nils-herrmann/clean_scirepeval huggingface dataset:
Weighted Local Variance-Based Edge Detection and Its Application to Vascular Segmentation in Magnetic Resonance Angiography

Accurate detection of vessel boundaries is particularly important for a precise extraction of vasculatures in magnetic resonance angiography (MRA). In this paper, we propose the use of weighted local variance (WLV)-based edge detection scheme for vessel boundary detection in MRA. The proposed method is robust against changes of intensity contrast of edges and capable of giving high detection responses on low contrast edges. These robustness and capabilities are essential for detecting the boundaries of vessels in low contrast regions of images, which can contain intensity inhomogeneity, such as bias field, interferences induced from other tissues, or fluctuation of the speed related vessel intensity. The performance of the WLV-based edge detection scheme is studied and shown to be able to return strong and consistent detection responses on low contrast edges in the experiments. The proposed edge detection scheme can be embedded naturally in the active contour models for vascular segmentation. The WLV-based vascular segmentation method is tested using MRA image volumes. It is experimentally shown that the WLV-based edge detection approach can achieve high-quality segmentation of vasculatures in MRA images.
Augmented vessels for quantitative analysis of vascular abnormalities and endovascular treatment planning

Endovascular treatment plays an important role in the minimally invasive treatment of patients with vascular diseases, a major cause of morbidity and mortality worldwide. Given a segmentation of an angiography, quantitative analysis of abnormal structures can aid radiologists in choosing appropriate treatments and apparatuses. However, effective quantitation is only attainable if the abnormalities are identified from the vasculature. To achieve this, a novel method is developed, which works on the simpler shape of normal vessels to identify different vascular abnormalities (viz. stenotic atherosclerotic plaque, and saccular and fusiform aneurysmal lumens) in an indirect fashion, instead of directly manipulating the complex-shaped abnormalities. The proposed method has been tested on three synthetic and 17 clinical data sets. Comparisons with two related works are also conducted. Experimental results show that our method can produce satisfactory identification of the abnormalities and approximations of the ideal post-treatment vessel lumens. In addition, it can help increase the repeatability of the measurement of clinical parameters significantly
MULTI-MODAL IMAGE REGISTRATION USING ORDINAL FEATURES AND MULTI-DIMENSIONAL MUTUAL INFORMATION

This paper proposes to use a new feature, namely ordinal feature, for image registration tasks. The ordinal features are extracted by passing through the images through the generalized ordinal filter bank, which effectively encodes the spatial information between neighboring voxels and specific microstructural information in the images. The ordinal feature is integrated with the image intensity to form a two-element attribute vector. Then, four dimensional mutual information is used as the similarity measure. The experimental results show that the proposed method is more robust than the conventional MI-based method and the method using Gabor features. The accuracy is comparable for the three approaches
Multi-modal non-rigid image registration based on similarity and dissimilarity with the prior joint intensity distributions

Multi-modal non-rigid image registration is widely used in different areas, including medical image analysis and image processing. In this paper, we introduce a new learning-based method for non-rigid image registration. The proposed method is based on a priori knowledge of the joint intensity distribution of a pre-aligned image pair. The similarity and dissimilarity of the expected and observed joint intensity distributions are measured by two Kullback-Leibler distances (KLD). Free-Form Deformation (FFD) is employed as the transformation model along with the L-BFGS-B optimizer. The derivatives of KLDs are derived to work with the L-BFGS-B optimizer. Moreover, we have tested our method with CT-T1 image pairs and compared the results obtained by using the mutual information based FFD and the conventional KLD based FFD. The experimental results show that our method gives remarkable improvement on the registration quality.
Face recognition with salient local gradient orientation binary patterns

This paper proposes a new face recognition method. There are two novelties in the proposed method. First, a new saliency measure function is designed to detect the most salient regions in facial images and determine their corresponding best scales. Second, a new type of image feature, called local gradient orientation binary pattern (LGOBP) is proposed, which captures the neighborhood gradient orientation information which is not considered in the conventional local binary patterns (LBP) to give more discriminant power. LGOBPs are extracted from the most salient regions selected by the proposed saliency measure function. The proposed method is evaluated on the FRGC version 2 database by comparing it with several widely used methods. Experimental results show that the proposed method achieves the highest recognition rate among all the compared methods.
Facial Expression Recognition using Advanced Local Binary Patterns, Tsallis Entropies and Global Appearance Features

