nils-herrmann/clean_scirepeval huggingface dataset:
Classification of the dermal-epidermal junction using in-vivo confocal microscopy

Reflectance confocal microscopy (RCM) is a powerful tool to visualize the skin layers at cellular resolution. The dermal-epidermal junction (DEJ) is a thin complex 3D structure. It appears as a low-contrasted structure in confocal en-face sections, which is difficult to recognize visually, leading to uncertainty in the classification. In this article, we propose an automated method for segmenting the DEJ with reduced uncertainty. The proposed approach relies on a 3D Conditional Random Field to model the skin biological properties and impose regularization constraints. We improve the restitution of the epidermal and dermal labels while reducing the thickness of the uncertainty area in a coherent biological way from 16.9 µm (ground-truth) to 10.3 µm.
Shot noise adaptive bilateral filter

The bilateral filter (BF) is an important image denoising local filter. It reduces noise in images while preserving edges by means of nonlinear combination of local pixel values. Its formulation and implementation are both simple. However, the BF is not parameter-free. The set of the bilateral filter parameters has an important influence on its behavior and performance. They have to be chosen considering the end application. In the case of noise removal, the parameters have to be adapted to the noise level, while the bilateral filter adapts itself to the image details content. In this paper we propose a method to estimate the best bilateral filter intensity parameter set in the case of shot noise removal, the dominant form of natural noise in digital imaging.
Topology-Preserving Rigid Transformation of 2D Digital Images

We provide conditions under which 2D digital images preserve their topological properties under rigid transformations. We consider the two most common digital topology models, namely dual adjacency and well-composedness. This paper leads to the proposal of optimal preprocessing strategies that ensure the topological invariance of images under arbitrary rigid transformations. These results and methods are proved to be valid for various kinds of images (binary, gray-level, label), thus providing generic and efficient tools, which can be used in particular in the context of image registration and warping.
Towards automated lymphoma prognosis based on PET images

This paper proposes a simple method to identify candidate tumors in a set of Positron Emission Tomography (PET) images obtained from patients suffering from lymphoma, and then extract statistics from the image most active tumor. These statistics are used as input for three machine learning algorithms, which generate models for overall survival and event-free survival. The results obtained by these methods are better than the ones obtained by visual analysis, and competitive or better than the ones obtained by a quantitative measure of prognosis. Besides, the results indicate that there is a lot of redundant information coming from the images, and only 2 out of 10 attributes might be enough to predict prognosis.
Optimal consensus set for digital plane fitting

This paper presents a method for fitting a digital plane to a given set of points in a 3D image in the presence of outliers. We present a new method that uses a digital plane model rather than the conventional continuous model. We show that such a digital model allows us to efficiently examine all possible consensus sets and to guarantee the solution optimality and exactness. Our algorithm has a time complexity O(N3 logN) together with a space complexity O(N) where N is the number of points.
Curvilinear Structure Analysis by Ranking the Orientation Responses of Path Operators

The analysis of thin curvilinear objects in 3D images is a complex and challenging task. In this article, we introduce a new, non-linear operator, called RORPO (Ranking the Orientation Responses of Path Operators). Inspired by the multidirectional paradigm currently used in linear filtering for thin structure analysis, RORPO is built upon the notion of path operator from mathematical morphology. This operator, unlike most operators commonly used for 3D curvilinear structure analysis, is discrete, non-linear and non-local. From this new operator, two main curvilinear structure characteristics can be estimated: an intensity feature, that can be assimilated to a quantitative measure of curvilinearity; and a directional feature, providing a quantitative measure of the structure's orientation. We provide a full description of the structural and algorithmic details for computing these two features from RORPO, and we discuss computational issues. We experimentally assess RORPO by comparison with three of the most popular curvilinear structure analysis filters, namely Frangi Vesselness, Optimally Oriented Flux, and Hybrid Diffusion with Continuous Switch. In particular, we show that our method provides up to 8 percent more true positive and 50 percent less false positives than the next best method, on synthetic and real 3D images.
Directional mathematical morphology for the detection of the road network in Very High Resolution remote sensing images

This paper presents a new method for extracting roads in Very High Resolution remotely sensed images based on advanced directional morphological operators. The proposed approach introduces the use of Path Openings and Closings in order to extract structural pixel information. These morphological operators remain flexible enough to fit rectilinear and slightly curved structures since they do not depend on the choice of a structural element shape and hence outperform standard approaches using rotating rectangular structuring elements. The method consists in building a granulometry chain using Path Openings and Closing to perform Morphological Profiles. For each pixel, the Morphological Profile constitutes the feature vector on which our road extraction is based.
Automated 3D lymphoma lesion segmentation from PET/CT characteristics

