Comparative Evaluation of a Gaussian Mixture Models and a Seeded Region Growing Techniques for the Segmentation of Microarray Images
نویسندگان
چکیده
The purpose of the present study was to investigate and compare the segmentation ability of the Gaussian Mixture Models (GMM) against the Seeded Region Growing (SRG) methods in microarray spots segmentation. A simulated microarray image, each containing 200 spots, was produced. An automatic gridding process was developed in MATLAB and it was applied on the images for identifying the centers of spots and their surrounding borders (cells). The GMM, developed in MATLAB and the SRG algorithms, using MAGIC Tool software, were applied to each spot separately for discriminating foreground from background. The segmentation abilities of the GMM and SRG algorithms were evaluated by calculating the segmentation matching factor for each spot. Optimal segmentation results were obtained by the GMM, especially in cases where the spot’s mean intensity value was close to the background. The GMM technique was found to be an accurate algorithm in delineating the boundary of microarray spots and, thus, in discriminating the spot from its surrounding background.
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