scholarly journals Dense deformation field estimation for atlas-based segmentation of pathological MR brain images

2006 ◽  
Vol 84 (2-3) ◽  
pp. 66-75 ◽  
Author(s):  
M. Bach Cuadra ◽  
M. De Craene ◽  
V. Duay ◽  
B. Macq ◽  
C. Pollo ◽  
...  
Keyword(s):  
Deformation Field ◽  
Brain Images ◽  
Field Estimation ◽  
Mr Brain Images ◽  
Mr Brain

scholarly journals Symmetric Deformable Registration via Learning a Pseudomean for MR Brain Images

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xiaodan Sui ◽  
Yuanjie Zheng ◽  
Yunlong He ◽  
Weikuan Jia
Keyword(s):  
Anatomical Variations ◽  
Deformable Registration ◽  
Deformation Field ◽  
Image Pair ◽  
Imaging Analysis ◽  
Brain Images ◽  
Image Guided ◽  
Mr Brain Images ◽  
Image Pairs ◽  
Mr Brain

Image registration is a fundamental task in medical imaging analysis, which is commonly used during image-guided interventions and data fusion. In this paper, we present a deep learning architecture to symmetrically learn and predict the deformation field between a pair of images in an unsupervised fashion. To achieve this, we design a deep regression network to predict a deformation field that can be used to align the template-subject image pair. Specifically, instead of estimating the single deformation pathway to align the images, herein, we predict two halfway deformations, which can move the original template and subject into a pseudomean space simultaneously. Therefore, we train a symmetric registration network (S-Net) in this paper. By using a symmetric strategy, the registration can be more accurate and robust particularly on the images with large anatomical variations. Moreover, the smoothness of the deformation is also significantly improved. Experimental results have demonstrated that the trained model can directly predict the symmetric deformations on new image pairs from different databases, consistently producing accurate and robust registration results.

scholarly journals A Multi-Scale Framework for Bias Field Estimation in MRI Brain Images

2018 ◽  
Vol 7 (4.10) ◽  
pp. 197
Author(s):  
Maryjo M George ◽  
Kalaivani S
Keyword(s):  
Intensity Variation ◽  
Bias Field ◽  
Intensity Inhomogeneity ◽  
Brain Images ◽  
Multi Scale ◽  
Field Estimation ◽  
Corrected Data ◽  
Quantitative Analyses ◽  
Mr Brain Images ◽  
Mr Brain

Intensity inhomogeneity is an artifact in MR brain images and causes intensity variation of same tissues on the basis of location of the tissue within the image. It is crucial to minimize this phenomenon to improve the accuracy of the computer-aided diagnosis. Unlike the several methods proposed in the past to minimize intensity inhomogeneity, this proposed method uses a pyramidal decomposition strategy to estimate the bias field in MR brain images. The bias field estimated from the proposed multi-scale framework can be effectively used for intensity inhomogeneity correction of the acquired MR data. The proposed methodology has been tested on simulated database and quantitative analyses in terms of coefficient of variation in grey matter and white matter tissue regions separately and combined coefficient of joint variation are assessed. The qualitative and quantitative analyses on the corrected data indicate that the method is effective for intensity inhomogeneity on brain MR images.  

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

Automatic segmentation of MR brain images of preterm infants using supervised classification

NeuroImage ◽  
2015 ◽  
Vol 118 ◽  
pp. 628-641 ◽  
Author(s):  
Pim Moeskops ◽  
Manon J.N.L. Benders ◽  
Sabina M. Chiţǎ ◽  
Karina J. Kersbergen ◽  
Floris Groenendaal ◽  
...  
Keyword(s):  
Preterm Infants ◽  
Supervised Classification ◽  
Automatic Segmentation ◽  
Brain Images ◽  
Mr Brain Images ◽  
Mr Brain

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

Classification of MR brain images by combination of multi-CNNs for AD diagnosis

2017 ◽  
Author(s):  
Danni Cheng ◽  
Manhua Liu ◽  
Jianliang Fu ◽  
Yaping Wang
Keyword(s):  
Brain Images ◽  
Mr Brain Images ◽  
Mr Brain

Evaluation of an automatic brain segmentation method developed for neonates on adult MR brain images

2015 ◽  
Author(s):  
Pim Moeskops ◽  
Max A. Viergever ◽  
Manon J. N. L. Benders ◽  
Ivana Išgum
Keyword(s):  
Brain Segmentation ◽  
Segmentation Method ◽  
Brain Images ◽  
Mr Brain Images ◽  
Mr Brain
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