MR Brain Image Segmentation to Detect White Matter, Gray Matter, and Cerebro Spinal Fluid Using TLBO Algorithm

2021 ◽  
pp. 2150034
Author(s):  
Sandhya Gudise ◽  
Giri Babu Kande ◽  
T. Satya Savithri
Keyword(s):  
White Matter ◽  
Gray Matter ◽  
Population Based ◽  
Spinal Fluid ◽  
Brain Image ◽  
Tlbo Algorithm ◽  
Wide Range ◽  
Skull Stripping ◽  
The Brain ◽  
Mr Brain

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.

scholarly journals Multilevel Thresholding Method Based on Electromagnetism for Accurate Brain MRI Segmentation to Detect White Matter, Gray Matter, and CSF

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
G. Sandhya ◽  
Giri Babu Kande ◽  
T. Satya Savithri
Keyword(s):  
White Matter ◽  
Gray Matter ◽  
Brain Mri ◽  
Ground Truth ◽  
Gaussian Mixture ◽  
Multilevel Thresholding ◽  
Mri Segmentation ◽  
Thresholding Method ◽  
Skull Stripping ◽  
Mr Brain

This work explains an advanced and accurate brain MRI segmentation method. MR brain image segmentation is to know the anatomical structure, to identify the abnormalities, and to detect various tissues which help in treatment planning prior to radiation therapy. This proposed technique is a Multilevel Thresholding (MT) method based on the phenomenon of Electromagnetism and it segments the image into three tissues such as White Matter (WM), Gray Matter (GM), and CSF. The approach incorporates skull stripping and filtering using anisotropic diffusion filter in the preprocessing stage. This thresholding method uses the force of attraction-repulsion between the charged particles to increase the population. It is the combination of Electromagnetism-Like optimization algorithm with the Otsu and Kapur objective functions. The results obtained by using the proposed method are compared with the ground-truth images and have given best values for the measures sensitivity, specificity, and segmentation accuracy. The results using 10 MR brain images proved that the proposed method has accurately segmented the three brain tissues compared to the existing segmentation methods such as K-means, fuzzy C-means, OTSU MT, Particle Swarm Optimization (PSO), Bacterial Foraging Algorithm (BFA), Genetic Algorithm (GA), and Fuzzy Local Gaussian Mixture Model (FLGMM).

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

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.

scholarly journals ANALISIS PERBEDAAN CITRA MRI BRAIN PADA SEKUENT1SE DAN T1FLAIR

SINERGI ◽  
2015 ◽  
Vol 19 (3) ◽  
pp. 206
Author(s):  
Nursama Heru Apriantoro ◽  
Christianni Christianni
Keyword(s):  
White Matter ◽  
Gray Matter ◽  
Spin Echo ◽  
Spinal Fluid ◽  
Inversion Recovery ◽  
Field Of View ◽  
Slice Thickness ◽  
Weighted Image ◽  
Mri Brain ◽  
Fluid Attenuated Inversion Recovery

MRI adalah bagian dari ilmu kedokteran untuk mediagnosa kelainan organ dengan memanfaatkan medan magnet dan pergerakan proton atom hidrogen. Salah satu pemeriksaan MRI adalah pemeriksaan brain. Pemeriksaan MRI brain dapat dilakukan T1 weighted image Spin Echo (T1 SE) atau T1 Fluid Attenuated Inversion Recovery (T1 FLAIR). Kajian dilakukan untuk menentukan perbedaan T1 SE dan T1 FLAIR dari segi citra berdasarkan nilai Rasio Signal terhadap Noise (SNR) dengan MRI GE Type Signa HD xt 1.5 Tesla. Penelitian menggunakan pendekatan kuantitatif.  20 pasien  telah diambil pada pemeriksaan MRI brain pada potongan axial, dengan parameter T1 SE potongan axial dengan parameter Time Repetition (TR) 700 ms, Time Echo (TE) 20 ms, Field of View (FOV) 240 mm, Slice Thickness 5,0 mm, Spacing 1,0 mm, Number of Excitations (NEX) 1, Phase 224, dan total slice 20. T1 FLAIR  parameter TR 3000 ms, TE 13,9 ms, TI 920 ms, FOV 240 mm, slice thickness 5,0 mm, spacing 1,0 mm,   NEX 1, phase 224, dan total slice 20. SNR dihitung pada anatomi brain meliputi CSF (Cerebro Spinal Fluid), White Matter dan Gray Matter. Hasil penelitian kedua sequence tersebut menunjukkan bahwa sequence T1 SE lebih baik daripada sequence T1 FLAIR.

