Enhancing CT 3D Images by Independent Component Analysis of Projection Images
| Autor: | Markus Hannula, Jari Hyttinen, Jarno M. A. Tanskanen |
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| Přispěvatelé: | Henriques, Jorge, de Carvalho, Paulo, Neves, Nuno, Tampere University, BioMediTech, Research group: Computational Biophysics and Imaging Group |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry 213 Electronic automation and communications engineering electronics 3D reconstruction Multispectral image Image processing Independent component analysis Computer vision Segmentation Noise (video) Tomography Artificial intelligence Projection (set theory) business |
| Zdroj: | IFMBE Proceedings ISBN: 9783030316341 |
| Popis: | Computed tomography (CT) is an imaging modality producing 3D images from sets of 2D X-ray images taken around the object. The images are noisy by nature, and segmentation of the 3D images is tedious. Also, detection of low contrast objects may be difficult, if not impossible. Here, we propose an independent component analysis (ICA) based method to process sets of 2D projection images prior to 3D reconstruction to remove noise, and to enhance objects for detection and segmentation. In this paper, a proof-of-concept is provided: the proposed method was able to separate noise and image components, as well as to make visible objects that were not observable in 3D images without processing. We demonstrate our method in object separation with 2D slice image processing simulations, and by enhancing a 3D image of a polymer sample taken with Xradia MicroXCT-400. The method is applicable in any CT tomography for which a number of project image sets with different contrasts can be taken, e.g., in multispectral fashion. acceptedVersion |
| Databáze: | OpenAIRE |
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