nils-herrmann/clean_scirepeval huggingface dataset:
Active contours for cell tracking

This paper introduces an active contour or snake-based method for tracking cells within a video sequence. Specifically, we apply our cell tracking techniques to rolling leukocytes observed in vivo (in living animal) from video microscopy. The analysis of leukocyte motion reveals cues about the mechanism of inflammatory disease. To attack the problem of tracking leukocytes in vivo, the proposed snake tracker utilizes shape and size information specific to the leukocytes. The principal contribution of this work lies in introducing the shape and size constraint as a geometric primitive in the parametric snake energy model. The energy functional is then minimized through the basic principles of the calculus of variations to obtain the Euler equations used in contour updating. We have developed a partial differential equation (PDE) based generalized gradient vector flow (GVF) that accommodates for contrast changes and weak cell edges. Whereas previous GVF models are sensitive to initial contour placement, the modified GVF construction with Dirichlet type boundary condition (BC) allows a snake tracker to be robust for a wide range of initial positions. Another contribution in this work is to incorporate an energy term in the snake model that eliminates the need for explicitly resampling the snake contour intermittently as performed in traditional snake evolution. Using animal experiments, we compare the accuracy of the proposed snake tracker with the correlation and centroid based tracker and show that the proposed tracker is superior in terms of increased number of frames tracked and reduced localization error.
Path-space algebra for constructing an average neuronal atlas from multi-class neuronal datasets

An open problem in biological image processing and analysis is the automated generation of neural atlases from image data. Such a neural atlas should be capable of representing typical neurons for each possible class. Further, the functioning atlas, called a neurome in this work, should enable the computation of neuron-to-neuron distances for the purposes of comparison and classification. Three technical roadblocks stand between the state of the art and the realization of a neural atlas. These three problems are the automated segmentation or tracing of 3-D neurons from image data, the computation of a neuron-to-neuron distance and the generation of a prototypical average neuron from the descriptions of multiple neurons. This paper concentrates on the latter problem — that of generating an average neuron. Our approach is hinged on the fundamental assumption that the individual paths of a neuron can be analyzed individually and used to describe the neuron as unit. In this path-based description, we account for path concurrence (the coexistence of multiple paths at given point), hierarchy (the importance of a path segment based on the position in a tree structure) and the actual 3-D position. This proof of concept is demonstrated by way of computing average neurons for three classes of neurons.
Accelerating leukocyte tracking using CUDA: A case study in leveraging manycore coprocessors

The availability of easily programmable manycore CPUs and GPUs has motivated investigations into how to best exploit their tremendous computational power for scientific computing. Here we demonstrate how a systems biology application—detection and tracking of white blood cells in video microscopy—can be accelerated by 200× using a CUDA-capable GPU. Because the algorithms and implementation challenges are common to a wide range of applications, we discuss general techniques that allow programmers to make efficient use of a manycore GPU.
Spatiotemporal segmentation for validation of rolling leukocyte tracking data

Processing of bulk microscopy video data requires automated tracking of rolling leukocytes in the hundreds to compute a rolling velocity distribution, which is an indispensable descriptor in inflammation research and anti/pro-inflammatory drug testing. However, for any automated tracking method to be successful, an automated validation process must exist to accept or reject the output of tracking. In this paper, we propose an automated validation technique that first generates a spatiotemporal image from the cell locations output by a tracking method; then, it segments the spatiotemporal image to detect the presence or absence of a leukocyte by employing an edge-response filter followed by an active contour method. The proposed direction sensitive edge-response filter, the maximum absolute average directional derivative (MAADD), computes the magnitude of the mean directional derivative over an oriented line segment and chooses the maximum of all such values within a range of orientations of the line segment. Our validation experiments show that the proposed method is successful in 93% of the trials using manual tracking, in 83% using correlation tracking and in 84% using active contour tracking method.
UGraSP: A unified framework for activity recognition and person identification using graph signal processing

With the growing availability and wide distribution of low-cost, high-performance 3D imaging sensors, the image analysis community has witnessed an increased demand for solutions to the challenges of activity recognition and person identification. We propose an integrated framework, based on graph signal processing, that simultaneously performs both tasks using a single set of features. The novelty of our approach is based on the fact that the set of features used for activity recognition accommodates person identification without additional computation. The analysis is based on the extracted structure-invariant graph (skeleton). The Laplacian of the skeleton is used both to identify the person and recognize the performed activity. While person identification is achieved directly from the analysis of the Laplacian, activity recognition is obtained after transformation, into the graph spectral domain, of the vectorized form of the skeletal joints 3D coordinates. Feature vectors for activity recognition are then derived, in this domain, from the covariance matrices evaluated over fixed-length sequential video segments. Both classification tasks are implemented using linear support vector machines (SVM). When applied to real activity datasets, our approach shows an improved performance over the existing state-of-the-art.
Image enhancement using a contrast measure in the compressed domain

