nils-herrmann/clean_scirepeval huggingface dataset:
An optimization based approach embedded in a fuzzy connectivity algorithm for airway tree segmentation

The main problem with airway segmentation methods which significantly influences their accuracy is leakage into the extra-luminal regions due to thinness of the airway wall during the process of segmentation. This phenomenon potentially makes large regions of lung-parenchyma to be wrongly identified as airways. A solution to this problem in the previous methods was based on leak detection followed by reducing leakage during the segmentation process. This has been dealt with adjusting the segmentation parameters and performing the re-segmentation process on the pre-segmented area. This makes the algorithm very exhaustive and more dependent on the user interaction. The method presented here is to apply a mathematical shape optimization approach embedded in an efficient fuzzy connectivity based segmentation algorithm to preserve shape features of the object to perform a precise segmentation. The novelty of our proposed scheme is to prevent the leakage root rather than taking leak detection and reduction approaches. This is carried out by minimizing a gradient based cost function which employs shape features of circle/cylindrical structure of airway tree during the process of segmentation.
RISM: Single-Modal Image Registration via Rank-Induced Similarity Measure

Similarity measure is an important block in image registration. Most traditional intensity-based similarity measures (e.g., sum-of-squared-difference, correlation coefficient, and mutual information) assume a stationary image and pixel-by-pixel independence. These similarity measures ignore the correlation between pixel intensities; hence, perfect image registration cannot be achieved, especially in the presence of spatially varying intensity distortions. Here, we assume that spatially varying intensity distortion (such as bias field) is a low-rank matrix. Based on this assumption, we formulate the image registration problem as a nonlinear and low-rank matrix decomposition (NLLRMD). Therefore, image registration and correction of spatially varying intensity distortion are simultaneously achieved. We illustrate the uniqueness of NLLRMD, and therefore, we propose the rank of difference image as a robust similarity in the presence of spatially varying intensity distortion. Finally, by incorporating the Gaussian noise, we introduce rank-induced similarity measure based on the singular values of the difference image. This measure produces clinically acceptable registration results on both simulated and real-world problems examined in this paper, and outperforms other state-of-the-art measures such as the residual complexity approach.
A textural approach for recognizing architectural distortion in mammograms

Breast cancer is considered as the most important cause of death among women. Architectural distortions are very important signs of breast cancer and early detection of them is a rewarding work. In this paper we propose a method to recognize architectural distortion from normal parenchyma. In our proposed method, appropriate features are extracted by the analysis of oriented textures with the application of orientation component of recent the state-of-the-art local texture descriptor called Monogenic Binary Coding (MBC). In addition, we transform Region of Interests (ROIs) to polar coordinates in order to highlight some specific patterns in mammograms. Various classifiers are used over a set of mammograms from Digital Database for Screening Mammography (DDSM). The results show that proposed method is very encouraging. The best performance achieved is 91.25% in terms of the average accuracy using the Nearest Neighbor classifier.
Canonical polyadic decomposition for principal diffusion direction extraction in diffusion weighted imaging

Diffusion weighted imaging is a non-invasive method for investigation of brain fiber bundles. In diffusion tensor imaging (DTI), the diffusion of water molecules is assumed Gaussian, therefore, it can just show a single fiber direction in a voxel. To overcome this limitation, a number of high angular resolution diffusion imaging methods have been proposed. One of these techniques is Q-ball imaging. Using this method, we can extract orientation distribution function (ODF) that shows the orientations of multiple fibers in a voxel. For extracting the fiber directions, the maxima of the ODFs are conventionally determined. However, the results of this approach are sensitive to noise. To improve the results, we present a method for extracting principal diffusion directions using a tensor decomposition method. The results obtained using simulated and real data show that our proposed method has a better performance compared to the local maxima. For SNRs less than 30 dB, the similarity value of our method is 0.3 larger than that of the local maxima method.
Another Approach to Detection of Abnormalities in MR-Images Using Support Vector Machines

In this paper we will address two major problems in mammogram analysis for breast cancer in MR-images. The first is classification between normal and abnormal cases and then, discrimination between benign and malignant in cancerous cases. Our proposed method extracts textural and statistical descriptive features that are fed to a learning engine based on the use of support vector machine learning framework to categorize them. The obtained results show excellent accuracy in both classification problems, that proves the appropriate combination of our features and selecting powerful classifier i.e. Support Vector Machine leads us to a brilliant outcome.
GMWASC: Graph matching with weighted affine and sparse constraints

