nils-herrmann/clean_scirepeval huggingface dataset:
CLASSIFICATION TECHNIQUES FOR AUTISTIC VS. TYPICALLY DEVELOPING BRAIN USING MRI DATA

Autism is a neurodevelopmental disorder that disrupts social and cognitive functions. Various autism studies revealed abnormalities in several brain regions. There is an increasing agreement from structural imaging studies on the abnormal anatomy of the white matter (WM) in autistic brains. In addition, the deficits in the size of the corpus callosum (CC) and its sub-regions in patients with autism relative to controls are well established. This paper presents two novel classification techniques of autism based on structural MRI. Our analysis is based on shape descriptions and geometric models. We compute the 3D distance map to describe the shape of the WM and use it as a statistical feature to discriminate between the two groups. We also use our recently proposed non-rigid registration technique to devise another classification approach by statistically analyzing and comparing the deformation fields generated from registering CC's onto each others. The accuracy of our techniques was tested on postmortem and on in vivo brain MR data. The results are very promising and show that, contrary to traditional methods, the proposed techniques are less sensitive to age and volume effects
Autism diagnostics by centerline-based shape analysis of the Corpus Callosum

Autism severely impairs personal behavior and communication skills, so that improved diagnostic methods are called for. Neuropathological studies have revealed abnormal anatomy of the Corpus Callosum (CC) in autistic brains. We explore a possibility of distinguishing between autistic and normal (control) brains by quantitative CC shape analysis in the 3D magnetic resonance images (MRI). Our approach consists of the three steps: (i) segmenting the CC from a given 3D MRI using the learned CC shape and visual appearance; (ii) extracting a centerline of the CC; and (iii) classifying the subject as autistic or normal based on the estimated length of the centerline of the CC using a k-Nearest neighbor classifier. Experiments revealed significant differences (at the 95% confidence level) between the CC centerlines for 17 normal and 17 autistic subjects. Our initial classification suggests the proposed centerline-based shape analysis of the CC is a promising supplement to the current autism diagnostics.
Fast Unsupervised Segmentation of 3D Magnetic Resonance Angiography

A new physically justified adaptive probabilistic model of blood vessels on magnetic resonance angiography (MRA) images is proposed. The model accounts for both laminar (for normal subjects) and turbulent blood flow (in abnormal cases like anemia or stenosis) and results in a fast algorithm for extracting a 3D cerebrovascular system from the MRA data. Experiments with real data sets confirm the high accuracy of the proposed approach.
Accurate modeling of tagged CMR 3D image appearance characteristics to improve cardiac cycle strain estimation

To reduce noise within a tag line, unsharpen the tag edges in spatial domain, and amplify the tag-to-background contrast, a 3D energy minimization framework for the enhancement of tagged Cardiac Magnetic Resonance (CMR) image sequences, based on learning first- and second-order visual appearance models, is proposed. The first-order appearance modeling uses adaptive Linear Combinations of Discrete Gaussians (LCDG) to accurately approximate the empirical marginal probability distribution of CMR signals for a given sequence, and separates tag and background submodels. It is also used to classify the tag lines and the background. The second-order model considers image sequences as samples of a translation- and rotation-invariant 3D Markov-Gibbs Random Field (MGRF) with multiple pairwise voxel interactions. A 3D energy function for this model is built by using the analytical estimation of the spatio-temporal geometry and Gibbs potentials of interaction. To improve the strain estimation, by enhancing the tag and background homogeneity and contrast, the given sequence is adjusted using comparisons to the energy minimizer. Special 3D geometric phantoms, motivated by statistical analysis of the tagged CMR data, have been designed to validate the accuracy of our approach. Experiments with the phantoms and eight real data sets have confirmed the high accuracy of the functional parameters that are estimated for the enhanced tagged sequences when using popular spectral techniques, such as spectral Harmonic Phase (HARP).
Image Alignment Using Learning Prior Appearance Model

A new approach to align an image of a textured object with a given prototype is proposed. Visual appearance of the images, after equalizing their signals, is modeled with a Markov-Gibbs random field with pairwise interaction. Similarity to the prototype is measured by a Gibbs energy of signal cooccurrences in a characteristic subset of pixel pairs derived automatically from the prototype. An object is aligned by an affine transformation maximizing the similarity by using an automatic initialization followed by gradient search. Experiments confirm that our approach aligns complex objects better than popular conventional algorithms.
A new shape-based framework for the left ventricle wall segmentation from cardiac first-pass perfusion mri

