PCA based polarized fluorescence study for detecting human cervical dysplasia

Gharekhan, Anita H.; Devi, Sobha Lalitha; Jagtap, Jaidip; Panigrahi, Prasanta K.; Pradhan, Asima

doi:10.1117/12.2004597

Cited by 1 publication

(2 citation statements)

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“…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]…”

Section: Discussionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

2014

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Intrinsic fluorescence spectra of the human normal, cervical intraepithelial neoplasia 1 (CIN1), CIN2, and cervical cancer tissue have been extracted by effectively combining the measured polarized fluorescence and polarized elastic scattering spectra. The efficacy of principal component analysis (PCA) to disentangle the collective behavior from smaller correlated clusters in a dimensionally reduced space in conjunction with the intrinsic fluorescence is examined. This combination unambiguously reveals the biochemical changes occurring with the progression of the disease. The differing activities of the dominant fluorophores, collagen, nicotinamide adenine dinucleotide, flavins, and porphyrin of different grades of precancers are clearly identified through a careful examination of the sectorial behavior of the dominant eigenvectors of PCA. To further classify the different grades, the Mahalanobis distance has been calculated using the scores of selected principal components.

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