  With the heterogeneous nature of tissue texture, using a single resolution
approach for optimum classification might not suffice. In contrast, a
multiresolution wavelet packet analysis can decompose the input signal into a
set of frequency subbands giving the opportunity to characterise the texture at
the appropriate frequency channel. An adaptive best bases algorithm for optimal
bases selection for meningioma histopathological images is proposed, via
applying the fractal dimension (FD) as the bases selection criterion in a
tree-structured manner. Thereby, the most significant subband that better
identifies texture discontinuities will only be chosen for further
decomposition, and its fractal signature would represent the extracted feature
vector for classification. The best basis selection using the FD outperformed
the energy based selection approaches, achieving an overall classification
accuracy of 91.25% as compared to 83.44% and 73.75% for the co-occurrence
matrix and energy texture signatures; respectively.