Graph Matching (GM) plays an essential role in computer vision and machine learning. The ability of using pairwise agreement in GM makes it a powerful approach in feature matching. In this paper, a new formulation is proposed which is more robust when it faces with outlier points. We add weights to the one-to-one constraints, and modify them in the process of optimization in order to diminish the effect of outlier points in the matching procedure. We execute our proposed method on different real and synthetic databases to show both robustness and accuracy in contrast to several conventional GM methods.
Investigation of Brain Default Network's activation in autism spectrum disorders using Group Independent Component Analysis

Autism Spectrum Disorders (ADS), with unknown etiology, is one of the most understudy fields of research worldwide that requires complicated and delicate analytical study methods. The purpose of this study was to compare active regions of Brain Default Mode Network (DMN) using Group Independent Component Analysis (6ICA) among resting state patients with Autism Disorder and healthy subjects. Default Mode Network consists of posterior cingulate cortex (PCC), lateral parietal cortex/angular gyrus retrosplenial cortex, medial prefrontal cortex, superior frontal gyrus, parahippocampal gyrus and temporal lobe shows more prominent activity in passive resting conditions. The diagnosis of autism disorder was According to the clinical ADI-R of ADOS test. After Data preprocessing of rs-fMRI dataset by SPM toolbox, Group independent component analysis was performed in three steps of data reduction, ICA, and back reconstruction using the group ICA of fMRI toolbox (GIFT). Group ICA revealed sixteen Default mode network components which five of them were chosen as DMN components to compare between both groups. The number of voxels in each cluster of autistic individuals was significantly lower than in healthy individuals. Spatial group ICA of fMRI can be a useful approach to determine and study of differences in DMN of brain in patients with autism disorder.
High angular resolution diffusion image registration

Diffusion Tensor Imaging (DTI) is a common method for the investigation of brain white matter. In this method, it is assumed that diffusion of water molecules is Gaussian and so, it fails in fiber crossings where this assumption does not hold. High Angular Resolution Diffusion Imaging (HARDI) allows more accurate investigation of microstructures of the brain white matter; it can present fiber crossing in each voxel. HARDI contains complex orientation information of the fibers. Therefore, registration of these images is more complicated than the scalar images. In this paper, we propose a HARDI registration algorithm based on the feature vectors that are extracted from the Orientation Distribution Functions (ODFs) in each voxel. Hammer similarity measure is used to match the feature vectors and thin-plate spline (TPS) based registration is used for spatial registration of the skeleton and its neighbors. A re-orientation strategy is utilized to re-orient the ODFs after spatial registration. Finally, we evaluate our method based on the differences in principal diffusion direction and we will show that utilizing the skeleton as landmark in the registration results in accurate alignment of HARDI data.
Pain level estimation in video sequences of face using incorporation of statistical features of frames

Pain level estimation from videos of face has many benefits for clinical applications. Most of the previous works focused only on pain detection task. However, pain level estimation of video sequences has been discussed fewer. In this work, we have proposed a new regression-based approach to estimate the pain level of video sequences. As the first step, facial expression-related features were extracted from each frame, this task was done by reducing identity-related features using the robust principal component analysis decomposition. Then, we used the minimum, maximum, and mean of the features of frames in a sequence to represent that sequence by a fixed-length feature vector. After this, in order to incorporate the discriminant features of pain and reduce the computational complexity, we implemented dimension reduction by Supervised Kernel Locality Preserving Projection (SKLPP) method, and in the end, a linear regression was used to predict the pain level. Experiments on UNBC-McMaster shoulder pain expression archive database show that our method achieves the area under the curve of ROC measure of 88.43 percent in pain detection task that has better result compared to other state of the art methods.
Gradient vector flow snake segmentation of breast lesions in Dynamic Contrast-Enhanced MR images

The development of computer-aided diagnosis (CAD) for breast magnetic resonance (MR) images has encountered some big challenges. One of these challenges is related to breast lesion segmentation. Accurate segmentation of breast lesions has a vital role in other consequent applications such as feature extraction. Since malignant breast lesions typically appear with irregular borders and shapes in MR images whereas benign masses appear with more regular shapes, and smooth and lobulated borders, it seems that the accurate segmentation of breast lesion borders in MR images are important. To achieve this purpose, we have used the Gradient Vector Flow (GVF) snake segmentation method. This study included 52 (33 malignant and 19 benign) histopathologically proven breast lesions and the stages of the proposed method are as follows: selecting the region of interest (ROI), segmentation using GVF, evaluation of GVF snake segmentation method. The results of GVF segmentation method in this study were satisfactory referred to the radiologist's manual segmentation. The results showed the GVF snake segmentation method correctly segmented 97% of malignant lesion borders and 89.5% of benign lesion borders at the overlap threshold of 0.6. This indicates GVF snake segmentation method could provide us with a powerful method that can make an accurate segmentation in breast lesion borders.
Multiple Sclerosis Diagnosis Based on Analysis of Subbands of 2-D Wavelet Transform Applied on MR-images

