nils-herrmann/clean_scirepeval huggingface dataset:
Biomedical application of target tracking in clutter

The movement of leukocytes (white blood cells) and their interaction with the endothelium (vessel wall) provides valuable information about the mechanism of inflammation and inflammatory disease. In order to investigate leukocyte motion within living animals, advanced automated tracking algorithms are requisite. We introduce military target tracking algorithms for the purpose of tracking cell movement. In 33 experiments, we compare the tracking performance of five trackers. The trackers tested include the centroid tracker, the correlation tracker, an enhanced centroid tracker, an enhanced correlation tracker and an active contour (snake) tracker. Of the five methods, the snake tracker proved to be the most robust method in terms of the highest percentage of frames tracked and the lowest root mean-squared error. The paper provides an overview of the five trackers and gives experimental results.
Edge detection in ultrasound imagery using the instantaneous coefficient of variation

The instantaneous coefficient of variation (ICOV) edge detector, based on normalized gradient and Laplacian operators, has been proposed for edge detection in ultrasound images. In this paper, the edge detection and localization performance of the ICOV-squared (ICOVS) detector are examined. First, a simplified version of the ICOVS detector, the normalized gradient magnitude squared, is scrutinized in order to reveal the statistical performance of edge detection and localization in speckled ultrasound imagery. Both the probability of detection and the probability of false alarm are evaluated for the detector. Edge localization is characterized by the position of the peak and the 3-dB width of the detector response. Then, the speckle-edge response of the ICOVS as applied to a realistic edge model is studied. Through theoretical analysis, we reveal the compensatory effects of the normalized Laplacian operator in the ICOV edge detector for edge-localization error. An ICOV-based edge-detection algorithm is implemented in which the ICOV detector is embedded in a diffusion coefficient in an anisotropic diffusion process. Experiments with real ultrasound images have shown that the proposed algorithm is effective in extracting edges in the presence of speckle. Quantitatively, the ICOVS provides a lower localization error, and qualitatively, a dramatic improvement in edge-detection performance over an existing edge-detection method for speckled imagery.
Velocity guided segmentation of phase contrast magnetic resonance angiography

Atherosclerosis, the precursor to acute events such as myocardial infarction and stroke, is a focal inflammatory disease that is influenced by local hemodynamic forces such as wall shear stress (WSS). Phase contrast (PC) MRI is an established method that encodes blood velocity into the phase of an image. A requisite step for the calculation of WSS from PC-MRI data is delineation of the vasculature. Previous automatic segmentation algorithms have primarily relied on the image magnitude. Phase based segmentation algorithms have reduced the phase data to image features prior to segmentation. We outline a novel method that directly incorporates phase data as an additional external force in an active model. We demonstrate improved segmentation results over current methods on both synthetic and real data.
Deconvolutional speckle reducing anisotropic diffusion

In order to propel the analysis of medical ultrasound imagery from qualitative observation to quantitative measurement, the obstacles of distortion from speckle and from blurring due to the point spread function must be overcome. A recent partial differential equation (PDE) based enhancement technique has improved the ability to segment ultrasound images and to detect salient edges. However, this diffusion method often distorts the size of image features and may in fact efface subtle features. This paper proposes a new PDE that combines the enhancement of speckle reducing anisotropic diffusion (SRAD) with the mechanism of deconvolution. The resulting method, called deconvolutional speckle reducing anisotropic diffusion (DeSpeRADo), surpasses the edge localization ability of SRAD while yielding lower error in terms of area estimation and improved detection of fine features. A comparative study employs 100 experiments to contrast the quantification enabled by adaptive filtering, inverse filtering, diffusion and the new DeSpeRADo technique.
Shape model segmentation of long-axis contrast enhanced echocardiography

We segment long-axis, four-chamber contrast enhanced echocardiography imagery using active shape models. The active shape model algorithm uses principal component analysis to model the shape variability found in a database of training shapes. Accurate segmentation from this model is accomplished by applying a specialized gradient vector flow field to guide the contours to the myocardial borders. The success of the proposed algorithm was verified by application to 65 patient myocardial contrast echocardiography (MCE) studies and through comparison with manually drawn contours. This approach improved accuracy over previously reported data, providing an average accuracy of 0.98, sensitivity of 0.84, specificity of 0.99, and RMSE of 3.3 pixels, when compared to ground truth. Error and variability from automatic segmentation were found to be less than those among multiple human observers. The approach also compares favorably against a standard active contour
Anisotropic diffusion and local monotonicity

