nils-herrmann/clean_scirepeval huggingface dataset:
Similarity-weighted linear reconstruction of anterior chamber angles for glaucoma classification

We present a reconstruction-based method, called Similarity-Weighted Linear Reconstruction (SWLR), for glaucoma classification from OCT images containing the anterior chamber angle (ACA). SWLR identifies the glaucoma type via linear reconstruction of the ACA region from similar reference images, a classification approach that has recently been shown in certain computer vision applications to yield higher accuracy than classifiers, as it does not rely on feature set quality and it makes specific use of examples that have a similar appearance. The performance of a reconstruction-based approach, however, is greatly affected by how accurately the test image aligns with the references. To address this problem, we present a low-rank decomposition scheme for orientation correction that exploits the symmetry of anterior chamber cross-sections. Together with other techniques for translational alignment, this orientation correction leads to improved reconstruction-based glaucoma classification. Tests on a large-scale clinical dataset show the proposed SWLR classification algorithm to outperform the state-of-the-art methods.
Intelligent fusion of cup-to-disc ratio determination methods for glaucoma detection in ARGALI

Glaucoma is a leading cause of permanent blindness. ARGALI, an automated system for glaucoma detection, employs several methods for segmenting the optic cup and disc from retinal images, combined using a fusion network, to determine the cup to disc ratio (CDR), an important clinical indicator of glaucoma. This paper discusses the use of SVM as an alternative fusion strategy in ARGALI, and evaluates its performance against the component methods and neural network (NN) fusion in the CDR calculation. The results show SVM and NN provide similar improvements over the component methods, but with SVM having a greater consistency over the NN, suggesting potential for SVM as a viable option in ARGALI.
Structure-preserving multi-scale decompositions for skin and retinal OCT image

Optical coherence tomography (OCT) is an emerging technique for imaging skin and retina. However, severe noise and incompatible dynamic range between OCT image and display devices make OCT image analysis a challenging task. To address these problems, we present a structure-preserving multi-scale decomposition approach. Instead of outputting single image, it represents an OCT image by a set of images in multiple scales while preserved structures. Experimental results demonstrate that skin and retina structures can be well separated from noises and illuminations based on such decompositions.
AQUIR - a system to generate quantitative and customized vision measurement template

This paper presents a visual acuity measurement system, AQUIR, which provides the functionality to generate customized visual acuity charts quantitatively. Visual chart is the basic measure to evaluate the visual acuity. With low variability, the traditional visual chart is inflexible and the same chart is used repeatedly to conduct vision assessment. AQUIR system implements a real-time solution and it can customize the visual acuity test book with more flexibility. The process customizes the background, races and reading habits. The adjustability of font sizes and word sparsity subdivides the diagnostic result levels. Besides traditional printed text version, the codebook can be displayed on back-illuminated electronic devices. It widens the application areas for the AQUIR system and transforms the traditional style of the visual acuity measurement system. The dynamic visual acuity codebook will facilitate the operating procedures of visual acuity measurement and improve the accuracy of diagnosis. By breaking through traditional technology, the flexibility and accuracy of visual acuity measurement is improved and reproduction of the technology on multimedia can be performed.
Automatic glaucoma screening hybrid cloud system with pattern classification algorithms

Glaucoma, which is known as the "Sneak thief of sight", is chronic and gradually weakens vision in the unknowing patient. There are no obvious symptoms during early stage of glaucoma, at which time it can only be diagnosed by regular eye diagnosis. Early detection of glaucoma associated with timely treatment has been shown to prevent blindness and visual loss. Quite recently, considerable attention has been paid to the combination between automatic glaucoma screening and hybrid cloud technology. This paper presents a platform which provides automatic glaucoma screening service based on hybrid cloud framework. A novel pattern classification technique was proposed for cup to disc ratio (CDR) assessment using 2-D retinal fundus images and it uses the reconstruction coefficients from SDC to compute the CDR. The proposed technique is used in the glaucoma screening diagnosis module of the remote hybrid cloud system. The scalability and flexibility of hybrid cloud framework enable the platform to work as moving connectivity between patients and ophthalmologists. The system enables the patients to get remote diagnostics from distance with low-cost and blindness and visual loss can be prevented through early detection and timely management.
Using relative spatial relationships to improve individual region recognition

