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).

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 Development of a Stand-Alone Independent Graphical User Interface for Neurological Disease Prediction with Automated Extraction and Segmentation of Gray and White Matter in Brain MRI Images

2019 ◽  
Vol 2019 ◽  
pp. 1-21 ◽  
Author(s):  
Ayush Goyal ◽  
Sunayana Tirumalasetty ◽  
Gahangir Hossain ◽  
Rajab Challoo ◽  
Manish Arya ◽  
...  
Keyword(s):  
White Matter ◽  
Gray Matter ◽  
Neurological Disorders ◽  
Neurological Disease ◽  
Automatic Segmentation ◽  
Brain Mri ◽  
Image Data ◽  
Disease Prediction ◽  
Mri Segmentation ◽  
Segmentation Algorithms

This research presents an independent stand-alone graphical computational tool which functions as a neurological disease prediction framework for diagnosis of neurological disorders to assist neurologists or researchers in the field to perform automatic segmentation of gray and white matter regions in brain MRI images. The tool was built in collaboration with neurologists and neurosurgeons and many of the features are based on their feedback. This tool provides the user automatized functionality to perform automatic segmentation and extract the gray and white matter regions of patient brain image data using an algorithm called adapted fuzzy c-means (FCM) membership-based clustering with preprocessing using the elliptical Hough transform and postprocessing using connected region analysis. Dice coefficients for several patient brain MRI images were calculated to measure the similarity between the manual tracings by experts and automatic segmentations obtained in this research. The average Dice coefficients are 0.86 for gray matter, 0.88 for white matter, and 0.87 for total cortical matter. Dice coefficients of the proposed algorithm were also the highest when compared with previously published standard state-of-the-art brain MRI segmentation algorithms in terms of accuracy in segmenting the gray matter, white matter, and total cortical matter.

scholarly journals Long-term cerebral white and gray matter changes after preeclampsia

Neurology ◽  
2017 ◽  
Vol 88 (13) ◽  
pp. 1256-1264 ◽  
Author(s):  
Timo Siepmann ◽  
Henry Boardman ◽  
Amy Bilderbeck ◽  
Ludovica Griffanti ◽  
Yvonne Kenworthy ◽  
...  
Keyword(s):  
Cardiovascular Risk ◽  
White Matter ◽  
Temporal Lobe ◽  
Gray Matter ◽  
Diffusion Tensor ◽  
Brain Mri ◽  
Risk Profile ◽  
Lesion Volume ◽  
Cardiovascular Risk Profile ◽  
Microstructural Integrity

Objective:To determine whether changes in cerebral structure are present after preeclampsia that may explain increased cerebrovascular risk in these women.Methods:We conducted a case control study in women between 5 and 15 years after either a preeclamptic or normotensive pregnancy. Brain MRI was performed. Analysis of white matter structure was undertaken using voxel-based segmentation of fluid-attenuation inversion recovery sequences to assess white matter lesion volume and diffusion tensor imaging to measure microstructural integrity. Voxel-based analysis of gray matter volumes was performed with adjustment for skull size.Results:Thirty-four previously preeclamptic women (aged 42.8 ± 5.1 years) and 49 controls were included. Previously preeclamptic women had reduced cortical gray matter volume (523.2 ± 30.1 vs 544.4 ± 44.7 mL, p < 0.05) and, although both groups displayed white matter lesions, changes were more extensive in previously preeclamptic women. They displayed increased temporal lobe white matter disease (lesion volume: 23.2 ± 24.9 vs 10.9 ± 15.0 μL, p < 0.05) and altered microstructural integrity (radial diffusivity: 538 ± 19 vs 526 ± 18 × 10−6 mm2/s, p < 0.01), which also extended to occipital and parietal lobes. The degree of temporal lobe white matter change in previously preeclamptic women was independent of their current cardiovascular risk profile (p < 0.05) and increased with time from index pregnancy (p < 0.05).Conclusion:A history of preeclampsia is associated with temporal lobe white matter changes and reduced cortical volume in young women, which is out of proportion to their classic cardiovascular risk profile. The severity of changes is proportional to time since pregnancy, which would be consistent with continued accumulation of damage after pregnancy.

scholarly journals Brain Image Segmentation with Gradient Information

2018 ◽  
Vol 7 (4.38) ◽  
pp. 1392
Author(s):  
Sri Purwani ◽  
Julita Nahar ◽  
Carole Twining
Keyword(s):  
Ground Truth ◽  
Real Data ◽  
Gaussian Mixture ◽  
Previous Method ◽  
Brain Images ◽  
Brain Image ◽  
Gradient Information ◽  
Segmentation Methods ◽  
Mr Brain Images ◽  
Mr Brain

