nils-herrmann/clean_scirepeval huggingface dataset:
Computer-aided diagnostic tool for early detection of prostate cancer

In this paper, we propose a novel non-invasive framework for the early diagnosis of prostate cancer from diffusion-weighted magnetic resonance imaging (DW-MRI). The proposed approach consists of three main steps. In the first step, the prostate is localized and segmented based on a new level-set model. In the second step, the apparent diffusion coefficient (ADC) of the segmented prostate volume is mathematically calculated for different b-values. To preserve continuity, the calculated ADC values are normalized and refined using a Generalized Gauss-Markov Random Field (GGMRF) image model. The cumulative distribution function (CDF) of refined ADC for the prostate tissues at different b-values are then constructed. These CDFs are considered as global features describing water diffusion which can be used to distinguish between benign and malignant tumors. Finally, a deep learning auto-encoder network, trained by a stacked non-negativity constraint algorithm (SNCAE), is used to classify the prostate tumor as benign or malignant based on the CDFs extracted from the previous step. Preliminary experiments on 53 clinical DW-MRI data sets resulted in 100% correct classification, indicating the high accuracy of the proposed framework and holding promise of the proposed CAD system as a reliable non-invasive diagnostic tool.
Probabilistic Modeling of Blood Vessels for Segmenting MRA Images

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
Expectation-maximization for a linear combination of Gaussians

We propose a modified expectation-maximization algorithm that approximates an empirical probability density function of scalar data with a linear combination of Gaussians (LCG). Due to both positive and negative components, the LCG approximates inter-class transitions more accurately than a conventional mixture of only positive Gaussians. Experiments in segmenting multi-modal medical images show the proposed LCG-approximation results in more adequate region borders.
A new framework for automated segmentation of left ventricle wall from contrast enhanced cardiac magnetic resonance images

A novel automated framework for the segmentation of the left ventricle (LV) wall from contrast enhanced cardiac magnetic resonance images (CE-CMRI) is proposed. The framework consists of two main steps. First, the inner cavity of the LV is segmented from the surrounding tissues based on finding the Maximum A Posteriori (MAP) estimation of a new energy function using a graph-cuts-based optimization algorithm. The proposed energy function consists of three descriptors: 1st-order visual appearance descriptors of the CE-CMRI, a 2D spatially rotation-variant 2nd-order homogeneity descriptor, and a LV inner cavity shape descriptor. Second, the outer contour of the LV is segmented by generating an orthogonal wave, starting from the LV inner contour, by solving an Eikonal partial differential equation with a new speed function that combines the prior shape and current visual appearance models of the LV wall. The proposed framework was tested on in-vivo CE-CMR images and validated with manual expert delineations of left ventricle borders. Experiments and comparison results on real CE-CMR images confirm the robustness and accuracy of the proposed framework over the existing ones.
Dyslexia Diagnostics by Centerline-Based Shape Analysis of the Corpus Callosum

Dyslexia severely impairs learning abilities, so that improved diagnostic methods are called for. Neuropathological studies have revealed abnormal anatomy of the Corpus Callosum (CC) in dyslexic brains. We explore a possibility of distinguishing between dyslexic and normal (control brains by quantitative CC shape analysis in 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 the centerline of the CC; and (iii) classifying the subject as dyslexic or normal based on the estimated length of the CC centerline using a _-nearest neighbor classifier. Experiments revealed significant differences (at the 95% confidence level) between the CC centerlines for 14 normal and 16 dyslexic subjects. Our initial classification suggests the proposed centerline-based shape analysis of the CC is a promising supplement to the current dyslexia diagnostics.
Precise segmentation of multimodal images

We propose new techniques for unsupervised segmentation of multimodal grayscale images such that each region-of-interest relates to a single dominant mode of the empirical marginal probability distribution of grey levels. We follow the most conventional approaches in that initial images and desired maps of regions are described by a joint Markov-Gibbs random field (MGRF) model of independent image signals and interdependent region labels. However, our focus is on more accurate model identification. To better specify region borders, each empirical distribution of image signals is precisely approximated by a linear combination of Gaussians (LCG) with positive and negative components. We modify an expectation-maximization (EM) algorithm to deal with the LCGs and also propose a novel EM-based sequential technique to get a close initial LCG approximation with which the modified EM algorithm should start. The proposed technique identifies individual LCG models in a mixed empirical distribution, including the number of positive and negative Gaussians. Initial segmentation based on the LCG models is then iteratively refined by using the MGRF with analytically estimated potentials. The convergence of the overall segmentation algorithm at each stage is discussed. Experiments show that the developed techniques segment different types of complex multimodal medical images more accurately than other known algorithms.
Remote sensing image segmentation using SVM with automatic selection for the kernel parameters

