nils-herrmann/clean_scirepeval huggingface dataset:
Landmark and intensity based image registration using free form deformation

The registration of images is a fundamental task in numerous applications in medical image processing. In this paper, a novel registration technique is proposed which combines landmark and intensity based approaches. In this framework, the free form deformation (FFD) is used as transformation which is the key point of our algorithm. Landmarks with FFD transformation define the guidance surface which increases robustness of intensity based registration to bias field (bias noise). In fact, the performance of registration is improved by matching both landmark and intensity information. The experimental results show that the proposed method is more accurate than only intensity based method (Mutual Information). Also, user can identify the region of interest for registration which makes method more applicable and useful for practical uses.
Laplacian mixture model point based registration

Point base registration is an important part in many machine vision applications, medical diagnostics, agricultural studies etc. The goal of point set registration is to find correspondences between different data sets and estimate the appropriate transformation that can map one set to another. Here we introduce a novel method for matching of different data sets based on Laplacian distribution. We consider the alignment of two point sets as probability density estimation problem. By using maximum likelihood methods we try to fit the Laplacian mixture model (LMM) centroids (source point set) to the data point set.
Spectral clustering approach with sparsifying technique for functional connectivity detection in the resting brain

The aim of this study is to assess the functional connectivity from resting state functional magnetic resonance imaging (fMRI) data. Spectral clustering algorithm was applied to the realistic and real fMRI data acquired from a resting healthy subject to find functionally connected brain regions. In order to make computation of the spectral decompositions of the entire brain volume feasible, the similarity matrix has been sparsified with the t-nearest-neighbor approach. Realistic data were created to investigate the performance of the proposed algorithm and comparing it to the recently proposed spectral clustering algorithm with the Nystrom approximation and also with some well-known algorithms such as the Cross Correlation Analysis (CCA) and the spatial Independent Component Analysis (sICA). To enhance the performance of the methods, a variety of data pre and post processing steps, including data normalization, outlier removal, dimensionality reduction by using wavelet coefficients, estimation of number of clusters and optimal number of independent components (ICs). Results demonstrate the applicability of the proposed algorithm for functional connectivity analysis.
Finding protein active sites using approximate sub-graph isomorphism

Prediction of the amino acids that have a catalytic effect on the enzymes is a major stage in appointing the activity of the enzymes and classification. The biological activity of a protein usually depends on the existence of a small number of amino acids. Recently, many algorithms have been proposed in the literature for finding these amino acids which are complex and time consuming. In this paper, we will introduce a new method for predicting the active sites that will use the spatial coordinates and the type of amino acids that contain the active sites. In order to increase the speed we use an approximate graph isomorphism algorithm. Furthermore, this algorithm allows us to find several active sites for a protein and order them according to a RMSD (Root Mean Square Deviation) number which has several applications in biology.
Illumination and View Invariant Face Detection and Recognition in Images with Complex Background

This paper presents a novel approach for detection and recognition of multi-view faces whose location is unknown and the illumination conditions are varying. The illumination is a big problem in the face detection and recognition aspects. Two completely different methods are proposed for face detection in this paper. Our proposed methods do not use the skin colour information for face detection. The detection of faces is accomplished after cancelling the effect of the various illumination conditions. Because of the independency of the approaches to the face's skin colour, persons with every kind of skin colours are detected even in completely dark environments. Next, the detected faces are recognized. The experiments have shown that we can combine some proposed aspects of feature based methods with eigenface method and get successful results. The illumination dependency and scaling problems of eigenface method have also been solved by a new methodology in this paper.
Edge Sensitive Block Motion Estimation Employing Partial Ridgelet Distortion Search

Motion estimation is an important field of video processing. Conventional block motion estimation methods consider all pixels equally. In this paper a new edge sensitive algorithm is proposed in which distortion of ridgelet coefficients is calculated partially and compared over all candidates. Using an edge boosting distortion function, edge pixels will have more important role since they introduce larger ridgelet coefficients. Inherent ability of ridgelet in representing edges with various directions in multiscale, makes it a powerful tool for edge pursuit. Proposed partial ridgelet distortion search (PRDS) do not reject any candidate without assessment while performs relatively fast since it operates partially. Experimental results show that the total PSNR of PRDS is near traditional full search while its motion compensated frame has less distortion over edge pixels. It has also less blocking problem, which both are visually annoying effect of traditional methods. Despite larger loser rejection ratio of PRDS, normalized PRDS is also proposed as even faster search with results close to the PRDS
An entropy based method for activation detection of functional MRI data using Independent Component Analysis

