Groupwise Registration of Brain Images for Establishing Accurate Spatial Correspondence of Brain Structures

2014 ◽  
pp. 229-257
Author(s):  
Zhenyu Tang ◽  
Yong Fan
Keyword(s):  
Brain Structures ◽  
Brain Images ◽  
Spatial Correspondence ◽  
Groupwise Registration

scholarly journals Multi-Atlas Brain MRI Segmentation with Multiway Cut

2013 ◽  
Author(s):  
Duygu Sarikaya ◽  
Liang Zhao ◽  
Jason J. Corso
Keyword(s):  
Brain Mri ◽  
Brain Structures ◽  
Mri Segmentation ◽  
Weighted Voting ◽  
Brain Images ◽  
Anatomical Structures ◽  
Test Dataset ◽  
Multiway Cut ◽  
The Subject

Characterization of anatomical structure of the brain and efficient algorithms for automatically analyzing brain MRI have gained an increasing interest in recent years. In this paper, we propose an algorithm that automatically segments the anatomical structures of magnetic resonance human brain images. Our method uses the prior knowledge of labels given by experts to statistically investigate the spatial correspondences of brain structures in subject images. We create a multi-atlas by registering the training images to the subject image and then propagating corresponding labels to the graph of the image. Label fusion then combines these multiple labels of atlases into one label at each voxel with intensity similarity based weighted voting. Finally we cluster the graph using multiway cut in order to achieve the final 3D segmentation of the subject image. The promising evaluation results of our segmentation method on the MRBrainS13 Test Dataset shows the efficiency and robustness of our algorithm.

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 STEREOLOGICAL EVALUATION OF BRAIN MAGNETIC RESONANCE IMAGES OF SCHIZOPHRENIC PATIENTS

2013 ◽  
Vol 32 (3) ◽  
pp. 145
Author(s):  
Amani Abdelrazag Elfaki ◽  
Abdelrazag Elfaki ◽  
Tahir Osman ◽  
Bunyamin Sahin ◽  
Abdelgani Elsheikh ◽  
...  
Keyword(s):  
White Matter ◽  
Magnetic Resonance ◽  
Structural Changes ◽  
Volume Fraction ◽  
Brain Structures ◽  
Magnetic Resonance Images ◽  
Brain Images ◽  
Schizophrenic Patients ◽  
The Mean ◽  
Mr Brain

Advances in neuroimaging have enabled studies of specific neuroanatomical abnormalities with relevance to schizophrenia. This study quantified structural alterations on brain magnetic resonance (MR) images of patients with schizophrenia. MR brain imaging was done on 88 control and 57 schizophrenic subjects and Dicom images were analyzed with ImageJ software. The brain volume was estimated with the planimetric stereological technique. The volume fraction of brain structures was also estimated. The results showed that, the mean volume of right, left, and total hemispheres in controls were 551, 550, and 1101 cm³, respectively. The mean volumes of right, left, and total hemispheres in schizophrenics were 513, 512, and 1026 cm³, respectively. The schizophrenics’ brains were smaller than the controls (p < 0.05). The mean volume of total white matter of controls (516 cm³) was bigger than the schizophrenics’ volume (451 cm³), (p < 0.05). The volume fraction of total white matter was also lower in schizophrenics (p < 0.05). Volume fraction of the lateral ventricles was higher in schizophrenics (p < 0.05). According to the findings, the volumes of schizophrenics’ brain were smaller than the controls and the volume fractional changes in schizophrenics showed sex dependent differences. We conclude that stereological analysis of MR brain images is useful for quantifying schizophrenia related structural changes.

scholarly journals Review of Computational Methods on Brain Symmetric and Asymmetric Analysis from Neuroimaging Techniques

2016 ◽  
Author(s):  
P. Kalavathi ◽  
K. Senthamilselvi ◽  
V. B. Surya Prasath
Keyword(s):  
Right Hemisphere ◽  
Sagittal Plane ◽  
Correct Diagnosis ◽  
Brain Structures ◽  
Diagnostic Tools ◽  
Computational Techniques ◽  
Brain Images ◽  
Left And Right ◽  
The Brain ◽  
Neuroimaging Techniques

