nils-herrmann/clean_scirepeval huggingface dataset:
Poisson inverse gradient approach to vascular myocyte detection and segmentation

This paper addresses the detection and segmentation of vascular myocytes. The detection and segmentation of these cells are critical to the investigation of atherosclerosis among other cardiovascular diseases. Our approach to detection is unique in that it attempts to compute the underlying external energy in an active contour model. Isolines in this computed external energy can be employed to localize boundaries of the cell nuclei. The process used to solve the inverse problem of obtaining the energy from the force vectors is called Poisson inverse gradient due to the Poisson-based solution. From the initial contours given by the isolines in the computed energy, parametric active contours are used to find the subtle cell boundaries. The results indicate that the Poisson inverse gradient improves detection accuracy and reduces false positives compared to existing morphological methods. Furthermore, the contour-based detection allows segmentation of the cell boundaries.
Echocardiographic Simulation for Validation of Automated Segmentation Methods

Segmentation of echocardiographic imagery is central to the understanding, diagnosis, and treatment of cardiovascular disease. Although volumes of literature have been devoted to automated image segmentation, little work has been directed towards the validation of these techniques. An echocardiographic simulation has the advantage of exact knowledge of the myocardial borders, thus providing quantifiable measurements of an algorithms performance. Existing simulation tools either become intractable when generating multiple images, or accommodate only simplistic myocardial motion models. This paper proposes a novel tool for the simulation of short-axis echocardiographic image sequences towards the goal of automated segmentation algorithm validation. We consider a complete set of simulation concerns, including a realistic myocardial model, variable inter-frame speckle pattern correlation, and low computational cost for fast simulation. We demonstrate the value of the proposed tool by evaluating a speckle filtering algorithms effects on segmentation accuracy.
Tracking rolling leukocytes with motion gradient vector flow

Recording rolling leukocyte velocities from intravital microscopic video imagery is important to 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 by taking the 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 leukocytes, whereas the basic gradient vector flow (GVF) is suitable for tracking slow leukocytes.
Nonlinear pyramids for object identification

Image pyramids constructed via nonlinear filtering and subsampling are investigated for object identification and tracking task. Two nonlinear structures, the morphological pyramid and the anisotropic diffusion pyramid are used in coarse-to-fine target recognition algorithms. The background and theoretical development of the pyramidal strategies are presented, and important implementation decisions are discussed. Particularly, the analysis focuses on the sampling schemes and the selection of the pyramid root level for target identification. Experimental results are provided that demonstrate the performance of both nonlinear pyrimidal techniques on noisy infrared image sequences. The results show that the morphological and anisotropic diffusion pyramids allow reliable, efficient extraction of features for rapid object identification.
A stochastic approach to ultrasound despeckling

A novel stochastically driven filtering method to despeckle B mode ultrasound images is presented. This method is motivated by viewing the pixel values as a stochastic process and removing outliers, where outliers are defined by local extrema. These outliers are removed by local averaging. This produces another image with new outliers (local extrema) and the process is iteratively repeated. With each iteration homogeneous regions become smoother while edges that defined these regions remain preserved. To evaluate the performance of our proposed method in satisfying these two opposing goals we develop a modified Fisher discriminant contrast metric. Larger values of this metric indicate better performance in reducing each intraregion or intraclass variance and increasing the difference of interregion or interclass means
Image segmentation by curve evolution with clustering

A generalized approach to image segmentation is presented in this paper. The approach consists of two successive stages. First, a fuzzy c-means clustering algorithm is used to separate the image pixels into N classes. Second, a curve evolution based on partial differential equations is utilized to subdivide the image into an arbitrary number of closed regions. Our segmentation method uses level-set theory to evolve geometric snakes that delineate the image regions. The partial differential equations that govern the snake evolution are a steepest descent solution to an energy functional that penalizes both region inhomogeneity and increased segmentation boundary length.
Locally monotonic models for image and video processing

Definitions of locally monotonic images are introduced. The model definitions are complemented with algorithms that compute locally monotonic versions of a given image or video frame input. The property of local monotonicity provides a useful vehicle for image smoothing and denoising. Local monotonicity is also useful for scale space generation, wherein the degree of local monotonicity is the scale parameter. Currently, the property of local monotonicity is well defined for the 1-D case, but is not well defined for images or video. In this paper, models for multidimensional local monotonicity that extend the 1-D definition are rendered. Regression-based and diffusion-based processing methods are prescribed that yield meaningful locally monotonic images. The definitions and associated algorithms are applicable to image enhancement and a variety of multiscale tasks such as image segmentation and video coding.
Dictionary Learning Level Set

