nils-herrmann/clean_scirepeval huggingface dataset:
Brain tissue segmentation in PET-CT images using probabilistic atlas and variational Bayes inference

PET-CT provides aligned anatomical (CT) and functional (PET) images in a single scan, and has the potential to improve brain PET image segmentation, which can in turn improve quantitative clinical analyses. We propose a statistical segmentation algorithm that incorporates the prior anatomical knowledge represented by probabilistic brain atlas into the variational Bayes inference to delineate gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) in brain PET-CT images. Our approach adds an additional novel aspect by allowing voxels to have variable and adaptive prior probabilities of belonging to each class. We compared our algorithm to the segmentation approaches implemented in the expectation maximization segmentation (EMS) and statistical parametric mapping (SPM8) packages in 26 clinical cases. The results show that our algorithm improves the accuracy of brain PET-CT image segmentation.
PCR-based fair intelligent bandwidth allocation for rate adaptive video traffic

In this paper, we propose a network bandwidth sharing algorithm, Peak Cell Rate (PCR)-based Fair Intelligent Bandwidth Allocation (PFIBA) for transporting rate-adaptive video traffic using feedback, and report on its performance under a general PCR-based share policies. Through extensive simulations, we obtained following results. The PFIBA algorithm is capable of allocating bandwidth fairly for the minimum cell rate (MCR) plus PCR-proportional fairness criteria among competitive rate-adaptive video sources, is capable to reallocate smoothly when there are renegotiations of the minimum guaranteed cell rate or PCR by some connections, is able to reallocate smoothly when a new connection is admitted, and is able to reallocate smoothly when a connection is throttled somewhere earlier along the connection path. Furthermore, we show the algorithm prevents congestion, especially during the initial periods when buffer queues can build up significantly.
Enhanced formation of parametric images using fast regressive GLLS for noisy functional imaging

Parametric images derived in functional imaging can visualize the spatial distribution of physiological parameters in vivo. However, the high level of noise intrinsic in single photon emission computed tomography (SPECT) may lead to physiologically meaningless parameter estimates such as negative kinetic rate constants using the generalized linear least squares (GLLS) method for compartmental model fitting. In this study, an enhanced GLLS method using fast regressive adjustment of parameters was investigated for improving the reliability of GLLS applied to dynamic SPECT data. Monte Carlo simulation data were used to systematically evaluate accuracy and reliability of derived parametric images. The simulation and experimental results demonstrate that the enhanced GLLS method can achieve more reliable parametric images, while largely preserving computational efficiency.
Prior knowledge enhanced random walk for lung tumor segmentation from low-contrast CT images

The separation of a lung tumor from adjacent normal tissue, which has similar intensity values and indistinct boundaries on low-contrast CT images is a challenging task. In this paper, a prior knowledge enhanced random walk (RW) is proposed to account for the prior functional knowledge from PET and intensity information from CT. The prior knowledge acquired from PET is used for the automated selection of foreground seeds, defined as the tumor confidence region, the background seeds and the walking range to increase computational efficiency of the RW algorithm in CT. The tumor confidence region is also used for balancing transition, and thus limiting the information propagation range through a weight factor. The experimental evaluation on 18 low-contrast CT images with manual tumor segmentation demonstrated that our method outperformed RW and random walk from restart (RWR) as measured by the Dice similarity coefficient (DSC).
Elastic & Efficient Three-Dimensional Registration for Abdominal Images

Biomedical image registration plays an important role in the intelligent use of large amount of medical data to establish safe healthcare. One of main challenges in abdominal image registration is to deal with elastic deformations caused by the shape and volume changes of internal organs, as well as heartbeat and breath. Other challenges include high computational efficiency required for clinical applications and accurate registration for 3D multimodality images. In this paper, an elastic, efficient and automatic 3D abdominal image registration method is proposed. Our proposed algorithm is divided into two stages and applied to register slice pairs of 3D volume data. First, non-iterative affine registration is performed to fix the global deformation by minimizing mean squared error (MSE). Then, effective active contour-based elastic registration is performed to correct the local deformations. This method has been validated by the experiments on both 3D CT-CT registration and CT-PET registration
A Knowledge-Based Approach for Detecting Unattended Packages in Surveillance Video

This paper describes a novel approach for detecting unattended packages in surveillance video. Unlike the traditional approach to just detecting stationary objects in monitored scenes, our approach detects unattended packages based on accumulated knowledge about human and non-human objects from continuous object tracking and classification. We design different reasoning rules for detecting different scenarios of the unattended package events. In the case where a package is left unattended by a single person explicitly, a rule using human activity recognition is introduced to decide the package ownership. In the case where a suspicious package is dropped down by a group of humans or under heavy occlusions, a rule based on historic tracking and classification information is proposed. Furthermore, an additional rule is given to reduce false alarms that may happen with traditional stationary object detection methods.
Magnetic resonance image restoration via dictionary learning under spatially adaptive constraints