This paper investigates the relationship between anisotropic diffusion and local monotonicity. A diffusion technique that has locally monotonic root signals is presented. The enhancement algorithm rapidly converges to a locally monotonic signal of the desired degree. It is shown that the diffusion coefficient used here is the only formation that guarantees idempotence for locally monotonic signals. The signals resulting from locally monotonic diffusion are closer to the original signals than the corresponding median root signals. Furthermore, the diffusion algorithm does not have a difficulty with alternating signals, as does the median filter. In contrast to other anisotropic diffusion techniques, the diffusion method given here does not preserve outliers and does not require a gradient magnitude threshold in the diffusion coefficient.
Automated leukocyte detection in VIVO

In this paper we propose a technique for detecting rolling leukocytes within intravital microscopy based on Bayesian classification. The classification depends on a feature score, the gradient inverse coefficient of variation (GICOV), which serves to discriminate rolling leukocytes from a cluttered environment. An ellipse matching algorithm and B-spline snake for the aim of boundary extraction are discussed. The extracted contours that have a GICOV score above the analytically determined threshold are retained as identified objects. Experimental results using 327 rolling leukocytes were compared to those of human experts and currently used methods.
Speckle reducing anisotropic diffusion

This paper provides the derivation of speckle reducing anisotropic diffusion (SRAD), a diffusion method tailored to ultrasonic and radar imaging applications. SRAD is the edge-sensitive diffusion for speckled images, in the same way that conventional anisotropic diffusion is the edge-sensitive diffusion for images corrupted with additive noise. We first show that the Lee and Frost filters can be cast as partial differential equations, and then we derive SRAD by allowing edge-sensitive anisotropic diffusion within this context. Just as the Lee and Frost filters utilize the coefficient of variation in adaptive filtering, SRAD exploits the instantaneous coefficient of variation, which is shown to be a function of the local gradient magnitude and Laplacian operators. We validate the new algorithm using both synthetic and real linear scan ultrasonic imagery of the carotid artery. We also demonstrate the algorithm performance with real SAR data. The performance measures obtained by means of computer simulation of carotid artery images are compared with three existing speckle reduction schemes. In the presence of speckle noise, speckle reducing anisotropic diffusion excels over the traditional speckle removal filters and over the conventional anisotropic diffusion method in terms of mean preservation, variance reduction, and edge localization.
UGrAD: A graph-theoretic framework for classification of activity with complementary graph boundary detection

Activity recognition and activity boundary detection are two separate long-standing challenges in the image processing literature. In activity recognition, a predefined set of activities is classified using features. Often, subjects do not perform meaningful activities in all the frames, thus requiring the identification of the beginning and the end of the set of contiguous frames containing the activity. This is known as activity boundary detection problem. We seamlessly integrate both tasks by leveraging a single graph-theoretical framework: UGrAD. First, we model the data as a graph and use the spectral properties of its complementary to identify the boundaries of an activity. Next, we introduce the concept of normalized flow between consecutive frames, encoded using a bipartite graph formalism, and use it to capture the temporal evolution of the detected activity. This is accomplished by deriving a robust and reliable feature descriptor from fitting a set of Legendre polynomials to the flow matrix. Lastly, the features are used for activity classification using a linear support vector machine (SVM). Our approach shows substantial improvement over the state-of-the-art activity classification and boundary detection techniques.
Ultrasound Despeckling for Contrast Enhancement

Images produced by ultrasound systems are adversely hampered by a stochastic process known as speckle. A despeckling method based upon removing outlier is proposed. The method is developed to contrast enhance B-mode ultrasound images. The contrast enhancement is with respect to decreasing pixel variations in homogeneous regions while maintaining or improving differences in mean values of distinct regions. A comparison of the proposed despeckling filter is compared with the other well known despeckling filters. The evaluations of despeckling performance are based upon improvements to contrast enhancement, structural similarity, and segmentation results on a Field II simulated image and actual B-mode cardiac ultrasound images captured in vivo.
Shape descriptors based on compressed sensing with application to neuron matching

In this paper we propose a novel compressed sensing based Fourier shape descriptor method to compute the shape feature vector of an arbitrary object. First, the object contour obtained via segmentation is represented as a complex-valued signal. We then formulate an optimization problem that exploits the sparsity of the shape feature of the contour. This results in a reduced size feature vector, which can efficiently represent the shape of an object as illustrated by the reconstruction results. Appropriate for general shape retrieval problems, we demonstrate the efficacy of our algorithm by retrieving structurally similar neurons from a database. Currently, the representation and matching of neurons, given the heterogeneous nature of the neuronal morphology and the characteristically complex branching patterns, is an open problem. Retrieval of structurally similar neurons will potentially enable classification of neurons imaged. The retrieval results obtained using our method provide evidence of efficacy with a 27% improvement over Sholl analysis, which is a standard shape descriptor used in neuroscience.
Graph cut segmentation of sparsely sampled images with application to InSAR-measured changes in elevation