We propose a novel region based segmentation technique using dictionary learning. In a previous work we have developed a method which uses a set of pre-specified Legendre basis functions to perform region based segmentation of an object in presence of heterogeneous illumination. We hypothesize that in problems where a set of training images for the object is available for analysis (such as depth image sequence of blood vessels via ultrasound imaging), segmentation accuracy can be significantly improved by learning the basis functions instead of specifying them implicitly. The salient idea of this letter is to compute the optimal set of functions to model the region intensities. Our solution to this problem involves the integration of a level set segmentation methodology with the dictionary learning framework. This provides an elegant solution to deal with intensity inhomogeneities prevalent in many imaging applications such as ultrasound and fluorescence microscopy. The proposed algorithm, Dictionary Learning Level Set (DL2S) is used to segment ultrasound images of blood vessels captured using low cost, portable ultrasound devices employed in a phlebotomy application. Qualitative and quantitative results obtained from this dataset suggest efficacy of D2LS with an associated improvement in the average Dice index of 12% over the relevant competitors.
P2A-5 Guided Automatic Segmentation of the Murine Left Ventricle Using Conservation of Myocardial Volume

Computation of important metrics of cardiac function - specifically left ventricular end systolic and end diastolic volume - necessitate accurate segmentation of myocardial boundaries in 3D image data sets. Automatic segmentation is highly dependent on appropriate selection of segmentation algorithm parameters. We propose the use of conservation of myocardial volume - that is, the difference in volumes between the epicardial and endocardial surface segmentations - as a metric of efficacy of a particular set of model parameters for the gradient vector flow field algorithm. The metric was found to be an effective predictor for the efficacy of the gradient vector field parameter - models generated from parameter values which exhibited strong conservation of volume yielded more accurate segmentations of both synthetic and in vivo data.
Area morphological segmentation for content based retrieval

Content based retrieval (CBR) has emerged as a useful tool for management of digital image libraries. Traditional approaches based on metadata and global features are limited in effectiveness and suffer from various drawbacks involving the use of heuristics and postprocessing techniques. The automated segmentation technique presented is based on fuzzy clustering in a scaled texture space, which is generated via a bank of Gabor filters. Within the texture space, we scale the responses using the novel tool of area morphology. The segmentation algorithm provides segments satisfying a minimum scale requirement for use in CBR. The experiments indicate that our segmentation mechanism increases feature relevancy and reduces the computational costs of segment based matching.
Conditional anisotropic diffusion for leukocyte identification

Leukocyte identification is pursued in order to measure important parameters of inflammation including rolling leukocyte flux and rolling leukocyte volume fraction. The automated identification of leukocytes also serves as a precursor to leukocyte tracking. In this study, a conditional anisotropic diffusion model is used to enhance edges within video microscopy depicting rolling leukocytes within small venules. The detected edge chains are pruned according to the geometric information given in the leukocyte models, and candidate edge chains representing potential leukocytes are approximated by circular contours. The performance of the proposed approach is evaluated using images of TNF-/spl alpha/ treated venules within the mouse cremaster.
Inclusion filters: a class of self-dual connected operators

In this paper, we define a connected operator that either fills or retains the holes of the connected sets depending on application-specific criteria that are increasing in the set theoretic sense. We refer to this class of connected operators as inclusion filters, which are shown to be increasing, idempotent, and self dual (gray-level inversion invariance). We demonstrate self duality for 8-adjacency on a discrete Cartesian grid. Inclusion filters are defined first for binary-valued images, and then the definition is extended to grayscale imagery. It is also shown that inclusion filters are levelings, a larger class of connected operators. Several important applications of inclusion filters are demonstrated-automatic segmentation of the lung cavities from magnetic resonance imagery, user interactive shape delineation in content-based image retrieval, registration of intravital microscopic video sequences, and detection and tracking of cells from these sequences. The numerical performance measures on 100-cell tracking experiments show that the use of inclusion filter improves the total number of frames successfully tracked by five times and provides a threefold reduction in the overall position error.
Detection of microspheres in venules for automated particle image velocimetry

