scholarly journals Cortical Surface Reconstruction from High-Resolution MR Brain Images

2012 ◽  
Vol 2012 ◽  
pp. 1-19 ◽  
Author(s):  
Sergey Osechinskiy ◽  
Frithjof Kruggel
Keyword(s):  
High Resolution ◽  
Cortical Layer ◽  
Cortical Surface ◽  
Computationally Efficient ◽  
Mr Images ◽  
Brain Images ◽  
Standard Resolution ◽  
Geometric Deformable Model ◽  
Young Subjects ◽  
Mr Brain Images

Reconstruction of the cerebral cortex from magnetic resonance (MR) images is an important step in quantitative analysis of the human brain structure, for example, in sulcal morphometry and in studies of cortical thickness. Existing cortical reconstruction approaches are typically optimized for standard resolution (~1 mm) data and are not directly applicable to higher resolution images. A new PDE-based method is presented for the automated cortical reconstruction that is computationally efficient and scales well with grid resolution, and thus is particularly suitable for high-resolution MR images with submillimeter voxel size. The method uses a mathematical model of a field in an inhomogeneous dielectric. This field mapping, similarly to a Laplacian mapping, has nice laminar properties in the cortical layer, and helps to identify the unresolved boundaries between cortical banks in narrow sulci. The pial cortical surface is reconstructed by advection along the field gradient as a geometric deformable model constrained by topology-preserving level set approach. The method’s performance is illustrated on exvivo images with 0.25–0.35 mm isotropic voxels. The method is further evaluated by cross-comparison with results of the FreeSurfer software on standard resolution data sets from the OASIS database featuring pairs of repeated scans for 20 healthy young subjects.

scholarly journals Registration-Based Approach for Reconstruction of High-Resolution In Utero Fetal MR Brain Images

2006 ◽  
Vol 13 (9) ◽  
pp. 1072-1081 ◽  
Author(s):  
Francois Rousseau ◽  
Orit A. Glenn ◽  
Bistra Iordanova ◽  
Claudia Rodriguez-Carranza ◽  
Daniel B. Vigneron ◽  
...  
Keyword(s):  
High Resolution ◽  
In Utero ◽  
Brain Images ◽  
Mr Brain Images ◽  
Mr Brain ◽  
Fetal Mr

scholarly journals Interpolation of the Histogramed MR Brain Images for Resolution Enhancement

2019 ◽  
Vol 8 (11) ◽  
pp. 1253-1256 ◽  
Keyword(s):  
Performance Metrics ◽  
Interpolation Method ◽  
Signal To Noise Ratio ◽  
Resolution Enhancement ◽  
The Body ◽  
Mr Images ◽  
Brain Images ◽  
Method Performance ◽  
Mr Brain Images ◽  
Mr Brain

Magnetic resonance imaging (MRI) is an incredible testing method which provides appropriate anatomical images of the body. For the diagnosis, high resolution MR images are essential to extract the detailed information about the diseases. However, with the measured MR images it’s a challenging issue in extracting the detailed information associated to disease for the posterior analysis or treatment. Usually to improve the resolution of the MR image, histogram equalization process has to be applied. In this paper, interpolation method is applied to improve the resolution of MR brain images for the histogram-ed images. And for the assessment of the skillfulness of introduced method, performance metrics such as peak signal to noise ratio (PSNR) and absolute mean brightness error (AMBE) are measured. The peak of signal for the enhanced images through interpolation will be much better and may have the good variation to the mean brightness error. With this there can be potential to the artificial intelligence for better diagnosis in complex decisive instances

scholarly journals Classification of Alzheimer Condition using MR Brain Images and Inception-Residual Network Model

2021 ◽  
Vol 7 (2) ◽  
pp. 763-766
Author(s):  
Sreelakshmi Shaji ◽  
Nagarajan Ganapathy ◽  
Ramakrishnan Swaminathan
Keyword(s):  
Network Performance ◽  
Neurodegenerative Disorder ◽  
Automated Analysis ◽  
Brain Regions ◽  
Mr Images ◽  
Residual Network ◽  
Brain Images ◽  
Precise Diagnosis ◽  
Mr Brain Images ◽  
Mr Brain

