Falzon G, Pearson S, Murison R, Hall C, Siu K, Evans A, Rogers K, Lewis R
Department of Physics and Electronics, School of Biological, Biomedical and Molecular Sciences, University of New England, Armidale, NSW 2351, Australia.
Phys Med Biol. 2006 May 21;51(10):2465-77. doi: 10.1088/0031-9155/51/10/007. Epub 2006 Apr 26.
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.
本文报道了小波分解在同步辐射源产生的人体乳腺组织小角X射线散射（SAXS）图案中的应用。正常、良性和恶性组织类型的SAXS图案的像素强度被转换为小波系数。统计分析发现，描述不同组织类型产生的图案的小波系数之间存在显著差异。这些差异随后与图像中的位置相关联，并与疾病存在时乳腺组织中发生的超分子结构变化有关。具体而言，结果表明，在描述由胶原蛋白轴向d间距产生的峰的小波系数中，健康组织和患病组织之间存在显著差异。这些差异表明，可以基于轴向峰内的光谱信息开发一种有用的分类工具。