In this paper, we propose an automatic approach for detecting particle tracers (microspheres) in microscopic imagery obtained from mouse cremaster venules in vivo. Measurements of the translational speed and radial position of individual microspheres provide the input data needed to extract velocity profiles from steady blood flow in venules. These profiles provide information about local hemodynamics that is critical to a broad range of fields in microvascular physiology, including endothelial-cell mechanotransduction, inflammation, and microvascular resistance. In the preprocessing stage, an active contour method based on dynamic programming is used for vessel region extraction. Each microsphere is then identified using a process of coarse segmentation followed by verification. Segmentation is achieved using a morphological method for microsphere detection while verification is achieved using an analytical model tailored to the microsphere. Experimental results are obtained using the proposed scheme and compared with previously published manually acquired data.
Target tracking using the anisotropic diffusion pyramid

Forward-looking infrared (FLIR) systems are used to identify targets based on their features and emissions. With the new generation of FLIR systems that can produce sixty or more high resolution frames per second, multiscale algorithms that can operate accurately and efficiently in a variety of battlefield situations are paramount. The popular Gaussian pyramid tends to distort and even destroy object edge information with increasing iterations, leading to incorrect target identification. We propose a new approach to infrared searching and tracking based on the anisotropic diffusion pyramid (ADP). In contrast to the Gaussian pyramid, the ADP preserves edge information while increasing search speed.
Ankle cartilage surface segmentation using directional gradient vector flow snakes

Ankle cartilage surface segmentation is a prestep to compute the cartilage thickness which is used to evaluate the treatment of ankle joint diseases. Because of the un-ideal imaging condition, the acquired ankle images are often noised and the cartilage parts in the images are often disconnected, thus traditional techniques such as gradient-based edge detection are not suitable for the segmentation of these surfaces. In this paper, we developed an active contour model based ankle cartilage surface segmentation algorithm. The algorithm used gradient vector flow (GVF) as external forces. In order to make the GVF snake more stable and to converge at the correct surfaces, directional gradient is used to produce the gradient vector flow field. Experimental results indicate that the new approach utilizing directional gradient is more robust than the traditional GVF snake.
Momentum measure for quantifying dendritic cell movement

Discovery is ongoing regarding the causes and onset of atherosclerosis. Studying the movement of dendritic cells may provide insight to disease pathways and mechanisms, which will eventually lead to improved treatment. This paper develops an approach, inspired by momentum, to quantify the amount of quivering motion separately from translational motion. A simulation is developed to contrast the method performance against that of a graph-based approach. The resultant correlation coefficient between the simulated movement and the algorithm outcome is 0.85 for the proposed method, compared to 0.02 for a graph-based approach. In empirical tests, the momentum measure is shown to be robust to segmentation error.
Biomedical Image Analysis: Segmentation

The sequel to the popular lecture book entitled Biomedical Image Analysis: Tracking, this book on Biomedical Image Analysis: Segmentation tackles the challenging task of segmenting biological and medical images. The problem of partitioning multidimensional biomedical data into meaningful regions is perhaps the main roadblock in the automation of biomedical image analysis. Whether the modality of choice is MRI, PET, ultrasound, SPECT, CT, or one of a myriad of microscopy platforms, image segmentation is a vital step in analyzing the constituent biological or medical targets. This book provides a state-of-the-art, comprehensive look at biomedical image segmentation that is accessible to well-equipped undergraduates, graduate students, and research professionals in the biology, biomedical, medical, and engineering fields. Active model methods that have emerged in the last few years are a focus of the book, including parametric active contour and active surface models, active shape models, and geomet...
Matching and Retrieval of Tattoo Images: Active Contour CBIR and Glocal Image Features

Tattoos provide an important source of biometric information, particularly in gang-related criminal activity. The goal of this paper is the formation of an image analysis tool to match tattoos and to retrieve similar tattoos from a tattoo database. First, an existing content based image retrieval (CBIR) approach for tattoos is reviewed. Then, a new active contour CBIR approach is detailed. This method incorporates vector field convolution active contours for tattoo segmentation, Haar wavelet decomposition for texture analysis, hue-saturation-value histograms for color representation and Fourier shape descriptors for shape characterization. Finally, the glocal (global-local) image feature approach is introduced. Results are provided for two datasets that include both recreational and prison/gang tattoos.
Piecewise and local image models for regularized image restoration using cross-validation

