An algorithm based on support vector machines (SVM), the most recent advance in pattern recognition, is presented for use in classifying light-induced autofluorescence collected from cancerous and normal tissues. The in vivo autofluorescence spectra used for development and evaluation of SVM diagnostic algorithms were measured from 85 nasopharyngeal carcinoma (NPC) lesions and 131 normal tissue sites from 59 subjects during routine nasal endoscopy. Leave-one-out cross-validation was used to evaluate the performance of the algorithms. An overall diagnostic accuracy of 96%, a sensitivity of 94%, and a specificity of 97% for discriminating nasopharyngeal carcinomas from normal tissues were achieved using a linear SVM algorithm. A diagnostic accuracy of 98%, a sensitivity of 95%, and a specificity of 99% for detecting NPC were achieved with a nonlinear SVM algorithm. In a comparison with previously developed algorithms using the same dataset and the principal component analysis (PCA) technique, the SVM algorithms produced better diagnostic accuracy in all instances. In addition, we investigated a method combining PCA and SVM techniques for reducing the complexity of the SVM algorithms.

A miniaturized three-dimensional endoscopic imaging system is presented. The system consists of two imaging in channels that can be used to obtain an image from an object of interest and to project as tructured light onto the imaged object to measure the surface topology. The structured light was generated with a collimated monochromatic light source and a holographic binary phase grating. The imaging and projection channels were calibrated by use of a modified pinhole camera. The surface profile was extracted by use of triangulation between the projected feature points and the two channel ofthe endoscope. The imaging system was evaluated in three-dimensional measurements of several objects with known geometries. The results show that surface profiles of the objects with different surfaces and dimensions can be obtained at high accuracy. The in vivo measurements at tissue sites of human skin and an oral cavity demonstrated the potential of the technique for clinical applications.

We investigated a novel method combining principal component analysis (PCA) and supervised learning technique, support vector machine (SVM), for classifying carcinoma lesion from normal tissue with light-induced autofluorescence. The autofluorescence spectral signals were collected in vivo from 85 nasopharyngeal carcinoma lesions and 131 normal tissue sites from 59 subjects during routine nasal endoscopy. With the combined PCA and SVM classifying algorithm, the achieved overall accuracy is over 97%, companied with 95% sensitivity and 99% specificity for discriminating carcinoma from normal tissue. In comparison with the previously developed algorithms based on PCA method, this new method outperforms threshold-and probability-based PCA algorithms in all instances. The experimental results indicate great promise for autofluorescence spectroscopy based detection of small carcinoma lesion in the nasopharynx and other tissues.

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