We propose a shape-based approach for the segmentation of the left ventricle (LV) wall on cardiac first-pass magnetic resonance imaging (FP-MRI) using level sets. To reduce the variabilities of the LV wall in FP-MRI, it is first imperative to co-align the time series images to account for the global and local motions of the heart. Therefore, we developed a two-step registration methodology that includes an affine-based registration followed by a local B-splines based alignment to maximize a similarity function that accounts for the first- and second-order normalized mutual information (NMI). Additionally, myocardial signal intensity varies with the agent transit, which makes it difficult to control the level set evolution using image intensities alone. Thus, we constrained the level set evolution using three features: a weighted probabilistic shape prior, the first-order pixel-wise image intensities, and a second-order Markov-Gibbs random field (MGRF) spatial interaction model. We tested our approach on 24 data sets in 8 infarction patients using the Dice similarity coefficient (DSC), comparing our approach to other shape-based segmentation approaches. We also tested the performance of our segmentation approach using the receiver operating characteristics (ROC). Our approach achieved a mean DSC value of 0.910±0.037 compared to other shape-based methods that achieved 0.862±0.045 and 0.844±0.047. Finally, the ROC analysis for our segmentation method showed the best performance, with area under the ROC curve of 0.92, while that for intensity showed the worst performance, with area under the ROC curve of 0.69.
A novel image analysis approach for accurate identification of acute renal rejection

Acute renal rejection is the most common reason for graft (transplanted kidney) failure after kidney transplantation, and early detection is crucial to survival of function in the transplanted kidney. The current techniques for early detection of acute renal rejection are not accurate. For example, clearances of inulin and DTPA require multiple blood and urine tests, and they provide information on both kidneys together, but not unilateral information. Moreover, biopsy (the gold standard for diagnosis of acute renal rejection after renal transplantation) could cause bleeding and infection. Also, the relatively small needle biopsies may lead to over- or underestimation of the extent of inflammation in the entire graft. Hence, a noninvasive and repeatable technique would not only be useful but is needed to ensure survival of transplanted kidneys. For this reason, we introduced a new non-invasive framework for automatic classification of normal and acute renal rejection transplants using Dynamic Contrast Enhanced Magnetic Resonance Images (DCE-MRI). In this paper, we introduce a new approach for the automatic classification of normal and acute rejection transplants from Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI). The proposed algorithm consists of three main steps; the first step isolates the kidney from the surrounding anatomical structures. In the second step, new motion correction models are employed to account for both the global and local motion of the kidney due to patient moving and breathing. Finally, the perfusion curves that show the transportation of the contrast agent into the tissue are obtained from the kidney and used in the classification of normal and acute rejection transplants. In this paper, we will focus on the second and third steps and the first step is shown in detail in .
A new deep-learning approach for early detection of shape variations in autism using structural mri

This paper introduces a novel shape-based computer-aided diagnosis (CAD) system using magnetic resonance (MR) brain images for autism diagnosis at different life stages. To improve the classification robustness, the system fuses the shape features extracted from the cerebral cortex (Cx) and cerebral white matter (CWM). Fusion is conducted based on the findings suggesting that Cx changes in autism are related to CWM abnormalities. The CAD system starts with segmenting Cx and CWM using a 3D joint model that combines intensity, shape, and spatial information. Then, Spherical Harmonic (SPHARM) is applied to the re-constructed meshes of Cx to derive 4 metrics for each mesh point; normal curvature, mean curvature, gaussian curvature, and Cx surface reconstruction error. To analyze the CWM shape, distance maps of its gyri are computed and three more shape features are extracted for these gyri. Finally, all the extracted shape features are fed to a multi-level deep network for feature fusion and diagnosis. The CAD system has been evaluated using subjects from the ABIDE database (8–12.8 years), achieving an accuracy of 93%, and from NDAR/Pitt database (16–51 years), achieving an accuracy of 97%. Also in order to show the capability of the system for early diagnosis, it has been tested on NDAR/IBIS database for infants, resulting in an accuracy of 85%. These initial results on the 3 databases hold the promise of efficient autism diagnosis.
Detection of white matter abnormalities in MR brain images for diagnosis of autism in children

This paper introduces a novel computer-aided diagnosis (CAD) system for the diagnosis of autism from magnetic resonance (MR) brain images of children. The proposed framework has two main components. First, cerebral white matter (CWM) is segmented from the brain volume using an adaptive shape model that is built from a set of co-aligned training images. A higher-order Markov Gibbs random field (MGRF) spatial interaction model is then integrated with an intensity model to account for data in homogeneities. Secondly, CWM meshes are reconstructed and a curvature-based analysis is applied in order to differentiate between autistic and control brains. From the reconstructed CWM meshes, we estimated 3 shape features (curvedness, sharpness, and mean curvature) that could geometrically describe CWM folds. The statistical analysis conducted on 20 autistic and control brains using binomial regression on raw features as well as on their principal components reveals that the three curvature-based measures could be used as discriminatory features between autistic and control brains. Moreover, the accuracy of the diagnostic results using K-nearest neighbor (KNN) classifier is 95%. These results show the promise of the proposed technique as a supplement to current diagnostic instruments (e.g., Autism Diagnostic Interview, Revised (ADI-R)).
Robust Image Registration Based on Markov-Gibbs Appearance Model

