1 November 2010 Evaluation of linear discriminant analysis for automated Raman histological mapping of esophageal high-grade dysplasia
Author Affiliations +
Rapid Raman mapping has the potential to be used for automated histopathology diagnosis, providing an adjunct technique to histology diagnosis. The aim of this work is to evaluate the feasibility of automated and objective pathology classification of Raman maps using linear discriminant analysis. Raman maps of esophageal tissue sections are acquired. Principal component (PC)-fed linear discriminant analysis (LDA) is carried out using subsets of the Raman map data (6483 spectra). An overall (validated) training classification model performance of 97.7% (sensitivity 95.0 to 100% and specificity 98.6 to 100%) is obtained. The remainder of the map spectra (131,672 spectra) are projected onto the classification model resulting in Raman images, demonstrating good correlation with contiguous hematoxylin and eosin (HE) sections. Initial results suggest that LDA has the potential to automate pathology diagnosis of esophageal Raman images, but since the classification of test spectra is forced into existing training groups, further work is required to optimize the training model. A small pixel size is advantageous for developing the training datasets using mapping data, despite lengthy mapping times, due to additional morphological information gained, and could facilitate differentiation of further tissue groups, such as the basal cells/lamina propria, in the future, but larger pixels sizes (and faster mapping) may be more feasible for clinical application.
© (2010) Society of Photo-Optical Instrumentation Engineers (SPIE)
Joanne C. Hutchings, Joanne C. Hutchings, Catherine A. Kendall, Catherine A. Kendall, Neil Shepherd, Neil Shepherd, Hugh Barr, Hugh Barr, Nicholas Stone, Nicholas Stone, } "Evaluation of linear discriminant analysis for automated Raman histological mapping of esophageal high-grade dysplasia," Journal of Biomedical Optics 15(6), 066015 (1 November 2010). https://doi.org/10.1117/1.3512244 . Submission:

Back to Top