The kernel function plays a basic role in support vector machines (SVM) algorithms. This paper presents an automatic method for selecting the parameters of the Gaussian radial basis function (GRBF) kernel which is one of the commonly used forms in SVM regression algorithms. The proposed method uses the expectation maximization (EM) algorithm for the automatic selection. The SVM regression algorithm is used to solve two major problems in remote sensing image segmentation: the density estimation problem and the Markov random field modeling problem. The density estimation is used for class conditional probabilities in Bayesian setups. The MRF is used for region modeling in boosting the Bayesian image segmentation. The proposed algorithms are integrated in a framework for remote sensing image segmentation. Experimental evaluation of the proposed algorithms using synthetic data set and hyperspectral data set illustrates the outstanding performance of the proposed algorithms.
Detection of lung injury using 4D-CT chest images

This paper proposes a novel framework for the identification of the radiation-induced lung injury (RILI) after radiation therapy (RT) using 4D computed tomography (CT). The proposed methodology involves elastic image registration; segmentation of the lung fields; extraction of appearance and functional features; and classification of the lung tissues. The first step locally aligns the consecutive phases of the respiratory cycle using an elastic image registration approach based on descent minimization of the sum of squared difference similarity metric. Secondly, lung fields are segmented using a hybrid framework that integrates an adaptive shape prior model, a first-order intensity model, and a second order homogeneity descriptor of the lung tissues. Next, regional features that describe different types of lung functionality (e.g., ventilation and elasticity) are estimated from the segmented lungs. Finally, a random forest classifier has been applied to distinguish between injured and normal lung tissues. The proposed framework has been tested on data sets collected from 13 patients who had undergone RT treatment. Experimental results demonstrate the promise of the proposed framework for the identification of the injured lung region, and thus hold the promise as a valuable tool for early detection of RILI.
A random forest-based framework for 3D kidney segmentation from dynamic contrast-enhanced CT images

A framework for 3D kidney segmentation from abdominal computed tomography (CT) images is proposed. Accurate kidney segmentation from CT images is a challenging task due to the large inhomogeneity of the kidney (e.g., cortex and medulla), inter-patient anatomical differences, etc. To account for these challenges, a novel framework utilizing random forest (RF) classification that has the ability to cluster complex data is proposed. To build a robust classification model, discriminative features are needed for better separation of data classes. In this work, regional features from the CT appearance, a kidney shape prior model, and higher-order spatial interactions are extracted and are used for tissue classification. The shape model is constructed using a set of training images and is updated during segmentation using an appearance-based method taking into account both voxels' locations and appearances. The spatial interactions between CT data voxels are modeled using a higher-order spatial model that adds to the pairwise cliques the families of the triple- and quad cliques. The proposed framework has been tested on CT data that has been collected from 20 subjects and consist of multiple 3D CT scans acquired at the pre-and post-contrast agent administration. Evaluation results, using both volumetric and distance-based metrics, between manually drawn and automatically segmented contours confirm the high accuracy of the proposed technique.
A novel shape-based diagnostic approach for early diagnosis of lung nodules

An alternative method of diagnosing malignant lung nodules by their shape rather than conventional growth rate is proposed. The 3D surfaces of the detected lung nodules are delineated by spherical harmonic analysis that represents a 3D surface of the lung nodule supported by the unit sphere with a linear combination of special basis functions, called spherical harmonics (SHs). The proposed 3D shape analysis is carried out in five steps: (i) 3D lung nodule segmentation with a deformable 3D boundary controlled by two probabilistic visual appearance models (the learned prior and the estimated current appearance one); (ii) 3D Delaunay triangulation to construct a 3D mesh model of the segmented lung nodule surface; (iii) mapping this model to the unit sphere; (iv) computing the SHs for the surface; and (v) determining the number of the SHs to delineate the lung nodule. We describe the lung nodule shape complexity with a new shape index, the estimated number of the SHs, and use it for the K-nearest classification into malignant and benign lung nodules. Preliminary experiments on 109 lung nodules (51 malignant and 58 benign) resulted in the 94.4% correct classification (for the 95% confidence interval), showing the proposed method is a promising supplement to current technologies for the early diagnosis of lung cancer.
Shape-Appearance Guided Level-Set Deformable Model for Image Segmentation