A new approach to align an image of a textured object with a given prototype is proposed. Visual appearance of the images, after equalizing their signals, is modeled with a Markov-Gibbs random field with pairwise interaction. Similarity to the prototype is measured by a Gibbs energy of signal co-occurrences in a characteristic subset of pixel pairs derived automatically from the prototype. An object is aligned by an affine transformation maximizing the similarity by using an automatic initialization followed by gradient search. Experiments confirm that our approach aligns complex objects better than popular conventional algorithms
Automatic segmentation of pathological lung using incremental nonnegative matrix factorization

Accurate segmentation of pathological lungs from large-size chest computed tomographic images is crucial for computer-assisted lung cancer diagnostics. In this paper, a new framework for automatic pathological lung segmentation is proposed. The proposed INMF-based segmentation approach has the ability to handle the in-homogeneities caused by the arteries, veins, bronchi, and possible pathologies that may exist in the lung tissues, and to detect the number of clusters in the image in an automated manner. The proposed INMF-based segmentation framework is quantitatively validated on simulated realistic lung phantoms that mimic different lung pathologies (7 datasets), in vivo data sets for 17 subjects, and for lung disease with severe pathologies. Three metrics are used: the Dice coefficient, modified Hausdorff distance, and absolute lung volume difference. Results show that the proposed approach outperforms existing lung segmentation techniques and can handle in-homogenities caused by different pathologies.
Image segmentation using GMRF models: parameters estimation and applications

Stochastic models of images are commonly represented in terms of three random processes (random fields) defined on the region of support of the image. The observed image process G is considered as a composite of two random process: a high level process G/sup h/, which represents the regions (or classes) that form the observed image; and a low level process G/sup l/, which describes the statistical characteristics of each region (or class). The representation G = (G/sup h/, G/sup l/) has been widely used in the image processing literature in the past two decades. In this paper, we consider the low level process G/sup l/ as mixture of normal distributions, and we use the expectation-maximization (EM) algorithm to estimate the mean, the variance, and proportion for each distribution. A popular model for the high level process G/sup h/ has been the Gibbs-Markov random field (GMRF) model. We introduce a novel unsupervised approach to estimate the parameters of a GMRF model. In this approach, we estimate the model parameters that maximize the posteriori probability of each pixel in a given image. The MAP estimate is obtained using a combination of genetic search and deterministic optimization using the iterated conditional mode (ICM) approach of Besag. The desired estimate of the GMRF parameters is the one corresponding to the MAP estimate. The approach has been applied on real images (Spiral CT slices) and provides satisfactory results.
Dyslexia diagnostics by 3D texture analysis of cerebral white matter gyrifications

The importance of accurate early diagnostics of dyslexia that severely affects the learning abilities of children cannot be overstated. Neuropathological studies have revealed an abnormal anatomy of the cerebral white matter (CWM) in dyslexic brains. We explore a possibility of distinguishing between dyslexic and normal (control) brains by a quantitative shape analysis of CWM gyrifications on 3D Magnetic Resonance (MR) images. Our approach consists of (i) segmentation of the CWM on a 3D brain image using a deformable 3D boundary; (ii) extraction of gyrifications from the segmented CWM, and (iii) shape analysis to quantify thickness of the extracted gyrifications and classify dyslexic and normal subjects. The boundary evolution is controlled by two probabilistic models of visual appearance of 3D CWM: the learned prior and the current appearance model. Initial experimental results suggest that the proposed 3D texture analysis is a promising supplement to the current techniques for diagnosing dyslexia.
Image analysis approach for identification of renal transplant rejection

Acute renal rejection is the most common reason for graft (transplanted kidney) failure after kidney transplantation, and early detection is crucial to survival of function in the transplanted kidney. The current techniques for early detection of acute renal rejection are not accurate. For example, clearances of inulin and DTPA require multiple blood and urine tests, and they provide information on both kidneys together, but not unilateral information. Moreover, biopsy (the gold standard for diagnosis of acute renal rejection after renal transplantation) could cause bleeding and infection. Also, the relatively small needle biopsies may lead to over- or underestimation of the extent of inflammation in the entire graft. Hence, a noninvasive and repeatable technique would not only be useful but is needed to ensure survival of transplanted kidneys. For this reason, we introduced a new non-invasive framework for automatic classification of normal and acute renal rejection transplants using dynamic contrast enhanced magnetic resonance images (DCE-MRI). In this paper, we introduce a new approach for the automatic classification of normal and acute rejection transplants from Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI). The proposed algorithm consists of three main steps; the first step isolates the kidney from the surrounding anatomical structures. In the second step, new motion correction models are employed to account for both the global and local motion of the kidney due to patient moving and breathing. Finally, the perfusion curves that show the transportation of the contrast agent into the tissue are obtained from the kidney and used in the classification of normal and acute rejection transplants. In this paper, we will focus on the second and third steps and the first step is shown in detail by A. El-Baz et al (2005).
A NOVEL FRAMEWORK FOR ACCURATE LUNG SEGMENTATION USING GRAPH CUTS