In this paper, we outline an algorithm for the automatic segmentation of sparse data in order to detect possible terrain-deformation phenomena. Segmentation is accomplished through a graph cut technique. In the graph structure, for each edge, we derive a unique energy by combining multiple independent energies tailored toward accurately locating the boundaries of spot-like, subsiding regions. We then find the series of cuts with minimum total energy and fit splines to these cuts for smooth segment boundaries. The segmentation approach is applied to the problem of localizing sinkholes in karst regions. Test results indicate efficacy for a sufficient density of InSAR features.
Constraining active contour evolution via Lie Groups of transformation

We present a novel approach to constraining the evolution of active contours used in image analysis. The proposed approach constrains the final curve obtained at convergence of curve evolution to be related to the initial curve from which evolution begins through an element of a desired Lie group of plane transformations. Constraining curve evolution in such a way is important in numerous tracking applications where the contour being tracked in a certain frame is known to be related to the contour in the previous frame through a geometric transformation such as translation, rotation, or affine transformation, for example. It is also of importance in segmentation applications where the region to be segmented is known up to a geometric transformation. Our approach is based on suitably modifying the Euler-Lagrange descent equations by using the correspondence between Lie groups of plane actions and their Lie algebras of infinitesimal generators, and thereby ensures that curve evolution takes place on an orbit of the chosen transformation group while remaining a descent equation of the original functional. The main advantage of our approach is that it does not necessitate any knowledge of nor any modification to the original curve functional and is extremely straightforward to implement. Our approach therefore stands in sharp contrast to other approaches where the curve functional is modified by the addition of geometric penalty terms. We illustrate our algorithm on numerous real and synthetic examples.
X-ray collimator shutter detection by active-rods

We present Active-Rods, a technique for automatically detecting collimator shutter edges in digital radiographs. Unlike the active contour method designed for smooth boundary contouring, the Active-Rods approach seeks straight line open boundaries. The shutter detection problem is formulated as minimizing an energy functional of both local edges and regional statistics. Initial shutter positions are obtained through edge detection, modeling and grouping for desired collinear sets. Rod translations and rotations are driven by the forces and torques via energy minimization. This technique has demonstrated promising shutter detection on a broad range of clinical images.
Gradient independent translation via differential morphology

A new multi-scale image enhancement mechanism is presented. Derived from the differential representation of the morphological filters, it approximates a median filter and alleviates many of the image blotching and noise preserving characteristics of morphological filtering. In one-dimension, the process is shown to be idempotent and to converge. In two dimensions, experimental results demonstrate convergence and display the ability to remove impulsive noise. Gradient independent translation avoids the two dimensional convergence problems of the median filter and does not involve the expensive rank-ordering of pixel intensities. Describing a scale space with median filter characteristics, it provides a multi-scale analysis method suitable for compression, coding, and feature extraction.
Motion gradient vector flow: an external force for tracking rolling leukocytes with shape and size constrained active contours

Recording rolling leukocyte velocities from intravital microscopic video imagery is a critical task in inflammation research and drug validation. Since manual tracking is excessively time consuming, an automated method is desired. This paper illustrates an active contour based automated tracking method, where we propose a novel external force to guide the active contour that takes the hemodynamic flow direction into account. The construction of the proposed force field, referred to as motion gradient vector flow (MGVF), is accomplished by minimizing an energy functional involving the motion direction, and the image gradient magnitude. The tracking experiments demonstrate that MGVF can be used to track both slow- and fast-rolling leukocytes, thus extending the capture range of previously designed cell tracking techniques.
Content-aware neuron image enhancement

Neuron microscopy enables advances in the exploration of neural anatomy and function, and neuron image enhancement is typically necessary before higher-level analyses. Most existing neuron image enhancement methods exploit the unique tubular structure of neurons. However, such methods do not preserve neuron details. In this paper, we propose a content-aware neuron image enhancement (CaNE) method that combines the neuron image properties of gradient sparsity and tubular structure. Experiments show that neuron images enhanced by CaNE possess 20% more details in terms of correlation as compared to existing methods. With high-quality neuron images provided by CaNE, neuron analyses, such as segmentation and skeleton detection, are enabled at higher rates of success as described in this paper.
Agglomerative clustering for image segmentation