scholarly journals Effects of Temperature on Evoked Electrical Activity and Anoxic Injury in CNS White Matter

1992 ◽  
Vol 12 (6) ◽  
pp. 977-986 ◽  
Author(s):  
Peter K. Stys ◽  
Stephen G. Waxman ◽  
Bruce R. Ransom
Keyword(s):  
White Matter ◽  
Optic Nerve ◽  
Functional Outcome ◽  
Gray Matter ◽  
Functional Recovery ◽  
Temperature Sensitive ◽  
Anoxic Injury ◽  
Intracellular Ca ◽  
Effects Of Temperature ◽  
The Brain

Temperature is known to influence the extent of anoxic/ischemic injury in gray matter of the brain. We tested the hypothesis that small changes in temperature during anoxic exposure could affect the degree of functional injury seen in white matter, using the isolated rat optic nerve, a typical CNS white matter tract (Foster et al., 1982). Functional recovery after anoxia was monitored by quantitative assessment of the compound action potential (CAP) area. Small changes in ambient temperature, within a range of 32 to 42°C, mildly affected the CAP of the optic nerve under normoxic conditions. Reducing the temperature to <37°C caused a reversible increase in the CAP area and in the latencies of all three CAP peaks; increasing the temperature to >37°C had opposite effects. Functional recovery of white matter following 60 min of anoxia was strongly influenced by temperature during the period of anoxia. The average recovery of the CAP, relative to control, after 60 min of anoxia administered at 37°C was 35.4 ± 7%; when the temperature was lowered by 2.5°C (i.e., to 34.5°C) for the period of anoxic exposure, the extent of functional recovery improved to 64.6 ± 15% ( p < 0.00001). Lowering the temperature to 32°C during anoxic exposure for 60 min resulted in even greater functional recovery (100.5 ± 14% of the control CAP area). Conversely, if temperature was increased to >37°C during anoxia, the functional outcome worsened, e.g., CAP recovery at 42°C was 8.5 ± 7% ( p < 0.00001). Hypothermia (i.e., 32°C) for 30 min immediately following anoxia at 37°C did not improve the functional outcome. Many processes within the brain are temperature sensitive, including O2 consumption, and it is not clear which of these is most relevant to the observed effects of temperature on recovery of white matter from anoxic injury. Unlike the situation in gray matter, the temperature dependency of anoxic injury cannot be related to reduced release of excitotoxins like glutamate, because neurotransmitters play no role in the pathophysiology of anoxic damage in white matter (Ransom et al., 1990 a). It is more likely that temperature affects the rate of ion transport by the Na+–Ca2+ exchanger, the transporter responsible for intracellular Ca2+ loading during anoxia in white matter, and/or the rate of some destructive intracellular enzymatic mechanism(s) activated by pathological increases in intracellular Ca2+.

scholarly journals Brain imaging in myotonic dystrophy type 1

Neurology ◽  
2017 ◽  
Vol 89 (9) ◽  
pp. 960-969 ◽  
Author(s):  
Kees Okkersen ◽  
Darren G. Monckton ◽  
Nhu Le ◽  
Anil M. Tuladhar ◽  
Joost Raaphorst ◽  
...  
Keyword(s):  
White Matter ◽  
Brain Imaging ◽  
Longitudinal Studies ◽  
Myotonic Dystrophy ◽  
Gray Matter ◽  
Myotonic Dystrophy Type 1 ◽  
Myotonic Dystrophy Type ◽  
Pooled Prevalence ◽  
The Brain

