nils-herrmann/clean_scirepeval huggingface dataset:
Tracking multiple cells by correspondence resolution in a sequential Bayesian framework

We propose a multi-target tracking (MTT) algorithm in a sequential Bayesian framework that computes cell velocities from video microscopy. Unlike the traditional tracking methods, our formulation does not involve the estimation of target states; instead, we estimate one-to-one target correspondences by way of a sequential Markov chain Monte Carlo (MCMC) algorithm. The proposed probabilistic framework also automatically accounts for a variable number of targets. We have tested the proposed tracking algorithm on two different in vitro and one in vivo microscopy experiments. The three experiments show that the method holds promise in terms of low false positive and false negative rates as well as low rates of correspondence error.
On the Convergence of Bilateral Filter for Edge-Preserving Image Smoothing

The bilateral filter represents a wide group of nonlinear filters for edge-preserving image smoothing. In this work, we study the convergence properties of the bilateral filter algorithm. The understanding is established that the bilateral filter is an optimization procedure. We demonstrate that the bilateral filter is equivalent to minimizing a robust cost criterion using iterative reweighting, which is a good approximation to the very fast but unstable Newton's method. Further, the results of the analysis allow us to derive an improved hybrid smoothing scheme with concerns of computational efficiency and edge preservation.
Scale space classification using area morphology

We explore the application of area morphology to image classification. From the input image, a scale space is created by successive application of an area morphology operator. The pixels within the scale space corresponding to the same image location form a scale space vector. A scale space vector therefore contains the intensity of a particular pixel for a given set of scales, determined in this approach by image granulometry. Using the standard k-means algorithm or the fuzzy c-means algorithm, the image pixels can be classified by clustering the associated scale space vectors. The scale space classifier presented here is rooted in the novel area open-close and area close-open scale spaces. Unlike other scale generating filters, the area operators affect the image by removing connected components within the image level sets that do not satisfy the minimum area criterion. To show that the area open-close and area close-open scale spaces provide an effective multiscale structure for image classification, we demonstrate the fidelity, causality, and edge localization properties for the scale spaces. The analysis also reveals that the area open-close and area close-open scale spaces improve classification by clustering members of similar objects more effectively than the fixed scale classifier. Experimental results are provided that demonstrate the reduction in intra-region classification error and in overall classification error given by the scale space classifier for classification applications where object scale is important. In both visual and objective comparisons, the scale space approach outperforms the traditional fixed scale clustering algorithms and the parametric Bayesian classifier for classification tasks that depend on object scale.
Tree2Tree2: Neuron tracing in 3D

We seek a complete description for the neurome of the Drosophila, which involves tracing more than 20,000 neurons. The currently available tracings are sensitive to background clutter and poor contrast of the images. In this paper, we present Tree2Tree2, an automatic neuron tracing algorithm to segment neurons from 3D confocal microscopy images. Building on our previous work in segmentation , this method uses an adaptive initial segmentation to detect the neuronal portions, as opposed to a global strategy that often results in under segmentation. In order to connect the disjoint portions, we use a technique called Path Search, which is based on a shortest path approach. An intelligent pruning step is also implemented to delete undesired branches. Tested on 3D confocal microscopy images of GFP labeled Drosophila neurons, the visual and quantitative results suggest that Tree2Tree2 is successful in automatically segmenting neurons in images plagued by background clutter and filament discontinuities.
Counting cells from microscopy videos without tracking individual cells

Counting the unique number of cells in a microscopy video (i.e., counting a cell only once while the cell is within the field of view in the video), is required in many biological and pathological studies. Conventionally, cell counting from videos is computed by tracking individual cells. Because tracking cells is non-trivial, these methods are plagued with inaccuracies. In this paper, we engineer a novel and straightforward solution to the problem of unique cell counting by combining frame based cell count with simple pixel motion computation. We estimate the influx and/or the outflux rate of unique cells in a region of interest within the field of view of a microscopy video. The unique count is then obtained by summing the influx and/or the outflux rates. Our proposed framework avoids individual cell tracking altogether; thus, it is capable of overcoming various difficulties associated individual cell tracking. We validate the framework on 11 cell videos of human monocytes. The number of cells is numerous in these videos, yet we obtain a mean counting accuracy of 99%.
Image stabilization and registration for tracking cells in the microvasculature

