Automatic brain extraction and hemisphere segmentation in rat brain MR images after stroke using deformable models

2021 ◽  
Author(s):  
Herng‐Hua Chang ◽  
Shin‐Joe Yeh ◽  
Ming‐Chang Chiang ◽  
Sung‐Tsang Hsieh
Keyword(s):  
Rat Brain ◽  
Deformable Models ◽  
Mr Images ◽  
Brain Extraction ◽  
Brain Mr Images

scholarly journals Consistent 4D brain extraction of serial brain MR images

2013 ◽  
Author(s):  
Yaping Wang ◽  
Gang Li ◽  
Jingxin Nie ◽  
Pew-Thian Yap ◽  
Lei Guo ◽  
...  
Keyword(s):  
Mr Images ◽  
Brain Extraction ◽  
Brain Mr Images ◽  
Serial Brain

Segmentation of Rat Brain MR Images Using Artificial Neural Network Classifier

2013 ◽  
Author(s):  
K. N. Magdoom ◽  
Thomas H. Mareci ◽  
Malisa Sarntinoranont
Keyword(s):  
Neural Network ◽  
White Matter ◽  
Rat Brain ◽  
Gray Matter ◽  
Computational Models ◽  
Chemotherapeutic Agents ◽  
Mr Images ◽  
Artificial Neural Network Classifier ◽  
Brain Mr Images ◽  
The Brain

Recently MR image based computational models are being developed to assist targeted drug delivery in the brain by helping determine appropriate catheter position, drug dose among others to achieve the desired drug distribution [1–3]. Such a planning might be important to prevent damaging healthier tissues because many of the drugs (e.g. chemotherapeutic agents) are usually toxic and needs to be concentrated in specific regions of interest (e.g. tumor). However, for the image based model to make accurate predictions, it is important to segment the image and assign appropriate tissue properties such as hydraulic conductivity which are known to vary significantly within the brain. For example, it has been experimentally found that drugs injected into brain parenchyma get preferentially transported along the white matter tracts compared to the gray matter regions [4]. Segmenting MR images is a challenging task since the pixel intensities between different regions often overlap, hence traditional approaches based on thresholds might not provide reliable results. In this study, we used multi-layered perceptron (MLP) neural network to segment rat brain MR images into 3 different regions namely white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF).

Normal cerebral ventricular volume growth in childhood

2020 ◽  
Vol 26 (5) ◽  
pp. 517-524
Author(s):  
Noah S. Cutler ◽  
Sudharsan Srinivasan ◽  
Bryan L. Aaron ◽  
Sharath Kumar Anand ◽  
Michael S. Kang ◽  
...  
Keyword(s):  
Brain Mri ◽  
Volume Growth ◽  
Ventricular Volume ◽  
Growth Patterns ◽  
Diagnostic Process ◽  
Growth Charts ◽  
Normal Brain ◽  
Mr Images ◽  
Ventricular Volumes ◽  
Brain Mr Images

OBJECTIVENormal percentile growth charts for head circumference, length, and weight are well-established tools for clinicians to detect abnormal growth patterns. Currently, no standard exists for evaluating normal size or growth of cerebral ventricular volume. The current standard practice relies on clinical experience for a subjective assessment of cerebral ventricular size to determine whether a patient is outside the normal volume range. An improved definition of normal ventricular volumes would facilitate a more data-driven diagnostic process. The authors sought to develop a growth curve of cerebral ventricular volumes using a large number of normal pediatric brain MR images.METHODSThe authors performed a retrospective analysis of patients aged 0 to 18 years, who were evaluated at their institution between 2009 and 2016 with brain MRI performed for headaches, convulsions, or head injury. Patients were excluded for diagnoses of hydrocephalus, congenital brain malformations, intracranial hemorrhage, meningitis, or intracranial mass lesions established at any time during a 3- to 10-year follow-up. The volume of the cerebral ventricles for each T2-weighted MRI sequence was calculated with a custom semiautomated segmentation program written in MATLAB. Normal percentile curves were calculated using the lambda-mu-sigma smoothing method.RESULTSVentricular volume was calculated for 687 normal brain MR images obtained in 617 different patients. A chart with standardized growth curves was developed from this set of normal ventricular volumes representing the 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles. The charted data were binned by age at scan date by 3-month intervals for ages 0–1 year, 6-month intervals for ages 1–3 years, and 12-month intervals for ages 3–18 years. Additional percentile values were calculated for boys only and girls only.CONCLUSIONSThe authors developed centile estimation growth charts of normal 3D ventricular volumes measured on brain MRI for pediatric patients. These charts may serve as a quantitative clinical reference to help discern normal variance from pathologic ventriculomegaly.

