利用改进蒙特卡罗模拟和光密度算法评估线扫描成像系统对组织内部缺陷的检测能力

Evaluation of Line-Scan Imaging System’s Ability to Detect Internal Defects in Tissue Using Improved Monte Carlo Simulation and Optical Density Algorithm

利用蒙特卡罗模拟和光密度算法评估线扫描成像系统对被测样品内部缺陷的检测性能。首先,引入三维体素分割方法,实现对内部缺陷不规则组织边界的精细划分,以改善传统蒙特卡罗方法难以准确模拟复杂组织的光学传输问题;分析了仪器参数对光子在组织内部的穿透深度、探测器的探测深度和表面漫反射率的影响,确定了最佳的参数配置;最后,利用光密度算法评估了系统对不同大小和深度缺陷的检测性能。仿真结果表明,在光源入射角为15°、光源-探测器距离为1 mm的条件下,线扫描成像检测系统能够兼顾光子探测深度和表面反射率;对于大(a=2 mm,b=3 mm,c=1 mm)、中(a=2 mm,b=2 mm,c=1 mm)、小(a=2 mm,b=1.5 mm,c=1 mm)三种尺度的椭球体缺陷,系统的缺陷深度检测限分别为3.5 mm、3 mm、2.7 mm。本研究结果为面向水果等农产品内部缺陷检测的线扫描成像系统的参数优化和性能评估提供了理论依据。

We use Monte Carlo simulation and optical density algorithm to evaluate the detection performance of line-scan imaging system for internal defects of tested samples in this paper.First,a fine division for the irregular tissue boundary of the internal defects is achieved using a three-dimensional voxel segmentation method,as it is difficult to accurately simulate the optical transmission of complex tissues by the traditional Monte Carlo method.Then,the effects of the instrument parameters on the penetration depth of photons in the tissue,the detection depth of the detector,and the diffuse reflectance of surfaces are analyzed,and the optimal parameter configuration is determined.Finally,the optical density algorithm is used to evaluate the detection performance of the system for defects with different sizes and depths.The simulation results show that the line-scan imaging detection system can achieve a good balance between the photon detection depth and the surface reflectivity,under a light source with an incident angle of 15°and a distance of 1 mm between the light source and detector.For large(a=2 mm,b=3 mm,c=1 mm),medium(a=2 mm,b=2 mm,c=1 mm),and small(a=2 mm,b=1.5 mm,c=1 mm)ellipsoid defects,the defect depth detection limits of the system are 3.5 mm,3 mm,and 2.7 mm,respectively.Hence,the study provides a theoretical basis for parameter optimization and performance evaluation of the line-scan imaging system for detecting the internal defects in agricultural products such as fruits.