Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Diagnostic imaging --- Image registration

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book constitutes the refereed proceedings of the 11th International Workshop on Biomedical Image Registration, WBIR 2024, held in conjunction with the 27th International conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024, in Marrakesh, Morocco in October 2024. The 28 full papers presented in this book were carefully reviewed and selected from 32 submissions. These papers have been categorized under the following topical sections: Architectures; Robustness; Atlas/ Fusion; Feature/ Similarity Learning & Efficiency.

Image registration. --- Imaging systems in medicine

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

"Image registration is a digital processing discipline that studies how to bring two or more digital images into precise alignment for analysis and comparison. Accurate registration algorithms are essential in supporting Earth and planetary scientists as they mosaic remote sensing satellite images and track changes of the planet's surface over time for environmental, political and basic science studies. The book brings together invited contributions by 36 distinguished researchers in the field to present a coherent and detailed overview of current research and practice in the application of image registration to satellite imagery. The chapters cover the problem definition, theoretical issues in accuracy and efficiency, fundamental algorithms used in its solution, and real world case studies of image registration software applied to imagery from operational satellite systems. This book is an essential reference for Earth and space scientists who need a comprehensive and practical overview on how to obtain optimal georegistration of their data, an indispensable source for image processing researchers interested in current research, and the ideal text for teaching a special topic university graduate course"-- Provided by publisher

Image registration --- Image analysis --- Remote sensing --- Image processing

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The sun's corona exhibits a very high brightness gradient. The use of HDR (High dynamic range) imaging technique is an asset at improving the quality of the resulting image. By taking successive images with different exposure times, it is possible to enhance the contrast of the image. Short exposure time images being adapted for very bright parts of the corona but inadequate for dark parts. Whereas long exposure time images are adapted for dark parts but inadequate for bright parts where saturation may occur. Combining images of different exposure time allows to take advantage of both and is at the basis of HDR techniques. 
The combination of images requires a perfect alignment of the sun's coronal structures. Images of different exposure time are taken at successive instants which causes misalignment due to the relative motion of the objects in the scene. An image alignment algorithm based on contours of intensity in the image has been developed. It allows the alignment of multi-exposure images with a pixel precision.
The composition of multi-exposure images allows to get a higher dynamic range to capture the details of the corona of the sun. Special care must be taken to avoid problems due to saturation and noise of the different images as well as unwanted objects in the scene. An algorithm for the composition of an HDR image has been developed.
It is applied in the case of the PROBA-3 satellite project: a new type of external coronagraph. As this is a future project, data had to be simulated for this study. Rather than giving one method to achieve the goal, it is a study of the different possibilities and will serve as a guide of possible techniques for further implementation. 
It is part of the internship made at the Centre spatial de Liège (CSL).

Image registration --- coronagraph --- HDR --- Ingénierie, informatique & technologie > Ingénierie électrique & électronique

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Multimodal imaging analyses are large scale work, combining experience from many professionals in different disciplines, providing different modalities (i.e. data produced by an experiment) linked together.
The growth in multimodal analyses induces a demand for software to make some workflows possible, or help automate some other workflows, at lest partially.

Mass Spectrometry Imaging (MSI), although more than 50 years old, continues to see some development in the data processing domain, particularly with machine learning and deep learning applications, where some approaches tackle the preprocessing and the analysis of MSI datasets.
The analysis performed on MSI data greatly contributes from multimodal studies, providing a spatial distribution for the molecular content of the sample, thus adding valuable information to the study.

Multimodal analyses currently lack an open, collaborative web platform : such tools would allow for a greater share of experience thanks to the collaborative aspect, enable reproducibility because the analyses would run in the cloud, always on the same hardware, and the results would be available to all.
Such tools are being developed : Cytomine aims to add more effective multimodal tools to improve its capabilities, but integrating MSI data is not trivial.

The analysis of MSI data is not an easy task : file formats for this kind of data are abundant, but often vendor specific. imzML is an open effort to unify all these formats, which is supported by many pieces of software already. However, imzML is not the most appropriate format as its structure is very different from most imaging data format, making it ill-suited for visualization applications such as in Cytomine.

This master's thesis introduce a new, versatile and open format based on OME-Zarr, which is suitable for many modalities, including MSI. This file format is benchmarked against imzML to show its potential in server applications, such as Cytomine.

In addition to the new file format, the developed pieces of software includes a convertor from imzML and some preprocessing tools designed for the file format. Using the developed file format, a machine learning workflow classifies spectra from a multimodal dataset with label coming from other modalities, and provide a list of important features as a mean of interpretation.

While these pieces of software are currently developed to be run on a local machine, they lay the ground for cloud based application that can be integrated with Cytomine.

multimodal --- mass spectrometry imaging --- msi --- cytomine --- template matching --- machine learning --- image registration --- bioimaging --- Ingénierie, informatique & technologie > Sciences informatiques