Characterization of Ischemic Stroke in CT Images using Image Processing

2017 ◽  
Vol 7 (9) ◽  
pp. 18
Author(s):  
Amal Alzain ◽  
Suhaib Alameen ◽  
Rani Elmaki ◽  
Mohamed E. M. Gar-Elnabi
Keyword(s):  
Ischemic Stroke ◽  
White Matter ◽  
Classification Accuracy ◽  
Ct Images ◽  
Gray Level ◽  
Level Variation ◽  
Interactive Data ◽  
The Brain ◽  
Brain Tissues

This study concern to characterize the brain tissues to ischemic stroke, gray matter, white matter and CSF using texture analysisto extract classification features from CT images. The First Order Statistic techniques included sevenfeatures. To find the gray level variation in CT images it complements the FOS features extracted from CT images withgray level in pixels and estimate the variation of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level of images. The results show that the Gray Level variation and   features give classification accuracy of ischemic stroke 97.6%, gray matter95.2%, white matter 97.3% and the CSF classification accuracy 98.0%. The overall classification accuracy of brain tissues 97.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate brain tissues names.

Characterization of Pancreas at Diabetic Patients in CT images using Texture Analysis

2017 ◽  
Vol 7 (7) ◽  
pp. 8
Author(s):  
Mona E. Elbashier ◽  
Suhaib Alameen ◽  
Caroline Edward Ayad ◽  
Mohamed E. M. Gar-Elnabi
Keyword(s):  
Size Distribution ◽  
Texture Analysis ◽  
Classification Accuracy ◽  
Ct Images ◽  
Diabetic Patients ◽  
Gray Level ◽  
Grey Level ◽  
Run Length ◽  
Interactive Data

This study concern to characterize the pancreas areato head, body and tail using Gray Level Run Length Matrix (GLRLM) and extract classification features from CT images. The GLRLM techniques included eleven’s features. To find the gray level distribution in CT images it complements the GLRLM features extracted from CT images with runs of gray level in pixels and estimate the size distribution of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level distribution of images. The results show that the Gray Level Run Length Matrix and  features give classification accuracy of pancreashead 89.2%, body 93.6 and the tail classification accuracy 93.5%. The overall classification accuracy of pancreas area 92.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate pancreas area names.

Abstract MP16: Excessive White Matter Hyperintensity Burden and Functional Outcomes After Acute Ischemic Stroke

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Sungmin Hong ◽  
Anne-katrin Giese ◽  
Markus D Schirmer ◽  
Adrian V Dalca ◽  
Anna Bonkhoff ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
White Matter ◽  
Acute Ischemic Stroke ◽  
Functional Outcomes ◽  
Life Time ◽  
Stroke Recovery ◽  
White Matter Hyperintensity ◽  
Brain Health ◽  
Stroke Outcomes ◽  
The Brain

Objective: Ability of the brain to recover after an acute ischemic stroke (AIS) is linked to the pre-stroke burden of white matter hyperintensity (WMH), a radiographic marker of brain health. We sought to determine the excessive WMH burden in an AIS population and investigate its association with 3-month stroke outcomes. Data: We used 2,435 subjects from the MRI-GENIE study. Three-month functional outcomes of 872 subjects among those subjects were measured by 90-day modified Ranking Scale (mRS). Methods: We automatically quantified WMH volume (WMHv) on FLAIR images and adjusted for a brain volume. We modeled a trend using the factor analysis (FA) log-linear regression using age, sex, atrial fibrillation, diabetes, hypertension, coronary artery disease and smoking as input variables. We categorized three WMH burden groups based on the conditional probability given by the model (LOW: lower 33%, MED: middle 34%, and HIGH: upper 33%). The subgroups were compared with respect to mRS (median and dichotomized odds ratio (OR) (good/poor: mRS 0-2/3-6)). Results: Five FA components out of seven with significant relationship to WMHv (p<0.001) were used for the regression modeling (R 2 =0.359). The HIGH group showed higher median (median=2, IQR=2) mRS score than LOW (median=1, IQR=1) and MED (median=1, IQR=1). The odds (OR) of good AIS outcome for LOW and MED were 1.8 (p=0.0001) and 1.6 (p=0.006) times higher than HIGH, respectively. Conclusion: Once accounted for clinical covariates, the excessive WMHv was associated with worse 3-month stroke outcomes. These data suggest that a life-time of injury to the white matter reflected in WMH is an important factor for stroke recovery and an indicator of the brain health.