Positron Emission Tomography (PET) using 18F-FDG is recognized as the modality of choice for lymphoma, due to its high sensitivity and specificity. Its wider use for the detection of lesions, quantification of their metabolic activity and evaluation of response to treatment demands the development of accurate and reproducible quantitative image interpretation tools. An accurate tumour delineation remains a challenge in PET, due to the limitations the modality suffers from, despite being essential for quantifying reliable changes in tumour tissues. Due to the spatial and spectral properties of PET images, most methods rely on intensity-based strategies. Recent methods also propose to integrate anatomical priors to improve the segmentation process. However, the current routinely-used approach remains a local relative thresholding and requires important user interaction, leading to a process that is not only user-dependent but very laborious in the case of lymphomas. In this paper, we propose to rely on hierarchical image models embedding multimodality PET/CT descriptors for a fully automated PET lesion detection / segmentation, performed via a machine learning process. More precisely, we propose to perform random forest classification within the mixed spatial-spectral space of component-trees modeling PET/CT mages. This new approach, combining the strengths of machine learning and morphological hierarchy models leads to intelligent thresholding based on high-level PET/CT knowledge. We evaluate our approach on a database of multi-centric PET/CT images of patients treated for lymphoma, delineated by an expert. Our method provides good efficiency, with the detection of 92% of all lesions, and accurate segmentation results with mean sensitivity and specificity of 0.73 and 0.99 respectively, without any user interaction.
Anisotropic diffusion using power watersheds

Many computer vision applications such as image filtering, segmentation and stereo-vision can be formulated as optimization problems. Whereas in previous decades continuous-domain, iterative procedures were common, recently discrete, convex, globally optimal methods have received a lot of attention. However not all problems in computer vision are convex, for instance L0 norm optimization such as seen in compressive sensing. Recently, a novel discrete framework encompassing many known segmentation methods was proposed: power watershed. We are interested to explore the possibilities of this minimizer to solve other problems than segmentation, in particular with respect to unusual norms optimization. In this article we reformulate the problem of anisotropic diffusion as an L0 optimization problem, and we show that power watersheds are able to optimize this energy quickly and effectively. This study paves the way for using the power watershed as a useful general-purpose minimizer in many different computer vision contexts.
Spatially variant PSF modeling in confocal macroscopy

The point spread function (PSF) of imaging systems plays an essential role in image reconstruction. In the context of confocal microscopy, optical performance degrades towards the edge of the field of view. In confocal macroscopy, the related artifacts are even stronger, as the field of view is much larger. Because the related PSFs are strongly spatially variant, it is essential to be able to model them with few parameters. The field aberrations in fluorescence macroscopy were observed to be symmetrical and to increase with the distance from the center of the field of view. In this paper we propose an experiment and an optimization method for assessing the center of the field of view. The obtained results constitute an important step towards reducing the number of parameters in macroscopy PSF models.
Directional Morphological Filtering

We show that a translation invariant implementation of min/max filters along a line segment of slope in the form of an irreducible fraction dy/dx can be achieved at the cost of 2+k min/max comparisons per image pixel, where k=max(|dx|,|dy|). Therefore, for a given slope, the computation time is constant and independent of the length of the line segment. We then present the notion of periodic moving histogram algorithm. This allows for a similar performance to be achieved in the more general case of rank filters and rank-based morphological filters. Applications to the filtering of thin nets and computation of both granulometries and orientation fields are detailed. Finally, two extensions are developed. The first deals with the decomposition of discrete disks and arbitrarily oriented discrete rectangles, while the second concerns min/max filters along gray tone periodic line segments.
Directed Connected Operators: Asymmetric Hierarchies for Image Filtering and Segmentation

Connected operators provide well-established solutions for digital image processing, typically in conjunction with hierarchical schemes. In graph-based frameworks, such operators basically rely on symmetric adjacency relations between pixels. In this article, we introduce a notion of directed connected operators for hierarchical image processing, by also considering non-symmetric adjacency relations. The induced image representation models are no longer partition hierarchies (i.e., trees), but directed acyclic graphs that generalize standard morphological tree structures such as component trees, binary partition trees or hierarchical watersheds. We describe how to efficiently build and handle these richer data structures, and we illustrate the versatility of the proposed framework in image filtering and image segmentation.
Flicker removal and superpixel-based motion tracking for high speed videos

