3D Segmentation of MR Brain Images into White Matter, Gray Matter and Cerebro-Spinal Fluid by Means of Evidence Theory

2003 ◽  
pp. 112-116 ◽  
Author(s):  
Anne-Sophie Capelle ◽  
Olivier Colot ◽  
Christine Fernandez-Maloigne
Keyword(s):  
White Matter ◽  
Gray Matter ◽  
Evidence Theory ◽  
Spinal Fluid ◽  
3D Segmentation ◽  
Brain Images ◽  
Mr Brain Images ◽  
Mr Brain

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.

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.

Automated tissue segmentation of MR brain images in the presence of white matter lesions

2017 ◽  
Vol 35 ◽  
pp. 446-457 ◽  
Author(s):  
Sergi Valverde ◽  
Arnau Oliver ◽  
Eloy Roura ◽  
Sandra González-Villà ◽  
Deborah Pareto ◽  
...  
Keyword(s):  
White Matter ◽  
White Matter Lesions ◽  
Tissue Segmentation ◽  
Brain Images ◽  
Mr Brain Images ◽  
Mr Brain

Novel active contour model based on multi-variate local Gaussian distribution for local segmentation of MR brain images

2017 ◽  
Vol 24 (6) ◽  
pp. 653-659
Author(s):  
Qiang Zheng ◽  
Honglun Li ◽  
Baode Fan ◽  
Shuanhu Wu ◽  
Jindong Xu
Keyword(s):  
Gaussian Distribution ◽  
Active Contour ◽  
Active Contour Model ◽  
Brain Images ◽  
Model Based ◽  
Contour Model ◽  
Mr Brain Images ◽  
Mr Brain

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.

Sign in / Sign up

Export Citation Format

Share Document