Brain is the most complex organ in the human body and it is divided into two hemispheres - left and right hemispheres. Left hemisphere is responsible for control of right side of our body whereas right hemisphere is responsible for control of left side of our body. Brain image segmentation from different neuroimaging modalities is one of the important parts in clinical diagnostic tools. Neuroimaging based digital imagery generally contain noise, inhomogeneity, aliasing artifacts, and orientational deviations. Therefore, accurate segmentation of brain images is a very difficult task. However, the development of accurate segmentation of brain images is very important and crucial for a correct diagnosis of any brain related diseases. One of the fundamental segmentation tasks is to identify and segment inter-hemispheric fissure/mid-sagittal plane, which separate the two hemispheres of the brain. Moreover, the symmetric/asymmetric analyses of left and right hemispheres of brain structures are important for radiologists to analyze diseases such as Alzheimer's, Autism, Schizophrenia, Lesions and Epilepsy. Therefore, in this paper we have analyzed the existing computational techniques used to find brain symmetric/asymmetric analysis in various neuroimaging techniques (MRI/CT/PET/SPECT), which are utilized for detecting various brain related disorders.

scholarly journals SEGMENTATION OF BRAIN STRUCTURES IN ALZHEIMER MR IMAGES USING SPATIAL FUZZY CLUSTERING LEVEL SET

2021 ◽  
Vol 57 (2) ◽  
pp. 234-240
Author(s):  
Sreelakshmi Shaji ◽  
◽  
Ramakrishnan Swaminathan
Keyword(s):  
Level Set ◽  
Fuzzy Clustering ◽  
Structural Changes ◽  
Neurodegenerative Disorder ◽  
Ground Truth ◽  
Brain Structures ◽  
Initial Contour ◽  
Mr Images ◽  
Brain Images ◽  
Normal Controls

Alzheimer’s Disease (AD) is an irreversible neurodegenerative disorder that affects brain structures. Corpus Callosum (CC) atrophy and Lateral ventricle (LV) enlargement are useful structural biomarkers in distinguishing the preclinical stages of AD. The shape of CC appears to be homogeneous from normal controls to AD images and LV shows shape dissimilarity across subjects. Therefore, effective methods to segment CC and LV are essential to characterize the magnitude of morphometric changes. In this study, an attempt has been made to segment CC and LV from MR brain images using the Spatial Fuzzy Clustering based Level Set (SFC-LS) method. For this, T1-weighted MR images of AD, Mild Cognitive Impairment (MCI), and normal controls are obtained from a public database. Spatial fuzzy clustering forms the initial contour for the level set and regularizes the evolution of curve. The segmented images are validated against ground truth using standard measures. Results indicate that SFC-LS is able to segment CC and LV with automated contour initialization. The final contours obtained are sharp and distinct with a high validation performance of accuracy and specificity greater than 97% for normal controls, MCI, and AD. A dice score of 83% and 84% is achieved in segmenting CC and LV respectively. As structural changes in CC and LV have the potential to predict the early stages of AD, the proposed approach seems to be clinically significant.

scholarly journals Segmentation of Striatal Brain Structures from High Resolution PET Images

2009 ◽  
Vol 2009 ◽  
pp. 1-12 ◽  
Author(s):  
Ricardo J. P. C. Farinha ◽  
Ulla Ruotsalainen ◽  
Jussi Hirvonen ◽  
Lauri Tuominen ◽  
Jarmo Hietala ◽  
...  
Keyword(s):  
High Resolution ◽  
Ventral Striatum ◽  
Brain Research ◽  
Automatic Segmentation ◽  
Image Data ◽  
Brain Structures ◽  
Quantitative Information ◽  
Surface Model ◽  
Segmentation Method ◽  
Brain Images

We propose and evaluate an automatic segmentation method for extracting striatal brain structures (caudate, putamen, and ventral striatum) from parametricC11-raclopride positron emission tomography (PET) brain images. We focus on the images acquired using a novel brain dedicated high-resolution (HRRT) PET scanner. The segmentation method first extracts the striatum using a deformable surface model and then divides the striatum into its substructures based on a graph partitioning algorithm. The weighted kernelk-means algorithm is used to partition the graph describing the voxel affinities within the striatum into the desired number of clusters. The method was experimentally validated with synthetic and real image data. The experiments showed that our method was able to automatically extract caudate, ventral striatum, and putamen from the images. Moreover, the putamen could be subdivided into anterior and posterior parts. An automatic method for the extraction of striatal structures from high-resolution PET images allows for inexpensive and reproducible extraction of the quantitative information from these images necessary in brain research and drug development.

scholarly journals Groupwise registration of MR brain images with tumors

2017 ◽  
Vol 62 (17) ◽  
pp. 6853-6868 ◽  
Author(s):  
Zhenyu Tang ◽  
Yihong Wu ◽  
Yong Fan
Keyword(s):  
Brain Images ◽  
Groupwise Registration ◽  
Mr Brain Images ◽  
Mr Brain
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