A new speed function to guide evolution of a level-set based active contour is proposed for segmenting an object from its background in a given image. The guidance accounts for a learned spatially variant statistical shape prior, 1st-order visual appearance descriptors of the contour interior and exterior (associated with the object and background, respectively), and a spatially invariant 2nd-order homogeneity descriptor. The shape prior is learned from a subset of co-aligned training images. The visual appearances are described with marginal gray level distributions obtained by separating their mixture over the image. The evolving contour interior is modeled by a 2nd-order translation and rotation invariant Markov-Gibbs random field of object/background labels with analytically estimated potentials. Experiments with kidney CT images confirm robustness and accuracy of the proposed approach.
Infant Brain Extraction in T1-Weighted MR Images Using BET and Refinement Using LCDG and MGRF Models

In this paper, we propose a novel framework for the automated extraction of the brain from T1-weighted MR images. The proposed approach is primarily based on the integration of a stochastic model [a two-level Markov-Gibbs random field (MGRF)] that serves to learn the visual appearance of the brain texture, and a geometric model (the brain isosurfaces) that preserves the brain geometry during the extraction process. The proposed framework consists of three main steps: 1) Following bias correction of the brain, a new three-dimensional (3-D) MGRF having a 26-pairwise interaction model is applied to enhance the homogeneity of MR images and preserve the 3-D edges between different brain tissues. 2) The nonbrain tissue found in the MR images is initially removed using the brain extraction tool (BET), and then the brain is parceled to nested isosurfaces using a fast marching level set method. 3) Finally, a classification step is applied in order to accurately remove the remaining parts of the skull without distorting the brain geometry. The classification of each voxel found on the isosurfaces is made based on the first- and second-order visual appearance features. The first-order visual appearance is estimated using a linear combination of discrete Gaussians (LCDG) to model the intensity distribution of the brain signals. The second-order visual appearance is constructed using an MGRF model with analytically estimated parameters. The fusion of the LCDG and MGRF, along with their analytical estimation, allows the approach to be fast and accurate for use in clinical applications. The proposed approach was tested on in vivo data using 300 infant 3-D MR brain scans, which were qualitatively validated by an MR expert. In addition, it was quantitatively validated using 30 datasets based on three metrics: the Dice coefficient, the 95% modified Hausdorff distance, and absolute brain volume difference. Results showed the capability of the proposed approach, outperforming four widely used BETs: BET, BET2, brain surface extractor, and infant brain extraction and analysis toolbox. Experiments conducted also proved that the proposed framework can be generalized to adult brain extraction as well.
Modified Akaike information criterion for estimating the number of components in a probability mixture model

To estimate the number of unimodal components in a mixture model of a marginal probability distribution of signals while learning the model with a conventional Expectation-Maximization (EM) algorithm, a modification of the well-known Akaike information criterion (AIC) called the modified AIC (mAIC), is proposed. Embedding the mAIC into the EM algorithm allows us to exclude sequentially, one-by-one, the least informative components from their initially excessive, or over-fitting set. Experiments on modeling empirical marginal signal distributions with mixtures of continuous or discrete Gaussians in order to describe the visual appearance of synthetic phantoms and real medical 3D images (lung CT and brain MRI) demonstrate a marked and monotone increase of the mAIC towards its maximum at the proper number that is known for the synthetic phantom or practically justified for the real image. These results confirm the accuracy and robustness of the proposed automated mAIC-EM based learning.
A New CAD System for Early Diagnosis of Detected Lung Nodules

A pulmonary nodule is the most common manifestation of lung cancer. Lung nodules are approximately-spherical regions of relatively high density that are visible in X-ray images of the lung. Large (generally defined as greater than 1 cm in diameter) malignant nodules can be easily detected with traditional imaging equipment and can be diagnosed by needle biopsy or bronchoscopy techniques. However, the diagnostic options for small malignant nodules are limited due to problems associated with accessing small tumors, especially if they are located deep in the tissue or away from the large airways; therefore, additional diagnostic and imaging techniques are needed. One of the most promising techniques for detecting small cancerous nodules relies on characterizing the nodule based on its growth rate. The growth rate is estimated by measuring the volumetric change of the detected lung nodules over time, so it is important to accurately measure the volume of the nodules to quantify their growth rate over time. In this paper, we introduce a novel Computer Assisted Diagnosis (CAD) system for early diagnosis of lung cancer. The proposed CAD system consists of five main steps. These steps are: (i) segmentation of lung tissues from low dose computed tomography (LDCT) images, (ii) detection of lung nodules from segmented lung tissues, (iii) a non-rigid registration approach to align two successive LDCT scans and to correct the motion artifacts caused by breathing and patient motion, (iv) segmentation of the detected lung nodules, and (v) quantification of the volumetric changes. Our preliminary classification results based on the analysis of the growth rate of both benign and malignant nodules for 10 patients (6 patients diagnosed as malignant and 4 diagnosed as benign) were 100% for 95% confidence interval. The preliminary results of the proposed image analysis have yielded promising results that would supplement the use of current technologies for diagnosing lung cancer.
A new nonrigid registration framework for improved visualization of transmural perfusion gradients on cardiac first-pass perfusion MRI

