[1]罗思言,吴豆豆,刘茜玮,等.基于计算机视觉的点刺标注与检测[J].中国医学物理学杂志,2023,40(2):238-243.[doi:DOI:10.3969/j.issn.1005-202X.2023.02.019]
　LUO Siyan,WU Doudou,LIU Qianwei,et al.Labeling and detection of tongue spots based on computer vision[J].Chinese Journal of Medical Physics,2023,40(2):238-243.[doi:DOI:10.3969/j.issn.1005-202X.2023.02.019]
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基于计算机视觉的点刺标注与检测()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
40卷
期数:
2023年第2期
页码:
238-243
栏目:
医学人工智能
出版日期:
2023-03-03

文章信息/Info

Title:
Labeling and detection of tongue spots based on computer vision
文章编号:
1005-202X（2023）02-0238-06
作者:
罗思言1; 吴豆豆2; 刘茜玮2; 王心舟3; 饶向荣1
1.中国中医科学院广安门医院肾病科， 北京 100053； 2.北京中医药大学中日友好临床医学院皮肤科， 北京 100029； 3.同济大学电子与信息工程学院， 上海 201804
Author(s):
LUO Siyan1;  WU Doudou2;  LIU Qianwei2;  WANG Xinzhou3;  RAO Xiangrong1
1. Department of Nephrology, Guanganmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China 2. Department of Dermatology, China-Japan Friendship Hospital, Beijing University of Chinese Medicine, Beijing 100029, China 3. College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China
关键词:
机器视觉; 深度学习; 中医舌诊; 点刺检测
Keywords:
Keywords: machine vision deep learning tongue diagnosis spot detection
分类号:
R318；TP183
DOI:
DOI:10.3969/j.issn.1005-202X.2023.02.019
文献标志码:
A
摘要:
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。
Abstract:
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.

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备注/Memo

备注/Memo:
【收稿日期】2022-10-18 【基金项目】国家自然科学基金（81973683） 【作者简介】罗思言，硕士研究生，研究方向：中西医结合，E-mail: luosiyan8888@163.com 【通信作者】饶向荣，主任医师，教授，研究方向：中西医结合，E-mail: raoyisheng@163.com
更新日期/Last Update: 2023-03-03