The combination of classifiers has been proposed as a method allowing to improve the quality of recognition systems as compared to a single classifier. This paper describes a segmentation scheme based on a combination of pixel classifications. The aim of this paper is to show the influence of the neighborhood information and of the number of classifiers used in several combination processes. In the first part, we detail the ground of our study for a microscopic application. Then, we name the different steps of the new segmentation scheme. In the third and fourth part, we detail the different rules that can be used to combine classifiers and the classifications results obtained on colour microscopic images. Finally, we draw a conclusion on the improvement of the quality of the segmentation at the end of treatment.