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

Danni Sun; Qibing Zhu; Min Huang

doi:10.3788/LOP221515

Laser & Optoelectronics Progress, Volume. 60, Issue 12, 1215005(2023)

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

Danni Sun, Qibing Zhu^*, and Min Huang

Author Affiliations

Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, Jiangsu, China

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Figures & Tables(13)

Fig. 1. Defective tissue model

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Fig. 2. Schematic of line-scan virtual imaging system. (a) Line-scan imaging system; (b) line-scan schematic

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Fig. 3. Intersection of photon and voxel boundary

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Fig. 4. Comparison of differences between normal and damaged tissues by OD algorithm along same scan lines. (a) Normal tissue; (b) damage tissue

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Fig. 5. Simulation results of different incident angles of linear light sources

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Fig. 6. Relationship between diffuse reflectance and source-detector distance

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Fig. 7. Relationship between source-detector distance and detection depth and photon average path length

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Fig. 8. Optical density differential distribution images. (a) Small defect; (b) large defect

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Fig. 9. CNR change of three defects at different depths

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Fig. 10. Optical density difference curves of small defects with different depths

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Fig. 11. Two-dimensional optical density difference of small defects at a depth of 1 mm

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Table 1. Optical properties of defective tissue model

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Table 1. Optical properties of defective tissue model

Model	Type	Parameter	Value	Reference
Heterogeneous tissue		Refractive index $n_{1}$	1.35
	Fruit flesh	Absorption coefficient $μ_{a 1}$ /cm^-1	0.1523	［13-17］
		Scattering coefficient $μ_{s 1}$ /cm^-1	4.167
		Anisotropy factor $g_{1}$	0.66
		Refractive index $n_{2}$	1.365
	Internal defect	Absorption coefficient $μ_{a 2}$ /cm^-1	1.633	［13-17］
		Scattering coefficient $μ_{s 2}$ /cm^-1	4.564
		Anisotropy factor $g_{2}$	0.66

Table 2. Input parameters for IMC simulation of photon propagation

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Table 2. Input parameters for IMC simulation of photon propagation

Parameter	Value
Number of photons	3，000，000
Resolution of depth （ $d_{z}$ ）；Resolution along x-axis （ $d_{x}$ ）；Resolution along y-axis （ $d_{y}$ ）	0.1 mm；0.1 mm；0.1 mm
Number of grid elements $(z, x, y)$	400；400；400
Number of tissue types	3
Profile of the incident light beam	Linear light source；Gaussian；1-mm width
Total energy of the incident light beam	1 J
1/e² radius of the incident light beam	0.5 mm

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Danni Sun, Qibing Zhu, Min Huang. Evaluation of Line-Scan Imaging System's Ability to Detect Internal Defects in Tissue Using Improved Monte Carlo Simulation and Optical Density Algorithm[J]. Laser & Optoelectronics Progress, 2023, 60(12): 1215005

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Paper Information

Category: Machine Vision

Received: May. 5, 2022

Accepted: Jun. 16, 2022

Published Online: Jun. 1, 2023

The Author Email: Qibing Zhu (zhuqib@163.com)

DOI:10.3788/LOP221515

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology