nils-herrmann/clean_scirepeval huggingface dataset:
Non-rigid image registration using local histogram-based features

This paper proposes a new non-rigid image registration method based on the formulation of the Demons algorithm. The proposed method utilizes combined geometric moments of local histogram to form new feature images. It greatly improves the accuracy of the original Demons algorithm, which is easy to get trapped at local minima during optimization. The local histogram-based features are rotation invariant and can capture sufficient spatial image information. During the registration process, local histogram-based feature images are built to substitute the original intensity images. This can reduce the possibility of being trapped at local solutions, and consequently improve the registration accuracy. The experimental results on both the synthetic image and real MRI image show that the proposed method can achieve higher accuracy than the Demons algorithm especially when the images are noisy.
A decentralized approach to sensory data integration

In this paper, a decentralized approach based on the team consensus approach and Markovian model is proposed to integrate multisensory data. A team of sensors can estimate the local and global uncertainties utilizing self-entropy and conditional-entropy measures of the sensors. Consensus can be reached based on the initial expected values and "uncertainty" weights assigned by the sensors. The proposed approach is compared with the Bayesian approach via experiments on two independent sensors. Results showed that consensus reached are comparable. However, there are factors that indicated the decentralized approach requires less communication and computational effort to reach consensus among sensors.
An atlas-based deep brain structure segmentation method: from coarse positioning to fine shaping

Segmentation of deep brain structures is a challenging task for MRI images due to blurry structure boundaries, small object size and irregular shapes. In this paper, we present a new atlas-based segmentation method. It first uses a prior spatial dependency tree to constrain the relative positions between different deep brain structures and determine an optimal sequence for the structureby-structure segmentation. After positioning the structures, the segmentation result is further fine tuned by a non-rigid registration procedure between the atlas image and the target image using the histogram of the gradient magnitudes lying on the structure boundaries. The proposed method has been applied on a publicly available MRI brain database and can achieve comparatively high segmentation accuracy.
Markov Random Field Energy Minimization via Iterated Cross Entropy with Partition Strategy

This paper introduces a novel energy minimization method, namely iterated cross entropy with partition strategy (ICEPS), into the Markov random field theory. The solver, which is based on the theory of cross entropy, is general and stochastic. Unlike some popular optimization methods such as belief propagation (BP) and graph cuts (GC), ICEPS makes no assumption on the form of objective functions and thus can be applied to any type of Markov random field (MRF) models. Furthermore, compared with deterministic MRF solvers, it achieves higher performance of finding lower energies because of its stochastic property. We speed up the original cross entropy algorithm by partitioning the MRF site set and assure the effectiveness by iterating the algorithm. In the experiments, we apply ICEPS to two MRF models for medical image segmentation and show the aforementioned advantages of ICEPS over other popular solvers such as iterated conditional modes (ICM) and GC.
A novel multi-layer framework for non-rigid image registration

Brain magnetic resonance (MR) images consist of different structures and features when they are observed at different scales and layers. Conventional non-rigid brain MR image registration methods mainly estimate the optimum transformation by relying on the information of a single layer and this can lead to the loss of information contained in other layers. In this paper, we propose a multi-layer framework for non-rigid brain MR image registration with different kinds of features extracted from different layers. The input images are factorized into three layers: global intensity layer, texture information layer and local anatomical layer. The generalized survival exponential entropy based mutual information (GSEE-MI), multi-scale brainton features and rotation invariant feature transform (RIFT) are used to represent the global intensity layer, texture information layer and local anatomical layer respectively. Information extracted from all layers is then embedded into a new similarity measure function. The role of each layer is identified through systematic experiments and it is shown that information conveyed by different layers is complement with each other. The proposed framework exhibits significant improvement of registration accuracy as compared with other widely used registration methods on the real 3D databases obtained from IBSR.
Multi-scale model-based vessel enhancement using local line integrals

This paper presents a novel vessel enhancement method. By regarding a vessel segment as a local line and exploiting the second order information along the line, our method embeds a vessel model to capture vessel structures. The vessel model is the key to better performances of our method than the Hessian-based methods and makes the Hessian-based methods fall in an extreme case of our method. It is experimentally shown that our method gives more accurate “vesselness” measures and vessel direction estimations. In particular, our method achieves better background suppression, smoother “vesselness” measures inside vessels and better responses at crossings (where two relatively straight vessels meet).
Label inference encoded with local and global patch priors