Independent Component Analysis (ICA) can be used to decompose functional Magnetic Resonance Imaging (fMRI) data into a set of statistically independent images which are likely to be the sources of fMRI data. After applying ICA, a set of independent components are produced, and then, a “meaningful” subset from these components must be identified, because a large majority of components are non-interesting. So, interpreting the components is an important and also difficult task. In this paper, we propose a criterion based on the entropy of time courses to automatically select the components of interest. This method does not require to know the stimulus pattern of the experiment.
MRI image reconstruction via new K-space sampling scheme based on separable transform

Reducing the time required for MRI, has taken a lot of attention since its inventions. Compressed sensing (CS) is a relatively new method used a lot to reduce the required time. Usage of ordinary compressed sensing in MRI imaging needs conversion of 2D MRI signal (image) to 1D signal by some techniques. This conversion of the signal from 2D to 1D results in heavy computational burden. In this paper, based on separable transforms, a method is proposed which enables the usage of CS in MRI directly in 2D case. By means of this method, imaging can be done faster and with less computational burden
Wavelet-Based 2-D ECG Data Compression Method Using SPIHT and VQ Coding

An improved wavelet-based 2-D ECG data compression method is presented which employs a double stage compression. In the first stage the set partitioning in hierarchical trees (SPIHT) algorithm is used to compress the 2-D data array formed by cutting and beat-aligning the heartbeat data sequence. In the second stage vector quantization is applied to the residual image obtained from the previous stage. The 2-D approach utilizes both inter-beat and inter-sample redundancies in the ECG signal. The proposed algorithm is applied to several records in the MIT-BIH arrhythmia database. Results show lower Percent Root mean square Difference (PRD) than 1-D methods and several 2-D methods for the same Compression Ratio (CR).
Interpolation of orientation distribution functions (ODFs) in Q-ball imaging

Diffusion tensor magnetic resonance imaging (DTMRI) is a non-invasive method for investigating the brain white matter structure. It can be used to evaluate fiber bundles in the brain but in the regions with crossing fibers, it fails. To resolve this problem, high angular resolution diffusion imaging (HARDI) with a large number of diffusion encoding directions is used and for reconstruction, the Q-ball method is applied. In this method, orientation distribution function (ODF) of fibers can be calculated. Mathematical models play a crucial role in the field of ODF. For instance, in registering Q-ball images for applications like group analysis or atlas construction, one needs to interpolate ODFs. To this end, principal diffusion directions (PDDs) of each ODF are needed. In this paper, PDDs are defined as vectors that connect the corresponding local maxima of ODF values. Then, ODFs are interpolated using PDDs. The proposed method is evaluated and compared with previous protocols. Experimental results show that the proposed interpolation algorithm preserves the principal direction of fiber tracts without producing any deviations in the tracts. It is shown that changes in the entropy of the interpolated ODFs are almost linear and the bloating effect (blurring of the principal directions) can be removed.
Sparse-induced similarity measure: mono-modal image registration via sparse-induced similarity measure

Similarity measure is an important key in image registration. Most traditional intensity-based similarity measures (e.g. sum-of-squared-differences, correlation coefficient, mutual information and correlation ratio) assume a stationary image and pixel-by-pixel independence. These similarity measures ignore the correlation among pixel intensities; hence, a perfect image registration cannot be achieved especially in the presence of spatially varying intensity distortions and outlier objects that appear in one image but not in the other. It is supposed here that non-stationary intensity distortion (such as bias field) has a sparse representation in the transformation domain. Based on this assumption, a novel similarity measure is proposed here based on sparse representation in a mono-modal setting. The zero norm (l
 0
) in the transform domain is introduced as a new similarity measure in the presence of non-stationary intensity distortion. The present study defines a sparsity similarity measure that indicates the complexity of the residual image between two registered images in the transform domain such as discrete cosine transform or wavelet. It is attempted here to analytically and statistically illustrate that the proposed similarity measure has important properties such as metric properties in vector space, from correntropy perspective. This measure produces accurate registration results on both artificial and real-world problems which are examined in the present study.
Echocardiography frames quantification by empirical mode decomposition method