A Robust Line Filter for Automatic X-Ray/CT Image Segmentation

2014 ◽  
Vol 721 ◽  
pp. 783-787
Author(s):  
Shao Hu Peng ◽  
Hyun Do Nam ◽  
Yan Fen Gan ◽  
Xiao Hu
Keyword(s):  
Multiscale Analysis ◽  
Automatic Segmentation ◽  
Local Variation ◽  
Recognition System ◽  
Ct Images ◽  
Gray Level ◽  
Ct Image ◽  
Image Pyramid ◽  
Level Variation ◽  
X Ray

Automatic segmentation of the line-like regions plays a very important role in the automatic recognition system, such as automatic cracks recognition in X-ray images, automatic vessels segmentation in CT images. In order to automatically segment line-like regions in the X-ray/CT images, this paper presents a robust line filter based on the local gray level variation and multiscale analysis. The proposed line filter makes usage of the local gray level and its local variation to enhance line-like regions in the X-ray/CT image, which can well overcome the problems of the image noises and non-uniform intensity of the images. For detecting various sizes of line-like regions, an image pyramid is constructed based on different neighboring distances, which enables the proposed filter to analyze different sizes of regions independently. Experimental results showed that the proposed line filter can well segment various sizes of line-like regions in the X-ray/CT images, which are with image noises and non-uniform intensity problems.

Strain Rate Dependence in the Structure Property Relationship of Porcine Brain

2009 ◽  
Author(s):  
Mark G. T. Begonia ◽  
Jun Liao ◽  
Mark F. Horstemeyer ◽  
Lakiesha N. Williams
Keyword(s):  
White Matter ◽  
Microstructural Characterization ◽  
Structure Property ◽  
Control Center ◽  
Prediction And Prevention ◽  
The Central Nervous System ◽  
Subcortical Nuclei ◽  
Relationship Of ◽  
The Brain

The brain is the control center for the central nervous system (CNS), and it is composed of specialized divisions that are attributed to a vast assortment of structural, homeostatic, and cognitive functions. These distinct regions are surrounded by supportive tissue and comprised of a complex arrangement of neurons that can be further categorized as either gray or white matter. The cerebrum constitutes the larger surrounding portion of the forebrain and includes sinuous ridges called gyri that are separated by grooves or fissures called sulci. The intermediate layer of the cerebrum primarily consists of white matter tracts that are responsible for integrating various regions throughout the cerebrum. The innermost and outermost layers of tissue mainly contain gray matter and are collectively known as the subcortical nuclei and cerebral cortex, respectively, which are crucial integrating components of the CNS [1]. An investigation into the mechanical properties of this vital organ coupled with microstructural characterization of its constituents under varying deformation levels is therefore crucial for implementing more accurate prediction and prevention of traumatic brain injury (TBI).

scholarly journals A Robust Method for MR Image Segmentation and Multiple Scleroses Detection

2019 ◽  
Vol 9 (6) ◽  
pp. 1119-1130
Author(s):  
H. Zouaoui ◽  
A. Moussaoui ◽  
M. Oussalah ◽  
A. Taleb-Ahmed
Keyword(s):  
White Matter ◽  
Automatic Segmentation ◽  
Fuzzy Model ◽  
Mr Images ◽  
New Approach ◽  
Magnetic Resonance Imaging Mri ◽  
Mr Image Segmentation ◽  
The Brain

In the present article, we propose a new approach for the segmentation of the MR images of the Multiple Sclerosis (MS). The Magnetic Resonance Imaging (MRI) allows the visualization of the brain and it is widely used in the diagnosis and the follow-up of the patients suffering from MS. Aiming to automate a long and tedious process for the clinician, we propose the automatic segmentation of the MS lesions. Our algorithm of segmentation is composed of three stages: segmentation of the brain into regions using the algorithm Fuzzy Particle Swarm Optimization (FPSO) in order to obtain the characterization of the different healthy tissues (White matter, grey matter and cerebrospinal fluid (CSF)) after the extraction of white matter (WM), the elimination of the atypical data (outliers) of the white matter by the algorithm Fuzzy C-Means (FCM), finally, the use of a Mamdani-type fuzzy model to extract the MS lesions among all the absurd data.

