We investigate the use of Legendre moments as biomarkers for an efficient and accurate
classification of bone tissue on images coming from stem cell regeneration studies.
Regions of either existing bone, cartilage or new bone-forming cells are
characterized at tile level to quantify the degree of bone regeneration
depending on culture conditions. Legendre moments are analyzed
from three different perspectives:
(1) their discriminant properties in a wide set of preselected vectors of
features based on our clinical and computational experience, providing solutions
whose accuracy exceeds 90%.
(2) the amount of information to be retained when using Principal Component
Analysis (PCA) to reduce the dimensionality of the problem from 2 to 6 dimensions.
(3) the use of the (alpha-beta)-k-feature set problem to identify a k=4 number of
features which are more relevant to our analysis from a combinatorial optimization approach.
These techniques are compared in terms of computational complexity
and classification accuracy to assess the strengths and limitations of the use
of Legendre moments for this biomedical image processing application.