In this study a new method is proposed for quantification of cardiac muscle motions in echocardiography frames based on empirical mode decomposition (EMD) and manifold learning method. EMD algorithm is able to extract intrinsic mode functions (IMF) from a signal. In the first bi-dimension intrinsic mode functions (BIMF) of echocardiography frames myocardial is shown with more details than the second BIMF and the second BIMF shows more details than the third BIMF. By using manifold learning method, quantification difference between each pair of consecutive frames in the first, second and third BIMF series (similarities between the frames were extracted). Acquired trajectories of three manifolds for ischemie hearts are similar to each other but they are different for healthy hearts. This finding can be used for classifying ischemie and healthy hearts.
Sparse representation-based super-resolution for diffusion weighted images

Diffusion weighted imaging (DWI) is a non-invasive method for investigating the brain white matter structure. It can be used to evaluate fiber bundles in the brain. However, clinical acquisitions are often low resolution. This paper proposes a method for improving the resolution using sparse representation. In this method a non-diffusion weighted image (bO) is utilized to learn the patches and then diffusion weighted images are reconstructed based on the trained dictionary. Our method is compared with bilinear, nearest neighbor and bicubic interpolation methods. The proposed method shows improvement in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural SIMilarity (SSIM).
Adaptive watermarking scheme based on ICA and RDWT

This paper proposes a new approach to watermarking multimedia products based on the combination of redundant discrete wavelet transform and independent component analysis. The original image is decomposed by RDWT, and watermark is embedded in to LL sub-band frequency according mixing model of the ICA, after that for enhancing the robustness of the watermark, the perceptual model is applied via stochastic approach for watermark adapting. This is based on computation of a noise visibility function (NVF) which has local image properties so the strength of watermarking is controllable. Principal component analysis (PCA) whitening process and FastICA techniques are introduced to ensure a blind watermark extraction without requiring the original image. Experimental results show the proposed technique is robust under compression attacks. (4 pages)
Nonlinear Dimensionality Reduction via Path-Based Isometric Mapping

Nonlinear dimensionality reduction methods have demonstrated top-notch performance in many pattern recognition and image classification tasks. Despite their popularity, they suffer from highly expensive time and memory requirements, which render them inapplicable to large-scale datasets. To leverage such cases we propose a new method called “Path-Based Isomap”. Similar to Isomap, we exploit geodesic paths to find the low-dimensional embedding. However, instead of preserving pairwise geodesic distances, the low-dimensional embedding is computed via a path-mapping algorithm. Due to the much fewer number of paths compared to number of data points, a significant improvement in time and memory complexity with a comparable performance is achieved. The method demonstrates state-of-the-art performance on well-known synthetic and real-world datasets, as well as in the presence of noise.
Optimized kalman filter based on second momentum and triple rectangular for cell tracking on sequential microscopic images

Cell dynamics and motion stages are very important issues in the biological cell investigation in this novel method, we propose a novel method based on Kalman filter and second momentum for tracking cells on Sequential Microscopic Images. In proposed manner at first, we select a cell and cut covering rectangle. in the next step, we predict rectangle center of the cell in Next frame based on a modeling of velocity-acceleration using Kalman filter. The rectangle with triple covering area of previous cell rectangle and predicting center by Kalman filter is considered as a searching area. So, if all objects in the search areas have second momentum error less than threshold, it is selected as a chosen cell and we continue this process till all cells are track. OKF (optimized Kalman filter) algorithm uses second momentum for detecting cells because; value of second momentum doesn't change with rotating cells or changing size. OKF method removes cell after tracking in every frame. So, tracking has an acceptable and excellent precision for tracking in another cell. Also, the algorithm finds cells between frames by changing the reference frame and removing were tracked cells. Performance results of OKF show the proposed method is very admissible, effective method and the work shows great assistance for the cell Cytoplasm tracking. Finally, cells tracking with 98.17% accuracy were achieved by using OKF algorithm.
Designing an illumination effect canceling filter in facial images for multi-view face detection and recognition in images with complex background

This paper presents a novel approach for detection and recognition of multi-view faces whose location is unknown and the illumination conditions are varying. The illumination is a big problem in the face detection and recognition aspects. Our proposed method doesnpsilat use the skin color information for face detection. The detection of faces is accomplished after canceling the effect of the various illumination conditions. Two completely different methods are proposed for face detection in this paper. Because of the independency of the approaches to the facepsilas skin color, the persons with every kind of skin colors are detected even in completely dark environments. Next, the detected faces are recognized. The experiments have shown that we can combine some proposed aspects of the feature based methods with the eigenface method and get very successful results. The illumination dependency problem of the eigenface method has also been solved by a new methodology in this paper.
A Hybrid Fuzzy Based Algorithm for 3D Human Airway Segmentation

