waashk/dblp huggingface dataset:
a hybrid 1d and 3d approach to hemodynamics modelling for a patient-specific cerebral vasculature and aneurysm. in this paper we present a hybrid 1d/3d approach to haemodynamics modelling in a patient-specific cerebral vasculature and aneurysm. the geometric model is constructed from a 3d cta image. a reduced form of the governing equations for blood flow is coupled with an empirical wall equation and applied to the arterial tree. the equation system is solved using a maccormack finite difference scheme and the results are used as the boundary conditions for a 3d flow solver. the computed wall shear stress (wss) agrees with published data.
ecoc random fields for lumen segmentation in radial artery ivus sequences. the measure of lumen volume on radial arteries can be used to evaluate the vessel response to different vasodilators. in this paper, we present a framework for automatic lumen segmentation in longitudinal cut images of radial artery from intravascular ultrasound sequences. the segmentation is tackled as a classification problem where the contextual information is exploited by means of conditional random fields (crfs). a multi-class classification framework is proposed, and inference is achieved by combining binary crfs according to the error-correcting-output-code technique. the results are validated against manually segmented sequences. finally, the method is compared with other state-of-the-art classifiers.
global and local multi-valued dissimilarity-based classification: application to computer-aided detection of tuberculosis. in many applications of computer-aided detection (cad) it is not possible to precisely localize lesions or affected areas in images that are known to be abnormal. in this paper a novel approach to computer-aided detection is presented that can deal effectively with such weakly labeled data. our approach is based on multi-valued dissimilarity measures that retain more information about underlying local image features than single-valued dissimilarities. we show how this approach can be extended by applying it locally as well as globally, and by merging the local and global classification results into an overall opinion about the image to be classified. the framework is applied to the detection of tuberculosis (tb) in chest radiographs. this is the first study to apply a cad system to a large database of digital chest radiographs obtained from a tb screening program, including normal cases, suspect cases and cases with proven tb. the global dissimilarity approach achieved an area under the roc curve of 0.81. the combination of local and global classifications increased this value to 0.83.
automatic correction of intensity nonuniformity from sparseness of gradient distribution in medical images. we propose to use the sparseness property of the gradient probability distribution to estimate the intensity nonuniformity in medical images, resulting in two novel automatic methods: a non-parametric method and a parametric method. our methods are easy to implement because they both solve an iteratively re-weighted least squares problem. they are remarkably accurate as shown by our experiments on images of different imaged objects and from different imaging modalities.
steerable features for statistical 3d dendrite detection. most state-of-the-art algorithms for filament detection in 3-d image-stacks rely on computing the hessian matrix around individual pixels and labeling these pixels according to its eigenvalues. this approach, while very effective for clean data in which linear structures are nearly cylindrical, loses its effectiveness in the presence of noisy data and irregular structures.in this paper, we show that using steerable filters to create rotationally invariant features that include higher-order derivatives and training a classifier based on these features lets us handle such irregular structures. this can be done reliably and at acceptable computational cost and yields better results than state-of-the-art methods.
feature-based morphometry. this paper presents feature-based morphometry (fbm), a new, fully data-driven technique for identifying group-related differences in volumetric imagery. in contrast to most morphometry methods which assume one-to-one correspondence between all subjects, fbm models images as a collage of distinct, localized image features which may not be present in all subjects. fbm thus explicitly accounts for the case where the same anatomical tissue cannot be reliably identified in all subjects due to disease or anatomical variability. a probabilistic model describes features in terms of their appearance, geometry, and relationship to sub-groups of a population, and is automatically learned from a set of subject images and group labels. features identified indicate group-related anatomical structure that can potentially be used as disease biomarkers or as a basis for computer-aided diagnosis. scale-invariant image features are used, which reflect generic, salient patterns in the image. experiments validate fbm clinically in the analysis of normal (nc) and alzheimer's (ad) brain images using the freely available oasis database. fbm automatically identifies known structural differences between nc and ad subjects in a fully data-driven fashion, and obtains an equal error classification rate of 0.78 on new subjects.
