Automated, Feature-Based Image Alignment for High-Resolution Imaging Mass Spectrometry of Large Biological Samples.

@article{citeulike:2662203, abstract = {High-resolution imaging mass spectrometry of large biological samples is the goal of several research groups. In mosaic imaging, the most common method, the large sample is divided into a mosaic of small areas that are then analyzed with high resolution. Here we present an automated alignment routine that uses principal component analysis to reduce the uncorrelated noise in the imaging datasets, which previously obstructed automated image alignment. An additional signal quality metric ensures that only those regions with sufficient signal quality are considered. We demonstrate that this algorithm provides superior alignment performance than manual stitching and can be used to automatically align large imaging mass spectrometry datasets comprising many individual mosaic tiles.}, address = {Centrum voor Wiskunde en Informatica, Amsterdam, The Netherlands.}, author = {Broersen, Alexander and van Liere, Robert and Altelaar, A F Maarten F. and Heeren, Ron M A M. and McDonnell, Liam A A. }, citeulike-article-id = {2662203}, doi = {http://dx.doi.org/10.1016/j.jasms.2008.02.015}, issn = {1044-0305}, journal = {Journal of the American Society for Mass Spectrometry}, keywords = {imaging, maldi}, month = {March}, posted-at = {2008-04-12 21:26:33}, priority = {5}, title = {Automated, Feature-Based Image Alignment for High-Resolution Imaging Mass Spectrometry of Large Biological Samples.}, url = {http://dx.doi.org/10.1016/j.jasms.2008.02.015}, year = {2008} }

TY - JOUR ID - citeulike:2662203 L3 - citeulike-article-id:2662203 N2 - High-resolution imaging mass spectrometry of large biological samples is the goal of several research groups. In mosaic imaging, the most common method, the large sample is divided into a mosaic of small areas that are then analyzed with high resolution. Here we present an automated alignment routine that uses principal component analysis to reduce the uncorrelated noise in the imaging datasets, which previously obstructed automated image alignment. An additional signal quality metric ensures that only those regions with sufficient signal quality are considered. We demonstrate that this algorithm provides superior alignment performance than manual stitching and can be used to automatically align large imaging mass spectrometry datasets comprising many individual mosaic tiles. JF - Journal of the American Society for Mass Spectrometry SN - 1044-0305 AD - Centrum voor Wiskunde en Informatica, Amsterdam, The Netherlands. TI - Automated, Feature-Based Image Alignment for High-Resolution Imaging Mass Spectrometry of Large Biological Samples. KW - imaging KW - maldi AU - Broersen, Alexander AU - van Liere, Robert AU - Altelaar, A F Maarten AU - Heeren, Ron M A AU - McDonnell, Liam A PY - 2008/03/18/ UR - http://dx.doi.org/10.1016/j.jasms.2008.02.015 ER -