This paper proposes a spatially adaptive constrained dictionary learning (SAC-DL) algorithm for Rician noise removal in magnitude magnetic resonance (MR) images. This algorithm explores both the strength of dictionary learning to preserve image structures and the robustness of local variance estimation to remove signal-dependent Rician noise. The magnitude image is first separated into a number of partly overlapping image patches. The statistics of each patch are collected and analyzed to obtain a local noise variance. To better adapt to Rician noise, a correction factor is formulated with the local signal-to-noise ratio (SNR). Finally, the trained dictionary is used to denoise each image patch under spatially adaptive constraints. The proposed algorithm has been compared to the popular nonlocal means (NLM) filtering and unbiased NLM (UNLM) algorithm on simulated T1-weighted, T2-weighted and PD-weighted MR images. Our results suggest that the SAC-DL algorithm preserves more image structures while effectively removing the noise than NLM and it is also superior to UNLM at low noise levels.
A comparative study on the coherent approaches to cooperation between TCP and ATM congestion control algorithms

Numerous studies have indicated that ATM available bit rate (ABR) service can provide low-delay, fairness, and high throughput, and can handle congestion effectively inside the ATM network. However, network congestion is not really eliminated but rather it is pushed out to the edge of the ATM network, packets from TCP sources competing for the available ATM bandwidth are buffered in the routers or switches at the network edges, causing severe congestion, degraded throughput, and unfairness. This poor performance is mainly due to the uncoordinated interaction between the congestion control mechanism of TCP and ATM. It is well accepted that some form of cooperation at edge device would help to control TCP traffic flow over ATM more effectively. We have previously proposed the fair intelligent explicit window adaptation (FIEWA) scheme and fair intelligent ACK bucket control (FIABC) scheme. The key idea is to combine the feedback information from the receiver, from the underlying ATM network, and from the local information at the edge device intelligently to explicitly/implicitly control the TCP rate. We present a comparative simulation study on our schemes with other established schemes; to identify the characteristics of each different scheme; and to indicate the requirement for a fairer, simpler and more robust coherent approach at the edge device.
A knowledge-based mediator for dynamic integration of heterogeneous multimedia information sources

To facilitate the efficient and accurate access and operation of a huge amount of multimedia information from various data sources, we propose a mediator to integrate heterogeneous multimedia information sources and their corresponding data operation API dynamically. To achieve this purpose, a reflection technique is adopted to extract heterogeneous API features at run time; a classifier component is designed to obtain global API features; and an ontology mechanism is used for hiding information heterogeneity. Ultimately, by using the proposed mediator, a global knowledge-based multimedia information API is produced, which makes multimedia applications and Web applications "understand" and operate heterogeneous information automatically and transparently. The proposed mediator provides multimedia applications with a unified interface for information operation and facilitates communication and interoperation between disparate multimedia applications.
Differential Evolution Based Variational Bayes Inference for Brain PET-CT Image Segmentation

The variational expectation maximization (VEM) algorithm has recently been increasingly used to replace the expectation maximization (EM) algorithm in Gaussian mixture model (GMM) based statistical image segmentation. However, the VEM algorithm, similar to its traditional counterpart, suffers from the sensitiveness to initializations, and hence is prone to be trapped into local minima. In this paper, we introduce the differential evolution (DE), which is a population-based global optimization approach, to the variational Bayes inference of posterior distributions, and thus propose the DE-VEM algorithm for the segmentation of gray matter, white matter, and cerebrospinal fluid in brain PET-CT images. By combining the advantages of both variational inference and evolutionary computing, this algorithm has the ability to avoid over-fitting and local convergence. To use the prior anatomical knowledge available for brain images, we also incorporate the spatial constraints derived from the probabilistic brain atlas into the segmentation process. We compare our algorithm to the VEM algorithm and the segmentation routine used in the statistical parametric mapping package in 27 clinical PET-CT studies. Our results show that the proposed algorithm can segment brain PET-CT images more accurately.
Automated CT liver segmentation using improved Chan-Vese model with global shape constrained energy

