Method for measuring and calculating fiber brooming rate

Abstract

The invention provides a fiber brooming rate measurement and calculation method, which adopts an optical digital microscope to shoot fibers, and then calculates the fiber brooming rate through imaging processing. According to the imaging processing method, the brooming rate of the fibers is calculated through operations such as binary color inversion, morphological operation and fiber main body identification and then through the ratio of the fibril area of the fibers to the effective fiber object area. According to the method, on the basis of image shooting and image processing based on morphological operation, rapid identification of fiber trunks and fibrils and quantitative statistical calculation of the fiber brooming rate are achieved, and the problems that manual processing, subjective identification and quantification are difficult are solved.

Description

Technical Field

[0001] The invention relates to a method for measuring and calculating a fiber brooming rate, and in particular to a method for measuring and calculating a fiber brooming rate based on image analysis. Background Art

[0002] Cellulose fiber is a natural or artificial fiber, widely used in papermaking, textile, medicine and other fields. In the industrial production process, the outer surface of cellulose fiber will produce different degrees of "fluffing" after being subjected to force. Especially for papermaking, during the pulping process, the fiber is subjected to mechanical force, the cell wall will be deformed and broken, so that fine fiber filaments and fluff will be produced on the fiber surface. These fiber filaments and fluff increase the specific surface area of ​​the fiber, improve the bonding force between the fibers, and help improve the strength and performance of the paper. This kind of fiber cell wall produces fluffing, tearing, fibrillation, brooming and other phenomena, which is usually called fibrillation and brooming. By controlling the process parameters of pulping, such as pulping time, pulping concentration, pulping pressure, etc., the degree of fiber brooming can be controlled, so as to produce paper products that meet different requirements.

[0003] However, for cellulose fibers, characterization of the degree of brooming is extremely challenging. GB\T 22836-2008 is a semi-quantitative method that qualitatively classifies fibers with brooming and then performs statistical calculations. This method cannot accurately define the fiber brooming rate and is affected by human qualitative classification. How to quantitatively analyze the degree of brooming based on microscope images and achieve accurate quantification of the brooming rate has become a key issue in product refinement control. Summary of the invention

[0004] The purpose of the present invention is to address the deficiencies of the above-mentioned prior art and to propose a method for measuring and calculating the fiber brooming rate based on image processing. The method calculates the fiber brooming rate based on area through operations such as binarization, morphological calculation, fiber body / filament identification, etc. The measurement method uses traditional optical digital microscopes and computer image processing calculations to solve the problems of manual processing, subjective identification and quantification difficulties.

[0005] The present invention is achieved by at least one of the following technical solutions:

[0006] A method for measuring and calculating fiber brooming rate comprises the following steps:

[0007] (1) The fibers are dyed and photographed using an optical digital microscope to obtain a fiber image, and it is ensured that the fibers in the image are not intertwined.

[0008] (2) The fiber image is converted into a grayscale image, and then binarized and inverted in sequence to obtain a binary image 1, in which the fiber is white and the background is black.

[0009] (3) A circular morphological structure element Se_disk with a radius of 2-4 pixels is established. The circular morphological structure element Se_disk is used to perform a closing operation on the binary image 1. Then, the area of ​​the fiber objects in the binary image 1 is counted. Only the fiber object with the largest area in the binary image 1 is retained as the binary image 2 for analysis.

[0010] (4) Segment the fiber object in the binary image 2 and determine the angle between each fiber segment and the horizontal direction in the binary image 2; if the maximum difference between the angles of each fiber segment is less than 40 degrees, the fiber object is regarded as a valid fiber object, and the average value of the angles of each fiber segment is calculated, recorded as deg_f, and the area of ​​the valid fiber object is calculated, recorded as Sf0; otherwise, reselect a new fiber image and perform steps (2)-(4).

[0011] (5) A rectangular morphological structure element Se_trect with a length of m pixels, a width of n pixels and an inclination angle (the angle with the horizontal direction) is established. The rectangular morphological structure element Se_trect is used to perform an opening operation on the binary image 2. The fiber objects with an area greater than 0.1 times Sf0 are obtained as effective fiber trunk objects. The area of ​​the effective fiber trunk objects is calculated and recorded as Sfi.