computer-aided assessment of anomalies in the scoliotic spine in 3-d mri images. the assessment of anomalies in the scoliotic spine using magnetic resonance imaging (mri) is an essential task during the planning phase of a patient's treatment and operations. due to the pathologic bending of the spine, this is an extremely time consuming process as an orthogonal view onto every vertebra is required. in this article we present a system for computer-aided assessment (caa) of anomalies in 3-d mri images of the spine relying on curved planar reformations (cpr). we introduce all necessary steps, from the pre-processing of the data to the visualization component. as the core part of the framework is based on a segmentation of the spinal cord we focus on this. the proposed segmentation method is an iterative process. in every iteration the segmentation is updated by an energy based scheme derived from markov random field (mrf) theory. we evaluate the segmentation results on public available clinical relevant 3-d mri data sets of scoliosis patients. in order to assess the quality of the segmentation we use the angle between automatically computed planes through the vertebra and planes estimated by medical experts. this results in a mean angle difference of less than six degrees.
model completion via deformation cloning based on an explicit global deformation model. our main focus is the registration and visualization of a pre-built 3d model from preoperative images to the camera view of a minimally invasive surgery (mis). accurate estimation of soft-tissue deformations is key to the success of such a registration. this paper proposes an explicit statistical model to represent global non-rigid deformations. the deformation model built from a reference object is cloned to a target object to guide the registration of the pre-built model, which completes the deformed target object when only a part of the object is naturally visible in the camera view. the registered target model is then used to estimate deformations of its substructures. our method requires a small number of landmarks to be reconstructed from the camera view. the registration is driven by a small set of parameters, making it suitable for real-time visualization.
detection of spatially correlated objects in 3 images using appearance models and coupled active contours. we consider the problem of segmenting 3d images that contain a dense collection of spatially correlated objects, such as fluorescent labeled cells in tissue. our approach involves an initial modeling phase followed by a data-fitting segmentation phase. in the first phase, cell shape (membrane bound) is modeled implicitly using a parametric distribution of correlation function estimates. the nucleus is modeled for its shape as well as image intensity distribution inspired from the physics of its image formation. in the second phase, we solve the segmentation problem using a variational level-set strategy with coupled active contours to minimize a novel energy functional. we demonstrate the utility of our approach on multispectral fluorescence microscopy images.
parametric representation of cortical surface folding based on polynomials. the development of folding descriptors as an effective approach for describing geometrical complexity and variation of the human cerebral cortex has been of great interests. this paper presents a parametric representation of cortical surface patches using polynomials, that is, the primitive cortical patch is compactly and effectively described by four parametric coefficients. by this parametric representation, the patterns of cortical patches can be classified by either model-driven approach or data-driven clustering approach. in the model-driven approach, any patch of the cortical surface is classified into one of eight primitive shape patterns including peak, pit, ridge, valley, saddle ridge, saddle valley, flat and inflection, corresponding to eight sub-spaces of the four parameters. the major advantage of this polynomial representation of cortical folding pattern is its compactness and effectiveness, while being rich in shape information. we have applied this parametric representation for segmentation of cortical surface and promising results are obtained.
a generic probabilistic active shape model for organ segmentation. probabilistic models are extensively used in medical image segmentation. most of them employ parametric representations of densities and make idealizing assumptions, e.g. normal distribution of data. often, such assumptions are inadequate and limit a broader application. we propose here a novel probabilistic active shape model for organ segmentation, which is entirely built upon non-parametric density estimates. in particular, a nearest neighbor boundary appearance model is complemented by a cascade of boosted classifiers for region information and combined with a shape model based on parzen density estimation. image and shape terms are integrated into a single level set equation. our approach has been evaluated for 3-d liver segmentation using a public data base originating from a competition (http://sliver07.org). with an average surface distance of 1.0 mm and an average volume overlap error of 6.5 %, it outperforms other automatic methods and provides accuracy close to interactive ones. since no adaptions specific to liver segmentation have been made, our probabilistic active shape model can be applied to other segmentation tasks easily.
cell segmentation using front vector flow guided active contours. phase-contrast microscopy is a common approach for studying the dynamics of cell behaviors, such as cell migration. cell segmentation is the basis of quantitative analysis of the immense cellular images. however, the complicated cell morphological appearance in phase-contrast microscopy images challenges the existing segmentation methods. this paper proposes a new cell segmentation method for cancer cell migration studies using phase-contrast images. instead of segmenting cells directly based on commonly used low-level features, e.g. intensity and gradient, we first identify the leading protrusions, a high level feature, of cancer cells. based on the identified cell leading protrusions, we introduce a front vector flow guided active contour, which guides the initial cell boundaries to the real boundaries. the experimental validation on a set of breast cancer cell images shows that the proposed method demonstrates fast, stable, and accurate segmentation for breast cancer cells with wide range of sizes and shapes.