In this paper, a novel label inference method encoded with local and global patch priors is introduced for the segmentation of subcortical structures in brain MR images. Due to the serious overlap of intensity profiles among different tissues in brain MR images, the conventional patch prior estimated with similarity measurement can be adversely impacted and become misleading during the final label inference procedure. As such, to obtain a more discriminative patch representation, we propose to capture local patch prior using sparse learning. Besides the local and low-level patch prior, the high-level structural properties of each subcortical structure are also taken into consideration and global patch prior is extracted with Convolutional Neural Networks. Experiments have been carried out on two publicly available datasets and results indicate that the proposed method can obtain the best performance as compared with other state-of-the-art methods.
Feature Sensitive Label Fusion With Random Walker for Atlas-Based Image Segmentation

In this paper, a novel label fusion method is proposed for brain magnetic resonance image segmentation. This label fusion method is formulated on a graph, which embraces both label priors from atlases and anatomical priors from target image. To represent a pixel in a comprehensive way, three kinds of feature vectors are generated, including intensity, gradient, and structural signature. To select candidate atlas nodes for fusion, rather than exact searching, randomized k-d tree with spatial constraint is introduced as an efficient approximation for high-dimensional feature matching. Feature sensitive label prior (FSLP), which takes both the consistency and variety of different features into consideration, is proposed to gather atlas priors. As FSLP is a non-convex problem, one heuristic approach is further designed to solve it efficiently. Moreover, based on the anatomical knowledge, parts of the target pixels are also employed as the graph seeds to assist the label fusion process, and an iterative strategy is utilized to gradually update the label map. The comprehensive experiments carried out on two publicly available databases give results to demonstrate that the proposed method can obtain better segmentation quality.
A segmentation method using compound Markov random fields based on a general boundary model

Markov random field (MRF) theory has widely been applied to segmentation in noisy images. This paper proposes a new MRF method. First, it couples the original labeling MRF with a boundary MRF that can help improve the performance of segmentation. Second, the boundary model is general and does not need prior training. Third, unlike existing related work, our model offers more compact interaction between the two MRFs. Experiments on synthetic images and real clinical datasets show that the proposed approach is able to produce good segmentation results, especially removing noise in low signal-to-noise ratio regions.
Graph-based optimal cross section boundary for vessel segmentation and stenosis quantification

In this paper, we propose a graph-based method to find the optimal cross section boundary for vessel segmentation. The voxels on the cross sectional plane are assumed to lay on the circles around the centerline point. The voxels on the circles with different radii are then transformed to a graph, by which the objective of finding the optimal boundary is converted to choosing the optimal path in the graph. A new cost function for the edge cost of the graph is proposed to obtain a smooth, optimal boundary of the cross section. Based on the optimal cross section boundary, we also propose a method for stenosis detection and quantification. The proposed method for segmentation and stenosis detection has been evaluated to be accurate and highly computationally efficient.
Segmentation of Intracranial Vessels and Aneurysms in Phase Contrast Magnetic Resonance Angiography Using Multirange Filters and Local Variances

Segmentation of intensity varying and low-contrast structures is an extremely challenging and rewarding task. In computer-aided diagnosis of intracranial aneurysms, segmenting the high-intensity major vessels along with the attached low-contrast aneurysms is essential to the recognition of this lethal vascular disease. It is particularly helpful in performing early and noninvasive diagnosis of intracranial aneurysms using phase contrast magnetic resonance angiographic (PC-MRA) images. The major challenges of developing a PC-MRA-based segmentation method are the significantly varying voxel intensity inside vessels with different flow velocities and the signal loss in the aneurysmal regions where turbulent flows occur. This paper proposes a novel intensity-based algorithm to segment intracranial vessels and the attached aneurysms. The proposed method can handle intensity varying vasculatures and also the low-contrast aneurysmal regions affected by turbulent flows. It is grounded on the use of multirange filters and local variances to extract intensity-based image features for identifying contrast varying vasculatures. The extremely low-intensity region affected by turbulent flows is detected according to the topology of the structure detected by multirange filters and local variances. The proposed method is evaluated using a phantom image volume with an aneurysm and four clinical cases. It achieves 0.80 dice score in the phantom case. In addition, different components of the proposed method-the multirange filters, local variances, and topology-based detection-are evaluated in the comparison between the proposed method and its lower complexity variants. Owing to the analogy between these variants and existing vascular segmentation methods, this comparison also exemplifies the advantage of the proposed method over the existing approaches. It analyzes the weaknesses of these existing approaches and justifies the use of every component involved in the proposed method. It is shown that the proposed method is capable of segmenting blood vessels and the attached aneurysms on PC-MRA images.
Bayesian image segmentation using local iso-intensity structural orientation