In this paper, we propose an automated liver segmentation method to overcome the challenging issues of high degree of variations in liver shape / size and similar density distribution shared by the liver and its surrounding structures. To improve the performance of conventional statistical shape model for liver segmentation, in our method, the signed distance function is utilized so that the landmarks correspondence is not required when performing the principle component analysis. We improve the Chan-Vese model to bind the shape energy and local intensity feature to evolve the surface both globally and locally toward the closest shape driven by the PCA. In our experiments, 20 clinical CT studies were used for training and 25 clinical CT studies were used for validation. Our experimental results demonstrate that our method can achieve accurate and robust liver segmentation from both of low-contrast and high-contrast CT images.
Dynamic image data compression in the spatial and temporal domains: clinical issues and assessment

In our previous work, we developed a novel approach to dynamic image data compression, and demonstrated that very high compression ratios can be achieved while preserving relevant kinetic information. However, the technique has not yet been assessed with clinical data. Many issues need to be addressed to tailor the method for clinical use. In this paper, we apply the compression technique to dynamic [18F] 2-fluoro-deoxy-glucose (FDG) brain positron emission tomography (PET) data, using a five-parameter model to include cerebral blood volume (CBV) and partial volume (PV) effects. Functional images generated from the compressed data are compared with those from the original uncompressed data. We show that the storage requirements for a typical clinical dynamic PET image data set can be reduced by more than 95%, without degradation of image quality. Furthermore, the technique greatly reduces the computational complexity of further clinical image postprocessing such as smoothing and generation of functional images. It is expected that the compression technique will be of benefit in image data management and telemedicine.
A numerical observer study of MAP with anatomical and functional priors for lesion detection

In a previous study, we described the use of a functional prior in addition to an anatomical prior to improve the contrast and quantification of lesions in PET imaging. The functional prior is specified by applying a threshold to a preliminary reconstructed PET image. The segmented regions are quarantined from the regularization during reconstruction, resulting in a form of adaptive smoothing. One additional parameter was added to control the weighting of the functional prior. We call this dual-prior technique quarantine maximum a posteriori MAP reconstruction (QMAP). In this study, we used the channelized Hotelling observer (CHO) to investigate the effect of QMAP on lesion detectability. The results show that the inclusion of a functional prior in addition to an anatomical prior improves the performance of MAP reconstruction in a lesion detection task compared with the use of anatomical or smoothing priors on their own.
Space vector modulation for neutral point clamped multilevel inverter with even order harmonic elimination

Space vector modulation (SVM) is increasingly used in three-level neutral point clamped (NPC) inverters. However, the conventional SVM scheme produces even order harmonics in the inverter output voltages. We analyze the mechanism of even order harmonic production. A new SVM scheme, which can eliminate all the even order harmonics, is proposed. The performance of the new design is investigated and simulation results are provided. The proposed SVM scheme can effectively control the neutral point potential. The presented technique can also be applied to other types of converters for even order harmonic elimination.
Enhancing scene structure in prosthetic vision using iso-disparity contour perturbance maps

We present a novel approach for enhancing structurally significant features in a scene to facilitate safe mobility with prosthetic vision. Previous approaches rely on visually salient features (e.g., intensity gradients, size, texture), or surface fitting (e.g., ground plane extraction), to determine and convey regions of structural change in the scene. Such approaches can be costly to compute, and/or are not guaranteed to detect all features relevant to the needs of safe mobility (e.g., small, low-contrast trip hazards). Assuming a dense disparity image, we propose a novel feature using iso-disparity contours. Regions of significant structural change are detected via a cost function based on local comparisons of iso-disparity contour orientations. Through this, structurally interesting features such as surface boundaries and general clutter are extracted and emphasised in the output visual representation. Our approach is real-time, and requires no surface fitting. Experimental results quantitatively and qualitatively validate our approach.
Towards object based manipulation in remote guidance

This paper presents a method for using object based manipulation and spatial augmented reality for the purpose of remote guidance. Previous remote guidance methods have typically not made use of any semantic information about the physical properties of the environment and require the helper and worker to provide context. Our new prototype system introduces a level of abstraction to the remote expert, allowing them to directly specify the object movements required of a local worker. We use 3D tracking to create a hidden virtual reality scene, mirroring the real world, with which the remote expert interacts while viewing a camera feed of the physical workspace. The intended manipulations are then rendered to the local worker using Spatial Augmented Reality (SAR). We report on the implementation of a functional prototype that demonstrates an instance of this approach. We anticipate that techniques such as the one we present will allow more efficient collaborative remote guidance in a range of physical tasks.
Graph-based Mumford-Shah segmentation of dynamic PET with application to input function estimation