Objective:To systematically review brain imaging studies in myotonic dystrophy type 1 (DM1).Methods:We searched Embase (index period 1974–2016) and MEDLINE (index period 1946–2016) for studies in patients with DM1 using MRI, magnetic resonance spectroscopy (MRS), functional MRI (fMRI), CT, ultrasound, PET, or SPECT. From 81 studies, we extracted clinical characteristics, primary outcomes, clinical-genetic correlations, and information on potential risk of bias. Results were summarized and pooled prevalence of imaging abnormalities was calculated, where possible.Results:In DM1, various imaging changes are widely dispersed throughout the brain, with apparently little anatomical specificity. We found general atrophy and widespread gray matter volume reductions in all 4 cortical lobes, the basal ganglia, and cerebellum. The pooled prevalence of white matter hyperintensities is 70% (95% CI 64–77), compared with 6% (95% CI 3–12) in unaffected controls. DTI shows increased mean diffusivity in all 4 lobes and reduced fractional anisotropy in virtually all major association, projection, and commissural white matter tracts. Functional studies demonstrate reduced glucose uptake and cerebral perfusion in frontal, parietal, and temporal lobes, and abnormal fMRI connectivity patterns that correlate with personality traits. There is significant between-study heterogeneity in terms of imaging methods, which together with the established clinical variability of DM1 may explain divergent results. Longitudinal studies are remarkably scarce.Conclusions:DM1 brains show widespread white and gray matter involvement throughout the brain, which is supported by abnormal resting-state network, PET/SPECT, and MRS parameters. Longitudinal studies evaluating spatiotemporal imaging changes are essential.

Segmenting brain white matter, gray matter and cerebro-spinal fluid using diffusion tensor-MRI derived indices

2001 ◽  
Vol 19 (9) ◽  
pp. 1167-1172 ◽  
Author(s):  
Mara Cercignani ◽  
Matilde Inglese ◽  
Malgorzata Siger-Zajdel ◽  
Massimo Filippi
Keyword(s):  
White Matter ◽  
Gray Matter ◽  
Diffusion Tensor ◽  
Spinal Fluid ◽  
Diffusion Tensor Mri ◽  
Brain White Matter

scholarly journals Diffusion tensor imaging analysis of the brain in the canine model of Krabbe disease

2016 ◽  
Vol 29 (6) ◽  
pp. 417-424 ◽  
Author(s):  
Allison Bradbury ◽  
David Peterson ◽  
Charles Vite ◽  
Steven Chen ◽  
N Matthew Ellinwood ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Mr Imaging ◽  
Gray Matter ◽  
Diffusion Tensor ◽  
Ex Vivo ◽  
Small Animal ◽  
Krabbe Disease ◽  
Adc Values ◽  
The Brain

Purpose The goal of this study was to compare the diffusion tensor imaging (DTI) metrics from an end-stage canine Krabbe brain evaluated by MR imaging ex vivo to those of a normal dog brain. We hypothesized that the white matter of the canine Krabbe brain would show decreased fractional anisotropy (FA) values and increased apparent diffusion coefficient (ADC) and radial diffusivity (RD) values. Methods An 11-week-old Krabbe dog was euthanized after disease progression. The brain was removed and was placed in a solution of 10% formalin. MR imaging was performed and compared to the brain images of a normal dog that was similarly fixed post-mortem. Both brains were scanned using similar protocols on a 7 T small-animal MRI system. For each brain, maps of ADC, FA, and RD were calculated for 11 white-matter regions and five control gray-matter regions. Results Large decreases in FA values, increases in ADC values, and increases in RD (consistent with demyelination) values, were seen in white matter of the Krabbe brain but not gray matter. ADC values in gray matter of the Krabbe brain were decreased by approximately 29% but increased by approximately 3.6% in white matter of the Krabbe brain. FA values in gray matter were decreased by approximately 3.3% but decreased by approximately 29% in white matter. RD values were decreased by approximately 27.2% in gray matter but increased by approximately 20% in white matter. Conclusion We found substantial abnormalities of FA, ADC, and RD values in an ex vivo canine Krabbe brain.