scholarly journals Patterns of white matter degeneration in remote brain areas from the basal ganglion lesion of ischemic stroke patients with motor impairment

2020 ◽  
Author(s):  
xuejin cao ◽  
Zan Wang ◽  
Xiaohui Chen ◽  
Yanli Liu ◽  
Wei Wang ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
White Matter ◽  
Basal Ganglia ◽  
Structural Changes ◽  
Diffusion Tensor ◽  
Brain Magnetic Resonance Imaging ◽  
Stroke Patients ◽  
Brain Areas ◽  
Ischemic Lesion ◽  
The Brain

Abstract Background: Diffusion tensor imaging (DTI) studies have revealed distinct white matter characteristics of the brain following diseases. Beyond the lesion-symptom mapping, recent studies have demonstrated extensive structural and functional alterations of remote areas to local lesions caused by stroke in the brain. Here, we further investigated the structural changes from a global level using DTI data through multivariate pattern analysis (MVPA) and network-based statistic (NBS). Methods: Ten ischemic stroke patients with basal ganglia lesions and motor dysfunctions and eleven demographically matched adults as controls underwent brain Magnetic Resonance Imaging scans. DTI data were processed to obtain fractional anisotropy (FA) maps and MVPA was used to explore brain regions that play an important role in classification based on FA maps. The white matter (WM) structural network was constructed by the deterministic fiber tracking approach according to the Automated Anatomical Labeling (AAL) atlas. NBS was used to explore differences in structural networks between groups. Results: MVPA applied to FA images correctly identified stroke patients with a statistically significant accuracy of 100% (P≤0.001). Compared with the controls, the study patients showed FA reductions in the perilesional basal ganglia and brainstem, with a few showing reductions in bilateral frontal lobes. Using NBS, we found a significant decrease in FA-weighted WM subnetwork in stroke patients. Conclusions: We identified some patterns of WM degeneration affecting brain areas remote to the ischemic lesion, revealing the abnormal organization of WM network in stroke patients, which may be helpful for the understanding of the neural mechanisms of stroke sequela.

scholarly journals Circular RNA Expression in the Brain of a Neonatal Rat Model of Periventricular White Matter Damage

2018 ◽  
Vol 49 (6) ◽  
pp. 2264-2276 ◽  
Author(s):  
Lihua Zhu ◽  
Ruibin Zhao ◽  
Li Huang ◽  
Sisi Mo ◽  
Zhangbin Yu ◽  
...  
Keyword(s):  
White Matter ◽  
Target Genes ◽  
Expression Profiles ◽  
Pcr Analysis ◽  
Periventricular White Matter ◽  
White Matter Damage ◽  
Qrt Pcr ◽  
Hypoxia Ischemia ◽  
The Brain ◽  
Brain Tissues

Background/Aims: Periventricular white matter damage (PWMD) is the predominant neurologic lesion in preterm infants who survive brain injury. In this study, we assessed the global changes in and characteristics of the transcriptome of circular RNAs (circRNAs) in the brain tissues of rats with PWMD. Methods: We compared the expression profiles of circRNAs in brain samples from three rats with PWMD and three paired control tissues using deep RNA sequencing. Bioinformatics analysis was applied to investigate these differentially expressed circRNAs, and quantitative reverse-transcription polymerase chain reaction (qRT-PCR) analysis was performed to confirm the results. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to predict associated cell signaling pathways and functions. Network analysis was performed to predict circRNAs-microRNAs, and target genes related to PWMD. Results: A total of 2151 more reliable circRNAs were dysregulated in the brain tissues of rats with PWMD, indicating a potential role in the condition. Of the 98 circRNAs significantly differentially expressed in rat brains with PWMD (P< 0.05), 52 were significantly over-expressed and 46 were significantly under-expressed. The expression profiles of seven of 10 randomly selected circRNAs were confirmed by qRT-PCR analysis. The glutamatergic synapse pathway and the VEGF signaling pathway, both associated with hypoxia/ischemia induced brain damage, were inriched. Relationship between miRNA (rno-miR-433-3p and rno-miR-206-3p) and HIF-1α were evident and potential associations between chr6: 48820833|48857932 and their target genes (rno-miR-433-3p and rno-miR-206-3p) were identified. Conclusion: The distinct expression patterns of circRNAs in the brain tissues of rats with PWMD suggest that circRNAs actively respond to hypoxia-ischemia. These findings could assist the development of novel diagnostic and therapeutic targets for PWMD therapy.