An image enhancement algorithm for images compressed using the JPEG standard is presented. The algorithm is based on a contrast measure defined within the discrete cosine transform (DCT) domain. The advantages of the psychophysically motivated algorithm are 1) the algorithm does not affect the compressibility of the original image because it enhances the images in the decompression stage and 2) the approach is characterized by low computational complexity. The proposed algorithm is applicable to any DCT-based image compression standard, such as JPEG, MPEG 2, and H. 261.
Automated Affine Registration of First-Pass Magnetic Resonance Images

Quantitative first-pass magnetic resonance (MR) imaging studies assist in characterizing the severity of ischemic heart disease. Cardiac motion due to a patient's difficulty in maintaining the required breath hold is often severe, making registration necessary. In this paper we present a novel automatic affine registration algorithm. The algorithm utilizes an affine active contour to determine affine registration errors in first-pass MR studies. We compare our results to the original unregistered studies and a normalized cross-correlation registration method
NeuroBFD: Size-independent automated classification of neurons using conditional distributions of morphological features

Automated classification and characterization of digitally-traced neurons poses a challenge in constructing the neurome of human brain. The complex anatomical structure of each neuron causes observable morphological and geometrical variations between different cell types and within cell types. Given a graph model of a neuron, one of the major bottlenecks in encoding such variation followed by the within-cell and between-cell comparisons is the size of the neuron. In this work, we define size-independent statistical morphometrics as feature descriptors for each neuron. The customized morphometrics are built by extracting three raw features, which are bifurcation angle, branch fragmentation, and spatial arborization density of a neuron. Next, the local variation in each of the raw features is encoded by constructing a conditional distribution for that feature, which provides an effective and discriminatory feature assembly. In addition, the algorithm is automatic, scalable, and coordinate-independent. The comparative approach is shown to outperform the existing state-of-the-art methods.
Ultrasound Myocardial Tracking with Speckle Reducing Anisotropic Diffusion Assisted Initialization

Cardiac parameters such as end-systolic volume, ejection fraction and myocardial mass are essential to the diagnosis and treatment of cardiovascular disease (CVD). Traditionally, these parameters are calculated based on manual myocardial segmentation by a trained technician. Fast, accurate, and automatic segmentation would provide researchers with an increased subject pool, an enhanced understanding of CVD, and may lead to the development of new therapies. In this paper we propose an automated algorithm for myocardial segmentation. This method utilizes speckle reducing anisotropic diffusion to assist the automated contour initialization. Speckle tracking segmentation (STS) is then applied throughout the cardiac cycle to track the myocardial borders. This approach, compared to standard active contour techniques, reduces the RMSE to ground truth by an order of magnitude.
Image classification by multi-kernel dictionary learning

Recent studies have indicated the efficacy of selecting and combining the salient features from a pool of feature types in image retrieval and classification applications. In contrast to previous work, in this paper, we approach this problem as a selection and combination of the salient feature type(s) from a pool of feature types rather than selecting an individual feature. Our approach utilizes multiple kernels within the dictionary-learning framework where a combination of dictionary atoms represents individual categories. The category specific feature combination parameters or weights for kernel combination are determined by the mutual information techniques. The method is compared to a meta-algorithm for feature nomination. The multi-kernel dictionary learning method yields, on average, a 10% increase in classification accuracy with respect to the meta-algorithm in our preliminary experiments.
Assessment of Wallerian degeneration by automated image analysis

Axonal degeneration is a hallmark of both developmental and pathological events. Degenerating axons appear as either interruptions in the cytoskeletal network or aggregates along the axon. Since manual quantification of degeneration events is cumbersome and time consuming, automating this process promises to expedite drug discovery as treatment for axonal injury or neurodegenerative disorders such as Alzheimer's disease. In this paper we present a multiscale approach to segment the degenerative features from the intact axonal structure. The segmentation involves delineation of the axons and detection of blebbing in the axonal cytoskeleton. The solution makes use of tools from morphology and scale analysis. The delineated set of degenerative features is then quantified with respect to the overall neuronal mass and the mass of the intact axons and a degeneration index is derived.
Segmentation of ultrasound imagery using anisotropic diffusion

