nils-herrmann/clean_scirepeval huggingface dataset:
A novel 4D PDE-based approach for accurate assessment of myocardium function using cine cardiac magnetic resonance images

A novel framework for assessing wall thickening from 4D cine cardiac magnetic resonance imaging (CMRI) is proposed. The proposed approach is primarily based on using geometrical features to track the left ventricle (LV) wall during the cardiac cycle. The 4D tracking approach consists of the following two main steps: (i) Initially, the surface points on the LV wall are tracked by solving a 3D Laplace equation between two successive LV surfaces; and (ii) Secondly, the locations of the tracked LV surface points are iteratively adjusted through an energy minimization cost function using a generalized Gauss-Markov random field (GGMRF) image model in order to remove inconsistencies and preserve the anatomy of the heart wall during the tracking process. Then the myocardial wall thickening is estimated by co-allocation of the corresponding points, or matches between the endocardium and epicardium surfaces of the LV wall using the solution of the 3D Laplace equation. Experimental results on in vivo data confirm the accuracy and robustness of our method. Moreover, the comparison results demonstrate that our approach outperforms 2D wall thickening estimation approaches.
Segmentation of lung region based on using parallel implementation of joint MGRF: Validation on 3D realistic lung phantoms

The segmentation of the lung tissues in chest Computed Tomography (CT) images is an important step for developing any Computer-Aided Diagnostic (CAD) system for lung cancer and other pulmonary diseases. In this paper, we introduce a new framework to generate 3D realistic synthetic phantoms to validate our developed Joint Markov-Gibbs based lung segmentation approach from CT data. Our framework is based on using a 3D generalized Gauss-Markov Random Field (GGMRF) model of voxel intensities with pairwise interaction to model the 3D appearance of the lung tissues. Then, the appearance of the generated 3D phantoms is simulated based on iterative minimization of an energy function that is based on using the learned 3D-GGMRF image model. These 3D realistic phantoms can be used to evaluate the performance of any lung segmentation approach. In this paper, we used the 3D realistic phantoms to evaluate the performance of our developed lung segmentation approach based on using the Dice Similarity Coefficient (DSC) metric and the Receiver Operating Characteristics (ROC). The DSC demonstrated that our approach achieves a mean DSC value of 0.994 ± 0.0034. Moreover, the ROC analysis for our method showed the best performance (area 0.99), while intensity showed the worst performance (area 0.92).
A novel image-based approach for early detection of prostate cancer

A novel non-invasive approach for the early diagnosis of prostate cancer from diffusion-weighted MRI is proposed. The proposed diagnostic approach consists of three main steps. The first step is to isolate the prostate from the surrounding anatomical structures based on a Maximum a Posteriori (MAP) estimate of a new log-likelihood function that accounts for the shape priori, the spatial interaction, and the current appearance of prostate tissues and its background (surrounding anatomical structures). In the second step, a nonrigid registration algorithm is employed to account for any local deformation between the segmented prostates at different b-values that could occur during the scanning process due to patient breathing and local motion. In the final step, a kn-Nearest Neighbor-based classifier is used to classify the prostate into benign or malignant based on four appearance features extracted from registered images. Moreover, in this paper we introduce a new approach to generate color maps that illustrate the propagation of diffusion in 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 (GGMRF) image model. Finally, the tumor boundaries are determined using a level set deformable model controlled by the diffusion information and the spatial interactions between the prostate voxels. Experimental results on 28 clinical diffusion-weighted MRI data sets yield promising results.
Accurate Automatic Analysis of Cardiac Cine Images

Acquisition of noncontrast agent cine cardiac magnetic resonance (CMR) gated images through the cardiac cycle is, at present, a well-established part of examining cardiac global function. However, regional quantification is less well established. We propose a new automated framework for analyzing the wall thickness and thickening function on these images that consists of three main steps. First, inner and outer wall borders are segmented from their surrounding tissues with a geometric deformable model guided by a special stochastic speed relationship. The latter accounts for Markov-Gibbs shape and appearance models of the object-of-interest and its background. In the second step, point-to-point correspondences between the inner and outer borders are found by solving the Laplace equation and provide initial estimates of the local wall thickness and the thickening function index. Finally, the effects of the segmentation error is reduced and a continuity analysis of the LV wall thickening is performed through iterative energy minimization using a generalized Gauss-Markov random field (GGMRF) image model. The framework was evaluated on 26 datasets from clinical cine CMR images that have been collected from patients with eleven independent studies, with chronic ischemic heart disease and heart damage. The performance evaluation of the proposed segmentation approach, based on the receiver operating characteristic (ROC) and Dice similarity coefficients (DSC) between manually drawn and automatically segmented contours, confirmed a high robustness and accuracy of the proposed segmentation approach. Furthermore, the Bland-Altman plot is used to assess the limit of agreement of our measurements of the global function parameters compared to the ground truth. Importantly, comparative results on the publicly available database (MICCAI 2009 Cardiac MR Left Ventricle Segmentation) demonstrated a superior performance of the proposed segmentation approach over published methods.
A novel 3D segmentation approach for segmenting the prostate from dynamic contrast enhanced MRI using current appearance and learned shape prior

