Sjoerd Krab successfully defended his MSc. thesis on:
Parasite Detection using Hyperspectral Microscopy in Malaria and Schistosoma diagnostics: the approximation and detection of spectral signatures
The first goal of his thesis work was to estimate the spectral signature of Malaria parasites in non-stained or Giemsa-stained thin smear blood samples and of Schistosoma parasite eggs in urine samples. For this different endmember extraction algorithms are combined with various methods of pre-processing and dimensionallity reduction. The used endmember extraction methods are pure pixel index (PPI), NFINDR, Statistics Based and simplex identification via split augmented Lagrangian (SISAL). For denoising Savitzky Golay and 3 dimensional gaussian filtering is used and the dimensionallity reduction is done with PCA, ICA or HySime. The resulting spectral signatures of the algorithms are validated by inspecting the endmember locations, spectra and abundance maps.
They have furthermore been compared by the classification performance where the spectral signatures are used in the feature derivation. This is done by deriving a detection map using OSP or CEM detection and then using the SVM or random forest classifiers to classify cells as being infected or not. These performances are furthermore compared to RGB image based classification.
The full report can be found here.