Abstract Alzheimer’s Disease (AD) is an irreversible progressive neurodegenerative disorder. Magnetic Resonance (MR) imaging based deep learning models with visualization capabilities are essential for the precise diagnosis of AD. In this study, an attempt has been made to categorize AD and Healthy Controls (HC) using structural MR images and an Inception-Residual Network (ResNet) model. For this, T1- weighted MR brain images are acquired from a public database. These images are pre-processed and are applied to a two-layer Inception-ResNet-A model. Additionally, Gradient weighted Class Activation Mapping (Grad-CAM) is employed to visualize the significant regions in MR images identified by the model for AD classification. The network performance is validated using standard evaluation metrics. Results demonstrate that the proposed Inception-ResNet model differentiates AD from HC using MR brain images. The model achieves an average recall and precision of 69%. The Grad- CAM visualization identified lateral ventricles in the mid-axial slice as the most discriminative brain regions for AD classification. Thus, the computer aided diagnosis study could be useful in the visualization and automated analysis of AD diagnosis with minimal medical expertise.

scholarly journals FCM Clustering Algorithms for Segmentation of Brain MR Images

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Yogita K. Dubey ◽  
Milind M. Mushrif
Keyword(s):  
Clustering Algorithms ◽  
Intensity Inhomogeneity ◽  
Noise Robustness ◽  
Mr Images ◽  
Tissue Segmentation ◽  
Brain Images ◽  
Fcm Clustering ◽  
Weak Boundaries ◽  
Brain Mr Images ◽  
Mr Brain Images

The study of brain disorders requires accurate tissue segmentation of magnetic resonance (MR) brain images which is very important for detecting tumors, edema, and necrotic tissues. Segmentation of brain images, especially into three main tissue types: Cerebrospinal Fluid (CSF), Gray Matter (GM), and White Matter (WM), has important role in computer aided neurosurgery and diagnosis. Brain images mostly contain noise, intensity inhomogeneity, and weak boundaries. Therefore, accurate segmentation of brain images is still a challenging area of research. This paper presents a review of fuzzyc-means (FCM) clustering algorithms for the segmentation of brain MR images. The review covers the detailed analysis of FCM based algorithms with intensity inhomogeneity correction and noise robustness. Different methods for the modification of standard fuzzy objective function with updating of membership and cluster centroid are also discussed.

scholarly journals Gaussian Intensity Model with Neighborhood Cues for Fluid-Tissue Categorization of Multi-Sequence MR Brain Images

2013 ◽  
Author(s):  
Ranveer Katyal ◽  
Sahil Paneri ◽  
Manohar Kuse
Keyword(s):  
Brain Mri ◽  
Spatial Location ◽  
Mr Images ◽  
Brain Images ◽  
Probability Estimates ◽  
Expert Annotation ◽  
Mri Sequences ◽  
Mr Brain Images ◽  
Intensity Model

This work presents an automatic brain MRI segmentation method which can classify brain voxels into one of three main tissue types: gray matter (GM), white matter (WM) and Cerebro-spinal Fluid (CSF). Intensity-model based classification of MR images has proven problematic. The statistical approach does not carry any spatial, textural and neighborhood information in it. We propose to use a computationally fast and novel feature-set to facilitate voxel wise classification based on regional intensity, texture, spatial location of voxels in addition to posterior probability estimates. Information available through T1-weighted (T1), T1-weighted inversion recovery (IR) and T2-weighted FLAIR (FLAIR) MRI sequences was also leveraged. An aggregate overlap of 90.21% for all intracranial structures was reported between the automatic classification and available expert annotation as measured by the DICE coefficient.

scholarly journals Modified Kernel Based Fuzzy Clustering for MR Brain Image Segmentation using Deep Learning

2019 ◽  
Vol 8 (6) ◽  
pp. 2881-2887
Keyword(s):  
Fuzzy Clustering ◽  
Bias Field ◽  
Mr Images ◽  
Brain Images ◽  
Normal Tissues ◽  
Segmentation Algorithms ◽  
Segmentation Approach ◽  
Mr Brain Images ◽  
Mr Brain ◽  
Abnormal Tissue

The segmentation procedure might cause error in diagnosing MR images due to the artifacts and noises that exist in it. This may lead to misclassifying normal tissue as abnormal tissue. In addition, it is also required to model the ontogenesis of a tumour, as they propagate at distinctive rates in contrast to their surroundings. Hence, it is still a challenging task to segment MR brain images due to possible noise presence, bias field and impact of partial volume. This article presents an efficient approach for segmenting MR brain images using a modified kernel based fuzzy clustering (MKFC) algorithm. In addition, this approach computes the weight of each picture element based on the local mutation coefficient (LMC). The proposed system would reflexively group normal tissues like white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) respectively, from abnormal tissue, such as a tumour region, in MR brain images. Simulation outcomes have shown that the performance of the proposed segmentation approach is superior to the existing segmentation algorithms in terms of both ocular and quantitative analysis

scholarly journals Differentiation of Alzheimer Conditions in MR Brain Images Using a Single Inception Module Network

