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Small angle x-ray scattering (SAXS) patterns of benign and malignant brain tumour tissue were examined. Independent component analysis was used to find a feature set representing the images collected. A set of coefficients was then used to describe each image, which allowed the use of the statistical technique of flexible discriminant analysis to discover a hidden order in the data set. The key difference was found to be in the intensity and spectral content of the second and fourth order myelin scattering peaks. This has clearly demonstrated that significant differences in the structure of myelin exist in the highly malignant glioblastoma multiforme as opposed to the benign: meningioma and schwannoma.

This paper reports on the application of wavelet decomposition to small-angle x-ray scattering (SAXS) patterns from human breast tissue produced by a synchrotron source. The pixel intensities of SAXS patterns of normal, benign and malignant tissue types were transformed into wavelet coefficients. Statistical analysis found significant differences between the wavelet coefficients describing the patterns produced by different tissue types. These differences were then correlated with position in the image and have been linked to the supra-molecular structural changes that occur in breast tissue in the presence of disease. Specifically, results indicate that there are significant differences between healthy and diseased tissues in the wavelet coefficients that describe the peaks produced by the axial d-spacing of collagen. These differences suggest that a useful classification tool could be based upon the spectral information within the axial peaks.