We present an anisotropic diffusion technique, tailored to ultrasound images, for the purpose of enhancing and segmenting medical ultrasound imagery. Taking into account the speckle characteristics of ultrasound imaging, we introduce a specialized set of partial differential equations to adaptively enhance the raw ultrasound data. The diffusion method mimics traditional anisotropic diffusion in the sense of directional sensitivity. As a result, homogeneous regions are smoothed in an intra-region manner and edges can be enhanced and extracted in the presence of speckle. We provide a series of experiments using synthetically developed models of the carotid artery. The experiments investigate the effect of varying edge orientation, level of noise, and amount of image contrast. Results from the novel diffusion technique are compared with the conventional approaches, revealing the efficacy of the proposed method.
A geometric-statistical approach toward neuron matching

In the same vein as the genome project that mapped the genetic structure of complex organisms such as the mouse, those pursuing the neurome are seeking a map the neural anatomy. In this massive biological investigation, the tools of imaging and biological experimentation are outpacing the requisite tools in image analysis. In terms of comparing neurons, based on the geometrical structure and features within the structure, the accepted approaches are largely manual. In this paper, we propose a combined geometric-statistical approach toward automated neuron matching. We utilize the geometric information of a neuron and compute a pairwise distance histogram based on the geometric information, to find a similarity measure between neurons. The distribution function is so chosen such that it reflects the structural pattern of a set of neuronal points, and is rotationally invariant. Preliminary experiments on a set of three different classes of neurons, with six neurons in each class, provides evidence of efficacy, with the best two matches to a given query producing a retrieval error of 0% and the third best match producing an error of only 11.2%. In future work, the proposed method can be used as a component in the retrieval of similar neurons from neuronal database.
A pyramidal edge detector based on anisotropic diffusion

A novel approach to diffusion-based edge detection is presented. The method utilizes a multiresolution, multiscale pyramidal structure created via successive anisotropic diffusion and subsampling of the original image. Coarse, low resolution image representations of the anisotropic diffusion pyramid (ADP) guide boundary detection at higher resolutions. The edges computed by this hierarchical technique are resilient to image corruption and reflect features of a desired scale. The ADP edge detector offers the advantages of decreased edge localization error and region merging, as compared to Gaussian-based pyramid edge detectors. The ADP-based approach also improves upon the performance of fixed-resolution anisotropic diffusion in noise and alleviates the need for post-processing such as edge thinning and linking.
Image segmentation techniques for object-based coding

Two image segmentation methods are presented and compared terms of rate-distortion within an object-based coding scheme. The LOMO segmentation exploits the relationship between mathematical morphology and local monotonicity in producing a multiscale segmentation. The process is a morphological analogy to the Laplacian of Gaussian. The level set approach used area morphology to generate segmented regions having a specified minimum area. Segments are optimally chosen from the connected components of the image level sets. A simple object-based coding scheme using the discrete cosine transform is used to avoid the artifacts produced by conventional block-based coding at segment boundaries. Results of each segmentation method are given and compared to another and to conventional JPEG coding by rate-distortion and the presence of boundary artifacts.
Evaluation of intravital tracking algorithms

We are exploring automated tracking for cells observed in vivo by video microscopy. In the past, three tracking algorithms have been utilized for tracking cells: the normalized cross correlation tracking algorithm, the centroid tracking algorithm and the snake based tracking algorithm. This paper focuses on the analysis, evaluation and comparison of the above three tracking algorithms using some performance measures. These performance measures include: (1) the average number of consecutive frames tracked; (2) the localization error; (3) correlation with ground truth, and (4) tracking time. One hundred in vivo (intravital) videos were used in our experiments for analysis, evaluation and comparison. The results show that snake based tracking provides the best performance, and that automated tracking of leukocytes is indeed possible.
Feature Weighted Active Contours for Image Segmentation

A novel external energy construction for active contours is proposed in this paper. In contrast to the standard approach of using linear filtering to smooth the external energies and to avoid noise, we define feature weighted active contours for extracting features of interest without distortion. The advantages of this innovation are demonstrated by examples and comparisons with Gaussian filtered external energy. Compared to the linear filtering approach, the feature weighted active contours yield lower root mean squared errors of contour position and improve upon the ability to capture fine details in noisy images
Retrieving similar images in an image database using a relational matrix