2021 ◽  
Author(s):  
Sreelakshmi Shaji ◽  
Nagarajan Ganapathy ◽  
Ramakrishnan Swaminathan
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Input Image ◽  
Mr Images ◽  
Brain Images ◽  
Medical Expertise ◽  
Module Network ◽  
Mr Brain Images ◽  
Mr Brain ◽  
Normal Controls

In this study, an attempt has been made to differentiate Alzheimer’s Disease (AD) stages in structural Magnetic Resonance (MR) images using single inception module network. For this, T1-weighted MR brain images of AD, mild cognitive impairment and Normal Controls (NC) are obtained from a public database. From the images, significant features are extracted and classified using an inception module network. The performance of the model is computed and analyzed for different input image sizes. Results show that the single inception module is able to classify AD stages using MR images. The end-to-end network differentiates AD from NC with 85% precision. The model is found to be effective for varied sizes of input images. Since the proposed approach is able to categorize AD stages, single inception module networks could be used for the automated AD diagnosis with minimum medical expertise.

scholarly journals CLUSTERING MULTIPLE SCLEROSIS SUBGROUPS WITH MULTIFRACTAL METHODS AND SELF-ORGANIZING MAP ALGORITHM

Fractals ◽  
2017 ◽  
Vol 25 (04) ◽  
pp. 1740001 ◽  
Author(s):  
YELIZ KARACA ◽  
CARLO CATTANI
Keyword(s):  
Multiple Sclerosis ◽  
Self Organizing Map ◽  
Mr Images ◽  
Brain Images ◽  
Som Algorithm ◽  
Mr Brain Images ◽  
Magnetic Resonance Imaging Mri ◽  
Mr Brain ◽  
Multifractal Method ◽  
Self Organizing

Magnetic resonance imaging (MRI) is the most sensitive method to detect chronic nervous system diseases such as multiple sclerosis (MS). In this paper, Brownian motion Hölder regularity functions (polynomial, periodic (sine), exponential) for 2D image, such as multifractal methods were applied to MR brain images, aiming to easily identify distressed regions, in MS patients. With these regions, we have proposed an MS classification based on the multifractal method by using the Self-Organizing Map (SOM) algorithm. Thus, we obtained a cluster analysis by identifying pixels from distressed regions in MR images through multifractal methods and by diagnosing subgroups of MS patients through artificial neural networks.

Brain Tumor Detection and Classification by Hybrid CNN-DWA Model Using MR Images.

2021 ◽  
Vol 17 ◽  
Author(s):  
Isselmou Abd El Kader ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Sani Saminu
Keyword(s):  
Brain Tumor ◽  
Early Detection ◽  
Hybrid Model ◽  
High Performance ◽  
Tumor Detection ◽  
Classification Model ◽  
Mr Images ◽  
Brain Images ◽  
Mr Brain Images ◽  
Mr Brain

Objective: Medical image processing is an exciting research area. In this paper, we proposed new brain tumor detection and classification model using MR brain images to help the doctors in early detection and classification of the brain tumor with high performance and best Accuracy. Materials: we trained and validated our model using five databases, including BRATS2012, BRATS2013, BRATS2014, BRATS2015, and ISLES-SISS 2015. Methods: The advantage of the hybrid model proposed is its novelty that is used for the first time; our new method is based on a hybrid deep convolution neural network and deep watershed auto-encoder (CNN-DWA) model. The method consists of six phases, first phase is input MR images, second phase is preprocessing using filter and morphology operation, third phase is matrix that represents MR brain images, fourth is applying the hybrid CNN-DWA, fifth is brain tumor classification, and detection, while sixth phase is the performance of the model using five values. Results and Conclusions: The novelty of our hybrid CNN-DWA model showed the best results and high performance with Accuracy around 98% and loss validation 0, 1. Hybrid model can classify and detect the Tumor clearly using MR images; comparing with other models like CNN, DNN, and DWA, we discover that the proposed model performs better than the above-mentioned models. Depending on the better performance of the proposed hybrid model, this helps in developing computer-aided system for early detection of brain tumors and helps the doctors to diagnose the patients better.

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