Optimization of projections for parallel-ray transmission tomography using genetic algorithm

Abstract

In this work, a Hybrid Continuous Genetic Algorithm (HCGA) based methodology has been used for the optimization of number of projections for parallel-beam transmission tomography. Image quality has been measured using root-mean-squared error, Euclidean error and peak signal-to-noise ratio. The sensitivity of the reconstructed image quality has been analyzed with the number of projections used for the estimation of the inverse Radon transform. The number of projections has resulted in the maximization of image quality while minimizing the radiation hazard involved. The results have been compared with the intensity levels of the original phantom and the image reconstructed by the Filtered Back-Projection (FBP) technique, by using Matlab® functions radon and iradon. For the 8 × 8 Head and Lung phantoms, HCGA and FBP have resulted in PSNR values of 40.47 & 8.28 dB and 26.38 & 12.98 dB respectively with the optimum number of projections. © 2008 Springer-Verlag.