基于计算机视觉的点刺标注与检测

目的：智能化地识别点刺在舌体不同区域的分布情况。方法：首先利用LoG算子对舌体图像进行卷积运算,对舌体上的斑点进行初步检测;随后利用人工交互的方式微调点刺标注,并训练卷积神经网络模型Fast-RCNN。结果：将同一舌象仪采集的240张图像作为训练集,60张图像作为测试集,达到了90.78%的召回率,优于已有的方法。结论：本文提出的数据预标注与人工微调方法将细粒度的点刺标注变为了可能。在精确到点刺个体的数据集基础之上,本文引入卷积神经网络进行亚像素级的点刺分布检测,其结果可为中医临床诊断提供客观化、定量化、自动化的参考依据。

Labeling and detection of tongue spots based on computer vision

Abstract: Objective To identify the distribution of spots in different areas of tongue intelligently. Methods After the initial detection of tongue spots by convolving the tongue image with LoG operator, the labeling of spots were fine-tuned by human interaction, and the convolutional neural network model (Fast-RCNN) was trained with the labeled dataset. Results With the 240 images collected by the instrument for tongue image as training set and 60 images as test set, a recall rate of 90.78% was obtained, indicating that the proposed method was superior to the existing methods. Conclusion The data pre-labeling and manual fine-tuning method proposed in the study makes it possible to label fine-grained spots. Based on the dataset accurate to a spot, convolutional neural network is introduced to detect the spot distribution at sub-pixel level, and the results can provide objective, quantitative and automatic reference for clinical diagnosis of traditional Chinese medicine.