Identification of Panax notoginseng origin using terahertz precision spectroscopy and neural network algorithm.

Panax notoginseng (P. notoginseng), a Chinese herb containing various saponins, benefits immune system in medicines development, which from Wenshan (authentic cultivation) is often counterfeited by others for large demand and limited supply. Here, we proposed a method for identifying P. notoginseng origin combining terahertz (THz) precision spectroscopy and neural network. Based on the comparative analysis of four qualitative identification methods, we chose high-performance liquid chromatography (HPLC) and THz spectroscopy to detect 252 samples from five origins. After classifications using Convolutional Neural Networks (CNNs) model, we found that the performance of THz spectra was superior to that of HPLC. The underlying mechanism is that there are clear nonlinear relations among the THz spectra and the origins due to the wide spectra and multi-parameter characteristics, which makes the accuracy of five-classification origin identification up to 97.62%. This study realizes the rapid, non-destructive and accurate identification of P. notoginseng origin, providing a practical reference for herbal medicine.

An Automatic Method for Sublingual Image Segmentation and Color Analysis.

Automatic segmentation of sublingual images and color quantification of sublingual vein are of great significance to disease diagnosis in traditional Chinese medicine. With the development of computer vision, automatic sublingual image processing provides a noninvasive way to observe patients' tongue and is convenient for both doctors and patients. However, current sublingual image segmentation methods are not accurate enough. Besides, the differences in subjective judgments by different doctors bring more difficulties in color analysis of sublingual veins. In this paper, we propose a method of sublingual image segmentation based on a modified UNet++ network to improve the segmentation accuracy, a color classification approach based on triplet network, and a color quantization method of sublingual vein based on linear discriminant analysis to provide intuitive one-dimensional results. Our methods achieve 88.2% mean intersection over union (mIoU) and 94.1% pixel accuracy on tongue dorsum segmentation, and achieves 69.8% mIoU and 82.7% pixel accuracy on sublingual vein segmentation. Compared with the state-of-art methods, the segmentation mIoUs are improved by 5.8% and 5.3% respectively. Our sublingual vein color classification method has the highest overall accuracy of 81.2% and the highest recall for the minority class of 77.5%, and the accuracy of color quantization is 90.5%.Clinical Relevance- The methods provide accurate and quantified information of the sublingual image, which can assist doctors in diagnosis.

An Automatic Petechia Dots Detection Method on Tongue.

Tongue diagnosis with features like tongue coating, petechia, color, size and so on is of great effectiveness and convenience in traditional Chinese medicine. With the development of image processing techniques, automatic image processing can reduce hospital inspection for patients. However, there are ubiquitous problems of inadequate accuracy in petechia dots detection with previous methods. In this paper, we propose a method of petechia dots detection on tongue based on SimpleBlobDetector function in OpenCV library and support vector machines model, which improves the detective accuracy. We test 128 clinic tongue images and select 9 of the images with plentiful petechia dots for further experiments. Our method achieves mean value of false alarm rate 4.6% and missing alarm rate 11.8%, which have 19.4% and 8.2% reduction respectively compared to previous work.Clinical Relevance-The method can provide detailed information of tongue, which assists doctors to investigate curative effect.