Enhancing CT 3D Images by Independent Component Analysis of Projection Images
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.
KeywordsComputed tomography CT µCT Micro-CT Independent component analysis Image processing 3D imaging
The work of J. M. A. Tanskanen has been supported by Jane and Aatos Erkko Foundation, Finland, under the project Biological Neuronal Communications and Computing with ICT. The work of M. Hannula has been supported by the Human Spare Parts Project funded by Finnish Funding Agency for Technology and Innovation (TEKES).
Conflict of Interest Declaration
No conflict of interest.
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