Prostate segmentation is an essential step in developing any non-invasive Computer-Assisted Diagnostic (CAD) system for the early diagnosis of prostate cancer using Magnetic Resonance Images (MRI). In this paper, we propose, a novel framework for 3D segmentation of the prostate region from Dynamic Contrast Enhancement Magnetic Resonance Images (DCE-MRI). The framework is based on a maximum aposteriori (MAP) estimate of a new log-likelihood function that consists of three descriptors: (i) 1st-order visual appearance descriptors of the DCE-MRI, (ii) a 3D spatially invariant 2nd-order homogeneity descriptor, and (iii) a 3D prostate shape descriptor. The shape prior is learned from the co-aligned 3D segmented prostate DCE-MRI data. The visual appearances of the object and its background are described with marginal gray-level distributions obtained by separating their mixture over prostate volume. The spatial interactions between the prostate voxels are modeled by a 3D 2nd-order rotation-variant Markov-Gibbs random field (MGRF) of object/background labels with analytically estimated potentials. Experiments with real in vivo prostate DCE-MRI confirm the robustness and accuracy of the proposed approach.
A novel approach for global registration of medical images based on learning the prior appearance model

A new approach to align an image of a medical object with a given prototype (reference object) is proposed. Visual appearance of the images, after equalizing their signals, is modeled with a new variant rotation and scaling Markov-Gibbs random field with pairwise interaction model. Similarity to the prototype (reference object) is measured by a Gibbs energy of signal co-occurrences in a characteristic subset of pixel pairs derived automatically from the prototype (reference object) using our previous Linear Combination of Discrete Gaussians (LCDG) probabilistic model. 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.
An ISO-surfaces based local deformation handling framework of lung tissues

Handling the deformations of the lung tissues in successive chest computed tomography (CT) scans of a patient is a vital step in any computer-aided diagnostic (CAD) system for lung cancer diagnosis. In this paper, we propose a new nonrigid registration methodology for the segmented lung region from CT data that involves two steps. The first step globally aligns the target-to-reference CT scans using an affine transformation based on ascent maximization of the estimated mutual information of the calculated distance map using the fast marching level sets method inside the segmented lung for both the target and reference objects. The second step is the local alignment of the target lung object in order to correct for intricate relative deformations due to breathing and heart beats. The local deformations are handled based on displacing each voxel of the target over evolving closed equi-spaced surfaces (iso-surfaces) to closely match the reference object. In order to displace the voxel on the iso-surfaces of the target lung object, the initial voxel-to-voxel match between target and reference lung objects is estimated by solving the 3D Laplace equation between each two corresponding iso-surfaces on the reference and target objects. Finally, the estimation of voxel-to-voxel match is refined through iterative energy minimization using a generalized Gauss-Markov random field (GGMRF) model. Qualitative and quantitative results demonstrate the promise of the proposed nonrigid registration framework.
3D diffusion MRI-based CAD system for early diagnosis of acute renal rejection

This paper introduces a novel computer aided diagnostic (CAD) system for early diagnosis of acute renal rejection (ARR) from 3D diffusion-weighted MRI (DW-MRI) data (2D + fe-value). The proposed CAD system starts with aligning the DW-MRI data using B-splines based registration to handle the motion effects, which come from breathing and heart beats. This is followed by the segmentation of the kidney tissue using geometric (level-sets based) de-formable model, which is guided by a new stochastic speed relationship that takes into account an adaptive kidney shape prior and the visual appearance of the kidney. The pixel-wise guidance of the level-sets is obtained by integrating these image features into a joint Markov-Gibbs random field (MGRF) model of the kidney and its background. The final step of the proposed CAD system is to calculate the apparent diffusion coefficients (ADCs) between different b-values of the segmented DW-MRI data to distinguish between rejection and nonrejection renal transplants. Experimental results on 36 subjects, using a KStar classifier and leave-one-subject-out, have classified 87% of the subjects correctly (26 out of 30 rejection kidneys and 5 out of 6 nonrejection kidneys). These initial diagnostic results hold promise of the proposed CAD system as a reliable non-invasive diagnostic tool.
3D shape analysis of the brain cortex with application to dyslexia

