nils-herrmann/clean_scirepeval huggingface dataset:
Real-time tracking of non-rigid objects using mean shift

A new method for real time tracking of non-rigid objects seen from a moving camera is proposed. The central computational module is based on the mean shift iterations and finds the most probable target position in the current frame. The dissimilarity between the target model (its color distribution) and the target candidates is expressed by a metric derived from the Bhattacharyya coefficient. The theoretical analysis of the approach shows that it relates to the Bayesian framework while providing a practical, fast and efficient solution. The capability of the tracker to handle in real time partial occlusions, significant clutter, and target scale variations, is demonstrated for several image sequences.
Using needle detection and tracking for motion compensation in abdominal interventions

In this paper, we present a method of using the needle detection and tracking to compensate breathing motion in 2D fluoroscopic videos. The method can robustly detect and tracking needles, even with the presence of image noises and large needle movements. The method first introduces an offline learned needle segment detector that detects needle segments at individual frames. Based on detected needle segments, a needle is interactively detected at the beginning of an intervention, and then is automatically tracked based on a probabilistic tracking framework. A multi-resolution kernel density estimation is applied to handle large needle movements efficiently and effectively. Experiments on phantom and clinical sequences demonstrate that the method can successfully track needles in fluoroscopy, and can provide motion compensation for abdominal interventions.
Molecular Diagnosis and Biomarker Identification on SELDI proteomics data by ADTBoost method

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 algorithm 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 (ALS) disease data acquired by surface enhanced laser-desorption/ionization-time-of-flight mass spectrometry (SELDI-TOF MS) 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. It will have broad application in molecular diagnosis through proteomics data analysis and personalized medicine in this post-genomic era
Computer-assisted discrimination among malignant lymphomas and leukemia using immunophenotyping, intelligent image repositories, and telemicroscopy

The process of discriminating among pathologies involving peripheral blood, bone marrow, and lymph node has traditionally begun with subjective morphological assessment of cellular materials viewed using light microscopy. The subtle visible differences exhibited by some malignant lymphomas and leukemia, however, give rise to a significant number of false negatives during microscopic evaluation by medical technologists. We have developed a distributed, clinical decision support prototype for distinguishing among hematologic malignancies. The system consists of two major components, a distributed telemicroscopy system and an intelligent image repository. The hybrid system enables individuals located at disparate clinical and research sites to engage in interactive consultation and to obtain computer-assisted decision support. Software, written in Java, allows primary users to control the specimen stage, objective lens, light levels, and focus of a robotic microscope remotely while a digital representation of the specimen is continuously broadcast to all session participants. Primary user status can be passed as a token. The system features shared graphical pointers, text messaging capability, and automated database management. Search engines for the database allow one to automatically identify and retrieve images, diagnoses, and correlated clinical data of cases from a gold standard database which exhibit spectral and spatial profiles which are most similar to a given query image.
Precise segmentation of the left atrium in C-arm CT volumes with applications to atrial fibrillation ablation

A patient-specific left atrium (LA) model extracted from intra-operative C-arm CT plays an important role in planning for transcatheter left atrial fibrillation ablation. Overlaying the LA model onto 2D fluoroscopic images provides valuable visual guidance during the intervention. However, automatic segmentation of the LA, together with the left atrial appendage (LAA) and the pulmonary vein (PV) trunks, is challenging due to the large structural variations and imaging artifacts. In this paper we exploit a part based LA model to handle the structural variations and different parts are then merged into a consolidated mesh. The connection region between the PV/LAA and the LA chamber is segmented precisely by enforcing both image boundary delineation accuracy and mesh smoothness. Furthermore, the boundary between parts is optimized to improve the mesh part labeling accuracy.
Patient-Specific Modeling and Quantification of the Aortic and Mitral Valves From 4-D Cardiac CT and TEE