In this study, we have proposed a novel approach to investigate the features of four subbands of 2-D wavelet transform in magnetic resonance images (MRIs) for normal and abnormal brains which defected by multiple sclerosis (MS). Concurrently, another method extracts different kinds of features in spatial domain. Totally, 116 features have been extracted. Before applying the algorithm, we have to use a registration method because of variety in size of brain images. All extracted features have been passed over the principal component analysis (PCA) and have been pushed to an artificial neural network (ANN) that is a feed-forward type. According to changing in position of defected parts of brain, we have analyzed four different MRI datasets in different stages of MS progression, including 101 MRIs of normal and abnormal brain images. In all cases, certain diagnosis is gained. Meantime, 40 percent of the datasets have been reserved as the "test data ".
A heuristic method for finding the optimal number of clusters with application in medical data

In this paper, a heuristic method for determining the optimal number of clusters is proposed. Four clustering algorithms, namely K-means, Growing Neural Gas, Simulated Annealing based technique, and Fuzzy C-means in conjunction with three well known cluster validity indices, namely Davies-Bouldin index, Calinski-Harabasz index, Maulik-Bandyopadhyay index, in addition to the proposed index are used. Our simulations evaluate capability of mentioned indices in some artificial and medical datasets.
Feature Extraction Using Gabor-Filter and Recursive Fisher Linear Discriminant with Application in Fingerprint Identification

Fingerprint is widely used in identification and verification systems. In this paper, we present a novel feature extraction method based on Gabor filter and Recursive Fisher Linear Discriminate (RFLD) algorithm, which is used for fingerprint identification. Our proposed method is assessed on images from the biolab database. Experimental results show that applying RFLD to a Gabor filter in four orientations, in comparison with Gabor filter and PCA transform, increases the identification accuracy from 85.2% to 95.2% by nearest cluster center point classifier with Leave-One-Out method. Also, it has shown that applying RFLD to a Gabor filter in four orientations, in comparison with Gabor filter and PCA transform, increases the identification accuracy from 81.9% to 100% by 3NN classifier. The proposed method has lower computational complexity and higher accuracy rates than conventional methods based on texture features.
Automatic B-spline image registration using histogram-based landmark extraction

Recognition and correction of inhomogeneous displacement caused by patient's movement has been recently discussed as an interesting topic in medical image processing. Considering consistency in general structure of the image during distortion, histogram could be employed as a fast implementation method in feature domain. Accordingly, attribute vectors could be defined for each pixel based on spatial features to find corresponding points in two images. Consequently a point-based and non-rigid transformation approach will be designed. A B-spline image registration has been applied to match those pairs with a defined smoothness factor. This algorithm is a step-by-step registration process controlled by this factor. The proposed algorithm has been applied to brain MR images. The normal mean square error value has been measured between registered and original images and the result shows a significant improvement in the proposed algorithm.
A new ROI extraction method for FKP images using global intensity

Finger-Knuckle - Print (FKP) is one of the newest biometrics. In this paper, a novel approach has been proposed to segment the Region of Interest (ROI) of a FKP image using the global intensity. This method upgrades the speed and accuracy of segmentation stage, as well as the pace of other steps of the procedure. This has been achieved by employing the area with maximum intensity in ROI extraction, instead of using the creases of the knuckle image. To confirm this improvement, lots of experiments have been performed and the method has been compared with the only existing schemes for ROI extraction suggested by Zhang and Kekre. At the end, the captured ROI images obtained by three methods have been performed on a common procedure of a feature extraction and matching scores, and the results have been compared.
Mammogram image retrieval via sparse representation

In recent years there has been a great effort to enhance the computer-aided diagnosis systems, since proven similar pathologies, in the past, plays an important role in diagnosis of the current cases, content based medical image retrieval has been emerged. In this work we have designed a decision making machine in which utilizes sparse representation technique to preserve semantic category relevance among the retrieved images and the query image, this machine comprises optimized wavelets (adapted using lifting scheme) to extract appropriate visual features in order to grasp visual content of the images, afterwards by using some classical methods, Raw data vectors become applicable for sparse representation. We implemented our algorithm on the DDSM database which consists of 2500 studies and their annotations provided by specialists.
Transform domain semi-super resolution

This paper presents a transform-based approach to semi-super resolution. The idea of semi-super resolution is to enhance low-resolution frames from a video sequence encoded with different resolutions among frames. The proposed framework uses a DCT-based down-sampling method at the encoder process. At the decoder, DCT-based up-sampling plus high-frequency information from adjacent frames in full resolution are used to enhance interpolated low-resolution frames. The results show an increase of the overall quality without using any extra information from the encoder. The proposed technique can also outperfom previous semi-super resolution methods operating in pixel domain.
Example-based Enhancement of Degraded Video

