Determination of optimal number of projections and parametric sensitivity analysis of operators for parallel‐ray transmission tomography using hybrid continuous genetic algorithm

Abstract

In this work, a hybrid continuous genetic algorithm (HCGA) based methodology has been developed for optimization of number of projections for parallel-ray transmission tomography. The HCGA calculations with filtered back-projection (FBP) utilize 8 bits for both head and lung phantoms. The effect of selection operator through proportionate, truncation, and tournament schemes has been analyzed along with the introduction of a mixed-selection scheme. Image quality has been measured using root-mean-squared error, Euclidean error and peak signal-to-noise ratios. The sensitivity of reconstructed image quality on various mutation operators, namely standard, gradient-, and offset-based schemes, has been analyzed along with the effect of number of projections. The number of projections has resulted in maximization of image quality while minimizing the radiation hazard involved. The results of HCGA have been compared with FBP as a deterministic technique and simulated annealing (SA) as a stochastic technique for IRT approximation. For the 8 Â 8 head and lung phantoms, HCGA, SA, FBP, have resulted PSNR values correspondingly as 40.47, 33.92, 8.28 and 26.38, 20.36, 12.98 dB.

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“…Same set of parameters have been employed as found suitable in [20]. Binary mutation has been used with P m = n -1 where n represents the length of chromosome.…”

“…A mixed selection scheme has been used consisting of truncation scheme (TS) [17] followed by roulette wheel scheme (RWS) [16,18,19] with elitism. The details may be seen in [20].…”

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“…These parents are used to produce the children for next generation. The selection of parents is based on the fitness function and the population evolves to the optimal solution in this way [11], [12], [13].…”