CN110146027B - Method for measuring thickness of celadon glaze layer of SD-OCT image - Google Patents
Method for measuring thickness of celadon glaze layer of SD-OCT image Download PDFInfo
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Abstract
The invention discloses a method for measuring the thickness of a celadon glaze layer of an SD-OCT image. Establishing a database of refractive indexes of different types of celadon glaze layers by measuring the thickness of the sample celadon glaze layer; collecting an SD-OCT image of the celadon glaze layer; positioning the upper boundary of the glaze layer by filtering and binaryzation of the SD-OCT image of the celadon glaze layer; designing structural elements by edge detection, performing closed operation on the image subjected to edge detection by using the structural elements, and extracting the lower boundary of the celadon glaze layer; and the thickness of the celadon glaze layer is calculated by multiplying the difference value of the upper boundary pixel and the lower boundary pixel of the glaze layer by the physical depth of each pixel. The method realizes the nondestructive real-time measurement of the thickness of the celadon glaze layer, the measurement precision reaches micron level, the accuracy is high, the thickness of the glaze layer of various celadons can be measured according to the established celadon glaze layer refraction rate database, the method has strong adaptability, and the measurement efficiency is improved.
Description
Technical Field
The invention belongs to the field of automatic measurement of the thickness of a celadon glaze layer, relates to a processing method of an SD-OCT image, and particularly relates to a method for measuring the thickness of the celadon glaze layer of the SD-OCT image.
Background
China has a long history of ceramics, and celadon is one of the main types of ceramics, and is widely concerned and researched by people at home and abroad. The thickness of the glaze layer and the uniformity of the whole glaze layer of the porcelain are one of main factors for judging the quality of the celadon, and the traditional method for measuring the thickness of the celadon glaze layer is used for destroying the porcelain to carry out physical measurement, has certain precision, but cannot measure the thickness of the glaze layer of all parts of the porcelain and is a method for destructive measurement, so that the method for realizing nondestructive measurement has great significance.
The method of nondestructive measurement is not invented so far, the spectral domain optical coherence tomography (SD-OCT) shows the internal structure morphology and distribution of the porcelain by measuring the optical reflection scattering property of the substance, the SD-OCT image of the porcelain is used for the structural research, classification and qualitative identification of the porcelain at present, and reports show that the image can clearly show the internal structure of the porcelain. The SD-OCT imaging technology is used for measuring the thickness of the celadon glaze layer, and the measurement which is lossless, real-time and not limited by a measuring position has important research value.
Disclosure of Invention
Aiming at the problems in the background art, the invention aims to provide a method for measuring the thickness of the celadon glaze layer of an SD-OCT image, which can detect the thickness of the celadon glaze layer in the SD-OCT image in a real-time and nondestructive manner, and lays a technical foundation for the detection of the glazing uniformity of the celadon by matching with a method for manufacturing the celadon.
As shown in fig. 1, the technical scheme adopted by the invention comprises the following frame steps:
1) establishing a database of refractive indexes of different types of celadon glaze layers;
2) collecting an SD-OCT image of the celadon glaze layer;
3) extracting the upper boundary of the celadon glaze layer;
4) extracting the lower boundary of the celadon glaze layer;
5) and calculating the thickness of the celadon glaze layer.
The step 1) is specifically as follows:
1.1) collecting celadon samples of different types and with known glaze layer thickness;
1.2) acquiring an SD-OCT image of the celadon glaze layer by using an OQ LabScope system, taking a 'Depth Per Pixel' parameter in the OQ LabScope system as the physical resolution Pr of each Pixel in the SD-OCT image, measuring the thickness of the celadon glaze layer by using a system self-provided caliper tool, adjusting the 'Depth Per Pixel' parameter, and further adjusting the physical resolution Pr until the thickness of the celadon glaze layer measured by the system self-provided caliper tool is the same as the actual thickness of the celadon glaze layer;
1.3) recording the physical resolution Pr of each pixel of the SD-OCT image of the celadon glaze layer, and calculating the refractive index n of each celadon glaze layer according to the optical resolution Or and the physical resolution Pr as follows:
the optical resolution Or is obtained by processing of an optical acquisition system.