The definition of perceptual similarity between two images is a function of the distance between image features, i.e, shape, color and texture features of dominant image regions. We propose a relational matrix based distance measure that can be used to measure the similarity between images in a given image database. A relational matrix, individualized for an image, describes the spatial and functional relations between neighboring image segments in the image intensity matrix. Automated image segmentation using agglomerative clustering generates the relational matrix. We propose a distance measure between corresponding relational matrices representing any two images that can be used in content based image retrieval. Simulation results are given to demonstrate the potential of the relational matrix in the digital library application.
SSPARED: Saliency and sparse code analysis for rare event detection in video

The problem of detecting rare and unusual events in video is critical to the analysis of large video datasets. Such events are identified as those occurrences within a sequence that cause a significant change in the scene. We propose to determine the significance of a frame, while preserving its compact representation, by introducing a saliency-driven dictionary learning technique. The derived sparse codes are then leveraged, together with the Kullback-Leibler divergence, in the design of a histogram-based metric that we use to evaluate the scene changes between consecutive frames. Our method, SSPARED, is compared with two state of the art methods for anomaly detection and shows significant improvement in detecting abnormal incidents and reduced false alarm generation.
Image edges from area morphology

This paper introduces an edge detection process based on area morphology. Area open-close and area close-open operators are used to generate scaled image representations for feature extraction. The edges are defined by the boundaries of the scaled objects in the area-filtered images. From the area open-close and close-open operators, thin closed contours suitable for image segmentation are produced. The edge maps allow exact specification of the minimum area for the extracted regions and are Euclidean invariant and causal through scale space. Results are given that demonstrate the effectiveness of the area operator-based edge detection. In contrast to traditional edge detectors, edge detection via area morphology provides well-localized boundaries and does not require thresholding.
3D-to-2D mapping for user interactive segmentation of human leg muscles from MRI data

The automated computation of muscle volume from MRI of human legs is an open problem in the biomedical imaging community. Such automation has the potential to provide an objective measure of effectiveness of pre- and post-surgery treatments. In this paper, we take a step toward automation by proposing a framework for user interactive segmentation of MRI of human leg muscles. Our framework is built upon the strategy of bootstrapping: after the first few tedious segmentation results are achieved, and once a small database of segmented muscles is built, user interaction is reduced. Further, as the database of segmented muscles grows, the user interaction becomes more efficient. At the heart of this proposed framework is a simple, computationally attractive 3D representation of muscles. By a generalized cylinder model, we represent a 3D human leg muscle by two smooth 2D images, which enables application of 2D image processing and analysis methods in this complex multi-segment 3D problem. The smoothness of a leg muscle is modeled by the smoothness of the 2D images. Interdependence and relative positions of leg muscles are represented by a linear combination (basically, convolutions) of such 2D images. We demonstrate that fitting and editing of these models during user interactive segmentation of MRI data are computationally efficient, because our linear interaction model can be represented in the Fourier domain.
A variational method for leukocyte detection

In this paper, we propose a variational method for the detection of leukocytes observed in vivo. An adaptive threshold surface is constructed automatically using boundary information from the image. The surface is created using an objective functional that is minimized via a variational approach. This surface is constrained by an edge field that is also computed with a variational method. Objects extracted from background are pruned according to two geometric criteria. In the experiments, we find the false positive rate of the detector and show that the proposed approach can automatically and accurately identify multiple rolling leukocytes in vivo.
Detection of radioactive seeds in ultrasound images of the prostate

This paper presents a robust technique for automatically detecting radioactive seeds that are used for prostate cancer therapy. The main innovation of the detection technique is the utilization of distributed constant false alarm rate (CFAR) processors and orientation-sensitive morphological filtering to locate the seeds in the ultrasound imagery. CFAR detection is utilized to detect the seed candidates with high signal-to-clutter ratios (SCR). The CFAR problem is posed as the detection of a fluctuating target against K-distributed clutter. Adaptive template matching is used to detect the weak seed signals and to discriminate the seed-like clutter from the seed candidates. To reduce the speckle noise, the adaptive template matching uses orientation-sensitive morphological filters. The complete detection algorithm has been tested using a set of phantom ultrasound images containing radioactive seeds. In the process of implantation of seeds for radiotherapy, the detection method can be used to evaluate seed placement before 3D reconstruction is accomplished.