Polarization Multi-parameter Recognition and Texture Feature Analysis of Cancerous Tissue

Lili ZHANG; Danfei HUANG; Junzhao GAO; Dong SONG; Jinghui HONG; Yong ZHANG; Hongyu TANG; Lechao ZHANG

doi:10.3788/gzxb20245306.0610002

Acta Photonica Sinica, Volume. 53, Issue 6, 0610002(2024)

Polarization Multi-parameter Recognition and Texture Feature Analysis of Cancerous Tissue

Lili ZHANG^1,2, Danfei HUANG^1,2、*, Junzhao GAO^1,2, Dong SONG³, Jinghui HONG³, Yong ZHANG^1,2, Hongyu TANG^1,2, and Lechao ZHANG^1,2

Author Affiliations

¹College of Optoelectronic Engineering， Changchun University of Science and Technology， Changchun 130022， China

²Zhongshan Research Institute of Changchun University of Science and Technology， Zhongshan 528437， China

³The First Hospital of Jilin University， Changchun 130021， China

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Figures & Tables(14)

Fig. 1. Backscatter polarization imaging system and optical path diagram

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Fig. 2. Physical meaning distribution of Muller matrix

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Fig. 3. Unstained pathological section sample

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Fig. 4. Normalized backward Muller matrix for lung cancer tissue

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Fig. 5. Mueller matrix decomposition parameter results for unstained lung cancer tissue

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Fig. 6. Mueller matrix transformation parameter results for unstained lung cancer tissue

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Fig. 7. Rotational invariance parameter results for unstained lung cancer tissue

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Fig. 8. Schematic diagram of high-dimensional polarization parameter set

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Fig. 9. Polarization multi-parameter feature curve of lung cancer tissue

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Fig. 10. Polarization multi-parameter feature curve of basal cell carcinoma tissue

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Fig. 11. Texture features of GLCM for polarization parameter set of lung cancer tissue

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Fig. 12. Texture features of Tamura for polarization parameter set of lung cancer tissue

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Table 1. Polarization parameter set and physical significance

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Table 1. Polarization parameter set and physical significance

Parameter expressions	Physical meanings
$D = m_{11}^{- 1} \sqrt[]{m_{12}^{2} + m_{13}^{2} + m_{14}^{2}}$	Dichroism
$Δ = 1 - \frac{\| t r (M_{Δ} - 1) \|}{3}$	Scattering depolarization
$R = a r c c o s [\frac{t r (M_{R})}{2} - 1]$	Phase delay
$δ = a r c c o s {[(M_{R} (2,2) + M_{R} {(3,3))}^{2} + (M_{R} (3,2) - M_{R} {(2,3))}^{2}]^{\frac{1}{2}} - 1}$	Linear phase delay
$θ = \frac{1}{2} a r c t a n [\frac{r_{2}}{r_{1}}]$	Orientation of fast axis
$ϕ = \frac{1}{2} a r c t a n [\frac{M_{R} (3,2) - M_{R} (2,3)}{M_{R} (2,2) - M_{R} (3,3)}]$	Optical rotation
$A = \frac{2 b \cdot t_{1}}{b^{2} + t_{1}^{2}} \in [0,1]$	Normalized anisotropy
$b = \frac{m_{22} + m_{33}}{2}$	Sensitive to scatterer size
$x = \frac{1}{2} a r c t a n (m_{31} / m_{21})$	Anisotropic angular orientation
$x_{3} = \frac{1}{2} a r c t a n (m_{42} / m_{34})$	Anisotropic angular orientation
$t_{1} = \frac{\sqrt[]{{(m_{22} - m_{33})}^{2} + {(m_{23} + m_{32})}^{2}}}{2}$	Amplitude parameters related to the degree of anisotropy，among them， t₁ and t₂ are not normalized， the main role of t₃ in thick and thin tissues is different
$t_{2} = \frac{\sqrt[]{{(m_{21})}^{2} + {(m_{31})}^{2}}}{2}$
$t_{3} = \frac{\sqrt[]{{(m_{42})}^{2} + {(m_{34})}^{2}}}{2}$
$P_{L} = \sqrt[]{(m_{21})^{2} + (m_{31})^{2}} \in [0,1]$	Linear and circular polarizance
$P_{C} = m_{41} \in [- 1,1]$	Linear and circular polarizance
$D_{L} = \sqrt[]{(m_{12})^{2} + (m_{13})^{2}}$	Linear and circular diattenuation
$D_{C} = m_{14} \in [- 1,1]$	Linear and circular diattenuation
$q_{L} = \sqrt[]{(m_{42})^{2} + (m_{43})^{2}} \in [0,1]$	Conversion ability between linear polarization and circular polarization
$r_{L} = \sqrt[]{(m_{24})^{2} + (m_{34})^{2}} \in [0,1]$
$α_{P} = \frac{1}{2} a r c t a n (\frac{m_{31}}{m_{21}})$	Angular orientation of linear polarization and diattenuation
$α_{D} = \frac{1}{2} a r c t a n (\frac{m_{13}}{m_{12}})$
$α_{q} = \frac{1}{2} a r c t a n (\frac{m_{42}}{- m_{43}})$	Angular orientation of linear delay
$α_{r} = \frac{1}{2} a r c t a n (\frac{- m_{24}}{m_{34}})$	Angular orientation of linear delay

