scholarly journals Optical Flow-Based Image Registration In Flair MRI

2021 ◽  
Author(s):  
Sergiu Mocanu
Keyword(s):  
Image Registration ◽  
Optical Flow ◽  
Video Processing ◽  
Registration Accuracy ◽  
Brain Images ◽  
Brain Volumes ◽  
Domain Specific ◽  
Tissue Integrity ◽  
Tissue Changes ◽  
Geometric Space

<div>Medical imaging is one of the most common areas of computer vision research and algorithm development. FLAIR-MRI is particularly useful in highlighting damaged and necrotic tissue in brain images due to high contrast and resolution. Image registration is a method of warping images to the same geometric space to quantify tissue changes with accuracy. With advances in deep-learning via convolutional neural networks, complex problems can now move closer to some semblance of a solution with purpose-built and domain specific models. To overcome the non-learnable nature of current registration algorithms, ideas are adapted from video processing solutions of calculating optical flow between temporally spaced frames using unsupervised CNN-based methods to warp moving medical images to a fixed image space. The proposed total network loss combines pixelwise photometric differences, flow smoothness, and intensity correlation. Registration accuracy of the proposed and four other registration algorithms is measured by examining tissue integrity, pixelwise</div><div>alignment, orientation, and global intensity similarity. The results, tested on two large FLAIRMRI datasets consisting of 700 and 4000 brain volumes, show that the optical-flow registration technique is able to obtain maximal alignment while maintaining structural tissue integrity.</div>

scholarly journals Optical Flow-Based Image Registration In Flair MRI

2021 ◽  
Author(s):  
Sergiu Mocanu
Keyword(s):  
Image Registration ◽  
Optical Flow ◽  
Video Processing ◽  
Registration Accuracy ◽  
Brain Images ◽  
Brain Volumes ◽  
Domain Specific ◽  
Tissue Integrity ◽  
Tissue Changes ◽  
Geometric Space

<div>Medical imaging is one of the most common areas of computer vision research and algorithm development. FLAIR-MRI is particularly useful in highlighting damaged and necrotic tissue in brain images due to high contrast and resolution. Image registration is a method of warping images to the same geometric space to quantify tissue changes with accuracy. With advances in deep-learning via convolutional neural networks, complex problems can now move closer to some semblance of a solution with purpose-built and domain specific models. To overcome the non-learnable nature of current registration algorithms, ideas are adapted from video processing solutions of calculating optical flow between temporally spaced frames using unsupervised CNN-based methods to warp moving medical images to a fixed image space. The proposed total network loss combines pixelwise photometric differences, flow smoothness, and intensity correlation. Registration accuracy of the proposed and four other registration algorithms is measured by examining tissue integrity, pixelwise</div><div>alignment, orientation, and global intensity similarity. The results, tested on two large FLAIRMRI datasets consisting of 700 and 4000 brain volumes, show that the optical-flow registration technique is able to obtain maximal alignment while maintaining structural tissue integrity.</div>

scholarly journals Transitively Consistent and Unbiased Multi-Image Registration using Numerically Stable Transformation Synchronisation

2015 ◽  
Author(s):  
Florian Bernard ◽  
Johan Thunberg ◽  
Andreas Husch ◽  
Luis Salamanca ◽  
Peter Gemmar ◽  
...  
Keyword(s):  
Image Registration ◽  
Medical Images ◽  
Desirable Property ◽  
Stable Transformation ◽  
Affine Transformations ◽  
Registration Accuracy ◽  
Brain Images ◽  
Registration Algorithm ◽  
Image Registration Algorithm ◽  
Affine Image

Transitive consistency of pairwise transformations is a desirable property of groupwise image registration procedures. The transformation synchronisation method (Bernard et al., 2015) is able to retrieve transitively consistent pairwise transformations from pairwise transformations that are initially not transitively consistent. In the present paper, we present a numerically stable implementation of the transformation synchronisation method for affine transformations, which can deal with very large translations, such as those occurring in medical images where the coordinate origins may be far away from each other. By using this method in conjunction with any pairwise (affine) image registration algorithm, a transitively consistent and unbiased groupwise image registration can be achieved. Experiments involving the average template generation from 3D brain images demonstrate that the method is more robust with respect to outliers and achieves higher registration accuracy compared to reference-based registration.

scholarly journals Development of an Image Registration Technique for Fluvial Hyperspectral Imagery Using an Optical Flow Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2407
Author(s):  
Hojun You ◽  
Dongsu Kim
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Optical Flow ◽  
Hyperspectral Imaging ◽  
Spatial Resolution ◽  
Template Matching ◽  
Hyperspectral Images ◽  
Spatial Accuracy ◽  
Flow Algorithm ◽  
Fluvial Remote Sensing

Fluvial remote sensing has been used to monitor diverse riverine properties through processes such as river bathymetry and visual detection of suspended sediment, algal blooms, and bed materials more efficiently than laborious and expensive in-situ measurements. Red–green–blue (RGB) optical sensors have been widely used in traditional fluvial remote sensing. However, owing to their three confined bands, they rely on visual inspection for qualitative assessments and are limited to performing quantitative and accurate monitoring. Recent advances in hyperspectral imaging in the fluvial domain have enabled hyperspectral images to be geared with more than 150 spectral bands. Thus, various riverine properties can be quantitatively characterized using sensors in low-altitude unmanned aerial vehicles (UAVs) with a high spatial resolution. Many efforts are ongoing to take full advantage of hyperspectral band information in fluvial research. Although geo-referenced hyperspectral images can be acquired for satellites and manned airplanes, few attempts have been made using UAVs. This is mainly because the synthesis of line-scanned images on top of image registration using UAVs is more difficult owing to the highly sensitive and heavy image driven by dense spatial resolution. Therefore, in this study, we propose a practical technique for achieving high spatial accuracy in UAV-based fluvial hyperspectral imaging through efficient image registration using an optical flow algorithm. Template matching algorithms are the most common image registration technique in RGB-based remote sensing; however, they require many calculations and can be error-prone depending on the user, as decisions regarding various parameters are required. Furthermore, the spatial accuracy of this technique needs to be verified, as it has not been widely applied to hyperspectral imagery. The proposed technique resulted in an average reduction of spatial errors by 91.9%, compared to the case where the image registration technique was not applied, and by 78.7% compared to template matching.

scholarly journals Detection of vessel bifurcations in CT scans for automatic objective assessment of deformable image registration accuracy

2021 ◽  
Author(s):  
Guillaume Cazoulat ◽  
Brian M Anderson ◽  
Molly M McCulloch ◽  
Bastien Rigaud ◽  
Eugene J Koay ◽  
...  
Keyword(s):  
Image Registration ◽  
Objective Assessment ◽  
Deformable Image Registration ◽  
Ct Scans ◽  
Registration Accuracy ◽  
Vessel Bifurcations
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