image-driven cardiac left ventricle segmentation for the evaluation of multiview fused real-time 3-dimensional echocardiography images. real-time 3-dimensional echocardiography (rt3de) permits the acquisition and visualization of the beating heart in 3d. despite a number of efforts to automate the left ventricle (lv) delineation from rt3de images, this remains a challenging problem due to the poor nature of the acquired images usually containing missing anatomical information and high speckle noise. recently, there have been efforts to improve image quality and anatomical definition by acquiring multiple single-view rt3de images with small probe movements and fusing them together after alignment. in this work, we evaluate the quality of the multiview fused images using an image-driven semi-automatic lv segmentation method. the segmentation method is based on an edge-driven level set framework, where the edges are extracted using a local-phase inspired feature detector for low-contrast echocardiography boundaries. this totally image-driven segmentation method is applied for the evaluation of end-diastolic (ed) and end-systolic (es) single-view and multiview fused images. experiments were conducted on 17 cases and the results show that multiview fused images have better image segmentation quality, but large failures were observed on ed (88.2%) and es (58.8%) single-view images.
mkl for robust multi-modality ad classification. we study the problem of classifying mild alzheimer's disease (ad) subjects from healthy individuals (controls) using multi-modal image data, to facilitate early identification of ad related pathologies. several recent papers have demonstrated that such classification is possible with mr or pet images, using machine learning methods such as svm and boosting. these algorithms learn the classifier using one type of image data. however, ad is not well characterized by one imaging modality alone, and analysis is typically performed using several image types --- each measuring a different type of structural/functional characteristic. this paper explores the ad classification problem using multiple modalities simultaneously. the difficulty here is to assess the relevance of each modality (which cannot be assumed a priori), as well as to optimize the classifier. to tackle this problem, we utilize and adapt a recently developed idea called multi-kernel learning (mkl). briefly, each imaging modality spawns one (or more kernels) and we simultaneously solve for the kernel weights and a maximum margin classifier. to make the model robust, we propose strategies to suppress the influence of a small subset of outliers on the classifier --- this yields an alternative minimization based algorithm for robust mkl. we present promising multi-modal classification experiments on a large dataset of images from the adni project.
supervised nonparametric image parcellation. segmentation of medical images is commonly formulated as a supervised learning problem, where manually labeled training data are summarized using a parametric atlas. summarizing the data alleviates the computational burden at the expense of possibly losing valuable information on inter-subject variability. this paper presents a novel framework for supervised nonparametric image parcellation (snip). snip models the intensity and label images as samples of a joint distribution estimated from the training data in a non-parametric fashion. by capitalizing on recently developed fast and robust pairwise image alignment tools, snip employs the entire training data to segment a new image via expectation maximization. the use of multiple registrations increases robustness to occasional registration failures. we report experiments on 39 volumetric brain mri scans with manual labels for the white matter, cortex and subcortical structures. snip yields better segmentation than state-of-the-art algorithms in multiple regions of interest.
an interactive geometric technique for upper and lower teeth segmentation. due to the complexity of the dental models in semantics of both shape and form, a fully automated method for the separation of the lower and upper teeth is unsuitable while manual segmentation requires painstakingly user interventions. in this paper, we present a novel interactive method to segment the upper and lower teeth. the process is performed on 3d triangular mesh of the skull and consists of four main steps: reconstruction of 3d model from teeth ct images, curvature estimation, interactive segmentation path planning using the shortest path finding algorithm, and performing actual geometric cut on 3d models using a graph cut algorithm. the accuracy and efficiency of our method were experimentally validated via comparisons with ground truth (manual segmentation) as well as the state of art interactive mesh segmentation algorithms. we show the presented scheme can dramatically save manual effort for users while retaining an acceptable quality (with an averaged 0.29 mm discrepancy from the ideal segmentation).
