计算机与现代化Issue(10)：14-19,6.DOI:10.3969/j.issn.1006-2475.2025.10.003
基于变核卷积的HRNetV2模型对舌下络脉图像分割算法
Sublingual Vein Image Segmentation Based on HRNetV2 Model with Variable Kernel Convolution
摘要
Abstract
The existing analysis of sublingual vein often uses convolutional neural network(CNN)image classification methods or image segmentation methods to extract.But there is a problem of low accuracy in extracting details of meridians.Therefore,an improved HRNetV2 high-resolution semantic segmentation network algorithm is proposed to extract sublingual meridians.Adopt-ing a high-resolution HRNetV2 network structure,the outputs of sub network structures from high to low resolution are con-nected in parallel to form multi-scale fused feature maps with higher spatial accuracy,improving the problem of loss of detailed information in sublingual veins.In addition,AKConv,convolutional kernel with arbitrary sampled shapes and arbitrary number of parameters instead of ordinary convolution can improve the convolution's adaptability to change pulse structure and reduce the problem of under segmentation.The algorithm is validated through data extraction on the tongue image open platform of Anhui university of Traditional Chinese Medicine(TCM)cloud diagnosis technology,with pixel accuracy(PA),mean pixel accuracy(mPA),and mean intersection over union(mIoU)of 95.28%,92.33%,and 93.42%,respectively,which is superior to the Mask-RCNN model,U-Net models,and HRNetV2 model.The improved HRNetV2 method has high accuracy in segmenting sublingual vein images,providing a new method for further quantitative research on pulse color and shape features.关键词
舌下络脉分割/高分辨率分割/变核卷积/不规则卷积/多尺度特征融合Key words
sublingual vein segmentation/high-resolution segmentation/variable kernel convolution/irregular convolution/multi-scale feature fusion分类
计算机与自动化引用本文复制引用
蒋冬梅,杨诺,陈仁明,董昌武,彭成东..基于变核卷积的HRNetV2模型对舌下络脉图像分割算法[J].计算机与现代化,2025,(10):14-19,6.基金项目
安徽省重点研究与开发计划项目(2022h11020018) （2022h11020018）
安徽省科技重大专项计划(202303a07020008) （202303a07020008）
新安医学与中医药现代化研究所揭榜挂帅项目(2024CXMMTCM002) （2024CXMMTCM002）
安徽省高校自然科学研究项目(2022AH052293) （2022AH052293）
