Computerized White Matter and Gray Matter Extraction from MRI of Brain Image

This paper proposes an advanced and precise technique for the segmentation of Magnetic Resonance Image (MRI) of the brain. Brain MRI segmentation is to be familiar with the anatomical structure, to recognize the deformities, and to distinguish different tissues which help in treatment planning and diagnosis. Nature’s inspired population-based evolutionary algorithms are extremely popular for a wide range of applications due to their best solutions. Teaching Learning Based Optimization (TLBO) is an advanced population-based evolutionary algorithm designed based on Teaching and Learning process of a classroom. TLBO uses common controlling parameters and it won’t require algorithm-specific parameters. TLBO is more appropriate to optimize the real variables which are fuzzy valued, computationally efficient, and does not require parameter tuning. In this work, the pixels of the brain image are automatically grouped into three distinct homogeneous tissues such as White Matter (WM), Gray Matter (GM), and Cerebro Spinal Fluid (CSF) using the TLBO algorithm. The methodology includes skull stripping and filtering in the pre-processing stage. The outcomes for 10 MR brain images acquired by utilizing the proposed strategy proved that the three brain tissues are segmented accurately. The segmentation outputs are compared with the ground truth images and high values are obtained for the measure’s sensitivity, specificity, and segmentation accuracy. Four different approaches, namely Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Bacterial Foraging Algorithm (BFA), and Electromagnetic Optimization (EMO) are likewise implemented to compare with the results of the proposed methodology. From the results, it can be proved that the proposed method performed effectively than the other.

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AN EFFICIENT APPROACH FOR MR BRAIN IMAGE MULTILEVEL SEGMENTATION AND PERFORMANCE ANALYSIS

International Journal of Engineering Technologies and Management Research ◽

10.29121/ijetmr.v5.i3.2018.195 ◽

2020 ◽

Vol 5 (3) ◽

pp. 230-233

Author(s):

S.Sri Devi ◽

Abhisha Mano

Keyword(s):

White Matter ◽

Gray Matter ◽

Neurological Diseases ◽

Brain Image ◽

Firefly Optimization ◽

And Performance ◽

Firefly Optimization Algorithm ◽

Mr Brain ◽

Abnormal Brain ◽

Complex Organ

Complex organs can be analysed by using the Magnetic Resonance Image (MRI). This kind of imaging helps the doctors for diagnosis and treatment of neurological diseases. Brain is the complex organ of the human body. It controls the all the organs in our body. Accurate segmentation and analysis of brain tissues such as Gray Matter and White Matter help the doctors for the diagnosing of some complex diseases and neuro surgery. In this paper an efficient method for the segmentation of Gray Matter, White Matter and Cerebrospinal Fluid, Skull regions from MRI brain image using Spatial Fuzzy C-Means was proposed. However, accuracy of this algorithm is not efficient for abnormal brain. To improve the accuracy of segmentation Firefly Optimization algorithm was implemented. Proposed method was implemented using MATLAB 8.6.0.267246 (R2015a) and various parameters were analysed.

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FT-IR microspectroscopic analysis of diseased white matter brain tissues

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100141214 ◽

1995 ◽

Vol 53 ◽

pp. 968-969

Author(s):

Steven M. Le Vine ◽

David L. Wetzel

Keyword(s):

White Matter ◽

Gray Matter ◽

Twitcher Mouse ◽

Grid Pattern ◽

Marked Contrast ◽

Spatially Resolved ◽

Ft Ir ◽

Ir Microspectroscopy ◽

Chemical Evidence

In situ FT-IR microspectroscopy has allowed spatially resolved interrogation of different parts of brain tissue. In previous work the spectrrscopic features of normal barin tissue were characterized. The white matter, gray matter and basal ganglia were mapped from appropriate peak area measurements from spectra obtained in a grid pattern. Bands prevalent in white matter were mostly associated with the lipid. These included 2927 and 1469 cm-1 due to CH2 as well as carbonyl at 1740 cm-1. Also 1235 and 1085 cm-1 due to phospholipid and galactocerebroside, respectively (Figs 1and2). Localized chemical changes in the white matter as a result of white matter diseases have been studied. This involved the documentation of localized chemical evidence of demyelination in shiverer mice in which the spectra of white matter lacked the marked contrast between it and gray matter exhibited in the white matter of normal mice (Fig. 3).The twitcher mouse, a model of Krabbe’s desease, was also studied. The purpose in this case was to look for a localized build-up of psychosine in the white matter caused by deficiencies in the enzyme responsible for its breakdown under normal conditions.