a fully automatic random walker segmentation for skin lesions in a supervised setting. we present a method for automatically segmenting skin lesions by initializing the random walker algorithm with seed points whose properties, such as colour and texture, have been learnt via a training set. we leverage the speed and robustness of the random walker algorithm and augment it into a fully automatic method by using supervised statistical pattern recognition techniques. we validate our results by comparing the resulting segmentations to the manual segmentations of an expert over 120 cases, including 100 cases which are categorized as difficult (i.e.: low contrast, heavily occluded, etc.). we achieve an f-measure of 0.95 when segmenting easy cases, and an f-measure of 0.85 when segmenting difficult cases.
vascular territory image analysis using vessel encoded arterial spin labeling. arterial spin labeling (asl) permits the non-invasive assessment of cerebral perfusion, by magnetically labeling all the blood flowing into the brain. vessel encoded (ve) asl extends this concept by introducing spatial modulations of the labeling procedure, resulting in different patterns of label applied to the blood from different vessels. here a bayesian inference solution to the analysis of ve-asl is presented based on a description of the relative locations of labeled vessels and a probabilistic classification of brain tissue to vessel source. in simulation and on real data the method is shown to reliably determine vascular territories in the brain, including the case where the number of vessels exceeds the number of independent measurements.
toward 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. in this paper, we focus on generating new probabilistic models for the estimated growth rate of the detected lung nodules from low dose computed tomography (ldct). we propose a new methodology for 3d ldct data registration which is non-rigid and involves two steps: (i) global target-to-prototype alignment of one scan to another using the learned prior appearance model followed by (ii) local alignment in order to correct for intricate relative deformations. visual appearance of these chest images is described using a markov---gibbs random field (mgrf) model with multiple pairwise interaction. an affine transformation that globally registers a target to a prototype is estimated by the gradient ascent-based maximization of a special gibbs energy function. to handle local deformations, we displace 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 a speed function in the directions that minimize distances between the corresponding voxel pairs on the iso-surfaces in both the data sets. preliminary results show that the proposed accurate registration could lead to precise diagnosis and identification of the development of the detected pulmonary nodules.
automated calibration for computerized analysis of prostate lesions using pharmacokinetic magnetic resonance images. the feasibility of an automated calibration method for estimating the arterial input function when calculating pharmacokinetic parameters from dynamic contrast enhanced mri is shown. in a previous study [1], it was demonstrated that the computer aided diagnoses (cadx) system performs optimal when per patient calibration was used, but required manual annotation of reference tissue. in this study we propose a fully automated segmentation method that tackles this limitation and tested the method with our cadx system when discriminating prostate cancer from benign areas in the peripheral zone.a method was developed to automatically segment normal peripheral zone tissue (pz). context based segmentation using the otsu histogram based threshold selection method and by hessian based blob detection, was developed to automatically select pz as reference tissue for the per patient calibration.in 38 consecutive patients carcinoma, benign and normal tissue were annotated on mr images by a radiologist and a researcher using whole mount step-section histopathology as standard of reference. a feature set comprising pharmacokinetic parameters was computed for each roi and used to train a support vector machine (svm) as classifier.in total 42 malignant, 29 benign and 37 normal regions were annotated. the diagnostic accuracy obtained for differentiating malignant from benign lesions using a conventional general patient plasma profile showed an accuracy of 0.65 (0.54-0.76). using the automated segmentation per patient calibration method the diagnostic value improved to 0.80 (0.71-0.88), whereas the manual segmentation per patient calibration showed a diagnostic performance of 0.80 (0.70-0.90).these results show that an automated per-patient calibration is feasible, a significant better discriminating performance compared to the conventional fixed calibration was obtained and the diagnostic accuracy is similar to using manual per-patient calibration.
a computational model of cerebral cortex folding. folding of the human cerebral cortex has intrigued many people for many years. quantitative description of cortical folding pattern and understanding of the underlying mechanisms have emerged as an important research goal. this paper presents a computational 3d geometric model of cerebral cortex folding that is initialized by mri data of human fetus brain and deformed under the governance of partial differential equations modeling the cortical growth. the simulations of this 3d geometric model provide computational experiment support to the following hypotheses: 1) mechanical constraints of the brain skull regulate the cortical folding process. 2) the cortical folding pattern is dependent on the global cell growth rate in the whole cortex. 3) the cortical folding pattern is dependent on relative degrees of tethering of different cortical areas and the initial geometry.