Donuts, scratches and blanks: robust model-based segmentation of microarray images

Inner holes, artifacts and blank spots are common in microarray images, but current image analysis methods do not pay them enough attention. We propose a new robust model-based method for processing microarray images so as to estimate foreground and background intensities. The method starts with a very simple but effective automatic gridding method, and then proceeds in two steps. The first step applies model-based clustering to the distribution of pixel intensities, using the Bayesian Information Criterion (BIC) to choose the number of groups up to a maximum of three. The second step is spatial, finding the large spatially connected components in each cluster of pixels. The method thus combines the strengths of histogram-based and spatial approaches. It deals effectively with inner holes in spots and artifacts. It also provides a formal inferential basis for deciding when the spot is blank, namely when the BIC favors one group over two or three. In experiments, our method had better ...

• Type: Journal
• Year: 2005
• Where: BIOINFORMATICS
• Authors: Qunhua Li, Chris Fraley, Roger Eugene Bumgarner, Ka Yee Yeung, Adrian E. Raftery
• Keywords: BIOINFORMATICS 2005, Effective Automatic Gridding, Inner Holes, Robust Model-based Method