nils-herrmann/clean_scirepeval huggingface dataset:
Real-Time Processing of Ultrasound Images with Speckle Reducing Anisotropic Diffusion

Real-time image processing is desirable for many medical imaging applications. However, many sophisticated image processing algorithms are so computationally intensive that general-purpose CPUs cannot satisfy real-time requirements. In this work, we present a design flow graph approach and several design considerations for implementing complex image processing algorithms in hardware. As a case study, an FPGA implementation of a novel ultrasound image enhancement algorithm - speckle reducing anisotropic diffusion (SRAD) - is discussed. A considerable reduction of hardware complexity is achieved through the co-optimization of both the algorithm and its hardware implementation. Experimental results reveal a 30X performance improvement over the original MATLAB implementation and a 20X performance improvement over the C implementation, with a relative intensity error of less than 2%.
Target tracking using the snake particle filter

This paper presents a method, the snake particle filter (SPF), for tracking targets in video sequences. Manual or semi-automated solutions are both expensive and susceptible to error. In the SPF algorithm, automated tracking is accomplished by combining the particle filter with the snake. Here we employ the snake to establish the target shape, which is used to assign the weight for each particle in the particle filter. The snake provides a likelihood measure in the flexible particle filter framework that accommodates non-linear, non-Gaussian systems. Our results show that the SPF algorithm has an associated low RMSE value of approximately five pixels in the sequences tested for this study.
Path2Path: Hierarchical path-based analysis for neuron matching

An active area of biological research is the construction of neural atlases and repositories of 3D neural images. The goal is to achieve insight into the structural and functional characteristics of classes of similar as well as dissimilar neurons with a view to understand how cellular structure regulates function. However, at present there is no well- established framework that can compare, analyze, and decompose the morphological and geometric information from the databases quantitatively. Current morphology comparison techniques for graphs are not suitable for this purpose since they frequently impose restrictions on the connectivity and degree of the graphs. More importantly, they do not take into account the geometric similarities between branches which are crucial in identifying similar neurons. In this paper, we develop Path2Path, which achieves a fusion of path-matching and morphology comparison into a common mathematical framework. Path2Path handles arbitrary connectivity and number of edges and decomposes the neurons into a connectivity component and the path resemblance component that aides in distinguishing neurons between different functional classes. Preliminary tests on classes of three neurons show an approximate average interclass to intraclass distance ratio of 2.74.
A DCT based gradient vector flow snake for object boundary detection

The paper proposes a new gradient vector flow (GVF) snake based on the discrete cosine transform (DCT) for object boundary detection. In order to develop the DCT GVF snake, for each pixel in the spatial domain, we compute the two dimensional DCT in its local neighborhood. A contrast measure derived from the local DCT coefficients is used to compute the gradient vector flow field, which is employed as the external force to drive the snake to the object edges. The advantage of the DCT GVF snake lies in its robustness to multiplicative noise. The DCT GVF snake is applied to object boundary detection in images with multiplicative noise. Experiments show the boundary detection result obtained by the DCT GVF snake is more accurate than that obtained by the original GVF snake.
Region Based Segmentation in Presence of Intensity Inhomogeneity Using Legendre Polynomials

We propose a novel region based segmentation method capable of segmenting objects in presence of significant intensity variation. Current solutions use some form of local processing to tackle intra-region inhomogeneity, which makes such methods susceptible to local minima. In this letter, we present a framework which generalizes the traditional Chan-Vese algorithm. In contrast to existing local techniques, we represent the illumination of the regions of interest in a lower dimensional subspace using a set of pre-specified basis functions. This representation enables us to accommodate heterogeneous objects, even in presence of noise. We compare our results with three state of the art techniques on a dataset focusing on biological/biomedical images with tubular or filamentous structures. Quantitatively, we achieve a 44% increase in performance, which demonstrates efficacy of the method.
Biomedical Image Analysis at the Cellular Level

The future of biomedical image analysis is no longer in anatomical imaging but in imaging of pathways and mechanisms at the cellular level and below. In this talk I present image analysis techniques for rolling leukocytes observed in vivo. Rolling leukocytes are activated white blood cells. The motion, shape, flux, number and position of these cells are important indicators of the inflammatory process. Measuring image-derived parameters are vital to validating anti-inflammatory drugs and to understanding the basic mechanism of inflammatory diseases such as atherosclerosis and arthritis. To date, these image features are typically derived manually due to the difficulty associated with intravital image clutter, noise, occlusion, instability, poor contrast, contrast changes and shape deformation. The first part of the talk details tracking methods used for rolling leukocytes. These are divided into two categories: active contour approaches and particle filter approaches. The second portion of the talk focuses on novel cell detection methods for intravital microscopy. The methods include a level set solution, the gradient inverse coefficient of variation (GICOV) technique, and the more recent Poisson inverse gradient approach. For both detection and tracking, real video data examples show the efficacy of the developed techniques. New directions in cellular image analysis are discussed including high content screening and future work in image analysis for systems biology.
Cloud tracking by scale space classification

