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

Authors:
Omar S. Al-Kadi
Affiliations:
Department of Informatics, University of Sussex, Brighton, UK
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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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 cooccurrence matrix and energy texture signatures; respectively.