1.4) sequentially scanning all types of celadon glaze layers, calculating the refractive indexes of all types of celadon glaze layers according to the step 1.3) and establishing a celadon glaze layer refractive index database.
The step 2) is specifically as follows: and (3) acquiring an SD-OCT (secure digital-optical coherence tomography) image of the celadon with the glaze layer thickness to be measured by using an OQ LabScope system, and performing median filtering on the image by using a 3 multiplied by 5 size template.
The step 3) is specifically as follows:
3.1) carrying out binarization processing on the filtered image;
3.2) searching each row of pixel points in the binarized image from top to bottom to present a pixel point with a first gray value of 1, recording the point as a pixel point to be fitted, and recording the number of rows Top (i) of the pixel point to be fitted in each row, wherein i represents the number of rows of the pixel point to be fitted;
3.3) recording the median of the number of rows top (i) where the to-be-fitted pixel points of all columns are located as m, and starting to check and process the value of the number of rows top (i) where the to-be-fitted pixel points of each column are located from i ═ 1: if the absolute value of the difference between the number of rows Top (1) where the pixel point to be fitted in the first row is located and the median m is larger than 30, updating and replacing the number of rows Top (1) with the median between the number of rows Top (2) where the pixel point to be fitted in the second row is located and the number of rows Top (I) where the pixel point to be fitted in the last row is located, wherein I represents the number of columns in the last row; if the absolute value of the difference between the number of rows Top (2) where the pixel points to be fitted in the second row are located and the median m is greater than 30, updating the value of the number of rows Top (2) into the number of rows Top (1); if i is greater than 2 and the absolute value of the difference between the row number Top (i) of the pixel point to be fitted in the ith column and the row number Top (i-1) of the pixel point to be fitted in the i-1 column is greater than 30, updating the value of the row number Top (i) by using a Lagrange interpolation method, and calculating as follows:
Top(i)＝2×Top(i-1)-Top(i-2)
wherein i represents the row ordinal number of the pixel point to be fitted;
3.4) after updating the number of rows Top (i) where the pixel points to be fitted of all columns are located, forming an upper boundary pixel point by all the updated pixel points to be fitted, and calculating to obtain the mean value Top of the number of rows Top (i) where the pixel points to be fitted of all columns are locatedAverage。
The step 4) is specifically as follows:
4.1) carrying out edge detection on the filtered image by using a canny operator, and taking the generated image as an edge detection image Fb;
4.2) defining a circular structural element se with the radius of five pixels, and performing a closing operation on the edge detection image Fb by using the structural element se to obtain an image Fc, wherein the closing operation is calculated as follows:
Fc＝Fb·se
wherein,
symbol
Representing dilation operations on images, symbols
Representing an erosion operation on the image; the effect of the dilation operation is to "grow" or "coarsen" objects in the binary image, and the effect of the erosion operation is to "shrink" or "refine" objects in the binary image, both morphological operations of the image.
4.3) taking connected pixel points as a target in the image Fc, wherein the connection means that eight neighborhoods of the Sudoku are connected around the pixel points; then counting the number of pixel points of the target, arranging all targets from small to large according to the number of the pixel points, taking the number of the pixel points positioned in the median as a point threshold, deleting the targets with the number of the pixel points smaller than the point threshold, and then dividing the targets into two types: the first-class target is a target with the number of lines occupied by the contained pixel points larger than the number of columns, and is reserved; the other type of target is a target that the number of lines occupied by the contained pixel points is less than the number of columns, and deletion is carried out;
4.4) counting the number of rows of the most-lower pixel points at the row positions in all the targets remained in the image Fc, and solving the average value Bot of the row positions of all the pixel pointsAverage；
4.5) in the SD-OCT image of the celadon glaze layer, the D pixel distance between each column of the upper glaze layer boundary and the lower glaze layer boundary is calculated as follows:
D＝BotAverage-TopAverage
wherein, TopAverageRepresenting the mean value of the number of rows top (i) where the pixel points to be fitted of all the columns after the updating is finished;
4.6) translating the upper boundary pixel point obtained in the step 3) downwards by a distance of D pixels according to a row to serve as the position of a lower boundary pixel point of the celadon glaze layer, and recording the position as bot (i).