As decisions in cardiology increasingly rely on noninvasive methods, fast and precise image processing tools have become a crucial component of the analysis workflow. To the best of our knowledge, we propose the first automatic system for patient-specific modeling and quantification of the left heart valves, which operates on cardiac computed tomography (CT) and transesophageal echocardiogram (TEE) data. Robust algorithms, based on recent advances in discriminative learning, are used to estimate patient-specific parameters from sequences of volumes covering an entire cardiac cycle. A novel physiological model of the aortic and mitral valves is introduced, which captures complex morphologic, dynamic, and pathologic variations. This holistic representation is hierarchically defined on three abstraction levels: global location and rigid motion model, nonrigid landmark motion model, and comprehensive aortic-mitral model. First we compute the rough location and cardiac motion applying marginal space learning. The rapid and complex motion of the valves, represented by anatomical landmarks, is estimated using a novel trajectory spectrum learning algorithm. The obtained landmark model guides the fitting of the full physiological valve model, which is locally refined through learned boundary detectors. Measurements efficiently computed from the aortic-mitral representation support an effective morphological and functional clinical evaluation. Extensive experiments on a heterogeneous data set, cumulated to 1516 TEE volumes from 65 4-D TEE sequences and 690 cardiac CT volumes from 69 4-D CT sequences, demonstrated a speed of 4.8 seconds per volume and average accuracy of 1.45 mm with respect to expert defined ground-truth. Additional clinical validations prove the quantification precision to be in the range of inter-user variability. To the best of our knowledge this is the first time a patient-specific model of the aortic and mitral valves is automatically estimated from volumetric sequences.
Robust information fusion using variable-bandwidth density estimation

We present a new approach to information fusion based on kernel density estimation. Employing a density estimation strategy with ddaptive bandwidths, we develop the variablebandwidth mean shift as an efficient technique for mode detection. Interestingly enough, the mean shifi procedure leads to the definition of a new fusion estimate as the location of the highest mode of a density function which takes into account the uncertainty of the estimates to be fused. We show that the new VariableBandwidth Density-based Fusion (VBDF) is consistent and conservative in the sense defined by Covariance Intersection, while being robust to outliers. At the same time, the new framework can reveal the presence of multiple source models in the input data. Experimental comparisons with the BLUE fision and Covariance Intersection show some of the advantages of our technique.
Total variation models for variable lighting face recognition

In this paper, we present the logarithmic total variation (LTV) model for face recognition under varying illumination, including natural lighting conditions, where we rarely know the strength, direction, or number of light sources. The proposed LTV model has the ability to factorize a single face image and obtain the illumination invariant facial structure, which is then used for face recognition. Our model is inspired by the SQI model but has better edge-preserving ability and simpler parameter selection. The merit of this model is that neither does it require any lighting assumption nor does it need any training. The LTV model reaches very high recognition rates in the tests using both Yale and CMU PIE face databases as well as a face database containing 765 subjects under outdoor lighting conditions
Parametric representations for nonlinear modeling of visual data

Accurate characterization of data distribution is of significant importance for vision problems. In many situations, multivariate visual data often spread into a nonlinear manifold in the high-dimensional space, which makes traditional linear modeling techniques ineffective. This paper proposes a generic nonlinear modeling scheme based on parametric data representations. We build a compact representation for the visual data using a set of parameterized basis (wavelet) functions, where the parameters are randomized to characterize the nonlinear structure of the data distribution. Meanwhile, a new progressive density approximation scheme is proposed to obtain an accurate estimate of the probability density, which imposes discrimination power on the model. Both synthetic and real image data are used to demonstrate the strength of our modeling scheme.
Meanshift clustering for DNA microarray analysis

Meanshift clustering is a well established algorithm that has been applied successfully in image processing and computer vision. Cluster centers are derived by local mode seeking identifying maxima in the normalized density of the data set. Recently, quantum clustering that highly resembles mean shift clustering has been proposed for analyzing microarray expression data. Quantum clustering is based on physical intuition derived from quantum mechanics. By an iterative process using a gradient descent procedure, the potential energy V belonging to the Hamiltonian of the time-independent Schrodinger equation develops minima that are identified with cluster centers. The analogies between the wavefunction in quantum clustering and the multivariate kernel density estimator in meanshift clustering are leading to closely related formulations. However, the approach towards the minima of the potential in quantum clustering needs to be performed unrelatedly to the formulation, by gradient descent steps. In contrast, in meanshift clustering the approach towards the maxima of the normalized density is performed by the meanshift vector that is derived by the formulation of the methodology. It points towards the direction of the maximum increase in the underlying density. Based on these observations, we propose implementing meanshift clustering to improve the efficiency of local mode seeking in analyzing expression data.
Visual Tracking by Continuous Density Propagation in Sequential Bayesian Filtering Framework