We propose a registration system to be used for tracking cells in intravital video microscopy that 1) stabilizes jitter-the undesired translational displacement of frames due to respiratory movement, etc ., and 2) registers frames in a moving field of view (FOV) to allow for cell tracking over an extended range. For the first time, tracking of rolling leukocytes in vivo over a moving FOV is demonstrated. In a fixed FOV, stable background regions are located using a morphological approach. Template subregions are then selected from the stable regions and matched to corresponding locations in a reference frame. We show the effectiveness of the stabilization algorithm by using an active contour to track 15 leukocytes previously untrackable due to jitter. For 30 fixed FOV sequences containing rolling leukocytes, the resulting root-mean-square error (RMSE) is less than 0.5 /spl mu/m. To align frames in a moving FOV, we present a modified correlation approach to estimate the common region between two consecutive fixed FOVs. We correlate the overlapping regions of the initial frame of the current fixed FOV and the final frame of the previous fixed FOV to register the images in the adjoining moving FOV. The RMSE of our moving FOV registration technique was less than 0.6 /spl mu/m. In 10 sequences from different venules, we were able to track 11 cells using an active contour approach over moving FOVs.
Particle Filter Tracking of Multiple Rolling Leukocytes in Vivo

In this paper, a novel multi-target tracking method is proposed, which is able to simultaneously track all the detected in vivo rolling leukocytes in the field of view, to stop tracking leukocytes moving out of the image, to identify new leukocytes in the frame and to initialize the tracking process. The proposed tracking method is based on the particle filter framework. The data augmentation algorithm is applied to reduce a high dimensional tracking problem to low dimensional one. The occlusion coefficient is introduced to handle occlusion between leukocytes. The target-measurement management is developed to accommodate the varying number of targets.
Tracking sunflower circumnutation using affine parametric active contours

Study of the sunflower movement may reveal clues regarding unknown mechanisms that regulate periodicity and spatial complexity in plant growth and development. In this paper, we introduce an automated process to track circumnutation of sunflower seedlings. The objective is to track the leaves of the sunflower plant in a video captured by an overhead camera. The tracking method presented predicts the translated and rotated boundary in the subsequent frames by active contour models. A salient feature of our solution is a constraint on affine transformation between updates. The constrained affine active contours used in this paper exhibit improvement over other traditional active contour approaches, with the new method yielding error less than one percent in the tracked sunflower centroid position.
Polarimetric SAR image segmentation using texture partitioning and statistical analysis

This paper presents a new technique for partitioning polarimetric synthetic aperture radar (POL-SAR) imagery into regions of homogeneous polarimetric backscattering properties. The method consists of two cascaded steps: an initial texture segmentation of one image derived from the POL-SAR data, and a polarimetric statistical region merging process that refines and modifies the initial segmentation. In the first stage, a morphological region-based image partitioning technique, the watershed algorithm, plays the key role. While in the second stage, the region adjacency graph and the segment dissimilarity measures derived from the Wishart model and the K distribution are applied. The overall segmentation algorithm has been tested using real 4-look POL-SAR imagery and sample results are provided in the paper. The main innovation of this work is the utilization of the K distribution in the segmentation process.
QUANTIFICATION OF CARDIAC DYSSYNCHRONY IN MICE AFTER MYOCARDIAL INFARCTION USING HIGH RESOLUTION ULTRASOUND

Contractile dyssynchrony in the left ventricles (LVs) of murine hearts post myocardial infarction (MI) was assessed using high resolution 30 MHz ultrasound. Long axis and mid-ventricular short axis image sequences were obtained from six post-Mi C57B1/6 mice. Two normal C57B1/6 mice were also scanned as non-infarcted controls. The ultrasound images had an axial resolution of approximately 50 mum, a lateral resolution of approximately 100 mum, and a temporal resolution of 1000 frames/s (achieved via retrospective compilation of LCG-gated acquisitions). Myocardial tissue displacement was measured using a 2D image tracking algorithm with sub-pixel resolution. Regional myocardial displacement analyses revealed prolonged regional systole and delay to peak displacement, as well as reduced displacement, in regions adjacent to infarcted zones. The regional delays in time to peak displacement ranged from 12 to 20 ms in mouse hearts with large MI. Thus, high resolution 2D ultrasound imaging followed by 2D image tracking analysis provides an efficient and cost-effective method for characterizing LV dyssynchrony in murine hearts post-MI
MRI ventilation analysis by merging parametric active contours