Table 2. Texture attributes and meanings

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Table 2. Texture attributes and meanings

Calculating expressions	Meanings of attributes
$C o n t r a s t = \sum_{n = 0}^{N g - 1} n^{2} \{\sum_{i = 1}^{N g} \sum_{\begin{array}{l} j = 1 \\ \| i - j \| = n \end{array}}^{N g} p (i, j)\}$	Reflect the differences in image grayscale values
$C o r r e l a t i o n = \frac{\sum_{i} \sum_{j} (i j) p (i, j) - u_{x} u_{y}}{σ_{x} σ_{y}}$	Reflect the grayscale correlation of local regions in the image
$E n e r g y = \sum_{i} \sum_{j} p {(i, j)}^{2}$	Reflect the uniformity of image texture
$H o m o g e n e i t y = \sum_{i} \sum_{j} \frac{1}{1 + {(i - j)}^{2}} p (i, j)$	Reflect the frequency of changes in local image texture features
$F_{c r s} = \frac{1}{m \times n} \sum_{i = 1}^{m} \sum_{j = 1}^{n} S_{b e s t} (i, j)$	Changing image quality by stretching or shrinking grayscale
$F_{c o n} = \frac{σ}{(α_{4})^{2}}$	Reflect the most basic structural features of an object
$F_{d i r} = 1 - r \cdot n_{p} \sum_{p}^{n_{p}} \sum_{ϕ \in ω_{p}} (ϕ - ϕ_{p})^{2} \cdot H_{D} (ϕ)$	Reflect directional or non-directional features of images
$F_{l i n} = \frac{\sum_{i = 1}^{m} \sum_{j = 1}^{n} P_{D, d} (i, j) c o s [(i - j) (2 π / n)]}{\sum i}$	Reflect the texture structure of lines and spots in the image
$F_{r e g} = 1 - r (σ_{c r s} + σ_{c o n} + σ_{d i r} + σ_{l i n})$	Reflect the regularity of image texture features
$F_{r g h} = F_{c r s} + F_{c o n}$	Reflect the roughness or smoothness of the image

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Lili ZHANG, Danfei HUANG, Junzhao GAO, Dong SONG, Jinghui HONG, Yong ZHANG, Hongyu TANG, Lechao ZHANG. Polarization Multi-parameter Recognition and Texture Feature Analysis of Cancerous Tissue[J]. Acta Photonica Sinica, 2024, 53(6): 0610002

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Paper Information

Category:

Received: Nov. 7, 2023

Accepted: Jan. 15, 2024

Published Online: Jul. 16, 2024

The Author Email: Danfei HUANG (huangd_f@163.com)

DOI:10.3788/gzxb20245306.0610002

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology