SVD-clustering, a general image-analyzing method explained and demonstrated on model and Raman micro-spectroscopic maps.

Scientific Reports
B SzalontaiCs Bagyinka

Abstract

An image analyzing method (SVD-clustering) is presented. Amplitude vectors of SVD factorization (V1…Vi) were introduced into the imaging of the distribution of the corresponding Ui basis-spectra. Since each Vi vector contains each point of the map, plotting them along the X, Y, Z dimensions of the map reconstructs the spatial distribution of the corresponding Ui basis-spectrum. This gives valuable information about the first, second, etc. higher-order deviations present in the map. We extended SVD with a clustering method, using the significant Vi vectors from the VT matrix as coordinates of image points in a ne-dimensional space (ne is the effective rank of the data matrix). This way every image point had a corresponding coordinate in the ne-dimensional space and formed a point set. Clustering was applied to this point set. SVD-clustering is universal; it is applicable to any measurement where data are recorded as a function of an external parameter (time, space, temperature, concentration, species, etc.). Consequently, our method is not restricted to spectral imaging, it can find application in many different 2D and 3D image analyses. Using SVD-clustering, we have shown on models the theoretical possibilities and limitations ...Continue Reading

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Methods Mentioned

BETA
imaging techniques
PCA

Software Mentioned

OPA
SIMPLISMA
HIA
MCR
Matlab
SIMPLE Mixture Analysis ( SIMPLISMA )
ALS

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