We describe two broad classes of useful and physically meaningful image models that can be used to construct novel smoothing constraints for use in the regularized image restoration problem. The two classes, termed piecewise image models (PIMs) and focal image models (LIMs), respectively, capture unique image properties that can be adapted to the image and that reflect structurally significant surface characteristics. Members of the PIM and LIM classes are easily formed into regularization operators that replace differential-type constraints. We also develop an adaptive strategy for selecting the best PIM or LIM for a given problem (from among the defined class), and we explain the construction of the corresponding regularization operators. Considerable attention is also given to determining the regularization parameter via a cross-validation technique, and also to the selection of an optimization strategy for solving the problem. Several results are provided that illustrate the processes of model selection, parameter selection, and image restoration. The overall approach provides a new viewpoint on the restoration problem through the use of new image models that capture salient image features that are not well represented through traditional approaches.
Bact-3D: A level set segmentation approach for dense multi-layered 3D bacterial biofilms

In microscopy, new super-resolution methods are emerging that produce three-dimensional images at resolutions ten times smaller than that provided by traditional light microscopy. Such technology is enabling the exploration of structure and function in living tissues such as bacterial biofilms that have mysterious interconnections and organization. Unfortunately, the standard tools used in the image analysis community to perform segmentation and other higher-level analyses cannot be applied naïvely to these data. This paper presents Bact-3D, a 3D method for segmenting super-resolution images of multi-leveled, living bacteria cultured in vitro. The method incorporates a novel initialization approach that exploits the geometry of the bacterial cells as well an iterative local level set evolution that is tailored to the biological application. In experiments where segmentation is used as precursor to cell detection, the Bact-3D matches or improves upon the Dice score and mean-squared error of two existing methods, while yielding a substantial improvement in cell detection accuracy. In addition to providing improvements in performance over the state-of-the-art, this report also characterizes the tradeoff between imaging resolution and segmentation quality.
Modified mean curvature motion for multispectral anisotropic diffusion

This paper introduces a new anisotropic diffusion algorithm for enhancing and segmenting multispectral image data. The algorithm is based upon mean curvature motion. Using a modified image gradient computation, the diffusion method is further improved by allowing the control of feature scale, and the sensitivity to heavy-tailed noise is eliminated. For comparison, a vector distance dissimilarity method is introduced and extended for multi-scale processing. The experiments on remotely sensed imagery and color imagery demonstrate the performance of the algorithms in terms of image entropy reduction and impulse elimination as well as visual quality.
A Transform Method to Remove Ultrasound Artifacts

Reverberation and multi-path reflection artifacts are a common problem in ultrasound imaging. We propose a novel method to remove these artifacts. Regions adversely affected by these artifacts are replaced with textures that resemble the underlying object(s), which were originally obscured. Our proposed method incorporates optimally soft thresholding the 2D discrete wavelet transform of the artifact regions to produce a near optimal estimate of the reflectivity values due only to the reverberation and multi-path reflection artifacts. Simply subtracting this estimate from the original reflectivity values, we attain a near optimal estimate of the artifact free reflectivity values. We provide simulated and B mode images to substantiate the benefits of this method in producing an artifact removed image
P3I-4 Multidimensional Segmentation of a Mouse Left Ventricle

This paper describes a method to determine three dimensional (3D) segmentation of a mouse left ventricle over time. The temporal varying 3D data consist of elevationally interpolating a temporal sequence of two dimensional (2D) short axis ultrasound slices. The gradient vector flow active surface is used to determine a 3D surface of the LV. The initial approximation of the gradient vector flow active surface is attained by temporally propagating the final surface from the previous time instance. An inherent problem with temporal propagation is that parts of two distinct surfaces in close proximity tend to converge to the same spatial location. We propose to embed a model correction method into the 4D segmentation algorithm to alleviate this problem
Self-dual inclusion filters for grayscale imagery

Using the structure of an adjacency-tree for binary-valued images, we define inclusion filters, a class of connected operators. Inclusion filters modify the image by filling or retaining the holes of the connected components of foreground and those of the background of a binary image depending on application-specific criteria, which are increasing in the set theoretic sense. We demonstrate a straightforward method to achieve self-duality (gray level inversion invariance) of inclusion filters on the discrete Cartesian domain by considering only 8-adjacency. Inclusion filters are extended to the grayscale images by the threshold decomposition principle. As an application, inclusion filters are shown to improve the performance of snake-based tracking of leukocytes observed in intravital microscopic video imagery. In this set of experiments the mean position error is reduced by a factor of 2.5 using the inclusion filter.