Particle filtering is frequently used for visual tracking problems since it provides a general framework for estimating and propagating probability density functions for nonlinear and non-Gaussian dynamic systems. However, this algorithm is based on a Monte Carlo approach and the cost of sampling and measurement is a problematic issue, especially for high-dimensional problems. We describe an alternative to the classical particle filter in which the underlying density function has an analytic representation for better approximation and effective propagation. The techniques of density interpolation and density approximation are introduced to represent the likelihood and the posterior densities with Gaussian mixtures, where all relevant parameters are automatically determined. The proposed analytic approach is shown to perform more efficiently in sampling in high-dimensional space. We apply the algorithm to real-time tracking problems and demonstrate its performance on real video sequences as well as synthetic examples.
Data-driven computational models of heart anatomy, mechanics and hemodynamics: An integrated framework

Cardiac therapies aim to correct pathological blood flow. Patient-specific therapy planning is challenging due to the large variability in disease cause, location and severity. A predictive framework is therefore needed to assess the optimal treatment for a patient in terms of maximizing effectiveness (blood flow velocity, vorticity, cardiac output, etc.) and minimizing the risk of complications. Multi-physics fluid structure interaction models have been proposed to investigate the function of the bi-ventricular myocardium. Yet, no or only partial patient-specific data have been used due to the lack of efficient methods for accurate modeling of patient's cardiac anatomy from images. Current imaging technologies enabling assessment of cardiac anatomy, dynamics and tissue structure, we propose an integrated framework for multi-physics heart modeling based on imaging data. Our approach relies on efficient machine learning methods to estimate an accurate and comprehensive model of patient's anatomy from MRI. A computational model of cardiac electrophysiology and biomechanics is then solved on patient's anatomy.
Automatic Aorta Segmentation and Valve Landmark Detection in C-Arm CT for Transcatheter Aortic Valve Implantation

Transcatheter aortic valve implantation (TAVI) is a minimally invasive procedure to treat severe aortic valve stenosis. As an emerging imaging technique, C-arm computed tomography (CT) plays a more and more important role in TAVI on both pre-operative surgical planning (e.g., providing 3-D valve measurements) and intra-operative guidance (e.g., determining a proper C-arm angulation). Automatic aorta segmentation and aortic valve landmark detection in a C-arm CT volume facilitate the seamless integration of C-arm CT into the TAVI workflow and improve the patient care. In this paper, we present a part-based aorta segmentation approach, which can handle structural variation of the aorta in case that the aortic arch and descending aorta are missing in the volume. The whole aorta model is split into four parts: aortic root, ascending aorta, aortic arch, and descending aorta. Discriminative learning is applied to train a detector for each part separately to exploit the rich domain knowledge embedded in an expert-annotated dataset. Eight important aortic valve landmarks (three hinges, three commissures, and two coronary ostia) are also detected automatically with an efficient hierarchical approach. Our approach is robust under all kinds of variations observed in a real clinical setting, including changes in the field-of-view, contrast agent injection, scan timing, and aortic valve regurgitation. Taking about 1.1 s to process a volume, it is also computationally efficient. Under the guidance of the automatically extracted patient-specific aorta model, the physicians can properly determine the C-arm angulation and deploy the prosthetic valve. Promising outcomes have been achieved in real clinical applications.
Reliable Detection of Overtaking Vehicles Using Robust Information Fusion

Early detection of overtaking vehicles is an important task for vision-based driver assistance systems. Techniques utilizing image motion are likely to suffer from spurious image structures caused by shadows and illumination changes, let alone the aperture problem. To achieve reliable detection of overtaking vehicles, the authors have developed a robust detection method, which integrates dynamic scene modeling, hypothesis testing, and robust information fusion. A robust fusion algorithm, based on variable bandwidth density fusion and multiscale mean shift, is introduced to obtain reliable motion estimation against various image noise. To further reduce detection error, the authors model the dynamics of road scenes and exploit useful constraints induced by the temporal coherence in vehicle overtaking. The proposed solution is integrated into a monocular vision system onboard for obstacle detection. Test results have shown superior performance achieved by the new method
Fractal clustering for microarray data analysis

DNA microarray experiments generate a substantial amount of information about global gene expression. Gene expression profiles can be represented as points in multi-dimensional space. It is essential to identify relevant groups of genes in biomedical research. Clustering is helpful in pattern recognition in gene expression profiles. Some clustering techniques have been introduced. However, these traditional methods mainly utilize shape-based assumption or distance metric to cluster the points in multi-dimension linear Euclidean space. Poor consistence with the functional annotation of genes is shown in their validation study. A fractal clustering method to cluster genes using intrinsic (fractal) dimension from modern geometry is proposed. Fractal dimension is used to characterize the degree of self similarity among the points in the clusters. The main idea of fractal clustering is to group points in a cluster in such a way that none of the points in the cluster changes the cluster's intrinsic dimension radically. Hausdorff fractal dimension is computed through the means of the box-counting plot algorithm, since it is the fastest and also robust enough. This method is assessed using validation assessment using public microarray dataset. It shows that this method is superior in identifying functional related gene groups than other traditional methods.
Illumination normalization for face recognition and uneven background correction using total variation based image models

