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Three-dimensional structure tensor based PET/CT fusion in gradient domain

Three-dimensional structure tensor based PET/CT fusion in gradient domain Gradient based image fusion can more effectively incorporate edge details using structure tensor, which is successfully used in 2D image fusion. In this study, we generalized and applied this gradient based image fusion method into 3D for non-small cell lung cancer PET/CT image fusion. According the characteristic of lung PET/CT images, we proposed a novel 3D structure tensor based feature, which can be used to construct a weighted structure tensor containing important local detail of both PET and CT images. The fusion gradient domain is deduced from a rank one tensor, which is the closest approximation of the weighted structure tensor in geometry. Based on the fusion gradient domain, final PET/CT fusion image is obtained by solving a Poisson equation. Comparing with the wavelet transform based fusion result, the average information entropy and average gradient measure of proposed fusion method increase 13.5% and 42.3%, respectively. The experimental results show that the proposed fusion method enables to effectively preserve lung vessel structure and sphere-like lesion detail while produces clear, stable and well consistent fusion images. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of X-Ray Science and Technology IOS Press

Three-dimensional structure tensor based PET/CT fusion in gradient domain

Wang, Yuanjun; Liu, Yu
Journal of X-Ray Science and Technology , Volume 27 (2): 13 – Jan 1, 2019
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References (26)

Publisher
IOS Press
Copyright
Copyright © 2019 © 2019 – IOS Press and the authors. All rights reserved
ISSN
0895-3996
eISSN
1095-9114
DOI
10.3233/XST-180451
Publisher site
See Article on Publisher Site

Abstract

Gradient based image fusion can more effectively incorporate edge details using structure tensor, which is successfully used in 2D image fusion. In this study, we generalized and applied this gradient based image fusion method into 3D for non-small cell lung cancer PET/CT image fusion. According the characteristic of lung PET/CT images, we proposed a novel 3D structure tensor based feature, which can be used to construct a weighted structure tensor containing important local detail of both PET and CT images. The fusion gradient domain is deduced from a rank one tensor, which is the closest approximation of the weighted structure tensor in geometry. Based on the fusion gradient domain, final PET/CT fusion image is obtained by solving a Poisson equation. Comparing with the wavelet transform based fusion result, the average information entropy and average gradient measure of proposed fusion method increase 13.5% and 42.3%, respectively. The experimental results show that the proposed fusion method enables to effectively preserve lung vessel structure and sphere-like lesion detail while produces clear, stable and well consistent fusion images.

Journal

Journal of X-Ray Science and Technology – IOS Press

Published: Jan 1, 2019

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