This paper proposes a novel facial expression recognition approach based on two sets of features extracted from the face images: texture features and global appearance features. The first set is obtained by using the extended local binary patterns in both intensity and gradient maps and computing the Tsallis entropy of the Gabor filtered responses. The second set of features is obtained by performing null-space based linear discriminant analysis on the training face images. The proposed method is evaluated by extensive experiments on the JAFFE database, and compared with two widely used facial expression recognition approaches. Experimental results show that the proposed approach maintains high recognition rate in a wide range of resolution levels and outperforms the other alternative methods.
Nonrigid Brain MR Image Registration Using Uniform Spherical Region Descriptor

There are two main issues that make nonrigid image registration a challenging task. First, voxel intensity similarity may not be necessarily equivalent to anatomical similarity in the image correspondence searching process. Second, during the imaging process, some interferences such as unexpected rotations of input volumes and monotonic gray-level bias fields can adversely affect the registration quality. In this paper, a new feature-based nonrigid image registration method is proposed. The proposed method is based on a new type of image feature, namely, uniform spherical region descriptor (USRD), as signatures for each voxel. The USRD is rotation and monotonic gray-level transformation invariant and can be efficiently calculated. The registration process is therefore formulated as a feature matching problem. The USRD feature is integrated with the Markov random field labeling framework in which energy function is defined for registration. The energy function is then optimized by the α-expansion algorithm. The proposed method has been compared with five state-of-the-art registration approaches on both the simulated and real 3-D databases obtained from the BrainWeb and Internet Brain Segmentation Repository, respectively. Experimental results demonstrate that the proposed method can achieve high registration accuracy and reliable robustness behavior.
Principal curves to extract vessels in 3D angiograms

Segmentation of blood vessels and extraction of their centerlines in 3D angiography are essential to diagnosis and prognosis of vascular diseases, and advanced image processing and analysis. In this paper, we propose a semi-automatic method to perform those two tasks simultaneously. A user supplies two end points to the algorithm and a vessel centerline between the two given points is extracted automatically. Local vessel widths are estimated as byproducts. Additional anchor points can be added in between to handle difficult situation. Our method is based upon a polygonal line algorithm. This algorithm is used to find principal curves, nonlinear generalization of principal components, from point clouds. We discuss an application of principal curve to vessel extraction from a theoretical view point. A novel algorithm is then proposed for the application. No data interpolation is needed in the algorithm and centerlines extracted are adaptive to the vasculature complexity on account of their nonparametric representation. We have tested the method on two synthetic data sets and two clinical data sets. Results show that it has high robustness to variation in image resolution, voxel anisotropy and noise. Moreover, centerlines obtained are in subvoxel precision and local widths estimated are accurate under limit of image resolution.
Learning-based multi-modal rigid image registration by using Bhattacharyya distances

Multi-modal image registration is a momentous technology in medical image processing and analysis. In order to improve the robustness and accuracy of multi-modal rigid image registration, a novel learning-based dissimilarity function is proposed in this paper. This novel dissimilarity function is based on measuring the dissimilarity between the joint intensity distribution of the testing image pair and the expected intensity distributions, which is learned from a registered image pair, with Bhattacharyya distances. Then, the aim of the registration process is to minimize the dissimilarity function. Eight hundred randomized CT — T1 registrations were performed and evaluated by the Retrospective Image Registration Evaluation (RIRE) project. The experimental results demonstrate that the proposed method can achieve higher robustness and accuracy, as compared with a closely related approach and a state-of-the-art method.
Non-rigid image registration using electric current flow

In this paper, a novel electric current flow (ECF) based model is proposed to perform feature based non-rigid brain image registration. The ECF features simultaneously capture both voxel intensity and inter-voxel distance information. In the proposed ECF framework, each voxel is regarded as exhibiting electric potential proportional to voxel intensity. Voxels are connected by conductive wires in a pairwise manner. Each conductive wire has resistance, in which the resistance value is proportional to the length of the wire. The electric potential difference among connected pixels induces electric current passing through their connected wire. The amount of the electric current is the ratio between the voxel potential difference and the wire resistance. The potential difference and resistance are respectively proportional to the voxel intensity difference and the inter-voxel distance. By analyzing the electric current induced by the connection between a reference voxel and its counterparts in a given range, the ECF algorithm searches for the most salient connection to construct the ECF features. The ECF features are incorporated in the Markov random field labeling framework for non-rigid image registration. The registration quality of the proposed method has been evaluated intensively on both BrainWeb and IBSR databases. It is compared with four related approaches. Experimental results illustrate that the proposed method consistently achieves the highest registration accuracy among all the compared methods on both databases.
VE-LLI-VO: Vessel Enhancement Using Local Line Integrals and Variational Optimization

