nils-herrmann/clean_scirepeval huggingface dataset:
Sequential Kernel Density Approximation and Its Application to Real-Time Visual Tracking

Visual features are commonly modeled with probability density functions in computer vision problems, but current methods such as a mixture of Gaussians and kernel density estimation suffer from either the lack of flexibility by fixing or limiting the number of Gaussian components in the mixture or large memory requirement by maintaining a nonparametric representation of the density. These problems are aggravated in real-time computer vision applications since density functions are required to be updated as new data becomes available. We present a novel kernel density approximation technique based on the mean-shift mode finding algorithm and describe an efficient method to sequentially propagate the density modes over time. Although the proposed density representation is memory efficient, which is typical for mixture densities, it inherits the flexibility of nonparametric methods by allowing the number of components to be variable. The accuracy and compactness of the sequential kernel density approximation technique is illustrated by both simulations and experiments. Sequential kernel density approximation is applied to online target appearance modeling for visual tracking, and its performance is demonstrated on a variety of videos.
Design, analysis, and engineering of video monitoring systems: an approach and a case study

Rapid improvement in computing power, cheap sensing, and more flexible algorithms are facilitating increased development of real-time video surveillance and monitoring systems. The deployment of video understanding systems in certain critical applications in the real world can be done only if performance guarantees can be provided for these systems. This paper reviews past work on a systematic engineering methodology for vision systems performance characterization and illustrates how it can be adapted in practice to develop a real-time people detection and zooming system to meet given application requirements. A case study involving dual-camera real-time video surveillance is used to illustrate that by judiciously choosing the system modules and by performing a careful analysis of the influence of various tuning parameters on the system it is possible to perform proper statistical inference, to automatically set control parameters and to quantify performance limits.
A novel coupling algorithm for computing blood flow in viscoelastic arterial models

We propose a novel coupling algorithm, based on the operator-splitting scheme, which implements the viscoelastic wall law at the coupling nodes of the vessels. Two different viscoelastic models are used (V1 and V2), leading to five different computational setups: elastic wall law, model V1 applied at interior and coupling grid points, model V1 applied only at the interior grid points (V1-int), model V2 applied at interior and coupling grid points, model V2 applied only at the interior grid points (V2-int). These have been tested with two arterial configurations: (i) single artery, and (ii) complete arterial tree. Models V1-int and V2-int lead to incorrect conclusions and to errors which can be of the same order as, and are at least 1/5 of, the difference between the results with the elastic and the viscoelastic laws. Both test cases demonstrate the importance of modeling the viscous component of the pressure-area relationship at all grid points, including the coupling points between vessels or at the inlet/outlet of the model.
Robust real-time myocardial border tracking for echocardiography: an information fusion approach

Ultrasound is a main noninvasive modality for the assessment of the heart function. Wall tracking from ultrasound data is, however, inherently difficult due to weak echoes, clutter, poor signal-to-noise ratio, and signal dropouts. To cope with these artifacts, pretrained shape models can be applied to constrain the tracking. However, existing methods for incorporating subspace shape constraints in myocardial border tracking use only partial information from the model distribution, and do not exploit spatially varying uncertainties from feature tracking. In this paper, we propose a complete fusion formulation in the information space for robust shape tracking, optimally resolving uncertainties from the system dynamics, heteroscedastic measurement noise, and subspace shape model. We also exploit information from the ground truth initialization where this is available. The new framework is applied for tracking of myocardial borders in very noisy echocardiography sequences. Numerous myocardium tracking experiments validate the theory and show the potential of very accurate wall motion measurements. The proposed framework outperforms the traditional shape-space-constrained tracking algorithm by a significant margin. Due to the optimal fusion of different sources of uncertainties, robust performance is observed even for the most challenging cases.
Diagnosis and biomarker identification on SELDI proteomics data by ADTBoost

Clinical proteomics is an emerging field that will have great impact on molecular diagnosis, identification of disease biomarkers, drug discovery and clinical trials in the post-genomic era. Protein profiling in tissues and fluids in disease and pathological control and other proteomics techniques will play an important role in molecular diagnosis with therapeutics and personalized healthcare. We introduced a new robust diagnostic method based on ADTboost algorithm, a novel method in proteomics data analysis to improve classification accuracy. It generates classification rules, which are often smaller and easier to interpret. This method often gives most discriminative features, which can be utilized as biomarkers for diagnostic purpose. Also, it has a nice feature of providing a measure of prediction confidence. We carried out this method in Amyotrophic lateral sclerosis disease data acquired by surface enhanced laser desorption/ionization-time-of-flight mass spectrometry experiments. Our method is shown to have outstanding prediction capacity through the cross-validation, ROC analysis results and comparative study. Our molecular diagnosis method provides an efficient way to distinguish ALS disease from neurological controls. The results are expressed in a simple and straightforward alternating decision tree format or conditional format. We identified most discriminative peaks in proteomic data, which can be utilized as biomarkers for diagnosis. ADTboost is not only useful in on proteomic data classification, it can also integrate other clinical, imaging data from heterogeneous sources for early diagnosis. It will have broad application in molecular diagnosis through proteomics and personalized medicine.
Estimation of patient-specific material properties of the mitral valve using 4D Transesophageal Echocardiography