Image segmentation is a fundamental problem in early computer vision. In segmentation of flat shaded, nontextured objects in real-world images, objects are usually assumed to be piecewise homogeneous. This assumption, however, is not always valid with images such as medical images. As a result, any techniques based on this assumption may produce less-than-satisfactory image segmentation. In this work, we relax the piecewise homogeneous assumption. By assuming that the intensity nonuniformity is smooth in the imaged objects, a novel algorithm that exploits the coherence in the intensity profile to segment objects is proposed. The algorithm uses a novel smoothness prior to improve the quality of image segmentation. The formulation of the prior is based on the coherence of the local structural orientation in the image. The segmentation process is performed in a Bayesian framework. Local structural orientation estimation is obtained with an orientation tensor. Comparisons between the conventional Hessian matrix and the orientation tensor have been conducted. The experimental results on the synthetic images and the real-world images have indicated that our novel segmentation algorithm produces better segmentations than both the global thresholding with the maximum likelihood estimation and the algorithm with the multilevel logistic MRF model.
A new active contour method based on elastic interaction

Image segmentation is defined as partitioning an image into non-overlapping regions based on the intensity or texture. The active contour methods provide an effective way for segmentation, in which the boundary of an object (usually with large image gradient value) is detected by an evolving curve. But, these methods have limitations due to the fact that real images may have objects with complex geometric structures and shapes, and are often corrupted by noise. Developing more robust and accurate active contour methods has been an active research area since the idea of the methods was proposed. In this paper, we propose a new active contour method and apply the method to medical image segmentation. This new method uses a long-ranged interaction between image boundaries and the moving curves, which is inspired by the elastic interaction between line defects in solids (dislocations). The new method is more efficient and effective, especially in detecting thin, weak and blurred structures such as the images of blood vessels.
2D-3D vascular registration between digital subtraction angiographic (DSA) and magnetic resonance angiographic (MRA) images

In this paper, we propose a 2D-3D vascular registration algorithm based on an efficient rendering method for generating 2D projected images from a segmented 3D MRA volume. At the preprocessing stage, vessels are segmented and represented by a number of spheres with centers on the skeleton points of the vessels and radii equal to the distance from the skeleton points to their closest boundary. To generate projected images for 2D-3D registration, instead of using the conventional ray-casting technique, the spheres are projected to the projection plane. The discrepancy between the projected image and the reference DSA image is measured by the sum of squared differences. Experimental results show that our method is computationally efficient. Moreover, based on manually selected markers, it is shown that the accuracies of our proposed method and the conventional ray-casting based registration method are comparable.
Multi-level non-rigid image registration using graph-cuts

Non-rigid image registration is widely used in medical image analysis and image processing. It remains a challenging research problem due to its smoothness requirement and high degree of freedoms in the deformation process. In , a method is proposed to solve non-rigid image registration via graph-cuts algorithm by modeling the registration process as a discrete labeling problem. A displacement label (vector) is assigned to each pixel in the source image to indicate the corresponding position in the floating image. The whole system is then optimized by using the graph-cuts algorithm via alpha-expansions . As the initial point is not required for the graph-cuts algorithm, the method proposed by  is a single-level registration. In this paper, rather single-level, we enable multi-level non-rigid image registration using graph-cuts by passing the deformation field of the current resolution level to the successive finer one. By applying the proposed multi-level registration method, the number of labels used in each level is greatly reduced due to lower image resolution being used in coarser levels. Therefore, the speed of the registration process is improved. We compare our results with the original single-level version, DEMONS and FFD. It is found that our method improves the speed of non-rigid image registration by 50% and meanwhile maintains similar robustness and registration accuracy with the single-level version.
Nonrigid Image Registration With Crystal Dislocation Energy

