The objective of this work was to investigate the segmentation ability of the Fuzzy Gaussian Mixture Models (FGMM) clustering algorithm, applied on complementary DNA (cDNA) images. A Simulated Microarray image of 200 cells, each containing one spot, was produced following standard established procedure. An automatic gridding process was developed and applied on the microarray image for the task of locating spot borders and surrounding background in each cell. The FGMM and the Gaussian Mixture Model (GMM) algorithms were applied to each cell, with the purpose of discriminating foreground from background. The segmentation abilities of both algorithms were evaluated by means of the segmentation matching factor in respect to the actual classes (foreground-background pixels) of the simulated spots. The FGMM was found to perform better and with equal processing time, as compared to the GMM, rendering the FGMM algorithm an efficient alternative for segmenting cDNA microarray images.