scholarly journals Edited Magnetic Resonance Spectroscopy Detects an Age-Related Decline in Nonhuman Primate Brain GABA Levels

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Xuanzi He ◽  
Bang-Bon Koo ◽  
Ronald J. Killiany
Keyword(s):  
White Matter ◽  
Gray Matter ◽  
Posterior Cingulate Cortex ◽  
Resonance Spectroscopy ◽  
Gamma Aminobutyric Acid ◽  
Gaba Level ◽  
Inhibitory Neurotransmitter ◽  
Independent Variables ◽  
Age Related ◽  
The Brain

Recent research had shown a correlation between aging and decreasing Gamma-aminobutyric acid (GABA), the primary inhibitory neurotransmitter in the brain. However, how GABA level varies with age in the medial portion of the brain has not yet been studied. The purpose of this study was to investigate the GABA level variation with age focusing on the posterior cingulate cortex, which is the “core hub” of the default mode network. In this study, 14 monkeys between 4 and 21 years were recruited, and MEGA-PRESS MRS was performed to measure GABA levels, in order to explore a potential link between aging and GABA. Our results showed that a correlation between age and GABA+/Creatine ratio was at the edge of significance (r=-0.523,p=0.081). There was also a near-significant trend between gray matter/white matter ratio and the GABA+/Creatine ratio (r=-0.518,p=0.0848). Meanwhile, the correlation between age and grey matter showed no significance (r=-0.028,p=0.93). Therefore, age and gray matter/white matter ratio account for different part ofR-squared (adjustedR-squared = 0.5187) as independent variables for predicting GABA levels. AdjustedR-squared is about 0.5 for two independent variables. These findings suggest that there is internal neurochemical variation of GABA levels in the nonhuman primates associated with normal aging and structural brain decline.

scholarly journals Impact on Brain due to Alcoholism using Improved Fuzzy C-Regression based Alcohol Detection

2019 ◽  
Vol 9 (2) ◽  
pp. 1317-1325
Keyword(s):  
Risk Factors ◽  
Human Brain ◽  
Gray Matter ◽  
Membership Functions ◽  
Brain Images ◽  
Men And Women ◽  
Brain Image ◽  
Left Thalamus ◽  
Posterior Insula ◽  
The Brain

In general, two risk factors such as alcohol expectancy and impulsivity have been concerned with alcohol abuse Currently, many people have been addicted to alcoholism and have an Alcohol Use Disorder (AUD) that affects neurons behavior in the human brain. Still, how such risk factors interrelate to estimate the alcoholism. To solve this problem, Fuzzy C-Regression based Alcoholism Detection (FCRAD) method has been proposed that segments the Region-Of-Interests (ROIs) from the human brain image to predict the Gray Matter Volume (GMV) reduction in the right posterior insula in women and the left thalamus in both men and women efficiently. However, it requires the detection of GMV reduction in the other brain image regions. This multi-modality can decrease the fuzziness of the partition and the crisp membership degrees were not derived easily. Therefore in this article, the GMV reduction in other regions of the brain images including right posterior insula in women and left thalamus in both men and women has been detected, an Improved FCRAD (IFCRAD) method is proposed to simplify the segmentation of the brain images by considering the second regularization term in the objective function of the FCR to take into account the noisy data. Also, the Euclidean distance is replaced with the Voronoi distance for computing different fuzzy membership functions. Moreover, new error measure and reward function are used in the objective function of the FCR to reward nearly crisp membership functions and to obtain more crisp partition. So, the brain images are segmented into gray-matter images that derive the ROIs to analyze the GMV reduction with less complexity. Finally, the experimental results illustrate the proposed IFCRAD method achieves higher accuracy than the existing AD methods.

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