A graph-theoretic three-dimensional (3-D) segmentation algorithm based on Mumford-Shah energy minimization is applied to the segmentation of brain /sup 18/F-fluoro-deoxyglucose (FDG) dynamic positron emission tomography data for the automated extraction of tissues with distinct time activity curves (TACs), and, in particular, extraction of the internal carotid arteries and venous sinuses for the noninvasive estimation of the input arterial TAC. Preprocessing by principal component analysis (PCA) and a Mahalanobis distance metric provide segmentation based on distinct TAC shape rather than simply activity levels. Evaluations on simulation and clinical FDG brain positron emission tomography (PET) studies demonstrate that differing tissue types can be accurately demarcated with superior performance to k-means clustering approaches, and, in particular, the internal carotids and venous sinuses can be robustly segmented in clinical brain dynamic PET datasets, allowing for the fully automatic noninvasive estimation of the arterial input curve.
Multiscale and multiorientation feature extraction with degenerative patterns for 3D neuroimaging retrieval

Accurate neuroimaging feature extraction is essential for effective content-based management of the large neuroimaging databases, as well as achieving improved diagnosis. In this paper, we presented a multiscale and multi-orientation neuroimaging feature extraction algorithm with degenerative patterns for content-based 3D neuroimaging analysis and retrieval, based on the localized 3D Gabor wavelets. Our proposed approach was evaluated with 209 3D clinical neurological imaging studies and compared with the 3D discrete curvelet transform based method and the 3D spatial grey level co-occurrence matrices based method. The preliminary results suggested that our algorithm could support more reliable 3D neuroimaging retrieval.
Unsupervised Spectral Mixture Analysis of Highly Mixed Data With Hopfield Neural Network

Hopfield Neural Network (HNN) has been demonstrated to be an effective tool for Spectral Mixture Analysis (SMA). However, the spectrum of pure ground objects, known as endmember, must be known previously. In this paper, the HNN is utilized to solve unsupervised SMA, in which Endmember Extraction (EE) and Abundance Estimation (AE) are performed iteratively. Two different HNNs are constructed to solve such multiplicative updating procedure, respectively. The proposed HNN based unsupervised SMA framework is then applied to solve three second-order constrained Nonnegative Matrix Factorization (NMF) models for SMA, including Minimum Distance Constrained NMF (MDC-NMF), Minimum endmember-wise Distance Constrained NMF (MewDC-NMF), and Minimum Dispersion Constrained NMF (MiniDisCo-NMF). As a result, our proposed HNN based algorithms are able to perform unsupervised SMA and extract virtual endmembers without assuming the presence of spectrally pure constituents in highly mixed hyperspectral data. Experimental results on both synthetic and real hyperspectral images demonstrate that our proposed HNN based algorithms clearly outperform traditional Projected Gradient (PG) based solutions for these constrained NMF based SMA.
A non-local post-filtering algorithm for PET incorporating anatomical knowledge

The maximum likelihood expectation maximization (MLEM) reconstruction method is known to yield noisy images at high iteration numbers because emission tomographic reconstruction is an ill-posed problem. The noise can be suppressed by post-filtering the ML estimate or imposing a priori knowledge as a constraint within a Bayesian reconstruction framework. Most of these filters and priors are based on weighting the intensity differences between neighbouring pixels within a small local neighbourhood. Therefore, they have limited information to distinguish edges from noise. We investigated the use of a non-local means (NLM) filter for post-filtering MLEM reconstructed positron emission tomography (PET) images. We further investigated the effect of incorporating anatomical side information obtained from co-registered computed tomography (CT) images into the NLM, resulting in an adaptive non-local means (A-NLM) filter which takes into account the variance within each anatomical region on the PET image. In simulated and physical phantom experiments, the A-NLM filter demonstrated superior performance tradeoff between lesion contrast and noise than conventional Gaussian post-filtering and NLM without anatomical prior. We conclude that the A-NLM filter has potential for improved lesion detection over Gaussian post-filtered MLEM images.
3-d visualization system of the cranium based on X-ray images

A 3-d visualization system of the cranium based on reconstruction from X-rays is presented. Since X-rays belong to the penetrating projection images, the objects do not have definite surface in images. To solve this problem, an approach of pasting the lead granules on the face of a patient and reconstructing the face through the correlated vision is adopted. Then the 3-d cranium model is built by subtracting the thickness of soft tissue from the face model. The whole system consists of image pre-processing, feature point recognition, matching, texture mapping, animation, and 3-d measurement. Only the X-ray machine, adhesive tapes with lead granules and M-ultrasonograph are needed. The experiment demonstrates that this approach s effective and of high precision. It can help doctors examine and measure the face and cranium of patients by computer. Hopefully, this product could he developed and applied in clinic one day.