Imaging mass spectrometry is a technique to determine of which materials a small, physical sample is made.
Current feature extraction techniques fail to extract certain small, high resolution characteristics from these
multi-spectral datacubes. Causes are a low signal-to-noise ratio, the presence of dominant but uninteresting
features, and the huge amount of variables in the dataset. In this paper, we present a zooming technique based on principal component analysis (PCA) to select regions
in a datacube for enhanced feature extraction at the highest possible resolution. It enables the selection of
spectral and spatial regions at a low resolution and recursively apply PCA to zoom in on interesting, correlated
features. This approach is not based on complex and data-specific denoising algorithms. Moreover, it decreases
execution time when additional filters have to be applied.
The technique utilizes a higher signal-to-noise ratio in the data, without losing the high resolution characteristics.
Less interesting and/or dominating features can be excluded in the spectral and spatial dimension. For
these reasons, more features can be distinguished and in greater detail. Analysts can zoom into a feature of
interest by increasing the resolution.