As well as words in text processing, image regions are polysemic and need some disambiguation. If the set of representations of two different objects are close or intersecting, a region that is in the intersection will be recognized as being possibly both objects. We propose here a way to disambiguate regions using some knowledge on relative spatial positions between these regions. Given a segmented image with a list of possible objects for each region, the objective is to find the best set of objects that fits the knowledge. A consistency function is constructed that attributes a score to a spatial arrangement of objects in the image. The proposed algorithm is demonstrated on an example where we try to recognize backgrounds (sky, water, snow, trees, grass, sand, ground, buildings) in images. An evaluation over a database of 10000 images shows that we can reduce the number of false positive while keeping almost the same recognition rate.
White blood cell segmentation using morphological operators and scale-space analysis

Cell segmentation is a challenging problem due to both the complex nature of the cells and the uncertainty present in video microscopy. Manual methods for this purpose are onerous, imprecise and highly subjective, thus requiring automated methods that perform this task in an objective and efficient way. In this paper, we propose a novel method to segment nucleus and cytoplasm of white blood cells (WBC). WBC composition of the blood provides important information to doctors and plays an important role in the diagnosis of different diseases. We use simple morphological operators and explore the scale-space properties of a toggle operator to improve the segmentation accuracy. The proposed scheme has been successfully applied to a large number of images, showing promising results for varying cell appearance and image quality, encouraging future works.
Joined segmentation of cortical surface and brain volume in MRI using a homotopic deformable cellular model

The paper presents a novel method to segment both the brain volume and the cortical surface from a MR image of the head using a single model. This method is based on an original deformable model which can handle jointly volumes and surfaces using a cellular complex based representation. The model is initialized on the outer brain surface and then deformed inside the cortex folds according to both volumic and surfacic constraints, while preserving topology.
Estimation of crop extent using multi-temporal PALSAR data

The aim of the approach proposed in this paper is to determine a potential crop extent prior to the crop season, by determining regions that might change in time vs. those that surely do not change. We use multi-annual PALSAR-1 data since in dry conditions, L-band HH/HV data have a potential of distinguishing between bare soil and other classes. In addition, a more accurate map can be reached with multi-temporal data than using a single date. We work on HH and HV data sets separately and analyze the two outputs using ground-truth information. In a final phase, we combine these two outputs and compare the result with the ground-truth too, to test the usefulness of fusing the HH/HV information. This approach is the first step in our three-step procedure for estimation of cultivated area in small plot agriculture in Malawi. Validation results show that the proposed approach is promising.
Photoreceptor detection in in-vivo Adaptive Optics images of the retina: Towards a simple interactive tool for the physicians

This article presents a novel photoreceptor detection algorithm applied to in-vivo Adaptive Optics (AO) images of the retina obtained from an advanced ophthalmic diagnosis device. Our algorithm is based on a recursive construction of thresholded connected components when the seeds of the recursions are the regional maxima of the image. This algorithm results in a labeling of the AO image which is then used to segment the image with a marker-controlled watershed algorithm. This method has been implemented in a software currently used by medical experts, and preliminary results are very encouraging.
Regularized reconstruction of 3D high-resolution magnetic resonance images from acquisitions of anisotropically degraded resolutions

We present an original method to reconstruct 3D magnetic resonance images of high resolution in the 3 directions of space from two anisotropic volumes. The resolution of each volume is degraded in a different direction. The reconstruction method is based on an optimization technique, the constraints being fidelity to the acquired data on the one hand, smoothness and edge preservation on the other hand. The interest of such a method is to significantly decrease the acquisition time of MR images, without degrading the spatial resolution.
Optical music recognition based on a fuzzy modeling of symbol classes and music writing rules

We propose an OMR method based on fuzzy modeling of the information extracted from the scanned score and of musical rules. The aim is to disambiguate the recognition hypotheses output by the individual symbol analysis process. Fuzzy modeling allows to account for imprecision in symbol detection, for typewriting variations, and for flexibility of rules. Tests conducted on a hundred of music sheets result in a global recognition rate of 98.55%, and show good performances compared to SmartScore.
Cellular complexes: a tool for 3d homotopic segmentation in brain images

Cortex surface modeling is important in several domains including for solving the inverse problem in EEG and MEG. Whereas MRI can be used to build such a model, problems arise due to the partial volume effect that makes the cortex surface disappear in the foldings. To solve this problem the authors use a model, based on cellular complexes, that has good topological properties. This model allows to represent any object which is the combination of volumes, surfaces and curves in a discrete space. The authors define homotopic deformations on such a model by adapting the notion of simple points to their model. They use this homotopic deformable model to segment the cortex surface and to preserve the spherical topology of the surface during the deformation process. The model is initialized from the external brain surface and is then deformed towards the minimum value of an energy function that pushes the surface inside the foldings.
GRAFIP: A FRAMEWORK FOR THE REPRESENTATION OF HEALTHY AND PATHOLOGICAL CEREBRAL INFORMATION