The closeness of the gray levels between lung tissues and the chest tissues makes lung segmentation based only on image signals difficult. This work proposes an automatic segmentation framework of lung consisting of three stages. First is the image signal modelling, and initial labelling stage. The labelling algorithm is based on a probabilistic model, which models the image signal with a linear combination of Gaussian distributions with positive and negative components. In the second stage a new analytically estimated potentials for Potts model parameter is used to identify the spatial interaction between the neighboring pixels. Finally the third stage, where an energy function using the previous models is formulated, and is globally minimized using s/t graph cuts. Experiments show that the developed technique segments CT lung images more accurately than other known algorithms.
Density estimation using modified expectation-maximization algorithm for a linear combination of Gaussians

In this paper we present a new approach for density estimation. The proposed approach is based on modifying expectation-maximization (EM) algorithm to approximate an empirical probability density function of scalar data with a linear combination of Gaussians (LCG). We also propose a novel EM-based sequential technique to get a close initial LCG approximation the modified EM algorithm should start with. Due to both positive and negative components, the LCG approximates inter-class transitions more accurately than a conventional mixture of only positive Gaussians. Experiments on simulated images demonstrate the accuracy of our approach.
Non-Invasive Image-Based Approach for Early Detection of Prostate Cancer

A novel noninvasive approach for early diagnosis of prostate cancer from Dynamic Contrast enhanced Magnetic Resonance Imaging is proposed. The proposed approach consists of four main steps. The first step is to isolate the prostate from the surrounding anatomical structures based on a Maximum a Posteriori estimate of a new log-likelihood function that accounts for the shape priori, the spatial interaction, and the current appearance of the prostate tissues and its background. In the second step, a non-rigid registration algorithm is employed to account for any local deformation that could occur in the prostate during the scanning process due to patient breathing and local motion. In the third step, the perfusion curves that show propagation of the contrast agent into the tissue are obtained from the segmented prostate of the whole image sequence of the patient. In the final step, we collect two features from these curves and use a kn nearest classifier to distinguish between malignant and benign detected tumors. Moreover, in this paper we introduce a new approach to generate color maps that illustrate the propagation of the contrast agent in the prostate tissues based on the analysis of the 3D spatial interaction of the change of the gray level values of prostate voxel using a Generalized Gauss Markov Random Field image model. Finally, the tumor boundaries are determined using a level set deformable model controlled by the perfusion information and the spatial interactions between the prostate voxels. Experimental results on 21 clinical Dynamic Contrast enhanced Magnetic Resonance Imaging data sets yield promising results.
Non-rigid biomedical image registration using graph cuts with a novel data term

Problem of non-rigid registration has become very important in the area of biomedical imaging. A non-rigid registration problem is modeled as an optimization problem and is solved using graph cuts and MRFs in recent years. In this paper, we have improved the graph cuts-based solution to non-rigid registration with a novel data term. The proposed data term has several advantages. Firstly, displacement labels can be directly assigned from this data term. Secondly, our data term imposes stricter penalty for intensity mismatches and hence yields higher registration accuracy. Finally, this data term can efficiently handle the dissimilarities in the intensity patterns between the floating and the reference images which may also arise due to some changes in illumination in addition to motion. We show the effectiveness of the proposed method on MRI images of brain and light microscopy images of retina. Experimental results indicate the superiority of our technique over some well-known non-rigid registration algorithms.
Early assessment of malignant lung nodules based on the spatial analysis of detected lung nodules

We propose a novel approach for diagnosing malignant lung nodules based on analyzing the spatial distribution of Hounsfield values for the detected lung nodules. Spatial distribution of image intensities (or Hounsfield values) comprising the malignant nodule appearance is accurately modeled with a new rotationally invariant second-order Markov-Gibbs Random Field (MGRF). In this paper, we introduce a new maximum likelihood estimation approach to estimate the neighborhood system of the proposed rotation invariant MGRF and its potentials from a training set of nodule images with normalized intensity ranges. Preliminary experiments on 327 lung nodules (153 malignant and 174 benign) resulted in the 91.1% correct classification (for the 95% confidence interval), showing the proposed method is a promising supplement to current technologies (biopsy-based diagnostic systems) for the early diagnosis of lung cancer.