Maximum likelihood thresholding algorithm based on four-parameter gamma distributions

In this contribution, we present a segmentation algorithm based on thresholding to subdivide an intensity image in the regions of object and background. The optimal threshold is found by maximizing a likelihood function derived from a novel intensity probability density function model, which consists of the sum of two weighted four-parameter gamma distributions, as a more flexible alternative to currently used models consisting of the sum of two weighted two-parameter Gaussian distributions. According to our experiments with 132 images, the proposed algorithm is in average slightly better than the best found in the scientific literature, performing particularly good in low contrast images. The additional parameters and complexity of its likelihood function resulted in an increase of the processing time by a factor of 3, from 0.003 sec/image to 0.009 sec/image.