A novel technique that combines MR imaging of hyperpolarized helium gas and conventional MR imaging facilitates, high resolution imaging of lung functionality for the first time. We put forth a segmentation method for measuring the total lung air space and a classification approach to computing the functional air space. For segmentation, we introduce a parametric active contour that allows automated merging of multiple contours. The active contour technique uses gradient vector flow modified and strengthened by a boundary condition that inhibits contour crossover. The active contour approach is computationally inexpensive and is independent of initial contour placement. For classification of the functional lung air space in the helium images, a fuzzy c-means technique is applied. The classification results, in conjunction with the segmentation, allow the analysis of ventilation. The resultant biomedical image analysis tool can used in determining the efficacy of certain respiratory treatments.
Generalized speckle reducing anisotropic diffusion for ultrasound imagery

We first derive rigorously a partial differential equation (PDE) for speckle reduction from minimizing a cost functional of the instantaneous coefficient of variation. Then, we show that the piecewise exponential function is the solution of the derived PDE. Next, we express the derived PDE using log-compressed ultrasound data, followed by a numerical implementation scheme. Finally, we demonstrate the performance of the proposed PDE using examples and compare the results with those obtained from the speckle reducing anisotropic diffusion (SRAD) algorithm.
The effect of initialization and registration on the active shape segmentation of the myocardium in contrast enhanced ultrasound

Ultrasound images inherently include speckle noise and other image artifacts resulting from various physical phenomena. Frequently, several ultrasound images are averaged together to improve image quality prior to automated processing. However, registration inaccuracy will produce blurred object edges and hamper the segmentation process. For automatic object identification in ultrasound imagery, model-based segmentation approaches are of particular interest because of their global approach and ability to infer edge-less parts of the object based on a priori information. These models are often implemented as active contours, which are iteratively deformed towards image objects. The associated segmentation results are strongly dependant on initial contour placement, as the contour often falls into local energy minima when initialized far from the desired image boundary. This paper presents results on the effects of initial contour placement and registration error on the success of an active contour model segmentation of the myocardium from contrast enhanced ultrasound. These data suggest that while a manual initialization provides superior segmentation results, a fully automatic segmentation approach is possible if some sacrifices are made in contour accuracy. Additionally, the registration error induced by the automatic registration algorithm presented here has no adverse effects on segmentation accuracy.
Murine Spatiotemporal Cardiac Segmentation

A promising frontier in the further understanding of cardiac dysfunction is the study of intra-myocardial mechanics, probing parameters such as myocardial strain, twist, and torsion. Techniques such as displacement encoding with stimulated echoes (DENSE) in cardiac magnetic resonance (cMR) imaging and speckle tracking in echocardiography (STE) are already providing quantitative measurements of myocardial function in a safe and non-invasive manner. However, extracting meaningful myocardial motion from the acquired imagery remains a tedious, time-consuming, and highly variable process. This research develops an automated method to recover myocardial tissue motion from murine DENSE cMR imagery. On average, the proposed automated technique is able to obtain a root mean squared error of less than a pixel when compared to semi-automatic methods.
Application of the Affine Transform Invariant Model to Cell Tracking

In target tracking, the movement of the target may involve translation as well as rotation, and the scale of the target in the image may also vary between frames. Severe image clutter, which may mimic the image intensity features of the target and distract the tracker, is another problem. In this paper, we develop a tracking method that is robust in the presence of image clutter and is able to accommodate linear affine transformations (translation, rotation, and/or scaling) of the target without the necessity of determining the exact transformations and the constituent parameters. Based on the affine transform invariant property, an energy function is defined such that locating the target contour in subsequent frames is equivalent to the minimization of the energy function. The particle filter is utilized to estimate the optimal solution
Generalized deterministic annealing

We develop a general formalism for computing high quality, low-cost solutions to nonconvex combinatorial optimization problems expressible as distributed interacting local constraints. For problems of this type, generalized deterministic annealing (GDA) avoids the performance-related sacrifices of current techniques. GDA exploits the localized structure of such problems by assigning K-state neurons to each optimization variable. The neuron values correspond to the probability densities of K-state local Markov chains and may be updated serially or in parallel; the Markov model is derived from the Markov model of simulated annealing (SA), although it is greatly simplified. Theorems are presented that firmly establish the convergence properties of GDA, as well as supplying practical guidelines for selecting the initial and final temperatures in the annealing process. A benchmark image enhancement application is provided where the performance of GDA is compared to other optimization methods. The empirical data taken in conjunction with the formal analytical results suggest that GDA enjoys significant performance advantages relative to current methods for combinatorial optimization.
Content based retrieval for remotely sensed imagery

