28 January 2008 Zooming in multi-spectral datacubes using PCA
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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.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Alexander Broersen, Alexander Broersen, Robert van Liere, Robert van Liere, Ron M. A. Heeren, Ron M. A. Heeren, } "Zooming in multi-spectral datacubes using PCA", Proc. SPIE 6809, Visualization and Data Analysis 2008, 68090C (28 January 2008); doi: 10.1117/12.766450; https://doi.org/10.1117/12.766450


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