Paper AS-WeM10
Comparison of MAF and PCA for Processing 3-D ToF-SIMS Images of Organic and Biological Samples

Wednesday, October 22, 2008, 11:00 am, Room 207

Recent technological advances have facilitated 3-D SIMS imaging of organic and biological samples. To fully realize the potential of this technology, new tools are needed to aid in the image analysis. Even in two-dimensions, obtaining clear contrast between chemically similar regions, distinguishing between chemical and topographical effects and identifying chemical species from a complex ToF-SIMS data set can be a formidable challenge. These challenges become even greater as the size and complexity of the data sets increase due to both the third dimension and the higher useful mass range commonly obtained with cluster ion sources. In the past, we have found that Maximum Autocorrelation Factors (MAF) provides significant improvement over PCA for enhancing image contrast, reducing spectral complexity and facilitating compound identification.^1,2 We have investigated several approaches to generalizing the MAF approach for use in 3 dimensions. These 3-D MAF algorithms have been tested on synthetic images and on a variety of organic and biological 3-D SIMS images. Results have been compared to converntion single-peak data analysis and to PCA results using various scaling options. MAF, which includes information on the nearest neighbors to each pixel, shows clear advantages over PCA, particularly for identifying spase of subtle components in the images. Additionally, MAF is insensitive to pre-processing choices that can dramatically influence PCA results.

¹ Tyler, B.J. Applied Surface Science, Volume 252, Issue 19,30 July 2006, Pages 6875-6882
²Tyler, B.J.,Rayal G, Castner D.G., Biomaterials 2007, May 28(15):2412-23.