The Bayesian ideal observer gives a measure for image quality since it uses all available statistical information for a given image data. A channelized-ideal observer (CIO), which reduces the dimensionality of integrals that need to be calculated for the ideal observer, has been introduced in the past. The goal of the CIO is to approximate the performance of the ideal observer in certain detection tasks. In this work, a CIO using Laguerre-Gauss (LG) channels is employed for detecting a rotationally symmetric Gaussian signal at a known location in the non-Gaussian distributed lumpy background. The mean number of lumps in the lumpy background is varied to see the impact of image statistics on the performance of this CIO and a channelized-Hotelling observer (CHO) using the same channels. The width parameter of LG channels is also varied to see its impact on observer performance. A Markov-chain Monte Carlo (MCMC) method is employed to determine the performance of the CIO using large numbers of LG channels. Simulation results show that the CIO is a better observer than the CHO for the task. The results also indicate that the performance of the CIO approaches that of the ideal observer as the mean number of lumps in the lumpy background decreases. This implies that LG channels may be efficient for the CIO to approximate the performance of the ideal observer in tasks using non-Gaussian distributed lumpy backgrounds.