To discriminate more accurately between dyslexic and normal brains, we detect the brain cortex variability through a spherical harmonic analysis that represents a 3D surface supported by the unit sphere, having a linear combination of special basis functions, called spherical harmonics (SHs). The proposed 3D shape analysis is carried out in five steps: (i) 3D brain cortex 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 brain cortex 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 brain cortex. We describe the brain shape complexity with a new shape index, the estimated number of the SHs, and use it for the K-nearest classification into the normal and dyslexic brains. Initial experiments suggest that our shape index is a promising supplement to the current dyslexia diagnostic techniques.
Global image registration based on learning the prior appearance model

A new approach to align an image of a textured object with a given prototype (learned reference object) 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 (learned reference object) 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. To get accurate appearance model, we developed a new approach to automatically select the most important cliques (neighborhood system) that describe the visual appearance of a texture object. Experiments confirm that our approach aligns complex objects better than popular conventional algorithms.
A unified framework for MAP estimation in remote sensing image segmentation

A complete framework is proposed for applying the maximum a posteriori (MAP) estimation principle in remote sensing image segmentation. The MAP principle provides an estimate for the segmented image by maximizing the posterior probabilities of the classes defined in the image. The posterior probability can be represented as the product of the class conditional probability (CCP) and the class prior probability (CPP). In this paper, novel supervised algorithms for the CCP and the CPP estimations are proposed which are appropriate for remote sensing images where the estimation process might to be done in high-dimensional spaces. For the CCP, a supervised algorithm which uses the support vector machines (SVM) density estimation approach is proposed. This algorithm uses a novel learning procedure, derived from the main field theory, which avoids the (hard) quadratic optimization problem arising from the traditional formulation of the SVM density estimation. For the CPP estimation, Markov random field (MRF) is a common choice which incorporates contextual and geometrical information in the estimation process. Instead of using predefined values for the parameters of the MRF, an analytical algorithm is proposed which automatically identifies the values of the MRF parameters. The proposed framework is built in an iterative setup which refines the estimated image to get the optimum solution. Experiments using both synthetic and real remote sensing data (multispectral and hyperspectral) show the powerful performance of the proposed framework. The results show that the proposed density estimation algorithm outperforms other algorithms for remote sensing data over a wide range of spectral dimensions. The MRF modeling raises the segmentation accuracy by up to 10% in remote sensing images.
Accurate Lungs Segmentation on CT Chest Images by Adaptive Appearance-Guided Shape Modeling.

To accurately segment pathological and healthy lungs for reliable computer-aided disease diagnostics, a stack of chest CT scans is modeled as a sample of a spatially inhomogeneous joint 3D Markov-Gibbs random field (MGRF) of voxel-wise lung and chest CT image signals (intensities). The proposed learnable MGRF integrates two visual appearance sub-models with an adaptive lung shape submodel. The first-order appearance submodel accounts for both the original CT image and its Gaussian scale space (GSS) filtered version to specify local and global signal properties, respectively. Each empirical marginal probability distribution of signals is closely approximated with a linear combination of discrete Gaussians (LCDG), containing two positive dominant and multiple sign-alternate subordinate DGs. The approximation is separated into two LCDGs to describe individually the lungs and their background, i.e., all other chest tissues. The second-order appearance submodel quantifies conditional pairwise intensity dependencies in the nearest voxel 26-neighborhood in both the original and GSS-filtered images. The shape submodel is built for a set of training data and is adapted during segmentation using both the lung and chest appearances. The accuracy of the proposed segmentation framework is quantitatively assessed using two public databases (ISBI VESSEL12 challenge and MICCAI LOLA11 challenge) and our own database with, respectively, 20, 55, and 30 CT images of various lung pathologies acquired with different scanners and protocols. Quantitative assessment of our framework in terms of Dice similarity coefficients, 95-percentile bidirectional Hausdorff distances, and percentage volume differences confirms the high accuracy of our model on both our database (98.4±1.0%, 2.2±1.0 mm, 0.42±0.10%) and the VESSEL12 database (99.0±0.5%, 2.1±1.6 mm, 0.39±0.20%), respectively. Similarly, the accuracy of our approach is further verified via a blind evaluation by the organizers of the LOLA11 competition, where an average overlap of 98.0% with the expert's segmentation is yielded on all 55 subjects with our framework being ranked first among all the state-of-the-art techniques compared.
Lung segmentation based on Nonnegative Matrix Factorization

