Application of a Mamdani-type fuzzy rule-based system to segment periventricular cerebral veins in susceptibility-weighted images

This paper presents an algorithm designed to segment veins in the periventricular region of the brain in susceptibility-weighted magnetic resonance images. The proposed algorithm is based on a Mamdani-type fuzzy rule-based system that enables enhancement of veins within periventricular regions of interest as the first step. Segmentation is achieved after determining the cut-off value providing the best trade-off between sensitivity and specificity to establish the suitability of each pixel to belong to a cerebral vein. Performance of the algorithm in susceptibility-weighted images acquired in healthy volunteers showed very good segmentation, with a small number of false positives. The results were not affected by small changes in the size and location of the regions of interest. The algorithm also enabled detection of differences in the visibility of periventricular veins between healthy subjects and multiple sclerosis patients. © Springer International Publishing Switzerland 2016.Postprint (author's final draft

A novel statistical cerebrovascular segmentation algorithm with particle swarm optimization

AbstractWe present an automatic statistical intensity-based approach to extract the 3D cerebrovascular structure from time-of flight (TOF) magnetic resonance angiography (MRA) data. We use the finite mixture model (FMM) to fit the intensity histogram of the brain image sequence, where the cerebral vascular structure is modeled by a Gaussian distribution function and the other low intensity tissues are modeled by Gaussian and Rayleigh distribution functions. To estimate the parameters of the FMM, we propose an improved particle swarm optimization (PSO) algorithm, which has a disturbing term in speeding updating the formula of PSO to ensure its convergence. We also use the ring shape topology of the particles neighborhood to improve the performance of the algorithm. Computational results on 34 test data show that the proposed method provides accurate segmentation, especially for those blood vessels of small sizes

Accurate Segmentation of Cerebrovasculature from TOF-MRA Images Using Appearance Descriptors

© 2013 IEEE. Analyzing cerebrovascular changes can significantly lead to not only detecting the presence of serious diseases e.g., hypertension and dementia, but also tracking their progress. Such analysis could be better performed using Time-of-Flight Magnetic Resonance Angiography (ToF-MRA) images, but this requires accurate segmentation of the cerebral vasculature from the surroundings. To achieve this goal, we propose a fully automated cerebral vasculature segmentation approach based on extracting both prior and current appearance features that have the ability to capture the appearance of macro and micro-vessels in ToF-MRA. The appearance prior is modeled with a novel translation and rotation invariant Markov-Gibbs Random Field (MGRF) of voxel intensities with pairwise interaction analytically identified from a set of training data sets. The appearance of the cerebral vasculature is also represented with a marginal probability distribution of voxel intensities by using a Linear Combination of Discrete Gaussians (LCDG) that its parameters are estimated by using a modified Expectation-Maximization (EM) algorithm. The extracted appearance features are separable and can be classified by any classifier, as demonstrated by our segmentation results. To validate the accuracy of our algorithm, we tested the proposed approach on in-vivo data using 270 data sets, which were qualitatively validated by a neuroradiology expert. The results were quantitatively validated using the three commonly used metrics for segmentation evaluation: the Dice coefficient, the modified Hausdorff distance, and the absolute volume difference. The proposed approach showed a higher accuracy compared to two of the existing segmentation approaches

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

A framework for intracranial saccular aneurysm detection and quantification using morphological analysis of cerebral angiograms

Reliable early prediction of aneurysm rupture can greatly help neurosurgeons to treat aneurysms at the right time, thus saving lives as well as providing significant cost reduction. Most of the research efforts in this respect involve statistical analysis of collected data or simulation of hemodynamic factors to predict the risk of aneurysmal rupture. Whereas, morphological analysis of cerebral angiogram images for locating and estimating unruptured aneurysms is rarely considered. Since digital subtraction angiography (DSA) is regarded as a standard test by the American Stroke Association and American College of Radiology for identification of aneurysm, this paper aims to perform morphological analysis of DSA to accurately detect saccular aneurysms, precisely determine their sizes, and estimate the probability of their ruptures. The proposed diagnostic framework, intracranial saccular aneurysm detection and quantification, first extracts cerebrovascular structures by denoising angiogram images and delineates regions of interest (ROIs) by using watershed segmentation and distance transformation. Then, it identifies saccular aneurysms among segmented ROIs using multilayer perceptron neural network trained upon robust Haralick texture features, and finally quantifies aneurysm rupture by geometrical analysis of identified aneurysmic ROI. De-identified data set of 59 angiograms is used to evaluate the performance of algorithms for aneurysm detection and risk of rupture quantification. The proposed framework achieves high accuracy of 98% and 86% for aneurysm classification and quantification, respectively

Extraction of Blood Vessels Geometric Shape Features with Catheter Localization and Geodesic Distance Transform for Right Coronary Artery Detection.

