基于粒子群优化算法的生物组织固有荧光光谱复原方法

Intrinsic Tissue Fluorescence Spectrum Recovery Based on Particle Swarm Optimization Algorithm

为减少吸收和散射对生物组织荧光光谱的干扰,使用蒙特卡罗(MC)方法模拟不同光学参数下的生物组织荧光和漫反射光,提出基于组织漫反射光谱的荧光复原方法。将复原算法中的经验参数编码为解空间中的一个粒子,以组织荧光临床应用效果作为适应值,构建粒子群优化(PSO)算法实现经验参数优化。利用已建立的用于糖尿病无创筛查的组织光谱测量系统,收集327例受试者的皮肤组织荧光光谱和漫反射光谱,使用基于PSO的组织荧光复原算法进行光谱复原。以复原前、后的组织荧光光谱强度作为输入变量进行受试者工作特性(ROC)曲线分析,观察其在糖尿病筛查中的应用价值。结果显示,采用复原前的组织荧光光谱作为输入变量用于糖尿病筛查时,ROC曲线覆盖面积为0.54,最佳诊断点对应的敏感性为32%、特异性为76%;以复原后的组织荧光光谱作为输入变量时,ROC曲线覆盖面积为0.86,最佳诊断点对应的敏感性和特异性分别为72%、86%。上述结果表明,使用基于PSO的组织固有荧光光谱复原算法能有效提高组织荧光光谱的临床应用价值。

In order to reduce the influence of absorption and scattering on tissue fluorescence spectra,the tissue fluorescence and diffuse reflection are simulated under different optical parameters with the Monte Carlo（MC）method,and a fluorescence recovery algorithm based on the tissue diffuse reflection spectrum is proposed.The empirical parameters in the proposed algorithm are coded as a particle in the solution domain,the classification performance is defined as fitness,and then a particle swarm optimization（PSO）algorithm is established to optimize empirical parameters.Skin fluorescence and diffuse reflection spectra of 327 subjects are collected with a tissue detection system for noninvasive screening of diabetes.The fluorescence spectra are recovered by the empirical approach,and the fluorescence intensity before and after recovery is selected as the input variable for the receiver operating characteristic（ROC）curve analysis,which is applied to evaluating the classification performance in diabetes screening.The sensitivity and specificity are 32%and 76%respectively,and the area under the ROC curveis 0.54 when the spectra before recovery are used,while the sensitivity and specificity are 72% and 86%respectively,and the area under the ROC curve is 0.86 when the spectra after recovery are used.The results indicate that using the tissue fluorescence spectrum recovery algorithm based on PSO can improve the application of tissue fluorescence spectroscopy effectively.