Results 51 to 60 of about 648,535 (141)

Fully automated convolutional neural network-based affine algorithm improves liver registration and lesion co-localization on hepatobiliary phase T1-weighted MR images. [PDF]

, 2019
BackgroundLiver alignment between series/exams is challenged by dynamic morphology or variability in patient positioning or motion. Image registration can improve image interpretation and lesion co-localization.
Bittencourt, Leornado Kayat, Brunsing, Ryan L, Cunha, Guilherme Moura, Delgado, Timo, Fowler, Katie J, Hasenstab, Kyle A, Higaki, Atsushi, Hsiao, Albert, Ichikawa, Shintaro, Schlein, Alexandra, Schwartzman, Armin, Sirlin, Claude B, Wang, Kang   +12 more
core  

Fast Predictive Simple Geodesic Regression

, 2017
Deformable image registration and regression are important tasks in medical image analysis. However, they are computationally expensive, especially when analyzing large-scale datasets that contain thousands of images.
CR Jack, M Niethammer, M Zhang, MA Ikram, PA Yushkevich, PT Fletcher, X Yang, Y Hong   +7 more
core   +1 more source

Multimodal Medical Image registration using Discrete Wavelet Transform

JISR on Computing, 2013
In image processing, image registration develops a relationship between two images using optimal transformation where the images could have been taken at various times, sources or devices, or from different perspectives.
Hina Shakir, S.Talha Ahsan
doaj   +1 more source

DTR-GAN: An Unsupervised Bidirectional Translation Generative Adversarial Network for MRI-CT Registration

Applied Sciences, 2023
Medical image registration is a fundamental and indispensable element in medical image analysis, which can establish spatial consistency among corresponding anatomical structures across various medical images.
Aolin Yang, Tiejun Yang, Xiang Zhao, Xin Zhang, Yanghui Yan, Chunxia Jiao   +5 more
doaj   +1 more source

Registration error of the liver CT using deformable image registration of MIM Maestro and Velocity AI

BMC Medical Imaging, 2017
Background Understanding the irradiated area and dose correctly is important for the reirradiation of organs that deform after irradiation, such as the liver.
Nobuyoshi Fukumitsu, Kazunori Nitta, Toshiyuki Terunuma, Toshiyuki Okumura, Haruko Numajiri, Yoshiko Oshiro, Kayoko Ohnishi, Masashi Mizumoto, Teruhito Aihara, Hitoshi Ishikawa, Koji Tsuboi, Hideyuki Sakurai   +11 more
doaj   +1 more source

Metric Learning for Image Registration

, 2019
Image registration is a key technique in medical image analysis to estimate deformations between image pairs. A good deformation model is important for high-quality estimates.
Kwitt, Roland, Niethammer, Marc, Vialard, Francois-Xavier   +2 more
core   +2 more sources

Multi-sensor remote sensing image alignment based on fast algorithms

Journal of Intelligent Systems, 2023
Remote sensing image technology to the ground has important guiding significance in disaster assessment and emergency rescue deployment. In order to realize the fast automatic registration of multi-sensor remote sensing images, the remote sensing image ...
Shu Tao
doaj   +1 more source

Symmetric image registration with directly calculated inverse deformation field [PDF]

, 2012
This paper presents a novel technique for a symmetric deformable image registration based on a new method for fast and accurate direct inversion of a large motion model deformation field.
Matuszewski, Bogdan, Papiez, Bartek
core  

Robust Registration of Calcium Images by Learned Contrast Synthesis

, 2015
Multi-modal image registration is a challenging task that is vital to fuse complementary signals for subsequent analyses. Despite much research into cost functions addressing this challenge, there exist cases in which these are ineffective. In this work,
Bogovic, John A., Hanslovsky, Philipp, Saalfeld, Stephan, Wong, Allan   +3 more
core   +1 more source

An Unsupervised Learning Model for Deformable Medical Image Registration

, 2018
We present a fast learning-based algorithm for deformable, pairwise 3D medical image registration. Current registration methods optimize an objective function independently for each pair of images, which can be time-consuming for large data.
Balakrishnan, Guha, Dalca, Adrian V., Guttag, John, Sabuncu, Mert R., Zhao, Amy   +4 more
core   +1 more source