[0012] (6) When the set conditions are met, a new rectangular morphological structure element Se_trect_i is established, so that the length m and the width n are both increased, and the inclination angle is the same as Se_trect; the new rectangular morphological structure element Se_trect_i is used to perform an opening operation on the binary image 2 to obtain the effective fiber trunk object, and the area Sfi of the effective fiber trunk object is calculated again until the set conditions are not met. The calculation is stopped, and the last new rectangular morphological structure element that meets the set conditions is recorded, which is recorded as Se_trect_n;

[0013] (7) Use the final new rectangular morphological structure element Se_trect_n to perform an opening operation on the binary image 2, and obtain the effective fiber trunk object as the effective fiber trunk, and calculate the area of ​​the effective fiber trunk, which is recorded as Sf_fiber;

[0014] (8) The fiber brooming rate is the ratio of the filament area Sf_fbril to the effective fiber object area Sf0, where Sf_fibril = Sf0-Sf_fiber;

[0015] (9) Analyze at least 100 fiber images, and use steps (2) to (8) to statistically calculate the fiber brooming rate, and calculate the average value as the average fiber brooming rate.

[0016] Preferably, the magnification of the optical digital microscope objective lens in step (1) is 10-40 times.

[0017] Preferably, in step (4), the fiber object is divided into 3-8 sections.

[0018] Preferably, the inclination angle of the rectangular morphological structural element Se_trect described in step (5) is equal to the average value deg_f of the angles of each fiber segment.

[0019] Preferably, in step (5), the length m of the rectangular morphological structure element Se_trect is 3-8, and the width n is 2-5.

[0020] Preferably, the setting conditions in step (6) should simultaneously satisfy the following three conditions: (1) the effective fiber trunk object is a single object; (2) Sf0-Sfi is less than 0.45 times Sf0; (3) the length m does not exceed 1 / 30 of the width of the binary image 2.

[0021] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0022] (1) The fiber trunk is identified based on the morphological calculation method of the present invention. Compared with fiber skeletonization or image segmentation, the image processing speed is faster and the image analysis time is saved, which is very beneficial for fiber samples that require a large amount of statistics.

[0023] (2) Based on the method of the present invention, the fiber trunk and filaments can be automatically identified and the fiber brooming rate can be statistically calculated, thus solving the problems of manual processing, subjective identification and quantification difficulties. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 Schematic diagram of fiber image and image processing in different steps of the present invention DETAILED DESCRIPTION

[0025] The present invention is further described in detail below in conjunction with the embodiments and drawings, but the embodiments of the present invention are not limited thereto. Figure 1 A method for measuring and calculating fiber brooming rate comprises the following steps:

[0026] (1) The fibers were dyed and 200 fiber images were obtained by taking them using an optical digital microscope (the magnification of the objective lens was 10 times) (see the typical fiber images). Figure 1 a), and ensure that the fibers in the image cannot be interwoven.

[0027] (2) The fiber image is converted into a grayscale image, and then binarized and inverted in sequence to obtain a binary image 1, in which the fiber is white and has a value of 1, and the background is black and has a value of 0.

[0028] (3) A circular morphological structure element Se_disk with a radius of 2 pixels is established. The circular morphological structure element Se_disk is used to perform a closing operation on the binary image 1. Then, the area of ​​the fiber objects in the binary image 1 is counted. Only the fiber object with the largest area in the binary image 1 is retained as the binary image 2 for analysis.

[0029] (4) The fiber object is divided into 5 segments, and a function similar to the regionprops function in Matlab is used to determine the angle between each segment of the fiber and the horizontal direction in the image. If the maximum difference between the angles of each segment of the fiber is less than 40 degrees, the fiber object is considered to be a valid fiber object, and the average value of the angles of each segment of the fiber is calculated, recorded as deg_f, and the area of ​​the valid fiber object is calculated, recorded as Sf0; if the maximum difference between the angles of each segment of the fiber is not less than 40 degrees, a new fiber image is selected to perform steps (2)-(4).

[0030] (1) Create a rectangular morphological structure element Se_trect with a length of 4 pixels, a width of 2 pixels, and an inclination angle (angle with the horizontal direction) of deg_f. Use the rectangular morphological structure element Se_trect to perform an opening operation on the binary image 2. The fiber objects with an area greater than 0.1 times Sf0 are valid fiber trunk objects (see Figure 1 b), calculate the area of ​​the effective fiber trunk object, recorded as Sfi. Note: If there are multiple effective fiber trunk objects, Sfi is saved in array form.

[0031] (5) The setting conditions should simultaneously meet the following three conditions: (1) the effective fiber trunk object is a single object; (2) Sf0-Sfi is less than 0.45 times Sf0; (3) the length m does not exceed 1 / 30 of the width of the binary image 2. When the setting conditions are met, a new rectangular morphological structure element Se_trect_i is established, so that both the length m and the width n are increased (m = 4 + 2*i, n = 2 + i, i is the number of iterations), and the tilt angle is the same as Se_trect; the new rectangular morphological structure element Se_trect_i is used to perform an open operation on the binary image 2 to obtain the effective fiber trunk object, and the area Sfi of the effective fiber trunk object is calculated again until the setting conditions are not met. The calculation is stopped, and the last new rectangular morphological structure element that meets the setting conditions is recorded, recorded as Se_trect_n.

