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.

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 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.

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

2015 ◽  
Vol 08 (09) ◽  
pp. 582-589
Author(s):  
Sudipta Roy ◽  
Debayan Ganguly ◽  
Kingshuk Chatterjee ◽  
Samir Kumar Bandyopadhyay
Keyword(s):  
White Matter ◽  
Gray Matter ◽  
Brain Image

FT-IR microspectroscopic analysis of diseased white matter brain tissues

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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