Flicker removal consists of filtering out rapid, artefactual changes of luminosity and colorimetry from image sequences in order to improve colorimetry consistency between video frames. It is a necessary and fundamental task in multiple applications, for instance in archived film sequences, image/video compression and time-lapse videos. In recent years, the wider availability of fast video acquisition technology has renewed the interest in the flicker removal problem, in particular periodic flicker. In this context, rapid, undesirable intensity and chroma variations are due to acquisition hardware performing faster than the frequency of alternating current powering artificial light sources. This paper proposes both theoretical and efficient experimental solutions for flicker removal from image sequences by performing simultaneous motion tracking and color correction using a superpixel image representation.
A variational model for thin structure segmentation based on a directional regularization

Tubular structure segmentation is an important task, with many applications in medical image analysis such as vessel segmentation both in 2D and 3D. However, this task is challenging due to the spatial sparsity of these objects, implying a high sensitivity to noise. An important cue in this context is the local orientation of the tubular structures. Using this information, it is possible to regularize the structures without destroying its integrity. In this article, we take advantage of recent advances in orientation estimation to propose a directional regularization prior for tubular structures, suitable for use in a variational framework. We illustrate on both synthetic and 2D real data.
Automated image splicing detection from noise estimation in raw images

Splicing is a common image manipulation technique in which a region from a first image is pasted onto a second image to alter its content. In this paper, we use the fact that different images have different noise characteristics, according to the camera and lighting conditions during the image acquisition. The proposed method automatically detects image splicing in raw images by highlighting local noise inconsistencies within a quadtree scan of the image. The image noise is modelized by both Gaussian and Poisson noise components. We demonstrate the efficiency and robustness of our method on several images generated with an automated image splicing.
Globally minimal surfaces by continuous maximal flows

In this paper, we address the computation of globally minimal curves and surfaces for image segmentation and stereo reconstruction. We present a solution, simulating a continuous maximal flow by a novel system of partial differential equations. Existing methods are either grid-biased (graph-based methods) or suboptimal (active contours and surfaces). The solution simulates the flow of an ideal fluid with isotropic velocity constraints. Velocity constraints are defined by a metric derived from image data. An auxiliary potential function is introduced to create a system of partial differential equations. It is proven that the algorithm produces a globally maximal continuous flow at convergence, and that the globally minimal surface may be obtained trivially from the auxiliary potential. The bias of minimal surface methods toward small objects is also addressed. An efficient implementation is given for the flow simulation. The globally minimal surface algorithm is applied to segmentation in 2D and 3D as well as to stereo matching. Results in 2D agree with an existing minimal contour algorithm for planar images. Results in 3D segmentation and stereo matching demonstrate that the new algorithm is robust and free from grid bias.
Morphological PDEs on graphs for analyzing unorganized data in 3D and higher

Mathematical morphology operators can be defined in terms of algebraic (discrete) sets or as partial differential equations (PDEs). In our previous works [1, 2], we have proposed a simple method to solve PDEs (Partial Differential Equations) on dataset using the framework PdEs (Partial difference Equations) on graphs. In this paper, we propose to apply morphological-based operators on unorganized dataset.
The phase only transform for unsupervised surface defect detection

We present a simple, fast, and effective method to detect defects on textured surfaces. Our method is unsupervised and contains no learning stage or information on the texture being inspected. The new method is based on the Phase Only Transform (PHOT) which correspond to the Discrete Fourier Transform (DFT), normalized by the magnitude. The PHOT removes any regularities, at arbitrary scales, from the image while preserving only irregular patterns considered to represent defects. The localization is obtained by the inverse transform followed by adaptive thresholding using a simple standard statistical method. The main computational requirement is thus to apply the DFT on the input image. The new method is also easy to implement in a few lines of code. Despite its simplicity, the methods is shown to be effective and generic as tested on various inputs, requiring only one parameter for sensitivity. We provide theoretical justification based on a simple model and show results on various kinds of patterns. We also discuss some limitations.
Efficiently Computing Optimal Consensus of Digital Line Fitting

Given a set of discrete points in a 2D digital image containing noise, we formulate our problem as robust digital line fitting. More precisely, we seek the maximum subset whose points are included in a digital line, called the optimal consensus. The paper presents an efficient method for exactly computing the optimal consensus by using the topological sweep, which provides us with the quadratic time complexity and the linear space complexity with respect to the number of input points.
Dual constrained TV-based regularization

