-automatic approach. MRI images were acquired using the Steady State Free Precession sequence. Taking advantage of the T2-weighting of this sequence, a set of fetal structures is identified and a simplified model of the fetal skeleton is instantiated in the images. An articulated model of a generic fetus is then registered in the images to initialize the fetal envelope segmentation and optimized using graph-cuts to refine the segmentation. Promising and robust results were obtained on a set of nine volumes.
Particle filtering with fuzzy spatial relations for object tracking

Dynamics modeling is of primal interest to track objects using particle filters. Even the choice of a well fitted noise parameter may lead to unsuccessful tracking when unexpected events arise, such as outliers, occultations, dynamics discontinuites… In this paper, we propose to introduce structural spatial information in particle filters. This information, expressed as spatial relations such as orientation or distance, is modeled in a fuzzy set framework, and is introduced in the dynamics in order to model the potential changes from one instant to the next one. The fuzzy modeling provides flexibility both in the semantics of the relations and in the transitions from one relation to another one. We show in our experiments that this kind of modeling is really adaptive to unexpected changes of dynamics, and outperforms classical filtering techniques while using only a small number of particles.
Segmentation-free and multiscale-free extraction of medial information using Gradient Vector Flow — Application to vascular structures

Gradient Vector Flow has become a popular method to recover medial information in medical imaging, in particular for vessels centerline extraction. This renewed interest has been motivated by its ability to process gray-scale images without prior segmentation. However, another interesting property lies in the diffusion process used to solve the underlying variational problem. We propose a method to recover scale information in the context of vascular structures extraction, relying on analytical properties of the Gradient Vector Flow only, with no multiscale analysis. Through simple one-dimensional considerations, we demonstrate the ability of our approach to estimate the radii of the vessels with an error of 10% only in the presence of noise and less than 3% without noise. Our approach is evaluated on convolved bar-like templates and is illustrated on 2D X-ray angiographic images.
Adaptive optics imaging of retinal microstructures: Image processing for medical applications

Adaptive optics (AO) fundus imaging is an optoelectronic technique allowing an improvement of an order of magnitude of lateral resolution of retinal images. Currently, its main applications in ophthalmology span from photoreceptor to retinal pigment epithelial cells and vessels, each of them being affected by specific diseases. Technological and image processing improvements are expanding the scope of its medical applications. Here we will review some of the current and envisioned applications of AO in clinical practice.
Explanatory Reasoning for Image Understanding Using Formal Concept Analysis and Description Logics

In this paper, we propose an original way of enriching description logics with abduction reasoning services. Under the aegis of set and lattice theories, we put together ingredients from mathematical morphology, description logics, and formal concept analysis. We propose computing the best explanations of an observation through algebraic erosion over the concept lattice of a background theory that is efficiently constructed using tools from formal concept analysis. We show that the defined operators are sound and complete and satisfy important rationality postulates of abductive reasoning. As a typical illustration, we consider a scene understanding problem. In fact, scene understanding can benefit from prior structural knowledge represented as an ontology and the reasoning tools of description logics. We formulate model based scene understanding as an abductive reasoning process. A scene is viewed as an observation and the interpretation is defined as the best explanation, considering the terminological knowledge part of a description logic about the scene context. This explanation is obtained from morphological operators applied on the corresponding concept lattice.
A denoising method forwhole-body low-dose x-ray imageswith adaptable parameter control

A denoising method is proposed for full body X-ray images, acquired under low dose conditions. The suggested algorithm is based on a non local means filter adapted to the statistics of Poisson noise. A new feature of the method is to locally set the filtering parameters in order to denoise while preserving details in low absorption regions. Thus, we propose to adapt the denoising parameters for each pixel by exploiting a global noise level measure and the standard deviation image of the gradient magnitude. Quantitative and visual results on phantom and clinical images show the interest of the method, achieving the objectives.
Semi-automatic teeth segmentation in Cone-Beam Computed Tomography by graph-cut with statistical shape priors

We propose a new semi-automatic framework for tooth segmentation in Cone-Beam Computed Tomography (CBCT) combining shape priors based on a statistical shape model and graph cut optimization. Poor image quality and similarity between tooth and cortical bone intensities are overcome by strong constraints on the shape and on the targeted area. The segmentation quality was assessed on 64 tooth images for which a reference segmentation was available, with an overall Dice coefficient above 0.95 and a global consistency error less than 0.005.
Whole-body pregnant woman modeling by digital geometry processing with detailed uterofetal unit based on medical images

Anatomical models of pregnant women are used in several applications, such as numerical dosimetry, to assess the potential effects of electromagnetic fields on biological tissues, or medical simulation. Recent advances in medical imaging have enabled the generation of realistic and detailed models of human beings. The construction of pregnant woman models remains a complex task, since it is not possible to acquire whole-body images. Only few models have been developed up to now, and they all present some limitations regarding the representation of anatomical variability of the fetus shape and position over the entire gestation. This paper describes a complete methodology that intends to automate each step of the construction of pregnant women models. The proposed approach relies on the segmentation of 3-D ultrasonic and 3-D magnetic resonance imaging (MRI) data, and on dedicated computer graphics tools. The lack of complete anatomical information for the mother in image data is compensated, in an original way, by merging the available information with a synthetic woman model, deformed to match the image-based information. A set of models anatomically validated by clinical experts is presented. They include detailed information on uterofetal units and cover different gestational stages with various fetal positions.