Vessel enhancement is a primary preprocessing step for vessel segmentation and visualization of vasculatures. In this paper, a new vessel enhancement technique is proposed in order to produce accurate vesselness measures and vessel direction estimations that are less subject to local intensity abnormalities. The proposed method is called vessel enhancement using local line integrals and variational optimization (VE-LLI-VO). First, vessel enhancement using local line integrals (VE-LLI) is introduced in which a vessel model is embedded by regarding a vessel segment as a straight line based upon the second order information of the local line integrals. Useful quantities similar to the eigenvalues and eigenvectors of the Hessian matrix are produced. Moreover, based upon the local line integrals, junctions can be detected and handled effectively. This can help deal with the bifurcation suppression problem which exists in the Hessian-based enhancement methods. Then a more generic curve model is embedded to model vessels and a variational optimization (VO) framework is introduced to generate optimized vesselness measures. Experiments have been conducted on both synthetic images and retinal images. It is experimentally demonstrated that VE-LLI-VO produces improved performance as compared with the widely used techniques in terms of both vesselness measurement and vessel direction estimation.
Integrating dependent sensory data

In sensory data fusion and integration consideration, sensor independence is a common assumption. We demonstrate the impact of including dependent information in the sensory data combination process. The team consensus approach based on information entropy can improve the measurement accuracy remarkably. The major benefits of the approach are: (a) the simple linear combination of the weighted initial local estimates for each sensor; and (b) the low order bivariate likelihood functions which can be represented easily. A comparison of the team consensus approach with the Bayesian approach is presented.
Multi-resolution LC-MS images alignment using dynamic time warping and Kullback-Leibler distance

Liquid chromatography mass spectrometry (LC-MS) is widely used in comparing proteomes for disease biomarker discovery. An LC-MS experiment produces a 2-D image, where the mass-to-charge ratio and the chromatographic retention time are the coordinates, and the signal intensities represent the abundance of detected peptides. However, there is always a non-linear retention time difference across replicate LC-MS images due to machine drift, such that synchronization of LC-MS images must be performed prior to any further analysis. In this paper, we propose a multi-resolution image alignment scheme to synchronize LC-MS images. Dynamic Time Warping (DTW) is used to reconcile the time differences among images and Kullback-Leibler distance (KLD) is used as a local distance measure. Our proposed scheme has been validated using two real data sets, and promising results have been obtained.
Vascular segmentation in three-dimensional rotational angiography based on maximum intensity projections

Three-dimensional rotational angiography (3D-RA) is a relatively new and promising technique for imaging blood vessels. In this paper, we propose a novel 3D-RA vascular segmentation algorithm, which is fully automatic and very computationally efficient, based on the maximum intensity projections (MIP) of 3D-RA images. Validation results on 13 clinical 3D-RA datasets reveal that, according to the agreement between the segmentation results and the ground truth, our method (a) outperforms both the maximum a posteriori-expectation maximization (MAP-EM)-based method and the MAP-Markov random field (MAP-MRF)-based segmentation method, and (b) works comparably to the optimal global thresholding method. Experimental results also show that our method can successfully segment major vascular structures in 3D-RA and produce a lower false positive rate than that of the MAP-EM-based and MAP-MRF-based methods.
Principal Curves for Lumen Center Extraction and Flow Channel Width Estimation in 3-D Arterial Networks: Theory, Algorithm, and Validation

We present an energy-minimization-based framework for locating the centerline and estimating the width of tubelike objects from their structural network with a nonparametric model. The nonparametric representation promotes simple modeling of nested branches and n -way furcations, i.e., structures that abound in an arterial network, e.g., a cerebrovascular circulation. Our method is capable of extracting the entire vascular tree from an angiogram in a single execution with a proper initialization. A succinct initial model from the user with arterial network inlets, outlets, and branching points is sufficient for complex vasculature. The novel method is based upon the theory of principal curves. In this paper, theoretical extension to grayscale angiography is discussed, and an algorithm to find an arterial network as principal curves is also described. Quantitative validation on a number of simulated data sets, synthetic volumes of 19 BrainWeb vascular models, and 32 Rotterdam Coronary Artery volumes was conducted. We compared the algorithm to a state-of-the-art method and further tested it on two clinical data sets. Our algorithmic outputs-lumen centers and flow channel widths-are important to various medical and clinical applications, e.g., vasculature segmentation, registration and visualization, virtual angioscopy, and vascular atlas formation and population study.
A novel Markov random field based deformable model for face recognition

