12/10/2010 Spatial segmentation of imaging mass spectrometry data with edge-preserving image denoising and clustering

Title : Spatial segmentation of imaging mass spectrometry data with edge-preserving image denoising and clustering

by Theodore Alexandrov, Michael Becker, Sören Deininger, Günther Ernst, Liane Wehder, Markus Grasmair, Ferdinand von Eggeling, Herbert Thiele, and Peter Maass

1. Background: MS Imaging enables visualization of the spatial distribution of e.g. compounds, biomarker, metabolites, peptides or proteins by their molecular masses.

2. What they did: the authors proposed a new procedure for spatial segmentation of MALDI-imaging dataset.

3. How they did: they built the pipeline that consists of
   • spectra preprocessing
   • peak picking
   • edge-preserving denoising of mz images
   • finally, clustering.
4. More in detail...
   • Spectra preprocessing uses baseline correction, which reduces the intensity errors.
   • Peak picking picks only 10 peaks in each 10th spectra, and keeps peaks at least 1% across entire sample. Orthogonal Matching Pursuit (OMP) is used since it is simple and fast.
   • Denoising uses Grasmair modification of Total Variation minimizing Chambolle algorithm. The parameter theta controls the smoothness.

• Clustering uses High Dimensional Discriminant Clustering (HDDC), where each cluster is modeled by a Gaussian distribution.

3. Results
   • Dataset : Rat brain coronal section and Section of neuroendocrine tumor (NET) invading the small intestine
   • Peak picking using OMP detects major peaks successfully.
   • Denoising with Grasmair method removed noise efficiently not smoothing out edges. The result illustrates the selection of parameter theta is important, though.
   • The clustered image by proposed pipeline and the segmentation map of rat brain were shown to be similar each other. The edge preserving denoising affects the clustering result.
   • 3 parameters for peak picking and 2 parameters for denoising and clustering should be tuned for good result.
4. conclusion
   • HDDC clustering is better than k-means but slow.
   • It is important for cancer study.
5. Criticism
   • What they are optimizing is unclear.
   • Too many parameters can influence the result yet no optimal values are given for various applications.
   • Slow running time makes it hard to run multiple trials.

Speaker: Jocelyne

Scribe: Kyowon

Slides: here