We present an example-based approach to general enhancement of degraded video frames. The method relies on building a dictionary with non-degraded parts of the video and to use such a dictionary to enhance the degraded parts. The image degradation has to originate from a “repeatable” process, so that the dictionary image patches (blocks) are equally degraded, thus originating a dictionary with degraded blocks and their residues (differences in between degraded and original blocks). Once a match is found between a degraded block in the video and a degraded block in the dictionary, the associated residue of the latter is soft-added to the block of the former. The method is a generalization of the method for example-based super-resolution. Results are presented to demonstrate the applicability of the method to many scenarios.
Clustering of matched features and gradient matching for mixed-resolution video super-resolution

This work presents a novel technique for image reconstruction applied to mixed-resolution video super-resolution. We segment an image into patches defined by the clustering of a vector flow generated from matching SIFT features. We reconstruct the segmented image by applying image projective transformation to a reference image. By varying the number of clusters, we composed a sequence of reconstructed images, which are then used to compose a codebook, through gradient matching. This idea is extended to use low and high-resolution image pairs for super-resolution. Our results indicate a 1.4dB gain, on average, over the use of overlapped-block motion-compensation (OBMC).
Video super-resolution based on local invariant features matching

This paper presents an algorithm for video super-resolution based on scale-invariant feature transform (SIFT) matching. SIFT features are known to be a robust method for locating keypoints. The matching of these keypoints from different frames in a video allows us to infer high-frequency information in order to perform example-based super-resolution. We first apply a block constrained keypoint detection for a more precise superposition of features. Later, we extract high-frequency information with a gradient-based matching scheme. Our results indicate gains over interpolation and previous example-based super-resolution approaches.
Lossless video coding using wavelet packet and subband domain motion estimation

Proposes a lossless video coding scheme based on a reversible wavelet packet, and subband domain motion estimation. In lossless transforms, integer input signals are transformed into integer transform coefficients and losslessly reconstructed. Our method permits both intra- and inter-frame coding to proceed in the wavelet transform domain. The subband divisions by wavelet packet are varied effectively. And the motions between two successive frames are described as the translation of subband blocks. Each frame is referred to the next frame. The prediction differences are encoded by a zero tree coding scheme to provide some desirable features including exact bit-rate control, quality and resolution scalability, progressive transmission, good compression performance and possibility of lossless coding. This paper offers the availability of our strategy in lossless video coding.
Image coding based on regular cosine-modulated filter banks

In this paper, a novel design method of regular cosine-modulated filter banks (CMFB's) have been presented for image coding. After introducing a regularity constraint on lattice parameters of a prototype filter in paraunitary (PU) CMFB's, we derive a regularity condition for perfect reconstruction (PR) CMFB's. Finally, we design regular 8-channel 32-length PUCMFB and PRCMFB by a unconstrained optimization of residual lattice parameters, and several simulation results for test images are shown for evaluating the proposed image coder based on the CMFB's with one degree of regularity.
Cosine-modulated 2 dimensional FIR filter banks satisfying perfect reconstruction

Considers the theory of cosine-modulated 2 dimensional (2-D) perfect reconstruction (PR) filter banks. First, a 2-D digital filter design with half passband, obtained by the sampling matrix, is discussed. Next, 2-D analysis filter banks are realized by cosine-modulating this prototype 2-D digital filter. It is shown that the modulation in the 2-D frequency plane is equivalent to the 1-D modulation. A necessary and sufficient condition for 2-D perfect reconstruction filter banks is derived. If the polyphase filter pairs of the prototype filter have a double-complement, the resulting 2-D filter bank satisfies the condition of perfect reconstruction.<<ETX>>
Vocal separation using improved robust principal component analysis and post-processing

Due to the spread of the Internet and improvement of the audio compression technology, we need technologies to handle a large number of audio files. Separating singing voice from music is one of them and it is used in many applications, such as lyric retrieval and singer recognition. Recently, robust principal component analysis (RPCA) has been proposed, which makes use of repetition of a phrase in an accompaniment. We assume that there are some points to be improved in RPCA. In this paper, we propose a developed method based on the RPCA algorithm. First, we propose the extended RPCA algorithm that makes use of the two features of the spectrogram. Second, we apply simple post-processing to remove noise effectively. Lastly, we experiment our algorithm using the MIR-1K dataset and confirm that the proposed algorithm shows better separation performance than the conventional RPCA method.