Segmentation is the process of extracting structures within the images. The purpose is to simplify the representation of the image into something meaningful and easier to analyse.  A magnetic resonance (MR) brain image can be represented as three main tissues, e.g. cerebrospinal fluid (CSF), grey matter and white matter. Although various segmentation methods have been developed, such images are generally segmented by modelling the intensity histogram by using a Gaussian Mixture Model (GMM). However, the standard use of 1D histogram sometimes fails to find the mean for Gaussians. We hence solved this by including gradient information in the 2D intensity and intensity gradient histogram. We applied our methods on real data of 2D MR brain images. We then compared the methods with the previous published method of Petrovic et al. on their dataset, as well as on our larger datasets extracted from the same database of 3D MR brain mages, where the ground-truth annotations are available. This shows that our method performs better than the previous method.  

scholarly journals MAP–Based Framework for Segmentation of MR Brain Images Based on Visual Appearance and Prior Shape

2013 ◽  
Author(s):  
Amir Alansary ◽  
Ahmed Soliman ◽  
Fahmi Khalifa ◽  
Ahmed Elnakib ◽  
Mahmoud Mostapha ◽  
...  
Keyword(s):  
White Matter ◽  
Gray Matter ◽  
Empirical Distribution ◽  
Brain Structures ◽  
Shape Prior ◽  
Unsupervised Segmentation ◽  
Brain Images ◽  
Visual Appearance ◽  
Mr Brain Images ◽  
Mr Brain

We propose a new MAP-based technique for the unsupervised segmentation of different brain structures (white matter, gray matter, etc.) from T1-weighted MR brain images. In this paper, we follow a procedure like most conventional approaches, in which T1-weighted MR brain images and desired maps of regions (white matter, gray matter, etc.) are modeled by a joint Markov-Gibbs Random Field model (MGRF) of independent image signals and interdependent region labels. However, we specifically focus on the most accurate model identification that can be achieved. The proposed joint MGRF model accounts for the following three descriptors: i) a 1st-order visual appearance descriptor(empirical distribution of signal intensity), ii) a 3D probabilistic shape prior, and iii) a 3D spatially invariant 2nd-order homogeneity descriptor. To better specify the 1st-order visual appearance descriptor, each empirical distribution of signals is precisely approximated by a Linear Combination of Discrete Gaussians (LCDG) having both positive and negative components. The 3D probabilistic shape prior is learned using a subset of 3D co-aligned training T1-weighted MR brain images. The 2nd-order homogeneity descriptor is modeled by a 2nd-order translation and rotation invariant MGRF of 3D T1-weighted MR brain region labels with analytically estimated potentials. The initial segmentation, based on a 1st-order visual appearance and 3D probabilistic shape, is then iteratively refined using a 3D MGRF model with analytically estimated potentials. Experiments on twelve 3D T1-weighted MR brain images confirm the high accuracy of the proposed approach.

Gray Matter Segmentation of Brain MRI Using Hybrid Enhanced Independent Component Analysis

2021 ◽  
pp. 2150029
Author(s):  
Shaik Basheera ◽  
M. Satya Sai Ram
Keyword(s):  
Independent Component Analysis ◽  
Gray Matter ◽  
Medical Image Analysis ◽  
Brain Mri ◽  
Spatial Relation ◽  
Component Analysis ◽  
Independent Component ◽  
Morphological Operations ◽  
Low Contrast ◽  
Skull Stripping

One of the primary pre-processing tasks of medical image analysis is segmentation; it is used to diagnose the abnormalities in the tissues. As the brain is a complex organ, anatomical segmentation of brain tissues is a challenging task. Segmented gray matter is analyzed for early diagnosis of neurodegenerative disorders. In this endeavor, we used enhanced independent component analysis to perform segmentation of gray matter in noise-free and noisy environments. We used modified [Formula: see text]-means, expectation–maximization and hidden Markov random field to provide better spatial relation to overcome inhomogeneity, noise and low contrast. Our objective is achieved using the following two steps: (i) Irrelevant tissues are stripped from the MRI using skull stripping algorithm. In this algorithm, sequence of threshold, morphological operations and active contour are applied to strip the unwanted tissues. (ii) Enhanced independent component analysis is used to perform segmentation of gray matter. The proposed approach is applied on both T1w MRI and T2w MRI images at different noise environments such as salt and pepper noise, speckle noise and Rician noise. We evaluated the performance of the approach using Jaccard index, Dice coefficient and accuracy. The parameters are further compared with existing frameworks. This approach gives better segmentation of gray matter for the diagnosis of atrophy changes in brain MRI.

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