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Altered gray matter structure and white matter microstructure in patients with congenital adrenal hyperplasia: Relevance for working memory performance

Yearbook of Paediatric Endocrinology ◽

10.1530/ey.17.8.15 ◽

2020 ◽

Author(s):

A Van’t Westeinde ◽

Karlsson L ◽

M Thomsen Sandberg ◽

Nordenstrom A ◽

Padilla N ◽

...

Keyword(s):

Working Memory ◽

White Matter ◽

Congenital Adrenal Hyperplasia ◽

Gray Matter ◽

Memory Performance ◽

Adrenal Hyperplasia ◽

White Matter Microstructure

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Longitudinal association between deep gray matter atrophy and lesion-based disruptions in connected white matter tracts

10.26226/morressier.59a3edaad462b8028d894aa5 ◽

2017 ◽

Author(s):

Tom Fuchs

Keyword(s):

White Matter ◽

Gray Matter ◽

White Matter Tracts ◽

Gray Matter Atrophy ◽

Longitudinal Association ◽

Deep Gray Matter

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Analysis of Fractional Anisotropy Measurements of Diffusion Tensor Images of Cerebral White Matter and Gray Matter Regions: For Male aged 30 to 50 years

Journal of the Korean Society of MR Technology ◽

10.31159/ksmrt.2018.28.2.1 ◽

2018 ◽

Vol 28 (2) ◽

pp. 1-10

Author(s):

Jong-Hyuk Kwak ◽

Bong-Gyeong Son ◽

Hyeon-Ah Park ◽

Jang-Seon Yu

Keyword(s):

White Matter ◽

Fractional Anisotropy ◽

Gray Matter ◽

Diffusion Tensor ◽

Cerebral White Matter ◽

Diffusion Tensor Images

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Faculty Opinions recommendation of Altered Gray Matter Structure and White Matter Microstructure in Patients with Congenital Adrenal Hyperplasia: Relevance for Working Memory Performance.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.737065571.793580490 ◽

2020 ◽

Author(s):

Mimi Kim

Keyword(s):

Working Memory ◽

White Matter ◽

Congenital Adrenal Hyperplasia ◽

Gray Matter ◽

Memory Performance ◽

Adrenal Hyperplasia ◽

White Matter Microstructure

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Topographic patterns of white matter hyperintensities are associated with multimodal neuroimaging biomarkers of Alzheimer’s disease

Alzheimer s Research & Therapy ◽

10.1186/s13195-020-00759-3 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Malo Gaubert ◽

Catharina Lange ◽

Antoine Garnier-Crussard ◽

Theresa Köbe ◽

Salma Bougacha ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

White Matter ◽

Glucose Metabolism ◽

Corpus Callosum ◽

Gray Matter ◽

Fdg Pet ◽

White Matter Hyperintensities ◽

Gray Matter Volume ◽

Matter Volume

Abstract Background White matter hyperintensities (WMH) are frequently found in Alzheimer’s disease (AD). Commonly considered as a marker of cerebrovascular disease, regional WMH may be related to pathological hallmarks of AD, including beta-amyloid (Aβ) plaques and neurodegeneration. The aim of this study was to examine the regional distribution of WMH associated with Aβ burden, glucose hypometabolism, and gray matter volume reduction. Methods In a total of 155 participants (IMAP+ cohort) across the cognitive continuum from normal cognition to AD dementia, FLAIR MRI, AV45-PET, FDG-PET, and T1 MRI were acquired. WMH were automatically segmented from FLAIR images. Mean levels of neocortical Aβ deposition (AV45-PET), temporo-parietal glucose metabolism (FDG-PET), and medial-temporal gray matter volume (GMV) were extracted from processed images using established AD meta-signature templates. Associations between AD brain biomarkers and WMH, as assessed in region-of-interest and voxel-wise, were examined, adjusting for age, sex, education, and systolic blood pressure. Results There were no significant associations between global Aβ burden and region-specific WMH. Voxel-wise WMH in the splenium of the corpus callosum correlated with greater Aβ deposition at a more liberal threshold. Region- and voxel-based WMH in the posterior corpus callosum, along with parietal, occipital, and frontal areas, were associated with lower temporo-parietal glucose metabolism. Similarly, lower medial-temporal GMV correlated with WMH in the posterior corpus callosum in addition to parietal, occipital, and fontal areas. Conclusions This study demonstrates that local white matter damage is correlated with multimodal brain biomarkers of AD. Our results highlight modality-specific topographic patterns of WMH, which converged in the posterior white matter. Overall, these cross-sectional findings corroborate associations of regional WMH with AD-typical Aß deposition and neurodegeneration.

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