Algorithms based on the minimization of the Total Variation are prevalent in computer vision. They are used in a variety of applications such as image denoising, compressive sensing and inverse problems in general. In this work, we extend the TV dual framework that includes Chambolle's and Gilboa-Osher's projection algorithms for TV minimization in a flexible graph data representation by generalizing the constraint on the projection variable. We show how this new formulation of the TV problem may be solved by means of a fast parallel proximal algorithm, which performs better than the classical TV approach for denoising, and is also applicable to inverse problems such as image deblurring.
Efficient Robust d-Dimensional Path Operators

Path openings and closings are efficient morphological operators that use flexible oriented paths as structuring elements. They are employed in a similar way to operators with rotated line segments as structuring elements, but are more effective at detecting linear structures that are not necessarily locally perfectly straight. While their theory has always allowed paths in arbitrary dimensions, de facto implementations were only proposed in 2D. Recently, a new implementation was proposed enabling the computation of efficient d -dimensional path operators. However this implementation is limited in the sense that it is not robust to noise. Indeed, in practical applications, for path operators to be effective, structuring elements must be sufficiently long so that they correspond to the length of the desired features to be detected. Yet, path operators are increasingly sensitive to noise as their length parameter L increases. To cope with this limitation, we propose an efficient d-dimensional algorithm, the Robust Path Operator, which uses a larger and more flexible family of flexible structuring elements. Given an arbitrary length parameter G, path propagation is allowed if disconnections between two pixels belonging to a path is less or equal to G and so, render it independent of L . This simple assumption leads to constant memory bookkeeping and results in a low complexity.
Power watersheds: A new image segmentation framework extending graph cuts, random walker and optimal spanning forest

In this work, we extend a common framework for seeded image segmentation that includes the graph cuts, random walker, and shortest path optimization algorithms. Viewing an image as a weighted graph, these algorithms can be expressed by means of a common energy function with differing choices of a parameter q acting as an exponent on the differences between neighboring nodes. Introducing a new parameter p that fixes a power for the edge weights allows us to also include the optimal spanning forest algorithm for watersheds in this same framework. We then propose a new family of segmentation algorithms that fixes p to produce an optimal spanning forest but varies the power q beyond the usual watershed algorithm, which we term power watersheds. Placing the watershed algorithm in this energy minimization framework also opens new possibilities for using unary terms in traditional watershed segmentation and using watersheds to optimize more general models of use in application beyond image segmentation.
Color deflickering for high-speed video in the presence of artificial lighting

When acquiring high-speed video (more than 100 frames per second), artificial lighting can cause severe non-uniform luminosity and chroma variation between frames, commonly labeled periodic flicker. Non-uniform periodic flicker is not easy to correct in the presence of general motion, since its estimation requires background and object tracking, and most tracking techniques assume consistent illumination. In this paper, we propose a joint tracking/color correction scheme using a block matching technique paired with color variation estimation. We introduce a robust method for stabilizing brightness variations in image sequences. A post-processing step is also proposed in order to deal with blocking artifacts. We demonstrate the efficacy of our method both on simulated and real data.
Combining interior tomography reconstruction and spatial regularization

Interior tomography, also called local or region-of-interest tomography is a special case of computed tomography, in which the object under study is larger than the detector. In this modality, reconstructing the tomography image is an even more ill-posed problem than standard tomography and characteristic artefacts are typically observed, even when a large number of measurements are taken. In this work we propose a reconstruction algorithm designed for interior tomography. We also investigate the case of under-sampled measurements in the case of gradient-sparse images. Our algorithm optimizes the sum of a spatial regularization terms for the image inside the region of interest, and a sinogram regularization term for the projection of the non-reconstructed part of the sample, using convex optimization techniques. We present results on simulated and real data.
Image restoration and segmentation using the Ambrosio-Tortorelli functional and Discrete Calculus

Essential image processing and analysis tasks, such as image segmentation, simplification and denoising, can be conducted in a unified way by minimizing the Mumford-Shah (MS) functional. Although seductive, this minimization is in practice difficult because it requires to jointly define a sharp set of contours and a smooth version of the initial image. For this reason, various relaxations of the original formulations have been proposed, together with optimisation methods. Among these, the Ambrosio-Tortorelli (AT) parametric functional is of particular interest, because minimizers of AT can be shown to converge to a minimizer of MS. However this convergence is difficult to achieve numerically using standard finite difference schemes. Indeed, with AT, discontinuities need to be represented explicitly rather than implicitly. In this work, we propose to formulate AT using the full framework of Discrete Calculus (DC), which is able to sharply represent discontinuities thanks to a more sophisticated topological framework. We present our proposed formulation, its resolution, and results on synthetic and real images. We show that we are indeed able to represent sharp discontinuities and as a result significantly better stability to noise, compared with finite difference schemes.