Segmentation of the human airway tree from volumetric computed tomography images is an important stage for many clinical applications such as virtual bronchoscopy. The main challenges of previously developed methods are to deal with two problems namely, leaking into the surrounding lung parenchyma during segmentation and the need to manually adjust the parameters. To overcome these problems, a multi- seeded fuzzy based region growing approach in conjunction with the spatial information of voxels is proposed. Comparison with a commonly used region growing segmentation algorithm shows that the proposed method retrieves more accurate results by achieving the specificity and sensitivity of 98.81% and 85.18%, respectively. The proposed algorithm needs no manually adjustment of parameters as well as any pre-filtering process, while leading to deliver the clinically accepted segmentation result with no leakage.
A two layer texture modeling based on curvelet transform and spiculated lesion filters for recognizing architectural distortion in mammograms

This paper presents a two layer texture modeling method to recognize architectural distortion in mammograms. We propose a method that models a Gaussian mixture on the Curvelet coefficients and the outputs of Spiculated Lesion Filters. The Curvelet transform and the Spiculated Lesion Filters have been applied to extract textural features of mammograms in literature. However the key difference between this study and the previous ones is that in our approach, a Gaussian mixture models the textural features extracted by the Curvelet transform and the Spiculated Lesion Filters. The results of the current study are shown in the form of accuracy and the area under the receiver operating characteristic curves on the DDSM and MIAS databases. The results suggest that the proposed method outperforms the previous work about 17.90% in accuracy and 0.19 in area under the receiver operating characteristic. The maximum achieved accuracy of our method is 92.78 %.
Statistical Association Mapping of Population-Structured Genetic Data

Association mapping of genetic diseases has attracted extensive research interest during the recent years. However, most of the methodologies introduced so far suffer from spurious inference of the associated sites due to population inhomogeneities. In this paper, we introduce a statistical framework to compensate for this shortcoming by equipping the current methodologies with a state-of-the-art clustering algorithm being widely used in population genetics applications. The proposed framework jointly infers the disease-associated factors and the hidden population structures. In this regard, a Markov Chain-Monte Carlo (MCMC) procedure has been employed to assess the posterior probability distribution of the model parameters. We have implemented our proposed framework on a software package whose performance is extensively evaluated on a number of synthetic datasets, and compared to some of the well-known existing methods such as STRUCTURE. It has been shown that in extreme scenarios, up to $10-15$10-15 percent of improvement in the inference accuracy is achieved with a moderate increase in computational complexity.
Extracting activated regions of fMRI data using unsupervised learning

Clustering approaches are going to efficiently define the activated regions of the brain in fMRI studies. However, choosing appropriate clustering algorithms and defining optimal number of clusters are still key problems of these methods. In this paper, we apply an improved version of Growing Neural Gas algorithm, which automatically operates on the optimal number of clusters. The decision criterion for creating new clusters at the heart of this online clustering is taken from MB cluster validity index. Comparison with other so-called clustering methods for fMRI data analysis shows that the proposed algorithm outperforms them in both artificial and real datasets.
Content based mammogram image retrieval based on the multiclass visual problem

Since expertise elicited from past resolved cases plays an important role in medical application and images acquired from various cases have a great contribution to diagnosis of the abnormalities, Content based medical image retrieval has become an active research area for many scientists, In this article we proposed a new framework to retrieve visually similar images from a large database, in which visual relevance is regarded as much as the semantic category similarity, we used optimized wavelet transform as the multi-resolution analysis of the images and extracted various statistical SGLDM features from different resolutions then after reducing feature space we used error correcting codes in order to untwist the existing multiclass visual problem introduced in preceding parts of the article, we implemented proposed algorithm on the 1000 mammograms provided by the DDSM database which consist of 2500 studies and their annotations provided by specialists.
A robust FCM algorithm for image segmentation based on spatial information and Total Variation

Image segmentation with clustering approach is widely used in biomedical application. Fuzzy c-means (FCM) clustering is able to preserve the information between tissues in image, but not taking spatial information into account, makes segmentation results of the standard FCM sensitive to noise. To overcome the above shortcoming, a modified FCM algorithm for MRI brain image segmentation is presented in this paper. The algorithm is realized by incorporating the spatial neighborhood information into the standard FCM algorithm and modifying the membership weighting of each cluster by smoothing it by Total Variation (TV) denoising. The proposed algorithm is evaluated with accuracy index in performing it on artificial synthesized images, and the results show the superior accuracy compared to some other state of the art FCM-based segmentation methods.