Speckle Analyzer: open-source package in MATLAB for finding metrics of
physical quantities based on laser speckle pattern analyzing

Shokouh Abedkarimi; Saeed Ghavami Sabouri

doi:10.1364/AO.438122

Applied Optics
Vol. 60,
Issue 31,
pp. 9728-9735
(2021)
•https://doi.org/10.1364/AO.438122

Speckle Analyzer: open-source package in MATLAB for finding metrics of physical quantities based on laser speckle pattern analyzing

Shokouh Abedkarimi and Saeed Ghavami Sabouri

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Shokouh Abedkarimi and Saeed Ghavami Sabouri, "Speckle Analyzer: open-source package in MATLAB for finding metrics of physical quantities based on laser speckle pattern analyzing," Appl. Opt. 60, 9728-9735 (2021)

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- Instrumentation and Measurements
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About this Article
History
- Original Manuscript: July 20, 2021
- Revised Manuscript: September 29, 2021
- Manuscript Accepted: September 29, 2021
- Published: October 25, 2021

Abstract

We provide an open-source user-friendly graphical-user interface software in a MATLAB environment, named Speckle Analyzer, as a tool for calculating and analyzing statistical parameters of a laser speckle pattern to find metrics for an object’s physical quantity. The first- and second-order statistical functions containing gray-level co-occurrence and gray-level run-length matrices and speckle grains geometrical properties are included in Speckle Analyzer. To validate the software’s operation, statistical parameters of the laser speckle pattern, to find metrics for the size and concentration of particles suspended in liquid, are investigated.

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Supplementary Material (1)

Name	Description
Code 1	Speckle Analyzer, user manual and an example folder.

Data Availability

Data underlying the results presented in this paper are available in Code 1, Ref. [35].

35. S. Abedkarimi and S. Ghavami Sabouri, “Speckle Analyzer: open-source software for finding metrics of physical quantities based on laser speckle pattern analyzing,” figshare, 2021, https://doi.org/10.6084/m9.figshare.16764481.

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Feature	Description
Energy	$E N R = \sum_{i = 0}^{N} \sum_{j = 0}^{N} G L C M {(i, j)}^{2}$
Homogeneity	$H o m = \sum_{i = 0}^{N} \sum_{i = 0}^{N} \frac{G L C M (i, j)}{1 + \| i - j \|}$
Contrast	$C o n = \sum_{i = 0}^{N} \sum_{j = 0}^{N} G L C M (i, j) \| i - j \|^{2}$
Maximum probability	$max G L = max (G L C M (i, j))$
Difference entropy	$D E N T = - \sum_{i = 0}^{N} {G L C M}_{x - y} (i) \log ({G L C M}_{x - y} (i))$
	${G L C M}_{x - y} (k) = \sum_{i = 0}^{N} \sum_{j = 0}^{N} δ_{\| i - j \|, k} G L C M (i, j)$

Feature	Description	Feature	Description
Short-run emphasis	$S R E = \sum_{i = 1}^{Q} \sum_{j = 1}^{W} \frac{G L R L M (i, j) / h}{i^{2}}$	High-run gray-level emphasis	$H G R E = \sum_{i = 1}^{Q} \sum_{j = 1}^{W} i^{2} G L R L M (i, j)$
Long-run emphasis	$L R E = \sum_{i = 1}^{Q} \sum_{j = 1}^{W} j^{2} G L R L M (i, j)$	Short-run low-gray-level emphasis	$S R L G E = \sum_{i = 1}^{Q} \sum_{j = 1}^{W} \frac{G L R L M (i, j)}{i^{2} j^{2}}$
Gray-level nonuniformity	$G L N = \frac{1}{h} \sum_{i = 1}^{Q} (\sum_{j = 1}^{W} G L R L M (i, j))^{2}$	Long-run low-gray-level emphasis	$L R L G E = \sum_{i = 1}^{Q} \sum_{j = 1}^{W} \frac{G L R L M (i, j) j^{2}}{i^{2}}$
Low-pun gray-level emphasis	$L G R E = \sum_{i = 1}^{Q} \sum_{j = 1}^{W} \frac{G L R L M (i, j)}{i^{2}}$	Long-run high-gray-level emphasis	$L R H G E = \sum_{i = 1}^{Q} \sum_{j = 1}^{W} G L R L M (i, j) i^{2} j$

Feature	Description
Area	Number of the image pixels included by a grain
Eccentricity	Eccentricity of the smallest ellipse surrounding a grain
Equivalent diameter	Diameter of a circle with the equal area with a grain
Convex area	Converts the speckle pattern to a binary image and the gray level of pixels related to a grain is set to 1 then calculates the area of the binary grain
Extent	Ratio of the grain pixels to the pixels of the closest surrounding square
Solidity	Ratio of the grain area to the convex area

Particle Sizes ( $µ m$ )	Mean ( $µ m$ )
$25 < x \leq 32$	28.5
$32 < x \leq 36$	34
$36 < x \leq 40$	38
$40 < x \leq 45$	42.5
$45 < x \leq 63$	54
$63 < x \leq 90$	76.5
$90 < x \leq 125$	107.5
$125 < x \leq 180$	152.5

Feature	Description
Energy	$E N R = \sum_{i = 0}^{N} \sum_{j = 0}^{N} G L C M {(i, j)}^{2}$
Homogeneity	$H o m = \sum_{i = 0}^{N} \sum_{i = 0}^{N} \frac{G L C M (i, j)}{1 + \| i - j \|}$
Contrast	$C o n = \sum_{i = 0}^{N} \sum_{j = 0}^{N} G L C M (i, j) \| i - j \|^{2}$
Maximum probability	$max G L = max (G L C M (i, j))$
Difference entropy	$D E N T = - \sum_{i = 0}^{N} {G L C M}_{x - y} (i) \log ({G L C M}_{x - y} (i))$
	${G L C M}_{x - y} (k) = \sum_{i = 0}^{N} \sum_{j = 0}^{N} δ_{\| i - j \|, k} G L C M (i, j)$

Abstract

Supplementary Material (1)

Data Availability

Cited By

Figures (7)

Tables (4)

Equations (2)

Applied Optics