A new framework for accurate registration of the segmented left ventricle (LV) on cardiac first-pass magnetic resonance imaging (FP-MRI) to precisely analyze the myocardial transit of contrast agent, especially in the ischemically damaged heart, is proposed. Due to the continuous physiological motion of the heart that causes the LV wall to change shape significantly, within the same scan, at the same cross section, we propose a new registration methodology that involves three steps: (i) global target-to-reference frame-to-frame alignment based on maximizing normalized mutual information (NMI); (ii) local alignment based on using a B-splines transformation model that maximizes a new similarity function that accounts for the 1st- and 2nd-order NMI between the globally aligned frames followed by (iii) a refinement step which is based on deforming each pixel of the target wall over evolving closed equi-spaced contours (iso-contours) to closely match the reference wall. Respective iso-contours in both reference and target frames are matched based on solving the Laplace equation. We have tested our framework on 20 FP-MRI datasets that have been collected from patients with ischemic damage resulting from heart attacks and who are undergoing experimental therapy, and have documented an improvement in the visualization and display of perfusion-related indexes.
A Framework for Automatic Segmentation of Lung Nodules from Low Dose Chest CT Scans

To accurately separate each pulmonary nodule from its background in a low dose computer tomography (LDCT) chest image, two new adaptive probability models of the visual appearance of small 2D and large 3D pulmonary nodules are jointly used to control the evolution of the de-formable model. The appearance prior is modeled with a translation and rotation invariant Markov-Gibbs random field of voxel intensities with pairwise interaction. The model is analytically identified from a set of training nodule images with normalized intensity ranges. Both the nodules and their background in each current multi-modal chest image are also modeled with a linear combination of discrete Gaussians that closely approximate the empirical marginal probability distribution of voxel intensities. Experiments with real LDCT chest images confirm the high accuracy of the proposed approach
Kidney segmentation from CT images using a 3D NMF-guided active contour model

A hybrid framework that is based on the integration of geometric deformable models and nonnegative matrix factorization (NMF) is introduced for 3D kidney segmentation from abdominal computed tomography (CT) images. The NMF is employed due to its ability to cluster complex data by extracting discriminative features from higher dimensional space. In this paper, regional features from CT appearance, a kidney shape model, and spatial interactions are fused using the NMF to produce a more robust model to guide the deformable model's evolution. The shape model is constructed using a set of training images and is updated during segmentation using an appearance-based method taking into account both voxels' locations and appearances. The spatial interactions are modeled using a pair-wise Potts Markov-Gibbs random field model. Our approach has been tested on 36 in-vivo 3D CT data sets and evaluated using, the Dice coefficient, the 95-percentile Hausdorff distance, and percentage kidney volume difference. Evaluation results show that feature fusion using NMF increases the deformable model's ability to accurately segment complex CT kidney data.
Dyslexia Diagnostics by 3-D Shape Analysis of the Corpus Callosum

Dyslexia severely impairs learning abilities; therefore, improved diagnostic methods are needed. Neuropathological studies have revealed an abnormal anatomy of the corpus callosum (CC) in dyslexic brains. We propose a new approach for the quantitative analysis of 3-D magnetic resonance images (MRI) of the brain that ensures a more accurate quantification of anatomical differences between the CC of dyslexic and control subjects. The proposed approach consists of three main processing steps: 1) segmenting the CC from a given 3-D MRI using the learned CC shape and visual appearance; 2) extracting the centerline of the CC; and 3) cylindrical mapping of the CC surface for its comparative analysis. Validation on 3-D simulated phantoms demonstrates the ability of the proposed approach to accurately detect the shape variability between two 3-D surfaces. Experimental results revealed significant differences (at the 95% confidence level) between 14 normal and 16 dyslexic subjects in all four anatomical divisions, i.e., splenium, rostrum, genu, and body of their CCs. Moreover, the initial classification results based on the centerline length and CC thickness suggest that the proposed shape analysis is a promising supplement to the current techniques for diagnosing dyslexia.