A Novel Technique for Detection of Anomalies in Brain MR Images

2015 ◽  
Vol 2 (2) ◽  
pp. 33-42
Author(s):  
K Bhima ◽  
◽  
A Jagan ◽  
Keyword(s):  
Mr Images ◽  
Novel Technique ◽  
Brain Mr Images

scholarly journals Content-Based Estimation of Brain MRI Tilt in Three Orthogonal Directions

2021 ◽  
Author(s):  
Pooja Prabhu ◽  
A. K. Karunakar ◽  
Sanjib Sinha ◽  
N. Mariyappa ◽  
G. K. Bhargava ◽  
...  
Keyword(s):  
Large Scale ◽  
Brain Mri ◽  
Similarity Measures ◽  
Principal Component ◽  
Brain Anatomy ◽  
Morphological Operations ◽  
Mr Images ◽  
Tilt Correction ◽  
Brain Mr Images ◽  
The Brain

AbstractIn a general scenario, the brain images acquired from magnetic resonance imaging (MRI) may experience tilt, distorting brain MR images. The tilt experienced by the brain MR images may result in misalignment during image registration for medical applications. Manually correcting (or estimating) the tilt on a large scale is time-consuming, expensive, and needs brain anatomy expertise. Thus, there is a need for an automatic way of performing tilt correction in three orthogonal directions (X, Y, Z). The proposed work aims to correct the tilt automatically by measuring the pitch angle, yaw angle, and roll angle in X-axis, Z-axis, and Y-axis, respectively. For correction of the tilt around the Z-axis (pointing to the superior direction), image processing techniques, principal component analysis, and similarity measures are used. Also, for correction of the tilt around the X-axis (pointing to the right direction), morphological operations, and tilt correction around the Y-axis (pointing to the anterior direction), orthogonal regression is used. The proposed approach was applied to adjust the tilt observed in the T1- and T2-weighted MR images. The simulation study with the proposed algorithm yielded an error of 0.40 ± 0.09°, and it outperformed the other existing studies. The tilt angle (in degrees) obtained is ranged from 6.2 ± 3.94, 2.35 ± 2.61, and 5 ± 4.36 in X-, Z-, and Y-directions, respectively, by using the proposed algorithm. The proposed work corrects the tilt more accurately and robustly when compared with existing studies.

A novel evolutionary row class entropy based optimal multi-level thresholding technique for brain MR images

2021 ◽  
Vol 168 ◽  
pp. 114426
Author(s):  
Rutuparna Panda ◽  
Leena Samantaray ◽  
Akankshya Das ◽  
Sanjay Agrawal ◽  
Ajith Abraham
Keyword(s):  
Mr Images ◽  
Multi Level ◽  
Brain Mr Images

Automated connectivity-based thresholding segmentation of midsagittal brain MR images

1996 ◽  
Author(s):  
Chulhee Lee ◽  
Michael A. Unser ◽  
Terence A. Ketter
Keyword(s):  
Mr Images ◽  
Brain Mr Images

scholarly journals Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images

2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Yunjie Chen ◽  
Tianming Zhan ◽  
Ji Zhang ◽  
Hongyuan Wang
Keyword(s):  
Magnetic Resonance ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Magnetic Resonance Images ◽  
Intensity Inhomogeneity ◽  
Mr Images ◽  
Proposed Model ◽  
Brain Mr Images ◽  
The Impact

We propose a novel segmentation method based on regional and nonlocal information to overcome the impact of image intensity inhomogeneities and noise in human brain magnetic resonance images. With the consideration of the spatial distribution of different tissues in brain images, our method does not need preestimation or precorrection procedures for intensity inhomogeneities and noise. A nonlocal information based Gaussian mixture model (NGMM) is proposed to reduce the effect of noise. To reduce the effect of intensity inhomogeneity, the multigrid nonlocal Gaussian mixture model (MNGMM) is proposed to segment brain MR images in each nonoverlapping multigrid generated by using a new multigrid generation method. Therefore the proposed model can simultaneously overcome the impact of noise and intensity inhomogeneity and automatically classify 2D and 3D MR data into tissues of white matter, gray matter, and cerebral spinal fluid. To maintain the statistical reliability and spatial continuity of the segmentation, a fusion strategy is adopted to integrate the clustering results from different grid. The experiments on synthetic and clinical brain MR images demonstrate the superior performance of the proposed model comparing with several state-of-the-art algorithms.

Sign in / Sign up

Export Citation Format

Share Document