摘要
分布式多位置散射光谱法从光子在组织中传播的"香蕉"路径这一理论出发,对测量组织的内部信息进行分析,有效消除了浅表组织对内部信息测量的干扰。设计了一种半透膜——牛奶双层结构仿体实验,以40组不同浓度的牛奶溶液代表需要被测的组织内部成分,牛奶上方盖以1至5层不同厚度的半透膜代表略有差异的浅表组织厚度干扰信息。通过贴合半透膜对该仿体采集分布式两点位置散射光谱数据样本200例,并以其中一单点位置的光谱数据为对照,均按3:1的比例用BP神经网络的方法分别对下层牛奶溶液的浓度进行建模和预测。实验结果显示单一位置散射光谱和两点位置散射光谱均达到90%以上的训练拟合率和预测精度,且两点位置散射光谱的预测精度更高,达到98.41%。证明了运用光子传播路径的散射光谱法可实现对底层牛奶溶液浓度的有效预测,消除浅表组织对于测量内部组织成分的影响,且将多点位置考虑到建模过程中可进一步提高预测精度。从而验证了该方法对于在不破坏组织完整性的前提下对组织表层以下内部某种成分或物质浓度进行分析的可行性。
The present paper describes the design of pellicle-milk double-layer phantom experiment .Milk solution of 40 different concentrations represents internal information of tissue ,1 to 5 pellicle which covers above the milk solution represents interfer-ence information of superficial tissue .The experiment collected 200 scattering spectral data of two positions and took the one sin-gle position spectral group as control ,and then respectively predicted the milk solution concentration on bottom layer with the ratio of 3：1 through the BP neural network method .The experimental results show that single position scattering spectrum and two-position scattering spectrum both reached more than 90% training fitting rates and prediction accuracy ,and the prediction accuracy of two-position scattering spectra is higher ,reaching 98.41% .It was verified by the experimental results that scatter-ing spectrum based on photon dissemination path can efficiently predict the milk solution concentration and eliminate the influence of superficial tissue for measurement of internal organization ,and considering multi-position in modeling process can improve the accuracy of the prediction .This study validates the feasibility of the method for exploring internal information of tissue without damaging tissue integrity .
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2014年第4期1026-1030,共5页
Spectroscopy and Spectral Analysis
基金
国家自然科学基金项目(30973964)
天津市应用基础及前沿技术研究计划(11JCZDJC17100)资助
关键词
散射光谱
光子传播路径
组织内部信息
表层干扰
BP神经网络
Scattering spectrum
Photon disseminated path
Internal information of tissue
Superficial interference
BP neural network
