The objective of this work was to investigate the segmentation capability of Gaussian Mixture Models (GMMs) on microarray images, using wavelet based image enhancement techniques. A Simulated Microarray image of 200 spots was produced using a Microarray Scan Simulator. The image was pre-processed using 3 different daub4 wavelet based filters, developed in MATLAB. The detail coefficients of the wavelet transform were processed up to scale 3 using Hard, Soft and Sigmoidal Thresholding. An automatic gridding process was developed and applied on the preprocessed microarray image with the purpose of identifying the spots. The GMMs algorithm was then applied to each spot in order to discriminate foreground from background. The segmentation capability of the GMMs was evaluated by calculating the segmentation matching factor for each spot. Optimal segmentation results were obtained by pre-processing the microarray image by all the waveletbased filters. Wavelet based preprocessing was found to improve the GMMs segmentability, in discriminating the spot’s foreground from background.