The problem of cloud tracking within a sequence of geo-stationary satellite images has direct relevance to the analysis of cloud life cycles and to the detection of cloud motion vectors (CMVs). The proposed approach first identifies a homogeneous consistent cloud mass for tracking and then establishes motion correspondence within an image sequence. In contrast to the crosscorrelation based approach as adopted in automatic CMV detection analysis, a scale space classifier is designed to detect cloud mass in the source image taken at time t and the destination image at time t+/spl delta/t. Boundaries of the extracted cloud segments are matched by computing a correspondence between high curvature points. This shape based method is capable of tracking in the cases of rotation, scaling, and shearing, while the correlation technique is limited to translational motion. The final tracking results provide motion magnitude and direction for each contour point, allowing reliable estimation of meteorological events and wind velocities aloft. With comparable computational expense, the scale space classification technique exceeds the performance of the traditional correlation-based approach in terms of reduced localization error and false matches.
Automated magnetic resonance assisted echocardiographic motion analysis

Competing technologies have recently emerged across the spectrum of cardiovascular imaging modalities that study intra-myocardial function, probing promising new parameters such as cardiac strain, twist, and torsion. Displacement encoding with stimulated echoes (DENSE) in cardiac magnetic resonance (cMR) imaging achieves accurate spatiotemporal measurements of tissue motion, but remains expensive and time-consuming. Speckle tracking in echocardiography uses an inexpensive and ubiquitous imaging modality, but often fails to fully capture tissue motion. This project strives to combine the desirable aspects of DENSE cMR and speckle tracking via a novel automated analysis technique termed active trajectory field models (ATFMs). The proposed ATFM analysis characterizes cardiac motion within a training set of DENSE cMR data, and uses this characterization to subsequently recover motion from a noisy and incomplete echocardiographic sequence. We demonstrate the effectiveness of the proposed technique via 2D short-axis murine image acquisitions, achieving results comparable to existing semi-automatic echocardiographic motion analysis methods.
Rolling Leukocyte Detection based on Teardrop Shape and the Gradient Inverse Coefficient of Variation

The efficient detection and quantification of rolling leukocytes within intravital microscopy is of both theoretical and practical interest. Currently, methods exist for tracking rolling leukocytes in vivo which rely on manual detection of the cells. In this paper, a new technique inspired by the teardrop shape of the rolling leukocytes is presented for accurately detecting rolling leukocytes. It is based on a feature score, the gradient inverse coefficient of variation (GICOV), which serves to discriminate rolling leukocytes from a cluttered environment. The leukocyte detection process consists of three sequential steps: The first step utilizes a novel teardrop shape matching algorithm to coarsely identify the leukocytes by finding the leukocytes resembling teardrops with a locally maximal GICOV. Second and third steps involve evolution of B-spline snake starting from each of the teardrops found in the first step to refine the leukocytes boundaries and associated GICOV score, and retaining contours with high GICOV scores. The detection process is observed to yield superior results compared to other methods currently in use. The deformation information inherent in the teardrop shapes gives valuable information about the extent to which the cells are adhering to the endothelium.
Automated 3D muscle segmentation from MRI data using convolutional neural network

In this paper, we propose an automated segmentation algorithm for human leg muscles from 3D MRI data using a deep convolutional neural network (CNN). Using a generalized cylinder model, a 3D human leg muscle is represented by two smooth 2D images. The CNN predicts these two images from raw 3D voxels. For our base CNN, we use a pre-trained AlexNet that is coupled with a principle components analysis (PCA)-head. The AlexNet predicts a compressed vector, which is then back-projected by the PCA-head into two 2D images representing a 3D leg muscle boundary. This structured-output CNN architecture is fine-tuned in an end-to-end fashion. Our proposed CNN outperforms the conventional model-based approach, the active appearance model (AAM) image segmentation algorithm. The average Dice score between the ground truth segmentation and the obtained segmentation image is 0.85 using our CNN model, whereas the AAM yields a Dice score of 0.60.
Vector field convolution medialness applied to neuron tracing