In this paper, a new scheme to address the face recognition problem is proposed. Different from traditional face recognition approaches which represent each facial image by a single feature vector as the classification problem, the proposed method establishes a new way to formulate the face recognition problem as a deformable image registration problem. The main contributions of the paper lie in the following aspects: (i) Each pixel is represented by an anatomical feature signature calculated from its corresponding best scale salient region by using a new salient region detector based on the survival exponential entropy (SEE); (ii) The face recognition problem is formulated as a deformable image registration problem, the deformation model is represented by a Markov random field (MRF) labeling framework. Explicit pixel correspondence is established by the deformation framework. (iii) The survival exponential entropy based normalized mutual information (SEE-NMI) is proposed and integrated with the MRF based deformation model as the similarity measure to reflect the similarity between two facial images. The proposed method is evaluated on the FERET and FRGC version 2 databases and compared with several state-of-the-art face recognition approaches. Experimental results show that the proposed method achieves the highest recognition rate among all the compared approaches.
A new subspace learning method in Fourier domain for texture classification

This paper proposes a new texture classification approach. There are two main contributions in the proposed method. First, input texture images are transformed to the composite Fourier domain (CFD) by using both the local and global Fourier transforms. The composite Fourier domain is rotation invariant and preserves the contextual information for the texture images in the original spatial domain. Second, the null-space based linear discriminant analysis (nLDA) is adopted to find the optimal representations of the texture images in the composite Fourier domain. This paper proposes a systematic way to cooperate subspace learning methods for texture classification in the frequency domain, which cannot be directly applied in the spatial domain for texture classification. The proposed method is evaluated on both the Brodatz and CUReT databases and compared with several state-of-the-art texture classification approaches. Experimental results show that the proposed method achieves the highest classification rate among all the compared methods.
Shape-Based Image Segmentation Using Normalized Cuts

To segment a whole object from an image is an essential and challenging task in image processing. In this paper, we propose a hybrid segmentation algorithm which combines prior shape information with normalized cut. With the help of shape information, we can utilize normalized cut to correctly segment the target whose boundary may be corrupted by noise or outliers. At the same time, we introduce the use of segmentation results of the normalized cut to guide the shape model, and thus avoid searching the shape space. The proposed method was demonstrated to be effective by our experiments on both synthetic and real data.
A Markov Random Field Groupwise Registration Framework for Face Recognition

In this paper, we propose a new framework for tackling face recognition problem. The face recognition problem is formulated as groupwise deformable image registration and feature matching problem. The main contributions of the proposed method lie in the following aspects: (1) Each pixel in a facial image is represented by an anatomical signature obtained from its corresponding most salient scale local region determined by the survival exponential entropy (SEE) information theoretic measure. (2) Based on the anatomical signature calculated from each pixel, a novel Markov random field based groupwise registration framework is proposed to formulate the face recognition problem as a feature guided deformable image registration problem. The similarity between different facial images are measured on the nonlinear Riemannian manifold based on the deformable transformations. (3) The proposed method does not suffer from the generalizability problem which exists commonly in learning based algorithms. The proposed method has been extensively evaluated on four publicly available databases: FERET, CAS-PEAL-R1, FRGC ver 2.0, and the LFW. It is also compared with several state-of-the-art face recognition approaches, and experimental results demonstrate that the proposed method consistently achieves the highest recognition rates among all the methods under comparison.
Centerline Extraction of 2D Vessels Based on Wavelet Magnitude

Centerline extraction is very important in interactive navigation and virtual endoscopy. However traditional centerline extraction techniques, such as minimal path, are sometimes incapable of providing a precise approximation of the ideal centerline of vessels because the minimal cost path is the shortest Euclidean path linking two user-supplied points. In this paper, by using wavelet magnitude, we propose a new algorithm to obtain the central region of the vessels and central points of the vessel junctions. Then the minimal path technique is used to link obtained central points and the user-supplied points within the central region. Experiment results on synthetical and clinical images show that our algorithm can extract high-quality centerline of the vessels.
Optimal and efficient segmentation for 3D vascular forest structure with graph cuts

In this paper, we propose an optimal segmentation method for vascular forest structure based on graph cuts framework, which has widely been used in recent years because of its global optimal object segmentation property. However, shrinking bias, a classical issue of the graph cuts methods, sets up a barrier for the use of these methods on elongated structures such as blood vessels, especially the complex vascular tree and forest structures. To deal with this problem, a new graph construction method and a new energy function are proposed in this paper. The global optimal segmentation of vascular forest structure can be obtained more efficiently, while the shrinking bias can be overcome by the proposed method. The method is compared with a classical graph cuts method  and two methods [2, 3] for vascular tree structure segmentation, and is demonstrated to be more accurate on both the synthetic and clinical images, especially on noisy images. Different from many other tree structure segmentation methods, the proposed method does not have to consider the bifurcations explicitly.