This paper presents a contribution to the large problematic of integrating medical image-based information into a structured framework (such as electronic patient records or anatomo-functional databases). In neuroscience, the complexity of the cerebral anatomy, the wealth of information embedded in imaging data, as well as the difficulty of their interpretation, can benefit from the use of a structural brain model representing prior generic knowledge, which includes information on anatomical structures and their spatial relations. In this paper we describe a novel generic brain model, based on graph representations, and an instantiation procedure for individual patients, based on image segmentation. A complete patient-specific modeling framework is proposed that can be integrated into powerful computational tools to assist image data reviewing, diagnosis and therapeutic patient follow up
Spatial reasoning with incomplete information on relative positioning

This paper describes a probabilistic method of inferring the position of a point with respect to a reference point knowing their relative spatial position to a third point. We address this problem in the case of incomplete information where only the angular spatial relationships are known. The use of probabilistic representations allows us to model prior knowledge. We derive exact formulae expressing the conditional probability of the position given the two known angles, in typical cases: uniform or Gaussian random prior distributions within rectangular or circular regions. This result is illustrated with respect to two different simulations: the first is devoted to the localization of a mobile phone using only angular relationships, the second, to geopositioning within a city. This last example uses angular relationships and some additional knowledge about the position.
Segmentation of fetal 3D ultrasound based on statistical prior and deformable model

A statistical variational framework is proposed for the fetus and uterus segmentation in ultrasound images. The Rayleigh and exponential distributions are used to model the pixel intensity. An energy is derived to perform an optimal partition of the 3D data into two classes corresponding to these two distributions, in a Bayesian MAP framework. Some numerical difficulties are raised by the combination of heterogeneous distributions in a variational level-set formulation, as discussed in the paper. Results on simulated and real data are presented and show that assuming different distributions provides better results than with the sole Rayleigh distribution.
Detection of aligned objects for high resolution image understanding

In this article we present a method for extracting groups of aligned objects from a labeled image. Our method is based on fuzzy measures of relative direction between the objects, leading to a fuzzy approach for defining alignment as a spatial relation. The method is able to capture the ambiguities presented when defining alignment between objects of different sizes. Two definitions of alignment are presented; local and global. The local alignments are first extracted and are used as candidates for the global alignments. Applications of the alignment relation on real images illustrates its interest for high level image interpretation.
Continuous label Bayesian segmentation, applications to medical brain images

Continuous label segmentation approaches have recently attracted much interest as they provide a formalism for handling image artifacts due to the partial volume effect which is common in for instance medical images. Here, the authors propose a new approach to this type of segmentation. Their work represents an extension of the now classic Markovian Bayesian discrete label segmentation approaches and provides good results on synthetic images simulating the presence of partial volumes as well as on real patient MR images.
Segmentation of fetal envelope from 3D ultrasound images based on pixel intensity statistical distribution and shape priors

This paper presents a novel shape-guided variational segmentation method for extracting the fetus envelope on 3D obstetric ultrasound images. Indeed, due to the inherent low quality of these images, classical segmentation methods tend to fail at segmenting these data. To compensate for the lack of contrast and of explicit boundaries, we introduce a segmentation framework that combines three different types of information: pixel intensity distribution, shape prior on the fetal envelope and a back model varying with fetus age. The intensity distributions, different for each tissue, and the shape prior, encoded with Legendre moments, are added as energy terms in the functional to be optimized. The back model is used in a post-processing step. Results on 3D ultrasound data are presented and compared to a set of manual segmentations. Both visual and quantitative comparisons show the satisfactory results obtained by this method on the tested data.
A new definition for fuzzy attributed graph homomorphism with application to structural shape recognition in brain imaging

We propose in this paper an original definition for fuzzy attributed graph homomorphism that deals with both the structural aspect and the maximization of similarity between attributes (on nodes and on arcs). A graph fuzzy homomorphism theory is developed to gather different pieces of information extracted from images, and to take full advantage of the whole graph structure, with several attached attributes. The homomorphism properties allow relevant and accurate graph mapping by evaluating graph structure deformation with fuzzy sets. Anticipated application is atlas-based labeling of brain MRI using graph homomorphism for spatial and temporal analysis of brain tumors and surrounding structures. The definitions and properties proposed in this paper are a first step towards this goal.
A contrario False Alarms Removal for Improving Blotch Detection in Digitized Films Restoration