In this paper we propose a novel approach to the extraction of medial axis for grayscale objects. The method utilizes a computationally efficient vector field convolution to enhance the medialness feature. Local maxima of medialness are analyzed in scale space, yielding a robust medial axis for grayscale imagery. An important application of this work is the segmentation of neurons from noisy, cluttered microscopy images. Existing neuron segmentation methods depend heavily on accurate, noise-insensitive medial axis extraction. We propose the vector field convolution medialness operation as a first step in segmenting neurons. The proposed method requires no complex parameters or an initial binarization step. The efficacy of the method is demonstrated by a 60% reduction root mean squared error (2.9 pixels) as compared to an approach based on gradient vector flow.
Level set analysis for leukocyte detection and tracking

We propose a cell detection and tracking solution using image-level sets computed via threshold decomposition. In contrast to existing methods where manual initialization is required to track individual cells, the proposed approach can automatically identify and track multiple cells by exploiting the shape and intensity characteristics of the cells. The capture of the cell boundary is considered as an evolution of a closed curve that maximizes image gradient along the curve enclosing a homogeneous region. An energy functional dependent upon the gradient magnitude along the cell boundary, the region homogeneity within the cell boundary and the spatial overlap of the detected cells is minimized using a variational approach. For tracking between frames, this energy functional is modified considering the spatial and shape consistency of a cell as it moves in the video sequence. The integrated energy functional complements shape-based segmentation with a spatial consistency based tracking technique. We demonstrate that an acceptable, expedient solution of the energy functional is possible through a search of the image-level lines: boundaries of connected components within the level sets obtained by threshold decomposition. The level set analysis can also capture multiple cells in a single frame rather than iteratively computing a single active contour for each individual cell. Results of cell detection using the energy functional approach and the level set approach are presented along with the associated processing time. Results of successful tracking of rolling leukocytes from a number of digital video sequences are reported and compared with the results from a correlation tracking scheme.
Object segmentation by traversing a pose-shape manifold

Recent investigations in estimating object shape in images and leveraging knowledge of expected shapes to perform object segmentation have necessitated the formalization of a rigorous mathematical theory of shape. Most of the existing theory in nonlinear shape manifolds lacks physically meaningful parameterization of the shape components, for e.g., pose. We build a novel pose-shape manifold in which manifold parameters signify physically meaningful pose/shape deformation modes. Geodesic distances on this manifold estimate dissimilarities in pose and shape. The segmentation method initializes a template point on the pose-shape manifold and navigates the manifold to converge on the correct pose and shape of the object to be segmented. We show that this method is superior to traditional active contour methods in robustness to edges from clutter. Application of this method to cell delineation of vascular myocytes from phase-contrast microscopy gives reliable segmentation (within ±5% RMS pixel error) of cell boundaries and reliable estimates of geodesic object deformation.
Image retrieval using segmentation

A novel algorithm for image retrieval is presented. The basic idea of the new algorithm is that the constituent segments of the images are used to retrieve images within a digital library. Image retrieval using segments is distinctive in that the local features of the image are used to retrieve the image instead of the typically utilized global features. In our algorithm, the given image is first segmented into dominant components and then the features of these components are extracted to perform retrieval. The features corresponding to each component are used to calculate the distance between components in the matching process. Each image is ranked based on the component wise distance measure with respect to the query component. One of the advantages of the algorithm is that, for a given retrieval image, the user can select a query segment with which to perform retrieval, thus it can satisfy different needs from different users.
Tracking leukocytes in vivo with shape and size constrained active contours

Inflammatory disease is initiated by leukocytes (white blood cells) rolling along the inner surface lining of small blood vessels called postcapillary venules. Studying the number and velocity of rolling leukocytes is essential to understanding and successfully treating inflammatory diseases. Potential inhibitors of leukocyte recruitment can be screened by leukocyte rolling assays and successful inhibitors validated by intravital microscopy. In this paper, we present an active contour or snake-based technique to automatically track the movement of the leukocytes. The novelty of the proposed method lies in the energy functional that constrains the shape and size of the active contour. This paper introduces a significant enhancement over existing gradient-based snakes in the form of a modified gradient vector flow. Using the gradient vector flow, we can track leukocytes rolling at high speeds that are not amenable to tracking with the existing edge-based techniques. We also propose a new energy-based implicit sampling method of the points on the active contour that replaces the computationally expensive explicit method. To enhance the performance of this shape and size constrained snake model, we have coupled it with Kalman filter so that during coasting (when the leukocytes are completely occluded or obscured), the tracker may infer the location of the center of the leukocyte. Finally, we have compared the performance of the proposed snake tracker with that of the correlation and centroid-based trackers. The proposed snake tracker results in superior performance measures, such as reduced error in locating the leukocyte under tracking and improvements in the percentage of frames successfully tracked. For screening and drug validation, the tracker shows promise as an automated data collection tool.
Estimation of Cell Statistics on a Cell Manifold for High Content Screening with Automated Cell Segmentation