4D Transesophageal Echocardiography (TEE) is a newly developed tool to visualize the morphology and dynamics of the mitral valve for diagnosis and treatment planning. Quantitative patient-specific modeling of the mitral valve is demanded since it allows for the reliable predictive simulation of medical intervention. State-of-the-art image-based and biomechanical models with generic material parameters have limited predictive power as they are only partially fitted to patient-data. As a step closer to a fully personalized model, an estimation algorithm is presented in this paper. The method combines image-derived mitral valve dynamics with a biomechanical model to estimate regional patient-specific material parameters in-vivo. In particular, the extended Kalman filter (EKF) is adopted in a way that it becomes flexible to integrate any biomechanical model and more parameters of interest in the estimation. The algorithm was verified on synthetic data with known Young's modulus and shear modulus, yielding less than 5% error. The algorithm was also evaluated on 4D TEE images of five patients. Estimated Young's moduli agree with the clinical observation that material parameters vary regionally and among the population. The estimated material parameters can be used in patient-specific modeling as well as the detection and evaluation of diseased areas.
Mean shift analysis and applications

A nonparametric estimator of density gradient, the mean shift, is employed in the joint, spatial-range (value) domain of gray level and color images for discontinuity preserving filtering and image segmentation. Properties of the mean shift are reviewed and its convergence on lattices is proven. The proposed filtering method associates with each pixel in the image the closest local mode in the density distribution of the joint domain. Segmentation into a piecewise constant structure requires only one more step, fusion of the regions associated with nearby modes. The proposed technique has two parameters controlling the resolution in the spatial and range domains. Since convergence is guaranteed, the technique does not require the intervention of the user to stop the filtering at the desired image quality. Several examples, for gray and color images, show the versatility of the method and compare favorably with results described in the literature for the same images.
Model based non-invasive estimation of PV loop from echocardiography

We introduce a model-based approach for the non-invasive estimation of patient specific, left ventricular PV loops. A lumped parameter circulation model is used, composed of the pulmonary venous circulation, left atrium, left ventricle and the systemic circulation. A fully automated parameter estimation framework is introduced for model personalization, composed of two sequential steps: first, a series of parameters are computed directly, and, next, a fully automatic optimization-based calibration method is employed to iteratively estimate the values of the remaining parameters. The proposed methodology is first evaluated for three healthy volunteers: a perfect agreement is obtained between the computed quantities and the clinical measurements. Additionally, for an initial validation of the methodology, we computed the PV loop for a patient with mild aortic valve regurgitation and compared the results against the invasively determined quantities: there is a close agreement between the time-varying LV and aortic pressures, time-varying LV volumes, and PV loops.
Decision support system for multiuser remote microscopy in telepathology

Recent advances in networking, robotics and computer technology allow real-time diagnosis, consultation, and education by using images obtained through remote microscopy. This paper presents a new approach in telepathology, the image guided decision support (IGDS) system, which integrates components for both remote microscope control and decision support. Using the micro-controller component the physician can command a robotic microscope from a distance, obtain high-quality images to be used in the diagnosis, and authorize other users to visualize the same images. The image understanding-based decision support component of the system locates, retrieves and displays cases which exhibit morphological profiles consistent to the case in question and suggests the most likely diagnosis based on majority logic. The IGDS system has a natural man-machine interface containing engines for speech recognition and voice feedback.
Robust anisotropic Gaussian fitting for volumetric characterization of Pulmonary nodules in multislice CT