This paper introduces a novel method for improving blotch detection in digital film archive restoration, by using a high-level interpretation of false alarms, and an adaptive decision criterion, based on the a contrario decision theory. Blotch detection is performed using a bidirectional motion compensated SROD detector, and false alarms interpretation is achieved in two steps, using the a contrario criterion. In the first step, meaningful spatio-temporal alignments of detected pixels arc found, while in the second step, corresponding meaningful regions in the current detection map are selected, and removed from the detection map. The introduction of the a contrario model allows adaptive and perceptually meaningful false alarms removal, improving the restoration workflow. Performance evaluation using real blotches ground truth of defects shows a decrease in the number of false alarms for sequences with fast or with complex motions.
Automatic Segmentation of Antenatal 3-D Ultrasound Images

The development of 3-D ultrasonic probes and 3-D ultrasound (3DUS) imaging offers new functionalities that call for specific image processing developments. In this paper, we propose an original method for the segmentation of the utero-fetal unit (UFU) from 3DUS volumes, acquired during the first trimester of gestation. UFU segmentation is required for a number of tasks, such as precise organ delineation, 3-D modeling, quantitative measurements, and evaluation of the clinical impact of 3-D imaging. The segmentation problem is formulated as the optimization of a partition of the image into two classes of tissues: the amniotic fluid and the fetal tissues. A Bayesian formulation of the partition problem integrates statistical models of the intensity distributions in each tissue class and regularity constraints on the contours. An energy functional is minimized using a level set implementation of a deformable model to identify the optimal partition. We propose to combine Rayleigh, Normal, Exponential, and Gamma distribution models to compute the region homogeneity constraints. We tested the segmentation method on a database of 19 antenatal 3DUS images. Promising results were obtained, showing the flexibility of the level set formulation and the interest of learning the most appropriate statistical models according to the idiosyncrasies of the data and the tissues. The segmentation method was shown to be robust to different types of initialization and to provide accurate results, with an average overlap measure of 0.89 when comparing with manual segmentations.
Automatic segmentation of head structures on fetal MRI

Recent improvements of fetal MRI acquisitions now allow three-dimensional segmentation of fetal structures, to extract biometrical measures for pregnancy follow-up. Automation of the segmentation process remains a difficult challenge, given the complexity of the fetal organs and their spatial organization. As a starting point, we propose in this paper a fully automated segmentation method to localize the eyes and segment the skull bone content (SBC). Priors, embedding contrast, morphological and biometrical information, are used to assist the segmentation process. A validation of the proposed segmentation method, on 24 MRI volumes of fetuses between 30 and 35 gestational weeks, demonstrated a high accuracy for eyes and SBC extraction.
Inexact graph matching using stochastic optimization techniques for facial feature recognition

We propose a formalization of model-based facial feature recognition as an inexact graph matching problem, one graph representing a model of a face and the other an image where recognition has to be performed. The graphs are built from regions and relationships between regions. Both nodes and edges are attributed. A global dissimilarity function is defined based on comparison of attributes of the two graphs, and accounting for the fact that several image regions can correspond to the same model region. This junction is then minimized using several stochastic algorithms.
Possibilistic multi-sensor fusion for humanitarian demining

We propose a method for combining humanitarian mine detection sensors based on possibility theory. Firstly, different features are extracted from the sensor data. Possibility distributions are then derived from the features based on prior information. After that, the combination of possibility degrees is performed in two steps, on separate sensor level and between the sensors. Combination operators are chosen to account for the different characteristics of the sensors. The final decision is obtained by thresholding the fusion result. Promising results have been obtained on a set of real mines and non-dangerous objects. In particular a 100% mine recognition rate was achieved, with a limited number of false alarms.
Fast detection of convergence areas in Digital Breast Tomosynthesis

In this paper we propose a fast method to detect spiculated lesions and architectural distortions in Digital Breast Tomosynthesis datasets. This approach relies on an a contrario modeling of the problem. First, an indicator corresponding to the convergence of structures is defined, then the a contrario framework is used to set a threshold on it in order to detect zones where its value is unlikely. We propose, as a main contribution of this paper, a fast algorithm to implement this method, which significantly reduces its computational cost. The method was evaluated on 38 breasts (10 containing a lesion), and a sensitivity of 0.8 at 1.68 false positive per breast was obtained.