Distinguishing autofluorescence of normal, benign, and cancerous breast tissues through wavelet domain correlation studies

Abstract

Using the multiresolution ability of wavelets and effectiveness of singular value decomposition (SVD) to identify statistically robust parameters, we find a number of local and global features, capturing spectral correlations in the co- and cross-polarized channels, at different scales (of human breast tissues). The copolarized component, being sensitive to intrinsic fluorescence, shows different behavior for normal, benign, and cancerous tissues, in the emission domain of known fluorophores, whereas the perpendicular component, being more prone to the diffusive effect of scattering, points out differences in the Kernel-Smoother density estimate employed to the principal components, between malignant, normal, and benign tissues. The eigenvectors, corresponding to the dominant eigenvalues of the correlation matrix in SVD, also exhibit significant differences between the three tissue types, which clearly reflects the differences in the spectral correlation behavior. Interestingly, the most significant distinguishing feature manifests in the perpendicular component, corresponding to porphyrin emission range in the cancerous tissue. The fact that perpendicular component is strongly influenced by depolarization, and porphyrin emissions in cancerous tissue has been found to be strongly depolarized, may be the possible cause of the above observation.

Monitoring breast tumor progression by photoacoustic measurements: a xenograft mice model study

“…In the case of D15 versus D20, the correct prediction accuracy as well as the sample membership association to a particular group was 80.77 and 94.23%, respectively, along with overall accuracy of 87.5% for the analysis as shown in Fig. 47,48,65 In order to test the reproducibility of the discrimination models for D10 versus D15, D15 versus D20, and D10 versus D20 analysis, the receiver operator characteristics (ROC) curves for the same were also plotted as shown in Fig. The prediction accuracy and the membership association for D10 versus D20 analysis was found to be 92.31 and 98.08%, respectively, along with the overall accuracy of 95.2% as shown in Fig.…”

“…In the present study, we have used Haar and Morlet wavelet methods [46][47][48][49] to analyze the PA spectra and attempted to evaluate the subtle differences between them. The scaling parameter is used to convert the mother wave (obtained when a ¼ 1 and b ¼ 0) into a more compressed or expanded form that would better suit the type of spectra being analyzed, and the translational parameter is used to translate the wavelet obtained by scaling along the length of the signal.…”

Detecting cervical cancer progression through extracted intrinsic fluorescence and principal component analysis

“…42,57 To identify the principal components (eigenvectors) of the spectra containing maximum variance of the complete data, an empirical correlation matrix C is constructed. [39][40][41][42]…”

“…The use of PCA with suitable classification functions has been found useful in differentiating various classes of human tissues. [30][31][32][33][34][35][36][37][38][39][40][41][42] It is worth pointing out that Ramanujam et al have developed a multivariate statistical algorithm for the screening and diagnosis of cervical precancers. 43,44 To illustrate the general applicability of our approach, we focus here on cervical precancers.…”

Differing self-similarity in light scattering spectra: A potential tool for pre-cancer detection

“…Wavelet transform due to it's multi-resolution analysis capability using the Daubechies' basis which extract the polynomial trends (for example, Db-4 and Db-6 extract the linear and quadratic trends respectively) has been shown to characterize the scaling behavior and selfsimilarity of empirical data sets quite faithfully [38,39]. Indeed, it has been initially explored to analyze tissue fluorescence spectra in an attempt to distinguish between normal and dysplastic tissue [40,41,42,43,44]. In this work, we employ this multi-resolution property of wavelets to ascertain the changes in the self-similarity of dysplastic human cervical tissues as opposed to healthy human cervical tissues by analyzing the esoteric nature of the fluctuations in tissue light scattering spectra.…”