We present a new algorithm for illumination normalization and uneven background correction in images, utilizing the recently proposed TV+L/sup 1/ model: minimizing the total variation of the output cartoon while subject to an L/sup 1/-norm fidelity term. We give intuitive proofs of its main advantages, including the well-known edge preserving capability, minimal signal distortion, and scale-dependent but intensity-independent foreground extraction. We then propose a novel TV-based quotient image model (TVQI) for illumination normalization, an important preprocessing for face recognition under different lighting conditions. Using this model, we achieve 100% face recognition rate on Yale face database B if the reference images are under good lighting condition and 99.45% if not. These results, compared to the average 65% recognition rate of the quotient image model and the average 95% recognition rate of the more recent self quotient image model, show a clear improvement. In addition, this model requires no training data, no assumption on the light source, and no alignment between different images for illumination normalization. We also present the results of the related applications - uneven background correction for cDNA mic roar ray films and digital microscope images. We believe the proposed works can serve important roles in the related fields.
Image-based Co-Registration of Angiography and Intravascular Ultrasound Images

In image-guided cardiac interventions, X-ray imaging and intravascular ultrasound (IVUS) imaging are two often used modalities. Interventional X-ray images, including angiography and fluoroscopy, are used to assess the lumen of the coronary arteries and to monitor devices in real time. IVUS provides rich intravascular information, such as vessel wall composition, plaque, and stent expansions, but lacks spatial orientations. Since the two imaging modalities are complementary to each other, it is highly desirable to co-register the two modalities to provide a comprehensive picture of the coronaries for interventional cardiologists. In this paper, we present a solution for co-registering 2-D angiography and IVUS through image-based device tracking. The presented framework includes learning-based vessel detection and device detections, model-based tracking, and geodesic distance-based registration. The system first interactively detects the coronary branch under investigation in a reference angiography image. During the pullback of the IVUS transducers, the system acquires both ECG-triggered fluoroscopy and IVUS images, and automatically tracks the position of the medical devices in fluoroscopy. The localization of tracked IVUS transducers and guiding catheter tips is used to associate an IVUS imaging plane to a corresponding location on the vessel branch under investigation. The presented image-based solution can be conveniently integrated into existing cardiology workflow. The system is validated with a set of clinical cases, and achieves good accuracy and robustness.
Vessel detection by mean shift based ray propagation

A robust and efficient method for the segmentation of vessel cross-sections in contrast enhanced CT and MR images is presented. The primary innovation of the technique is the boundary propagation by mean shift analysis combined with a smoothness constraint. Consequently, the robustness of the mean shift to noise is enhanced by the use of a priori information on boundary smoothness. This processing is integrated into our computationally efficient framework based on ray propagation. The new algorithm allows real time segmentation of medical structures found in multi-modality images (CT, MR) and various examples are shown to illustrate its effectiveness.
Automatic cardiac flow quantification on 3D volume color Doppler data

Valvular heart diseases are recognized as a significant cause of morbidity and mortality. Accurate quantification of cardiac flow volumes in patients is essential in evaluation of the progression of the disease and in determination of clinical options. Recent advances in the real-time 3D full volume echocardiography have enabled high frame rate acquisition of volumetric color Doppler flow images. In this paper, we propose a fully automated method to quantify the cardiac flow using instantaneous 3D+t ultrasound data. The anatomical information such as mitral annulus and left ventricle outflow tract (LVOT) are detected and tracked automatically accounting for the heart motion. Furthermore, the proposed method automatically detects and tracks the endocardial boundary of the left ventricle (LV) and computes the instantaneous change in LV volume. This information is used to overcome inherent limitation of the color Doppler velocity ambiguity such that de-aliasing parameters are computed and used to correct flow computations. Preliminary results with clinical data presented here agree well with accepted clinical measurements in a quantitative manner. The proposed method is efficient and achieves high speed performance of 0.2 second per volume of ultrasound data.