This paper proposes a robust statistical estimation and verification framework for characterizing the ellipsoidal (anisotropic) geometrical structure of pulmonary nodules in the Multislice X-ray computed tomography (CT) images. Given a marker indicating a rough location of a target, the proposed solution estimates the target's center location, ellipsoidal boundary approximation, volume, maximum/average diameters, and isotropy by robustly and efficiently fitting an anisotropic Gaussian intensity model. We propose a novel multiscale joint segmentation and model fitting solution which extends the robust mean shift-based analysis to the linear scale-space theory. The design is motivated for enhancing the robustness against margin-truncation induced by neighboring structures, data with large deviations from the chosen model, and marker location variability. A chi-square-based statistical verification and analytical volumetric measurement solutions are also proposed to complement this estimation framework. Experiments with synthetic one-dimensional and two-dimensional data clearly demonstrate the advantage of our solution in comparison with the /spl gamma/-normalized Laplacian approach (Linderberg, 1998) and the standard sample estimation approach (Matei, 2001). A quasi-real-time three-dimensional nodule characterization system is developed using this framework and validated with two clinical data sets of thin-section chest CT images. Our experiments with 1310 nodules resulted in 1) robustness against intraoperator and interoperator variability due to varying marker locations, 2) 81% correct estimation rate, 3) 3% false acceptance and 5% false rejection rates, and 4) correct characterization of clinically significant nonsolid ground-glass opacity nodules. This system processes each 33-voxel volume-of-interest by an average of 2 s with a 2.4-GHz Intel CPU. Our solution is generic and can be applied for the analysis of blob-like structures in various other applications.
Mean Shift: A Robust Approach Toward Feature Space Analysis

A general non-parametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure: the mean shift. For discrete data, we prove the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and, thus, its utility in detecting the modes of the density. The relation of the mean shift procedure to the Nadaraya-Watson estimator from kernel regression and the robust M-estimators; of location is also established. Algorithms for two low-level vision tasks discontinuity-preserving smoothing and image segmentation - are described as applications. In these algorithms, the only user-set parameter is the resolution of the analysis, and either gray-level or color images are accepted as input. Extensive experimental results illustrate their excellent performance.
Fast tracking of catheters in 2D fluoroscopic images using an integrated CPU-GPU framework

Catheter tracking has become more important in recent interventional applications for atrial fibrillation (AF) ablation procedures. It can provide real-time guidance for the physicians and be used for motion compensation by overlaying a 3D left atrium model on live 2D fluoroscopic images. To achieve that, this paper has two main contributions. We first propose a new approach to generate tracking hypotheses based on catheter electrode detection. The novelly-designed tracking hypotheses are evaluated by a Bayesian-framework that fuses learning-based detection and template matching. The second contribution is a novel integrated framework that efficiently distributes computation between a GPU (graphics processing unit) and a CPU. Our framework implements Probabilistic Boosting-Tree (PBT)-based [7] classification for object detection in 2D data on the GPU. Quantitative evaluation has been conducted on a databases of 1073 clinical fluoroscopic sequences. The new framework achieves robust performance with the median error at 0.5mm and the 95th percentile error at 1.0mm. The speed of tracking the coronary sinus (CS) catheter reaches more than 30 frames-per-second (fps) on most evaluation data. The achieved speed is faster than most real-time fluoroscopy frame rates.
Robust analysis of feature spaces: color image segmentation

A general technique for the recovery of significant image features is presented. The technique is based on the mean shift algorithm, a simple nonparametric procedure for estimating density gradients. Drawbacks of the current methods (including robust clustering) are avoided. Feature space of any nature can be processed, and as an example, color image segmentation is discussed. The segmentation is completely autonomous, only its class is chosen by the user. Thus, the same program can produce a high quality edge image, or provide, by extracting all the significant colors, a preprocessor for content-based query systems. A 512/spl times/512 color image is analyzed in less than 10 seconds on a standard workstation. Gray level images are handled as color images having only the lightness coordinate.
Detection and Measurement of Fetal Anatomies from Ultrasound Images using a Constrained Probabilistic Boosting Tree

We propose a novel method for the automatic detection and measurement of fetal anatomical structures in ultrasound images. This problem offers a myriad of challenges, including: difficulty of modeling the appearance variations of the visual object of interest, robustness to speckle noise and signal dropout, and large search space of the detection procedure. Previous solutions typically rely on the explicit encoding of prior knowledge and formulation of the problem as a perceptual grouping task solved through clustering or variational approaches. These methods are constrained by the validity of the underlying assumptions and usually are not enough to capture the complex appearances of fetal anatomies. We propose a novel system for fast automatic detection and measurement of fetal anatomies that directly exploits a large database of expert annotated fetal anatomical structures in ultrasound images. Our method learns automatically to distinguish between the appearance of the object of interest and background by training a constrained probabilistic boosting tree classifier. This system is able to produce the automatic segmentation of several fetal anatomies using the same basic detection algorithm. We show results on fully automatic measurement of biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), femur length (FL), humerus length (HL), and crown rump length (CRL). Notice that our approach is the first in the literature to deal with the HL and CRL measurements. Extensive experiments (with clinical validation) show that our system is, on average, close to the accuracy of experts in terms of segmentation and obstetric measurements. Finally, this system runs under half second on a standard dual-core PC computer.
Robust motion estimation using trajectory spectrum learning: Application to aortic and mitral valve modeling from 4D TEE

