频域纹理先验与特征增强的医学图像分割模型

doi:10.11896/jsjkx.241200125

摘要/Abstract

摘要： 提出的模型利用傅里叶变换提取的频域信息作为优化依据,增强网络在高相似性背景下伪装性病灶区域的识别能力。通过设计频域特征增强模块(Frequency Feature Enhancement Module,FFEM),网络能够显著增强病灶区域不同频率的特征信息,实现在复杂背景下更精准地捕捉伪装区域的细微特征。此外,创新性地将频域特征先验图加权融合到损失函数中,以在优化过程中引导网络关注病灶区域特征,提升网络在训练阶段的敏锐度和适应性。同时,设计了交叉注意力融合模块(Cross Attention Fusion Module,CAFM),针对不同频率特征进行差异化增强,进一步提升了网络对各频率特征的调节能力。提出的方法在多个医学影像数据集上(皮肤数据集:ISIC2016,ISIC2017,ISIC2018;结肠息肉数据集:CVC-Clinic,Kvasir,CVC-ColonDB,ETIS LaribPolyDB;乳腺数据集:BUSI)展现出卓越的分割性能;在定量指标,如Dice系数、交并比(IoU)和准确率(ACC)等指标方面,均优于现有模型,具有更好的准确性和鲁棒性。

关键词: 医学图像分割, 频域先验, 特征增强, 伪装区域分割, 注意力机制

Abstract: The proposed model leverages frequency-domain information extracted via Fourier transform as an optimization basis,enhancing the network’s ability to identify camouflaged lesion regions under highly similar backgrounds.By designing the Frequency Feature Enhancement Module (FFEM),the network can significantly strengthen lesion-related features across different frequencies,thereby enabling more precise capture of subtle camouflaged patterns in complex contexts.In addition,a novel strategy integrates frequency-domain prior maps into the loss function through weighted fusion,guiding the optimization process to focus on lesion features and improving the network’s sensitivity and adaptability during training.Furthermore,a Cross-Attention Fusion Module(CAFM) is designed to perform differentiated enhancement of multi-frequency features,further enhancing the network’s ability to regulate and balance frequency-specific information.The proposed method demonstrates outstanding segmentation performance across multiple medical imaging datasets(skin datasets ISIC 2016,ISIC 2017,ISIC 2018;colon polyp datasets CVC-Clinic,Kvasir,CVC-ColonDB,ETIS-LaribPolyDB;breast dataset BUSI).In quantitative evaluations,including Dice coefficient,Intersection over Union(IoU),and Accuracy(ACC),the method outperforms existing models,achieving superior accuracy and robustness.

Key words: Medical image segmentation, Frequency prior, Feature enhancement, Camouflaged lesion segmentation, Attention

中图分类号:

TP183

钟延杰, 蹇木伟, 张昊然, 凌钰坤. 频域纹理先验与特征增强的医学图像分割模型[J]. 计算机科学, 2025, 52(11A): 241200125-8. https://doi.org/10.11896/jsjkx.241200125

ZHONG Yanjie, JIAN Muwei, ZHANG Haoran, LING Yukun. Medical Image Segmentation Model Based on Frequency Texture Prior and Frequency Feature Enhancement Fusion[J]. Computer Science, 2025, 52(11A): 241200125-8. https://doi.org/10.11896/jsjkx.241200125

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