scholarly journals Patterns of white matter degeneration in remote brain areas from the basal ganglion lesion of ischemic stroke  patients  with motor impairment

2020 ◽  
Author(s):  
Xuejin Cao ◽  
Zan Wang ◽  
Xiaohui Chen ◽  
Yanli Liu ◽  
Wei Wang ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
White Matter ◽  
Basal Ganglia ◽  
Structural Changes ◽  
Diffusion Tensor ◽  
Brain Magnetic Resonance Imaging ◽  
Stroke Patients ◽  
Brain Areas ◽  
Ischemic Lesion ◽  
The Brain

Abstract Background: Diffusion tensor imaging (DTI) studies have revealed distinct white matter characteristics of the brain following diseases. Beyond the lesion-symptom mapping, recent studies have demonstrated extensive structural and functional alterations of remote areas to local lesions caused by stroke in the brain. Here, we further investigated the structural changes from a global level using DTI data through multivariate pattern analysis (MVPA) and network-based statistic (NBS). Methods: Ten ischemic stroke patients with basal ganglia lesions and motor dysfunctions and eleven demographically matched adults as controls underwent brain Magnetic Resonance Imaging scans. DTI data were processed to obtain fractional anisotropy (FA) maps and MVPA was used to explore brain regions that play an important role in classification based on FA maps. The white matter (WM) structural network was constructed by the deterministic fiber tracking approach according to the Automated Anatomical Labeling (AAL) atlas. NBS was used to explore differences in structural networks between groups.Results: MVPA applied to FA images correctly identified stroke patients with a statistically significant accuracy of 100% (P≤0.001). Compared with the controls, the study patients showed FA reductions in the perilesional basal ganglia and brainstem, with a few showing reductions in bilateral frontal lobes. Using NBS, we found a significant decrease in FA-weighted WM subnetwork in stroke patients. Conclusions: We identified some patterns of WM degeneration affecting brain areas remote to the ischemic lesion, revealing the abnormal organization of WM network in stroke patients, which may be helpful for the understanding of the neural mechanisms of stroke sequela.

scholarly journals Injury Biomechanics of a Child’s Head: Problems, Challenges and Possibilities with a New aHEAD Finite Element Model

2020 ◽  
Vol 10 (13) ◽  
pp. 4467
Author(s):  
Johannes Wilhelm ◽  
Mariusz Ptak ◽  
Fábio A. O. Fernandes ◽  
Konrad Kubicki ◽  
Artur Kwiatkowski ◽  
...  
Keyword(s):  
Finite Element ◽  
Brain Injury ◽  
White Matter ◽  
Numerical Models ◽  
Material Models ◽  
Detailed Model ◽  
Hexahedral Elements ◽  
The Impact ◽  
The Brain ◽  
Brain Tissues

Traumatic brain injury (TBI) is a major public health problem among children. The predominant causes of TBI in young children are motor vehicle accidents, firearm incidents, falls, and child abuse. The limitation of in vivo studies on the human brain has made the finite element modelling an important tool to study brain injury. Numerical models based on the finite element approach can provide valuable data on biomechanics of brain tissues and help explain many pathological conditions. This work reviews the existing numerical models of a child’s head. However, the existing literature is very limited in reporting proper geometric representation of a small child’s head. Therefore, an advanced 2-year-old child’s head model, named aHEAD 2yo (aHEAD: advanced Head models for safety Enhancement And medical Development), has been developed, which advances the state-of-the-art. The model is one of the first published in the literature, which entirely consists of hexahedral elements for three-dimensional (3D) structures of the head, such as the cerebellum, skull, and cerebrum with detailed geometry of gyri and sulci. It includes cerebrospinal fluid as Smoothed Particle Hydrodynamics (SPH) and a detailed model of pressurized bringing veins. Moreover, the presented review of the literature showed that material models for children are now one of the major limitations. There is also no unambiguous opinion as to the use of separate materials for gray and white matter. Thus, this work examines the impact of various material models for the brain on the biomechanical response of the brain tissues during the mechanical loading described by Hardy et al. The study compares the inhomogeneous models with the separation of gray and white matter against the homogeneous models, i.e., without the gray/white matter separation. The developed model along with its verification aims to establish a further benchmark in finite element head modelling for children and can potentially provide new insights into injury mechanisms.

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