The paper presents an agglomerative clustering technique for image segmentation. To initiate agglomeration, a set of homogeneous segments is found in the image using level set analysis. A relational matrix is then defined establishing relations between neighbouring segments present in the image. These relations are derived based on the intensity and boundary features of the segments. The agglomeration is performed on this relationship based on asymmetric agglomerative clustering criteria. The performance is demonstrated through results on a number of natural images and through cluster validity criteria.
Edge enhancement of infrared imagery by way of the anisotropic diffusion pyramid

The problem of enhancing and detecting edges in infrared images is addressed in this paper. The proposed solution utilizes a multiresolution structure called the anisotropic diffusion pyramid (ADP), which is created by successive application of anisotropic diffusion and subsampling. A pyramid node linking process is used to segment the image and to detect edges. For the slowly varying sigmoidal edges in the IR imagery, it is shown that the diffusion process is effective in edge enhancement. Furthermore, system parameters are derived to maximize the edge sharpening ability of the ADP. The ADP avoids the "staircase" artifacts of single-resolution diffusion algorithms and avoids the localization and region merging problems of pyramids based on linear filters.
Cardiac Motion Recovery via Active Trajectory Field Models

Cardiovascular researchers are constantly developing new and innovative medical imaging technologies, striving to improve the understanding, diagnosis, and treatment of cardiovascular dysfunction. Combining these sophisticated imaging methods with advancements in image understanding via computational intelligence will continue to advance the frontier of cardiovascular medicine. Recently, researchers have turned to a new class of tissue motion imaging techniques, including displacement encoding with stimulated echoes (DENSE) in cardiac magnetic resonance (cMR) imaging, to directly quantify cardiac displacement and produce accurate spatiotemporal measurements of myocardial strain, twist, and torsion. The associated analysis of DENSE cMR and other tissue motion imagery, however, represents a major bottleneck in the study of intramyocardial mechanics. In the computational intelligence area of deformable models, this paper develops an automated motion recovery technique termed active trajectory field models (ATFMs) geared toward these new motion imaging protocols, offering quantitative physiological measurements without the pains of manual analyses. This novel generative deformable model exploits both image information and prior knowledge of cardiac motion, utilizing a point distribution model derived from a training set of myocardial trajectory fields to automatically recover cardiac motion from a noisy image sequence. The effectiveness of the ATFM method is demonstrated by quantifying myocardial motion in 2-D short-axis murine DENSE cMR image sequences both before and after myocardial infarction, producing results comparable to existing semiautomatic analysis methods.
Surface extraction and thickness measurement of the articular cartilage from MR images using directional gradient vector flow snakes

The accuracy of the surface extraction of magnetic resonance images of highly congruent joints with thin articular cartilage layers has a significant effect on the percentage errors and reproducibility of quantitative measurements(e.g., thickness and volume) of the articular cartilage. Traditional techniques such as gradient-based edge detection are not suitable for the extraction of these surfaces. This paper studies the extraction of articular cartilage surfaces using snakes, and a gradient vector flow (GVF)-based external force is proposed for this application. In order to make the GVF snake more stable and converge to the correct surfaces, directional gradient is used to produce the gradient vector flow. Experimental results show that the directional GVF snake is more robust than the traditional GVF snake for this application. Based on the newly developed snake model, an articular cartilage surface extraction algorithm is developed. Thickness is computed based on the surfaces extracted using the proposed algorithm. In order to make the thickness measurement more reproducible, a new thickness computation approach, which is called T-norm,is introduced. Experimental results show that the thickness measurement obtained by the new thickness computation approach has better reproducibility than that obtained by the existing thickness computation approaches
Anisotropic diffusion pyramids for image segmentation

We introduce the Anisotropic Diffusion Pyramid (ADP), a structure for multiresolution image processing. We also develop the ADP for use in region-based segmentation. The pyramid is constructed using the anisotropic diffusion equations, creating an efficient scale-space representation. Segmentation is accomplished using pyramid node linking. Since anisotropic diffusion preserves edge localization as the scale is increased, the region boundaries in the coarse-to-fine ADP segmentation are accurately delineated. An application to segmentation of remotely sensed data is provided. The results of ADP segmentation are compared to Gaussian-based pyramidal segmentation. The examples show that the ADP has a superior ability to subdivide the image into integral groupings, minimizing the error in boundary localization and in pixel intensity.<<ETX>>
Vector Field Convolution for Image Segmentation using Snakes

Snakes, or active contours, have been widely used in image processing applications. Typical roadblocks to consistent performance include limited capture range, noise sensitivity, and poor convergence to concavities. This paper proposes a new design for the snake external force, called vector field convolution (VFC), to address these problems. Qualitative and quantitative comparisons with the gradient vector flow (GVF) external force are presented in this paper to show the advantages of this innovation.