A fractal dimension based optimal wavelet packet analysis technique for classification of meningioma brain tumours

TL;DR

This paper introduces an adaptive wavelet packet analysis method using fractal dimension for classifying meningioma brain tumors, significantly improving accuracy over traditional texture analysis techniques.

Contribution

It proposes a novel fractal dimension-based basis selection algorithm for wavelet packet analysis, enhancing texture classification in histopathological images.

Findings

Achieved 91.25% classification accuracy.

Outperformed energy-based and co-occurrence matrix methods.

Demonstrated effectiveness of fractal dimension in texture analysis.

Abstract

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.