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32 results for “white matter lesions”
Figure 2. Overall process of the system -An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images
<p>This paper mainly focuses on automated detection of White Matter Lesions of brain using<br> fast and efficient clustering algorithms. The goal of clustering a medical image is to simplify the<br> representation of an image into a meaningful image and makes it easier to analyze. As a first step,<br> MRI brain image is pre-processed using Contrast Stretching technique which is one of the efficient<br> image enhancement techniques. The pre-processed image is subjected to clustering. The clustering<br> algorithms include Fuzzy c-means Clustering (FCM), Geostatistical Possibilistic Clustering (GPC)<br> and Geostatistical Fuzzy Clustering Model (GFCM). However clustering techniques are sensitive to<br> initialization and are easily trapped in local optima. In order to obtain an optimized result, the<br> clustered images are undergone optimization. Particle swarm optimization (PSO) is a stochastic<br> global optimization tool which is used in many optimization problems. Figure 2 represents overall<br> process of automatic detection of WMLs of brain. Since MS lesions present different characteristics<br> from lesions in elderly individuals there are many clustering models to determine the accuracy but<br> those methods are not directly applicable to predict the accurate lesions because of the decreased<br> contrast between White Matter and Grey Matter in elderly people. The proposed clustering models<br> are derived by extending the objective functions of FCM and Possibilistic clustering with a<br> Geostatistical (spatial) model. These algorithms are applied to real magnetic resonance images and<br> is shown to be more robust to noise and other artifacts than competing approaches.</p>
Figure 9. Performance analysis of FCM-PSO, GPC-PSO and GFCM-PSO-An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images
<p>All scans obtained from different image clustering models are manually ranked based on<br> values in table 1. Table 2 represents WML detection rates of optimized images. FCM, GPC and<br> GFCM clustering methods and hybrid optimized methods (FCM-PSO, GPC-PSO and GFCM-PSO)<br> are applied on a dataset of 208 images and ranking is done in terms of under detected, over<br> detected, properly detected as shown in figure 8 and figure 9.</p>
Figure 8. Performance analysis of FCM, GPC and GFCM Figure 9.-An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images
<p>All scans obtained from different image clustering models are manually ranked based on<br> values in table 1. Table 2 represents WML detection rates of optimized images. FCM, GPC and<br> GFCM clustering methods and hybrid optimized methods (FCM-PSO, GPC-PSO and GFCM-PSO)<br> are applied on a dataset of 208 images and ranking is done in terms of under detected, over<br> detected, properly detected as shown in figure 8 and figure 9. The number of images detected<br> properly in GFCM is comparatively high than FCM and GPC. The optimized result of GFMC<br> provides accurate detection of WMLs and it properly detects 195 images.</p>
Figures -using Particle Swarm Optimization (PSO)-An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images
<p>The performance of WML quantification is evaluated using clustering algorithms. When the<br> image is pre-processed, contrast of the image is enhanced. The resulting enhanced image is<br> clustered using the effective clustering algorithms. Figure 3 represents the input image for WML<br> detection. In order to increase robustness, the noisy medical image is pre-processed. Figure 4<br> depicts the pre-processed image. Bright contrast stretching, which is one of the image enhancement<br> (pre-processing) techniques is applied. After pre-processing the enhanced image is subjected to<br> clustering. Three clustering models are proposed to provide accurate results.</p> <p>All scans obtained from different image clustering models are manually ranked based on<br> values in table 1. Table 2 represents WML detection rates of optimized images. FCM, GPC and<br> GFCM clustering methods and hybrid optimized methods (FCM-PSO, GPC-PSO and GFCM-PSO)<br> are applied on a dataset of 208 images and ranking is done in terms of under detected, over<br> detected, properly detected as shown in figure 8 and figure 9. The number of images detected<br> properly in GFCM is comparatively high than FCM and GPC.</p>
Figures a, b ,c -8-An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images
<p>In turn, the optimized results of<br> GFCM provide overall accuracy of 95%. Figures 8(a), 8(b) and 8(c) shows the comparison results<br> of clustering models and optimization technique in terms of Se, Sp and Acc.</p>
Figure 1 in Patients profiling for Botox® (onabotulinum toxin A) treatment for migraine: a look at white matter lesions in the MRI as a potential marker
Figure 1 Coronar brain MRI slices (FLAIR), in (a), on the left side, with one WML in a responder and in (b), on the right side, with three WML in a non-responder.
The Relationship Between Right-to-left Shunt and Brain White Matter Lesions in Patients With Migraine
ClinicalTrials.gov study NCT03418766. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Effect of Erythropoietin in Premature Infants on White Matter Lesions and Neurodevelopmental Outcome
ClinicalTrials.gov study NCT03110341. IPD Sharing: YES. Countries: 1. Publications: 5.
Cilostazol Verse Asprin for Vascular Dementia in Poststroke Patients With White Matter Lesions
ClinicalTrials.gov study NCT00847860. IPD Sharing: Not stated. Countries: 1. Publications: 1.
MRI-based Biomarkers for Predicting Punctate White Matter Lesions in Neonates
ClinicalTrials.gov study NCT02637817. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Mathematical modeling for the prediction of cerebral white matter lesions based on clinical examination data
Open the record for dataset details and reuse information.
Data from: White matter lesions: spatial heterogeneity, links to risk factors, cognition, genetics, atrophy
Open the record for dataset details and reuse information.
Tolebrutinib, a Brain-penetrant Bruton's Tyrosine Kinase Inhibitor, for the Modulation of Chronically Inflamed White Matter Lesions in Multiple Sclerosis
ClinicalTrials.gov study NCT04742400. IPD Sharing: YES. Countries: 1. Publications: 0.
Anakinra for the Treatment of Chronically Inflamed White Matter Lesions in Multiple Sclerosis
ClinicalTrials.gov study NCT04025554. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Spatially resolved transcriptome of brain white matter brain lesions of a patient with progressive multiple sclerosis
GEO Series GSE231586. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
Lesion-remote astrocytes govern microglia-mediated white matter repair
GEO Series GSE312911. Mus musculus. 256 samples. Type: Expression profiling by high throughput sequencing; Other.
Gene expression of white and grey matter demyelinated areas and normal appearing matter from Multiple Sclerosis leukocortial lesions of human post-mortem tissue
GEO Series GSE149326. Homo sapiens. 43 samples. Type: Expression profiling by high throughput sequencing.
Lesion-remote astrocytes govern microglia-mediated white matter repair III
GEO Series GSE312880. Mus musculus. 35 samples. Type: Expression profiling by high throughput sequencing.
Lesion-remote astrocytes govern microglia-mediated white matter repair IV
GEO Series GSE312910. Mus musculus. 16 samples. Type: Other.
Next-generation sequencing study in multiple sclerosis white matter brain lesions
GEO Series GSE138614. Homo sapiens. 98 samples. Type: Expression profiling by high throughput sequencing.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.