We present a framework for content based retrieval (CBR) of remotely sensed imagery. The main focus of our research is the segmentation step in CBR. A bank of Gabor filters is used to extract regions of homogeneous texture. These filter responses are utilized in a multiscale clustering technique to yield the final segmentation. Novel area morphological filters are utilized for the purpose of scaling. The resultant segmentation yields regions that are homogeneous in terms of texture and are significant in terms of scale. These regions are used for the purpose of extracting shape and textural features (on a global and local basis) that provide important similarity cues in CBR of remotely sensed imagery. In comparison to solutions which use region merging, the segmentation from the texture/scale space does not require heuristic post-processing, nor knowledge of the number of significant regions.
Neuron segmentation with level sets

We propose a variational scheme for neuron segmentation. The growing demand for an automated image analysis tool for efficient segmentation of neurites has led to significant focus and advancement in the field of neuro-image analysis. However, the complex structure of the neurites and imaging artifacts pose considerable challenge for automated image analysis. Analysis of the neuronal morphology is important to determine the cause of certain neurological diseases (e.g. Alzheimer's disease). Such applications demand accurate segmentation of the neuron that respect the geometric structure as well as the branching patterns. Our proposed method relies on level set evolution to perform segmentation. The propagation of the level sets is primarily governed by a medial flow vector field, which encourages the growth of the level set along the width and the axis of the vascular neurite structures. Experimental results on both 2D and 3D images suggest that the method is robust to imaging artifacts and is efficient in capturing the complex branching structures of the neurons.
Oriented texture completion by AM-FM reaction-diffusion

We provide an automated method to repair broken, occluded oriented image textures. Our approach is based on partial differential equations (PDEs) and AM-FM image modeling. Reconstruction of the texture occurs via simultaneous PDE-generated diffusion and reaction. In the diffusion process, the image is adaptively smoothed, preserving important boundaries and features. The reaction process produces the reconstructed textural information in the occluded image regions. Gabor (1946) filters are designed and used in the reaction process using an AM-FM dominant component analysis. An AM-FM model of the texture image is constructed, making it possible to localize the reaction filters spatio-spectrally. In contrast to previous disocclusion techniques that depend on interpolation, on continuity of the connected components within the image level sets, or on texture estimation, the reaction-diffusion process proposed here yields a seamless transition between the recreated region and the unoccluded image regions. Using AM-FM dominant component analysis, we avoid the ad hoc parameter selection typified with other reaction-diffusion approaches. As a useful example, we focus on the repair of broken, occluded fingerprints. We also treat several exemplary natural textures to demonstrate the technique's generality.
Tree2Tree: Neuron segmentation for generation of neuronal morphology

The knowledge of the structure and morphology of neurons is a central part of our understanding of the brain. There have been concerted efforts in recent years to develop libraries of neuronal structures that can be used for multiple purposes including modeling the brain connectivity and understanding how cellular structure regulates function. However, at present, tracing neuronal structures from microscopy images of neurons is very time consuming and somewhat subjective and therefore not practical for the current datasets. Current automatic state of the art algorithms for neuron tracing fail to work in neuron images which have low contrast, amorphous filament boundaries, branches, and clutter. In this paper, we develop Tree2Tree, a robust automatic neuron segmentation and morphology generation algorithm. It uses a local medial tree generation strategy for visible parts of the neuron and then uses a global tree linking approach to build a maximum likelihood global tree by combining the local trees. Tests on cluttered confocal microscopy images of Drosophila neurons give results that correspond to ground truth within ±5.3 pixel RMSE margin of error.
Segmentation-based image coding by morphological local monotonicity

Segmentation-based image and video coding is desirable for many multimedia applications due to the additional functionality provided by object-based representation. Methods of object-based coding have generally treated the segmentation and encoding processes as separate problems. Here, we present an integrated segmentation and coding method unified by the theoretical structure of morphological local monotonicity. This unified segmentation/coding scheme utilizes morphological operators within a nonlinear scale-space to generate a segmentation. The segmented regions are independently coded and reconstructed using morphological generalization of Laplace's equation in a multiresolution framework. The coding procedure is appropriate for non-textured imagery and avoids arbitrarily chosen constants. Examples are given for two-dimensional grayscale imagery.
Modulation domain texture retrieval for CBIR in digital libraries

We present a new texture retrieval algorithm that, for the first time, performs content-based image retrieval (CBIR) in the modulation domain by computing powerful low-level texture features based on computed AM-FM image models. Performance of the new algorithm is analyzed with respect to competing methods where texture features are computed from Gabor filter magnitude responses. Our experimental results show that the new algorithm achieves a significant performance advantage. We describe how the new algorithm will be used for the texture component of a novel CBIR service called DIRECT, which is designed to provide image based searches in distributed digital libraries without the need for manual entry of annotations and image metadata.