X-ray angiography is considered the standard imaging sensory system for diagnosing coronary artery diseases. For automated, accurate diagnosis of such diseases, coronary vessels’ detection from the captured low quality and noisy angiography images is challenging. It is essential to detect the main branch of the coronary artery, to resolve such limitations along with the problems due to the sudden changes in the lumen diameter, and the abrupt changes in local artery direction. Accordingly, this paper solved these limitations by proposing a computer-aided detection system for the right coronary artery (RCA) extraction, where geometric shape features with catheter localization and geodesic distance transform in the angiography images through two parts. In part 1, the captured image was initially preprocessed for contrast enhancement using singular value decomposition-based contrast adjustment, followed by generating the vesselness map using Jerman filter, and for further segmentation the K-means was introduced. Afterward, in part 2, the geometric shape features of the RCA, as well as the skeleton gradient transform, and the start/end points were determined to extract the main blood vessel of the RCA. The analysis of the skeletonize image was performed using Geodesic distance transform to examine all branches starting from the predetermined start point and cover the branching till the predefined end points. A ranking matrix, and the inverse of skeletonization were finally carried out to get the actual main branch. The performance of the proposed system was then evaluated using different evaluation metrics on the angiography images...

Regional White Matter Integrity Differentiates Between Vascular Dementia and Alzheimer Disease

BACKGROUND AND PURPOSE: Considerable clinical and radiological overlap between vascular dementia (VaD) and Alzheimer disease (AD) often makes the diagnosis difficult. Diffusion-tensor imaging studies showed that fractional anisotropy (FA) could be a useful marker for white matter changes. This study aimed to identify regional FA changes to identify a biomarker that could be used to differentiate VaD from AD. METHODS: T1-weighted and diffusion-tensor imaging scans were obtained in 13 VaD patients, 16 AD patients, and 22 healthy elderly controls. We used tract-based spatial statistics to study regional changes in fractional anisotropy in AD, VaD, and elderly controls. We then used probabilistic tractography to parcel the corpus callosum in 7 regions according to its connectivity with major cerebral cortices using diffusion-tensor imaging data set. We compared the volume and mean FA in each set of transcallosal fibers between groups using ANOVA and then applied a discriminant analysis based on FA and T2-weighted imaging measures. RESULTS: FA reduction in forceps minor was the most significant area of difference between AD and VaD. Segmentation of the corpus callosum using tractography and comparison of FA changes of each segment confirmed the FA changes in transcallosal prefrontal tracts of patients with VaD when compared to AD. The best discriminant model was the combination of transcallosal prefrontal FA and Fazekas score with 87.5% accuracy, 100% specificity, and 93% sensitivity (P<0.0001). CONCLUSIONS: Integrating mean FA in the forceps minor to the Fazekas score provides a useful quantitative marker for differentiating AD from VaD

Vascular Tree Structure: Fast Curvature Regularization and Validation

This work addresses the challenging problem of accurate vessel structure analysis in high resolution 3D biomedical images. Typical segmentation methods fail on recent micro-CT data sets resolving near-capillary vessels due to limitations of standard first-order regularization models. While regularization is needed to address noise and partial volume issues in the data, we argue that extraction of thin tubular structures requires higher-order curvature-based regularization. There are no standard segmentation methods regularizing surface curvature in 3D that could be applied to large 3D volumes. However, we observe that standard measures for vessels structure are more concerned with topology, bifurcation angles, and other parameters that can be directly addressed without segmentation. We propose a novel methodology reconstructing tree structure of the vessels using a new centerline curvature regularization technique. Our high-order regularization model is based on a recent curvature estimation method. We developed a Levenberg-Marquardt optimization scheme and an efficient GPU-based implementation of our algorithm. We also propose a validation mechanism based on synthetic vessel images. Our preliminary results on real ultra-resolution micro CT volumes are promising

Distinct White Matter Changes Associated with Cerebrospinal Fluid Amyloid-β\u3csub\u3e1-42\u3c/sub\u3e and Hypertension

BACKGROUND: Alzheimer\u27s disease (AD) pathology and hypertension (HTN) are risk factors for development of white matter (WM) alterations and might be independently associated with these alterations in older adults. OBJECTIVE: To evaluate the independent and synergistic effects of HTN and AD pathology on WM alterations. METHODS: Clinical measures of cerebrovascular disease risk were collected from 62 participants in University of Kentucky Alzheimer\u27s Disease Center studies who also had cerebrospinal fluid (CSF) sampling and MRI brain scans. CSF Aβ1-42 levels were measured as a marker of AD, and fluid-attenuated inversion recovery imaging and diffusion tensor imaging were obtained to assess WM macro- and microstructural properties. Linear regression analyses were used to assess the relationships among WM alterations, cerebrovascular disease risk, and AD pathology. Voxelwise analyses were performed to examine spatial patterns of WM alteration associated with each pathology. RESULTS: HTN and CSF Aβ1-42 levels were each associated with white matter hyperintensities (WMH). Also, CSF Aβ1-42 levels were associated with alterations in normal appearing white matter fractional anisotropy (NAWM-FA), whereas HTN was marginally associated with alterations in NAWM-FA. Linear regression analyses demonstrated significant main effects of HTN and CSF Aβ1-42 on WMH volume, but no significant HTN×CSF Aβ1-42 interaction. Furthermore, voxelwise analyses showed unique patterns of WM alteration associated with hypertension and CSF Aβ1-42. CONCLUSION: Associations of HTN and lower CSF Aβ1-42 with WM alteration were statistically and spatially distinct, suggesting independent rather than synergistic effects. Considering such spatial distributions may improve diagnostic accuracy to address each underlying pathology