Neural network segmented CD algorithm-based PET liver image reconstruction

摘要

In this paper, reconstruction of the Positron Emission Tomography (PET) images, a CD algorithm was instigated with NN based image segmentation techniques called Neural Network Segmentation based Coordinate Descent-Weighted Least Square (NNCD-WLS). Thus, NNCD-WLS of the function is not quadratic, but natural. The iterative algorithm achieve a fashion equivalent to an analytic derivation of the Maximum Likelihood-Expectation Maximisation (ML-EM) algorithm, which gives a different minimisation process between two convex sets of matrices. Conversely the distance metric is quite distinct, and more intricate to analyse. This algorithm is similar type, shares many properties acquainted with the ML-EM algorithm. Unlike WLS algorithm, NNCD-WLS method minimises the WLS objective function. The NNCD-WLS algorithm instigates via NN based segmentation process in image reconstruction. Image quality parameter of the PSNR value, NNCD-WLS algorithm and the denoising algorithm is compared. The PET input image is reconstructed and simulated in the MATLAB/Simulink package.