Quantifying the regeneration of bone tissue in biomedical images via Legendre moments

Abstract

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.