GENETIC ALGORITHM OPTIMISATION OF ADAPTIVE MULTI-SCALE GLCM FEATURES

We introduce a new second-order method of texture analysis called Adaptive Multi-Scale Grey Level Co-occurrence Matrix (AMSGLCM), based on the well-known Grey Level Co-occurrence Matrix (GLCM) method. The method deviates significantly from GLCM in that features are extracted, not via a fixed 2-D weighting function of co-occurrence matrix elements, but by a variable summation of matrix elements in 3-D localised neighbourhoods. We subsequently present a new methodology for extracting optimised, highly discriminant features from these localised areas using adaptive Gaussian weighting functions. Genetic Algorithm (GA) optimisation is used to produce a set of features whose classification `worth' is evaluated by discriminatory power and feature correlation considerations. We critically appraised the performance of our method and GLCM in pair-wise classification of images from visually similar texture classes, captured from Markov Random Field (MRF) synthesised, natural, and biological origins. In these cross-validated classification trials, our method demonstrated significant benefits over GLCM, including increased feature discriminatory power, automatic feature adaptability, and significantly improved classification performance.

Dr Ross Walker