Segmentation of diesel spray images with log-likelihood ratio test algorithm for non-Gaussian distributions

A methodology for processing images of diesel sprays under different experimental situations is presented. The new approach has been developed for cases where the background does not follow a Gaussian distribution but a positive bias appears. In such cases, the lognormal and the gamma probability density functions have been considered for the background digital level distributions. Two different algorithms have been compared with the standard log-likelihood ratio test (LRT): a threshold defined from the cumulative probability density function of the background shows a sensitive improvement, but the best results are obtained with modified versions of the LRT algorithm adapted to non-Gaussian cases.