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ANFIS‐EM approach for PET brain image reconstruction
Author(s) -
Thiyagarajan Arunprasath,
Murugan Pallikonda Rajasekaran,
Subramanian Kannan
Publication year - 2015
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22114
Subject(s) - adaptive neuro fuzzy inference system , expectation–maximization algorithm , maximum a posteriori estimation , computer science , artificial intelligence , segmentation , image segmentation , algorithm , iterative reconstruction , pattern recognition (psychology) , computer vision , mathematics , maximum likelihood , fuzzy logic , statistics , fuzzy control system
In this article, for the reconstruction of the positron emission tomography (PET) images, an iterative MAP algorithm was instigated with its adaptive neurofuzzy inference system based image segmentation techniques which we call adaptive neurofuzzy inference system based expectation maximization algorithm (ANFIS‐EM). This expectation maximization (EM) algorithm provides better image quality when compared with other traditional methodologies. The efficient result can be obtained using ANFIS‐EM algorithm. Unlike any usual EM algorithm, the predicted method that we call ANFIS‐EM minimizes the EM objective function using maximum a posteriori (MAP) method. In proposed method, the ANFIS‐EM algorithm was instigated by neural network based segmentation process in the image reconstruction. By the image quality parameter of PSNR value, the adaptive neurofuzzy based MAP algorithm and de‐noising algorithm compared and the PET input image is reconstructed and simulated in MATLAB/simulink package. Thus ANFIS‐EM algorithm provides 40% better peak signal to noise ratio (PSNR) when compared with MAP algorithm. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 1–6, 2015