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http://hdl.handle.net/10609/125327
Title: A novel method for reconstructing CT images in GATE/GEANT4 with application in medical imaging: a complexity analysis approach
Author: Gholami, Neda
Dehshibi, Mohammad Mahdi
Adamatzky, Andrew
Rueda Toicen, Antonio
Zenil, Hector
Fazlali, Mahmood
Masip Rodó, David
Others: Pattern Research Center
University of the West of England
Algorithmic Nature Group
Karolinska Institute
Oxford University Innovation
Shahid Beheshti University
Universitat Oberta de Catalunya (UOC)
Keywords: complexity
CT image
FCM
GATE/Geant4
Hounsfield unit
pixel-based attenuation matrix
Issue Date: Feb-2020
Publisher: Journal of Information Processing
Citation: Gholami, N., Dehshibi, M.M., Adamatzky, A., Rueda-Toicen, A., Zenil, H., Fazlali, M. & Masip, D. (2020). A novel method for reconstructing CT images in GATE/GEANT4 with application in medical imaging: A complexity analysis approach. Journal of Information Processing, 28, 161-168. doi: 10.2197/ipsjjip.28.161
Project identifier: info:eu-repo/grantAgreement/EP/P016677/1
info:eu-repo/grantAgreement/TIN2015-66951-C2-2-R
info:eu-repo/grantAgreement/RTI2018-095232-B-C22
Also see: https://doi.org/10.2197/ipsjjip.28.161
Abstract: For reconstructing CT images in the clinical setting, "effective energy" is usually used instead of the total X-ray spectrum. This approximation causes an accuracy decline. We proposed to quantize the total X-ray spectrum into irregular intervals to preserve accuracy. A phantom consisting of the skull, rib bone, and lung tissues was irradiated with CT configuration in GATE/GEANT4. We applied inverse Radon transform to the obtained Sinogram to construct a Pixel-based Attenuation Matrix (PAM). PAM was then used to weight the calculated Hounsfield unit scale (HU) of each interval's representative energy. Finally, we multiplied the associated normalized photon flux of each interval to the calculated HUs. The performance of the proposed method was evaluated in the course of Complexity and Visual analysis. Entropy measurements, Kolmogorov complexity, and morphological richness were calculated to evaluate the complexity. Quantitative visual criteria (i.e., PSNR, FSIM, SSIM, and MSE) were reported to show the effectiveness of the fuzzy C-means approach in the segmenting task.
Language: English
URI: http://hdl.handle.net/10609/125327
ISSN: 1882-6652MIAR
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