In this paper, we present a computational framework that provides the automatic analysis of spotted DNA microarray image data. The challenges are in providing an accurate representation of microarray hybridization observations while minimizing user interaction. To obtain this, we need to segment the observation data and subsequent correction for true hybridization level measurements must be accomplished against the backdrop of signal noise, background signal variation, and spatial non-uniformity in the array layout. With the requirements of automation and accuracy, an approach based on data-driven denoising, array addressing, background estimation, and spot segmentation was developed. We proceeded to validate our approach on synthetic data as well as the publicly available raw and analyzed microarray data from the published Stanford yeast cell cycle analysis project. Spot mean and total intensities were examined as well as spot background estimates. By minimizing the user role, a main bottleneck in microarray data analysis is removed, allowing for more immediate analysis of large observation data sets. Our implementation has proven to be relatively fast, and the results of our approach have been encouraging.