Conclusions and future works

In this study, we aimed to detect oral cancer using ERL-MHIS. The ERL-MHIS includes: a microscope, relay lens, spectrometer and EMCCD. With the relay lens, we can scan the image of a sample without the relative movement between the biopsies and the scanning system. After saving the hyperspectral information, we used algorithms to diagnose the oral cancer. We observed the change of morphology and spectrum in the sample. The methods of morphology include the fractal dimension and the correct rate of classification by KNN.

Because cancer cells destroy the structure of epithelial tissues, method 4-1 of calculating fractal dimension shows high sensitivity in distinguishing between normal and cancerous tissues. The methods of spectrum include: comparing the intensity, the ratio, the wavelength of a specific peak, the area under spectral curve and the FWHM. The spectral data include halogen transmittance and fluorescent spectrum with 330~385nm and 470~490nm excitation. After removing the noise, we found the decrease of halogen transmittance at 470-490nm.

After combining the methods of the penetration in the wavelength range of 460~480nm, the ratio of the penetration in the range 460~480nm to the penetration in the range 700~710nm and fractal dimension, the sensitivity and specificity were both more than 95%. ERL-MHIS with diagnoses using morphology and spectrum successfully distinguished normal and cancerous tissues. However, because of the irregular shape of nuclei, it is hard to automatically choose nuclei as training data. All of the methods are needed to choose the training data by hand, and it significantly influences sensitivity. In the fractal dimension method, the value of the critical point in the threshold method is decided according to the training data. In classification by KNN, we also need training data. In the spectral method, the coordinates of analyzed data are all chosen by hand. In the future, we will try to design a portable and real-time instrument and diagnose oral cancer automatically without biopsies or training data chosen by hand.

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Publications

Publications

Conference Papers

1. Chih-Hsien Chen, Yao-Fang Hsieh, Ou-Yang Mang, Jeng-Ren Duann,Jin-Chern Chiou, Yung-Jiun Lin, Ming-Hsui Tsai, Da-Tian Bau, Chang-Fang Chiu, Guan-Chin Teseng, Nai-Wen Chang, Wen-Chung Kao, and Shun-De Wu, " The experiment of analyzing oral cancer’s spectral characteristics by using UV fluorescence excitation," International

Conference on Photonics, Tainan, Taiwan, Dec. 8-10, 2011.

2. Chih-Hsien Chen, Sing-Tsung Lee, Yao-Fang Hsieh, Mang Ou-Yang, Jeng-Ren Duann, Jin-Chern Chiou, Yung-Jiun Lin, Ming-Hsui Tsai, Chang-Fang Chiu, Guan-Chin Teseng, Nai-Wen Chang, Wen-Chung Kao, and Shun-De Wu," Analysis of Spectral Image for Evaluation of Oral Cancer," Conference of Symposium on Engineering Medicine and Biology

Applications, Taichung, Taiwan, Feb. 11-13 ,2012

Patents

1. 歐陽盟，謝耀方，陳誌賢，“一種繼光鏡掃瞄超光譜數位典藏裝置與 方法”，專利申請中，中華民國，民國99年。