The goal of nonrigid image registration is to find a suitable transformation such that the transformed moving image becomes similar to the reference image. The image registration problem can also be treated as an optimization problem, which tries to minimize an objective energy function that measures the differences between two involved images. In this paper, we consider image matching as the process of aligning object boundaries in two different images. The registration energy function can be defined based on the total energy associated with the object boundaries. The optimal transformation is obtained by finding the equilibrium state when the total energy is minimized, which indicates the object boundaries find their correspondences and stop deforming. We make an analogy between the above processes with the dislocation system in physics. The object boundaries are viewed as dislocations (line defects) in crystal. Then the well-developed dislocation energy is used to derive the energy assigned to object boundaries in images. The newly derived registration energy function takes the global gradient information of the entire image into consideration, and produces an orientation-dependent and long-range interaction between two images to drive the registration process. This property of interaction endows the new registration framework with both fast convergence rate and high registration accuracy. Moreover, the new energy function can be adapted to realize symmetric diffeomorphic transformation so as to ensure one-to-one matching between subjects. In this paper, the superiority of the new method is theoretically proven, experimentally tested and compared with the state-of-the-art SyN method. Experimental results with 3-D magnetic resonance brain images demonstrate that the proposed method outperforms the compared methods in terms of both registration accuracy and computation time.
Dominant Local Binary Patterns for Texture Classification

This paper proposes a novel approach to extract image features for texture classification. The proposed features are robust to image rotation, less sensitive to histogram equalization and noise. It comprises of two sets of features: dominant local binary patterns (DLBP) in a texture image and the supplementary features extracted by using the circularly symmetric Gabor filter responses. The dominant local binary pattern method makes use of the most frequently occurred patterns to capture descriptive textural information, while the Gabor-based features aim at supplying additional global textural information to the DLBP features. Through experiments, the proposed approach has been intensively evaluated by applying a large number of classification tests to histogram-equalized, randomly rotated and noise corrupted images in Outex, Brodatz, Meastex, and CUReT texture image databases. Our method has also been compared with six published texture features in the experiments. It is experimentally demonstrated that the proposed method achieves the highest classification accuracy in various texture databases and image conditions.
Segmentation of Vessels Using Weighted Local Variances and an Active contour Model

Performing segmentation of vasculature with blurry and low contrast boundaries in noisy images is a challenging problem. This paper presents a novel approach to segmenting blood vessels using weighted local variances and an active contour model. In this work, the vessel boundary orientation is estimated locally based on the orientation that minimizes the weighted local variance. Such estimation is less sensitive to noise compared with other common approaches. The edge clearness is measured by the ratio of weighted local variances obtained along different orientations. It is independent of the edge intensity contrast and capable of locating weak boundaries. Integrating the orientation and clearness of edges, an active contour model is employed to align contours that match the contour tangent direction and edge orientation. The proposed method is validated by two synthetic images and two real cases. It is experimentally shown that our method is suitable for dealing with noisy images which consist of structures having blurry and low contrast boundaries, such as blood vessels.
Tubular anisotropy for 2D vessel segmentation

In this paper, we present a new approach for segmentation of tubular structures in 2D images providing minimal interaction. The main objective is to extract centerlines and boundaries of the vessels at the same time. The first step is to represent the trajectory of the vessel not as a 2D curve but to go up a dimension and represent the entire vessel as a 3D curve, where each point represents a 2D disc (two coordinates for the center point and one for the radius). The 2D vessel structure is then obtained as the envelope of the family of discs traversed along this 3D curve. Since this 2D shape is defined simply from a 3D curve, we are able to fully exploit minimal path techniques to obtain globally minimizing trajectories between two or more user supplied points using front propagation. The main contribution of our approach consists on building a multi-resolution metric that guides the propagation in this 3D space. We have chosen to exploit the tubular structure of the vessels one wants to extract to built an anisotropic metric giving higher speed on the center of the vessels and also when the minimal path tangent is coherent with the vessel's direction. This measure is required to be robust against the disturbance introduced by noise or adjacent structures with intensity similar to the target vessel. Indeed, if we examine the flux of the projected image gradient along a given direction on a circle of a given radius (or scale), one can prove that this flux is maximal at the center of the vessel, in its direction and with its exact radius. This approach is called optimally oriented flux. Combining anisotropic minimal paths techniques and optimally oriented flux we obtain promising results on noisy synthetic and real data.
Dependence in sensory data combination

It is common to assume sensor independence in the sensory data fusion and integration. The authors previously (1997, 1998) illustrated that the team consensus approach based on information entropy can remarkably improve the measurement accuracy. The major benefits of the approach are (a) the simple linear combination of the weighted initial expected estimates for each sensor; and (b) the low order bivariate likelihood functions which can be represented easily. In this paper, we demonstrate specifically both the positive and negative impacts of including dependent information in sensory data combination process; and show how the measurable consensus uncertainty level can be derived. A comparison of the team consensus approach with the Bayesian approach is presented.