Although minimum cross entropy thresholding(MCET) is efficient in the case of bilevel thresholding, it encounters
expensive computation when involving multilevel thresholding for exhaustive search on multiple thresholds. Therefore, an
improved scheme based on the genetic algorithm is presented for fastening threshold selection in multilevel MCET. Firstly,
image segmentation is considered as an optimization problem. Then, this scheme uses a recursive programming technique to
reduce the computational complexity of the objective function in multilevel MCET. Finally, a genetic algorithm is proposed
to search several near-optimal multilevel thresholds. Simulation results show that the multiple thresholds obtained by using
the proposed scheme are very close to the optimal ones via exhaustive search on the real images.