title: Pattern recognition of multiple excitation autofluorescence spectra for colon tissue classification.
abstract: OBJECTIVE
  The aim of this study was to explore the usefulness of multiple excitation autofluorescence (AF) and a spectral feature-based pattern recognition in classification of colon tissues.
  
  
  METHODS
  Under four different excitation wavelengths (337, 375, 405 and 460 nm), AF spectra of freshly excised normal and adenocarcinoma colon tissues were measured. Pattern recognition method including features extraction, data reduction using principal component analysis (PCA) and Fisher's discriminant analysis (FDA) were performed for classification.
  
  
  RESULTS
  There was a significantly difference between spectral patterns of normal and adenocarcinoma tissues. Compared with the other three excitation wavelengths, the AF spectra obtained under 337 nm excitation provided more diagnostic information, but also more sensitive to the trivial change resulted from neoplastic transformation. For discriminating normal from adenocarcinoma tissues, the sensitivity, specificity and accuracy using 337 nm excitation in the present study were 88.9%, 80.0% and 83.9%, respectively. Compared these values with those determined from multispectral data analysis, our findings indicate that the latter has higher specificity while maintaining the same sensitivity (sensitivity 88.9% vs. 88.9%, specificity 91.4% vs. 80.0%, and accuracy 90.3% vs. 83.9%).
  
  
  CONCLUSIONS
  This study suggests that the pattern recognition of the multiple excitation AF spectra is an effective algorithm for improving the diagnostic accuracy of adenocarcinoma.
authors.affiliation: Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou 350007, China.
authors.forename: Lina
authors.initials: L
authors.lastname: Liu
authors.forename: Yingbin
authors.initials: Y
authors.lastname: Nie
authors.forename: Lisheng
authors.initials: L
authors.lastname: Lin
authors.forename: Weihua
authors.initials: W
authors.lastname: Li
authors.forename: Zheng
authors.initials: Z
authors.lastname: Huang
authors.forename: Shusen
authors.initials: S
authors.lastname: Xie
authors.forename: Buhong
authors.initials: B
authors.lastname: Li
mesh_terms: D000230:Adenocarcinoma / Q000175:diagnosis*; D000368:Aged; D000465:Algorithms; D003110:Colonic Neoplasms / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D066298:In Vitro Techniques; D008297:Male; D036641:Microscopy, Fluorescence, Multiphoton / Q000379:methods*; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013050:Spectrometry, Fluorescence / Q000379:methods*