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ANFIS‐EM approach for PET brain image reconstruction
Authors:Arunprasath Thiyagarajan  Pallikonda Rajasekaran Murugan  Kannan Subramanian
Affiliation:1. Department of EEE, Kalasalingam University, Virudhunagar, Tamil Nadu, India;2. Department of Electronics and Communication Engineering, Kalasalingam University, Virudhunagar, Tamil Nadu, India;3. Department of EEE, Ramco Institute of Technology, Virudhunagar, Tamil Nadu, India
Abstract: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
Keywords:PET brain image  image reconstruction  adaptive neurofuzzy inference system  maximum a posteriori  expectation‐maximization (EM) algorithm  adaptive neurofuzzy inference system based expectation maximization algorithm (ANFIS‐EM)
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