In this paper, a new framework for 3D lung segmentation is proposed. The primary step of this framework is to model both the spatial interaction and first-order visual appearance of the lung tissue based on a new Nonnegative Matrix Factorization (NMF) approach that has the ability to handle the inhomogeneity in the lung regions caused by arteries, veins, bronchi, and possible pathological tissues. The performance of our framework is assessed on fourteen 3D CT images. Based on the Dice Similarity Coefficient (DSC), experimental results showed that the proposed approach outperforms other lung segmentation techniques.
Promising results for early diagnosis of lung cancer

Our long term research goal is to develop a fully automated, image- based diagnostic system for early diagnosis of pulmonary nodules that may lead to lung cancer. This paper focuses on monitoring the development of lung nodules detected in successive chest low dose (LD) CT scans of a patient. We propose a new methodology for 3D LDCT data registration which is non-rigid and involves two steps: (i) global alignment of one scan (target) to another scan (reference or prototype) using the learned prior appearance model followed by (ii) local alignment in order to correct for intricate deformations. After equalizing signals for two subsequent chest scans, visual appearance of these chest images is modeled with a Markov-Gibbs random field with pairwise interaction. We estimate the affine transformation that globally register the target to the prototype by gradient descent maximization of a special Gibbs energy function. To handle local deformations, we deform each voxel of the target over evolving closed equi-spaced surfaces (iso-surfaces) to closely match the prototype. The evolution of the iso-surfaces is guided by an exponential speed function in the directions that minimize distances between the corresponding voxel pairs on the iso-surfaces in both the data sets. Preliminary results on the 135 LDCT data sets from 27 patients show that our proper registration could lead to precise diagnosis and identification of the development of the detected pulmonary nodules.
Stochastic models in image analysis: parameter estimations and case studies in image segmentation

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 X, which represents the regions (or classes) that form the observed image; and a low level process Y, which describes the statistical characteristics of each region (or class). The representation G = (X, Y) has been widely used in the image processing literature in the past two decades. In this paper we show how to use expectation maximization (EM) algorithm to get accurate model for the low level image by using mixture of normal distribution. The main idea of the proposed algorithm is as follow: first, we will use the EM algorithm to get the most dominance mixtures in the given density (empirical density), and then we assume that the absolute error between the empirical density and the estimated density is another density and we use the EM algorithm to estimate the number of mixtures in this error and the parameters for each mixtures. Then the estimated density for the absolute error is added or subtracted from the estimated density according to the sign (error). Convergence to the true distribution is tested using the Levy distance. A popular model for the high level process X has been the Gibbs-Markov random field (GMRF) model. In this paper we will use the same approach, which described in A. El-Baz et al. (July 2003) to estimate the parameters of GMRF. The approach has been applied on real images (spiral CT slices) and provides satisfactory results.
Detection and recognition of lung nodules in spiral CT images using deformable templates and Bayesian post-classification

In this paper, we propose a novel algorithm for isolating lung abnormalities (nodules) from low dose spiral chest CT scans. The proposed algorithm consists of three main steps. The first step isolates the lung nodules, arteries, veins, bronchi, and bronchioles from the surrounding anatomical structures. The second step detects lung nodules using deformable 2D and 3D templates describing typical geometry and gray level distribution within the nodules of the same type. The detection combines the normalized cross-correlation template matching and genetic optimization algorithm. The final step eliminates the false positive nodules (FPNs) using three features that robustly define the true lung nodules. Accurate density estimation for these three features is obtained using logistic regression model and linear combination of Gaussians (LCG) with positive and negative components. This paper focuses on the second and third steps. Experiments with 200 patients' CT scans demonstrate the accuracy of our approach.
3D joint Markov-Gibbs model for segmenting the blood vessels from MRA

New techniques for more accurate segmentation of a 3D cerebrovascular system from time-of-flight (TOF) magnetic resonance angiography (MRA) data are proposed. In this paper, we describe TOF-MRA images and desired maps of regions (blood vessels and the other brain tissues) by a joint Markov-Gibbs random field model (MGRF) of independent image signals and interdependent region labels but focus on most accurate model identification. To better specify region borders, each empirical distribution of signals is precisely approximated by a Linear Combination of Discrete Gaussians (LCDG) with positive and negative components. We modify a conventional Expectation-Maximization (EM) algorithm to deal with the LCDG and develop a sequential EM-based technique to get an initial LCDG-approximation for the modified EM algorithm. The initial segmentation based on the LCDG-models is then iteratively refined using a GMRF model with analytically estimated potentials. To validate the accuracy of our algorithm, a special 3D geometrical phantom motivated by statistical analysis of the MRA-TOF data is designed. Experiments with both the phantom and 50 real data sets confirm high accuracy of the proposed approach.