Spatial segmentation of imaging mass spectrometry data with edge-preserving image denoising and clustering. Alexandrov, T., Becker, M., Deininger, S., Ernst, G. n., Wehder, L., Grasmair, M., von Eggeling, F., Thiele, H., & Maass, P. J Proteome Res, 9(12):6535–6546, ACS Publications, 2010. doi abstract bibtex In recent years, matrix-assisted laser desorption/ionization (MALDI)-imaging mass spectrometry has become a mature technology, allowing for reproducible high-resolution measurements to localize proteins and smaller molecules. However, despite this impressive technological advance, only a few papers have been published concerned with computational methods for MALDI-imaging data. We address this issue proposing a new procedure for spatial segmentation of MALDI-imaging data sets. This procedure clusters all spectra into different groups based on their similarity. This partition is represented by a segmentation map, which helps to understand the spatial structure of the sample. The core of our segmentation procedure is the edge-preserving denoising of images corresponding to specific masses that reduces pixel-to-pixel variability and improves the segmentation map significantly. Moreover, before applying denoising, we reduce the data set selecting peaks appearing in at least 1% of spectra. High dimensional discriminant clustering completes the procedure. We analyzed two data sets using the proposed pipeline. First, for a rat brain coronal section the calculated segmentation maps highlight the anatomical and functional structure of the brain. Second, a section of a neuroendocrine tumor invading the small intestine was interpreted where the tumor area was discriminated and functionally similar regions were indicated.
@Article{alexandrov10spatial,
author = {Alexandrov, Theodore and Becker, Michael and Deininger, S{\"o}ren-Oliver and Ernst, Gu nther and Wehder, Liane and Grasmair, Markus and von Eggeling, Ferdinand and Thiele, Herbert and Maass, Peter},
title = {Spatial segmentation of imaging mass spectrometry data with edge-preserving image denoising and clustering},
journal = {J Proteome Res},
year = {2010},
volume = {9},
number = {12},
pages = {6535--6546},
abstract = {In recent years, matrix-assisted laser desorption/ionization (MALDI)-imaging mass spectrometry has become a mature technology, allowing for reproducible high-resolution measurements to localize proteins and smaller molecules. However, despite this impressive technological advance, only a few papers have been published concerned with computational methods for MALDI-imaging data. We address this issue proposing a new procedure for spatial segmentation of MALDI-imaging data sets. This procedure clusters all spectra into different groups based on their similarity. This partition is represented by a segmentation map, which helps to understand the spatial structure of the sample. The core of our segmentation procedure is the edge-preserving denoising of images corresponding to specific masses that reduces pixel-to-pixel variability and improves the segmentation map significantly. Moreover, before applying denoising, we reduce the data set selecting peaks appearing in at least 1\% of spectra. High dimensional discriminant clustering completes the procedure. We analyzed two data sets using the proposed pipeline. First, for a rat brain coronal section the calculated segmentation maps highlight the anatomical and functional structure of the brain. Second, a section of a neuroendocrine tumor invading the small intestine was interpreted where the tumor area was discriminated and functionally similar regions were indicated.},
doi = {10.1021/pr100734z},
file = {:2010/AlexandrovEtAl_SpatialSegmentationImagingMS_JOProtRes_2010.pdf:PDF},
owner = {purva},
publisher = {ACS Publications},
timestamp = {2014.08.23},
}
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