Spatial Segmentation of Imaging Mass Spectrometry Data with Edge-Preserving Image Denoising and Clustering

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