We present a method for estimating cell population statistics on a high dimensional nonlinear cell manifold constructed from the output of a high throughput cytometer that allows microscopic imaging of each cell. After active contour segmentation of the cell images, we construct a nonlinear cell manifold from the morphology characteristics and multispectral properties of the cells. We estimate the geodesic distance between the cells on the cell manifold and use it to estimate the cell population statistics on the manifold and quantify dissimilarities between cells to enable meaningful clustering of similar cell populations.
Segmentation and Tracing of Single Neurons from 3D Confocal Microscope Images

In order to understand the brain, we need to first understand the morphology of neurons. In the neurobiology community, there have been recent pushes to analyze both neuron connectivity and the influence of structure on function. Currently, a technical roadblock that stands in the way of these studies is the inability to automatically trace neuronal structure from microscopy. On the image processing side, proposed tracing algorithms face difficulties in low contrast, indistinct boundaries, clutter, and complex branching structure. To tackle these difficulties, we develop Tree2Tree, a robust automatic neuron segmentation and morphology generation algorithm. Tree2Tree uses a local medial tree generation strategy in combination with a global tree linking to build a maximum likelihood global tree. Recasting the neuron tracing problem in a graph-theoretic context enables Tree2Tree to estimate bifurcations naturally, which is currently a challenge for current neuron tracing algorithms. Tests on cluttered confocal microscopy images of Drosophila neurons give results that correspond to ground truth within a margin of ±2.75% normalized mean absolute error.
Phase congruency eigendecomposition for multi-scale neuronal enhancement

In this paper, we present an algorithm for enhancing neuronal structure from 3D Confocal Microscopy Images. Our algorithm computes a multi-scale phase congruency value at every pixel from a 3D image, which assigns values that indicate the presence of image features such as edges and lines. The phase congruency of a 3D image is calculated by carefully combining the convolutions of the image with a quadrature filter bank, so we leverage this information to supplement phase features. We analyse the outputs of the quadrature filter bank to enhance neuronal structure. We compare our method with the Hessian based enhancement of neuronal structure to demonstrate the advantages/efficacy of our algorithm.
MISTICA: Minimum Spanning Tree-Based Coarse Image Alignment for Microscopy Image Sequences

Registration of an in vivo microscopy image sequence is necessary in many significant studies, including studies of atherosclerosis in large arteries and the heart. Significant cardiac and respiratory motion of the living subject, occasional spells of focal plane changes, drift in the field of view, and long image sequences are the principal roadblocks. The first step in such a registration process is the removal of translational and rotational motion. Next, a deformable registration can be performed. The focus of our study here is to remove the translation and/or rigid body motion that we refer to here as coarse alignment. The existing techniques for coarse alignment are unable to accommodate long sequences often consisting of periods of poor quality images (as quantified by a suitable perceptual measure). Many existing methods require the user to select an anchor image to which other images are registered. We propose a novel method for coarse image sequence alignment based on minimum weighted spanning trees (MISTICA) that overcomes these difficulties. The principal idea behind MISTICA is to reorder the images in shorter sequences, to demote nonconforming or poor quality images in the registration process, and to mitigate the error propagation. The anchor image is selected automatically making MISTICA completely automated. MISTICA is computationally efficient. It has a single tuning parameter that determines graph width, which can also be eliminated by the way of additional computation. MISTICA outperforms existing alignment methods when applied to microscopy image sequences of mouse arteries.
Piecewise and local class models for image restoration

In this paper, we present a new approach to image restoration based on a flexible constraint framework that encapsulates structural assumptions about the uncorrupted image. Piecewise and local class (PALC) models are defined and utilized to restore images degraded by linear blurring and additive noise. The restoration process is accomplished by iteratively deconvolving the solution image while simultaneously optimizing local image characteristics defined by the PALC models. Solution images to this ill-posed, combinatorial problem are computed using the novel generalized deterministic annealing (GDA) optimization technique. The results demonstrate high quality image restoration as measured by local feature integrity, improvement in signal-to-noise ratio, and reduction of restoration artifacts, especially in the presence of heavy-tailed additive noise.<<ETX>>
Segmentation of colloidal crystal images by local histogram analysis

The segmentation algorithm given herein is a development of a study of hard-sphere colloidal crystallization undertaken by the authors. The motivation for the development of the algorithm was the lack of a suitable commercially-available program. The desired algorithm is one capable of accurately segmenting a large set of digitized images with a sparse population of blobs varying in contrast to the local background. Furthermore, the program would also need to segment accurately images with backgrounds which varied locally and temporally. The algorithm used here appears to provide adequate results with stable sensitivity to low and high contrast crystal images when implemented recursively.