In this paper we propose a robust and efficient approach to localizing and estimating the motion of non-rigid and articulated objects using marginal trajectory spectrum learning. Detecting the motion directly in the Euclidean space is often found difficult to guarantee a smooth and accurate result and might be affected by drifting. These issues, however, can be addressed effectively by formulating the motion estimation problem as spectrum detection in the trajectory space. The full trajectory space can be decomposed into orthogonal subspaces defined by generic bases, such as the Discrete Fourier Transform (DFT). The obtained representation is shown to be compact, facilitating efficient learning and optimization in its marginal spaces. In the training stage, local features are extended in the temporal domain to integrate the time coherence constraint and selected via boosting to form strong classifiers. An incremental optimization is performed in sparse marginal spaces learned from the training data. To maximize efficiency and robustness we constrain the search based on clusters of hypotheses defined in each subspace. Experiments demonstrate the performance of the proposed method on articulated motion estimation of aortic and mitral valves from ultrasound data. Our method is evaluated on 65 4D TEE sequences (1516 volumes) with the accuracy in the range of the inter-user variability of expert users. It provides in less than 60 seconds with an precision of 1.36 ± 0.32mm a personalized 4D model of aortic and mitral valves crucial for the clinical workflow.
Multiple object detection by sequential monte carlo and Hierarchical Detection Network

In this paper, we propose a novel framework for detecting multiple objects in 2D and 3D images. Since a joint multi-object model is difficult to obtain in most practical situations, we focus here on detecting the objects sequentially, one-by-one. The interdependence of object poses and strong prior information embedded in our domain of medical images results in better performance than detecting the objects individually. Our approach is based on Sequential Estimation techniques, frequently applied to visual tracking. Unlike in tracking, where the sequential order is naturally determined by the time sequence, the order of detection of multiple objects must be selected, leading to a Hierarchical Detection Network (HDN). We present an algorithm that optimally selects the order based on probability of states (object poses) within the ground truth region. The posterior distribution of the object pose is approximated at each step by sequential Monte Carlo. The samples are propagated within the sequence across multiple objects and hierarchical levels. We show on 2D ultrasound images of left atrium, that the automatically selected sequential order yields low mean detection error. We also quantitatively evaluate the hierarchical detection of fetal faces and three fetal brain structures in 3D ultrasound images.
Constrained marginal space learning for efficient 3D anatomical structure detection in medical images

Recently, we proposed marginal space learning (MSL) as a generic approach for automatic detection of 3D anatomical structures in many medical imaging modalities. To accurately localize a 3D object, we need to estimate nine parameters (three for position, three for orientation, and three for anisotropic scaling). Instead of uniformly searching the original nine-dimensional parameter space, only low-dimensional marginal spaces are uniformly searched in MSL, which significantly improves the speed. In many real applications, a strong correlation may exist among parameters in the same marginal spaces. For example, a large object may have large scaling values along all directions. In this paper, we propose constrained MSL to exploit this correlation for further speed-up. As another major contribution, we propose to use quaternions for 3D orientation representation and distance measurement to overcome the inherent drawbacks of Euler angles in the original MSL. The proposed method has been tested on three 3D anatomical structure detection problems in medical images, including liver detection in computed tomography (CT) volumes, and left ventricle detection in both CT and ultrasound volumes. Experiments on the largest datasets ever reported show that constrained MSL can improve the detection speed up to 14 times, while achieving comparable or better detection accuracy. It takes less than half a second to detect a 3D anatomical structure in a volume.
An Algorithm for Data-Driven Bandwidth Selection

The analysis of a feature space that exhibits multiscale patterns often requires kernel estimation techniques with locally adaptive bandwidths, such as the variable-bandwidth mean shift. Proper selection of the kernel bandwidth is, however, a critical step for superior space analysis and partitioning. This paper presents a mean shift-based approach for local bandwidth selection in the multimodal, multivariate case. The method is based on a fundamental property of normal distributions regarding the bias of the normalized density gradient. This paper demonstrates that, within the large sample approximation, the local covariance is estimated by the matrix that maximizes the magnitude of the normalized mean shift vector. Using this property, the paper develops a reliable algorithm which takes into account the stability of local bandwidth estimates across scales. The validity of the theoretical results is proven in various space partitioning experiments involving the variable-bandwidth mean shift.