[0032] (6) Use the final new rectangular morphological structure element Se_trect_n to perform an opening operation on the binary image 2, and the obtained fiber object is regarded as a valid fiber trunk (see Figure 1 c), calculate the area of ​​the effective fiber trunk, denoted as Sf_fiber.

[0033] (7) Fibril is the difference between the effective fiber object and the effective fiber trunk (see Figure 1 d) The fiber fibrilization rate is the ratio of the filament area Sf_fibril to the effective fiber object area Sf0, where Sf_fibril = Sf0-Sf_fiber.

[0034] (8) Analyze all fiber images, and use steps (2) to (8) to statistically calculate the fiber brooming rate, and calculate the average value as the average fiber brooming rate.

Claims

1. A method for measuring and calculating fiber brooming rate, characterized in that: The following steps are involved: (1) Dyeing the fibers, taking a fiber image through an optical digital microscope, and ensuring that the fibers in the image are not intertwined; (2) The fiber image is converted into a grayscale image, and then binarized and inverted in sequence to obtain a binary image 1, in which the fiber is white and the background is black; (3) A circular morphological structure element Se_disk with a radius of 2-4 pixels is established, and the circular morphological structure element Se_disk is used to perform a closing operation on the binary image 1, and then the area of ​​the fiber objects in the binary image 1 is counted, and only the fiber object with the largest area is retained in the binary image 1 as the binary image 2 for analysis; (4) Segment the fibers in the binary image 2 and determine the angle between each fiber segment and the horizontal direction in the binary image 2; If the maximum difference between the angles of each fiber segment is less than 40 degrees, the fiber object is regarded as a valid fiber object, and the average value of the angles of each fiber segment is calculated, recorded as deg_f, and the area of ​​the valid fiber object is calculated, recorded as Sff); Otherwise, reselect a new fiber image and perform steps (2)-(4); (5) A rectangular morphological structure element Se_trect with a length of m pixels, a width of n pixels and an inclination angle (the angle with the horizontal direction) is established. The rectangular morphological structure element Se_trect is used to perform an opening operation on the binary image 2. The fiber objects with an area greater than 0.1 times Sf0 are obtained as effective fiber trunk objects. The area of ​​the effective fiber trunk objects is calculated and recorded as Sfi. (6) When the set conditions are met, a new rectangular morphological structure element Se_trect_i is established, so that the length m and the width n are both increased, and the inclination angle is the same as Se_trect; the new rectangular morphological structure element Se_trect_i is used to perform an opening operation on the binary image 2 to obtain the effective fiber trunk object, and the area Sfi of the effective fiber trunk object is calculated again until the set conditions are not met. The calculation is stopped, and the last new rectangular morphological structure element that meets the set conditions is recorded, which is recorded as Se_trect_n; (7) Use the final new rectangular morphological structure element Se_trect_n to perform an opening operation on the binary image 2, and the effective fiber trunk object is regarded as the effective fiber trunk, and the area of ​​the effective fiber trunk is calculated, which is recorded as Sf_fber; (8) The fiber brooming rate is the ratio of the filament area Sf_fibril to the effective fiber object area Sf0, where Sf_fibril = Sf0-Sf_fiber; (9) Analyze at least 100 fiber images, and use steps (2) to (8) to statistically calculate the fiber brooming rate, and calculate the average value as the average fiber brooming rate.

2. The method for measuring and calculating the fiber brooming rate according to claim 1, characterized in that: The optical digital microscope objective lens in step (1) has a magnification of 10-40 times.

3. The method for measuring and calculating the fiber brooming rate according to claim 1, characterized in that: The fiber object in step (4) is divided into 3-8 sections.

4. The method for measuring and calculating the fiber brooming rate according to claim 1, characterized in that: The inclination angle of the rectangular morphological structure element Se_trect described in step (5) is equal to the average value deg_f of the angles of each fiber segment.

5. The method for measuring and calculating the fiber brooming rate according to claim 1, characterized in that: The length m of the rectangular morphological structure element Se_trect in step (5) is 3-8, and the width n is 2-5.

6. The method for measuring and estimating fiber brooming rate according to claim 1, characterized in that: The setting conditions described in step (6) should simultaneously meet the following three conditions: (1) the effective fiber trunk object is a single object; (2) Sf0-Sfi is less than 0.45 times Sf0; (3) the length m does not exceed 1 / 30 of the width of the binary image 2.

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