The step 5) is specifically as follows:
5.1) obtaining the refractive index n corresponding to the celadon according to the type x of the measured celadonxThe physical resolution Pr of each pixel of the collected celadon SD-OCT image is obtained by the processing of the step 1)x；
5.2) converting the distance between the upper boundary and the lower boundary of the glaze layer on the celadon glaze layer SD-OCT image obtained in the step 4) into the actual glaze layer thickness T, and calculating as follows:
T＝D×Prx
wherein D represents the distance between the upper and lower boundaries of the glaze layer on the celadon glaze layer SD-OCT image.
The method comprises the steps of calculating the refractive indexes of different kinds of celadons by measuring the thickness of a sample celadon glaze layer, establishing a database of the refractive indexes of different celadon glaze layers, filtering and binarizing an SD-OCT image of the celadon glaze layer, positioning the upper boundary of the glaze layer, carrying out edge detection on the filtered image, designing a structural element, carrying out closed operation on the image after edge detection by using the structural element, marking all targets in the image after closed operation, deleting the targets which do not meet the requirements, counting the positions of all pixels of the targets which meet the requirements in the image, calculating the longitudinal mean value of the minimum longitudinal position pixel in all the targets, taking the longitudinal mean value as the mean value of the longitudinal positions of the lower boundary pixels of the glaze layer, calculating the mean value of the longitudinal positions of all the upper boundary pixels of the glaze layer, and multiplying the difference value of the longitudinal position mean value of the lower boundary pixels of the glaze layer by the physical depth of each pixel to obtain the thickness of the glaze layer And (4) degree. The key points are the calculated refractive index of the celadon glaze layer, a software algorithm and the logic of the whole process.
The method realizes the nondestructive real-time measurement of the thickness of the celadon glaze layer, the measurement precision reaches micron level, the accuracy is high, the thickness of the glaze layer of various celadons can be measured according to the established celadon glaze layer refraction rate database, the method has strong adaptability, and the measurement efficiency is improved.
The invention has the beneficial effects that:
the method can be used for nondestructively measuring the thickness of the glaze layer at any position of the celadon glaze layer in real time, and the problem that the thickness of the glaze layer can be detected only by destroying the celadon is solved.
The method accurately positions the upper boundary and the lower boundary of the celadon glaze layer in the image, ensures the accuracy and the robustness of measurement, establishes the refraction rate databases of different celadon glaze layers, can measure the thickness of various types of celadon glaze layers, and improves the measurement universality.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a sample drawing of a celadon sample with a glaze layer to be measured, wherein (a) - (d) correspond to the samples respectively.
Fig. 3 is an SD-OCT image of the collected celadon glaze layer, and (a) - (d) correspond to (a) - (d) in fig. 2 in sequence.
FIG. 4 shows the SD-OCT image after noise reduction according to the embodiment, wherein (a) - (d) correspond to (a) - (d) in FIG. 3 in sequence.
Fig. 5 is a graph showing the effect of extracting the upper boundary of the celadon glaze layer according to the embodiment, wherein (a) - (d) correspond to (a) - (d) in fig. 2 in sequence.
Fig. 6 is a drawing showing the effect of extraction of the lower boundary of the celadon glaze layer according to the embodiment, wherein (a) - (d) correspond to (a) - (d) in fig. 2 in sequence.
Detailed Description
The present invention will be described in further detail below with reference to the accompanying drawings and examples. The specific embodiments described herein are merely illustrative of the invention and do not delimit the invention.
The specific embodiment of the invention is as follows:
1. a method for measuring the thickness of a celadon glaze layer of an SD-OCT image is characterized by comprising the following steps
1) Establishing a database of refractive indexes of different types of celadon glaze layers;
1.1) collecting celadon samples of different types and with known glaze layer thickness;
1.2) an OQ LabScope system produced by Lumedica company is used for collecting an SD-OCT image of the celadon glaze layer, a parameter of 'Depth Per Pixel' in the OQ LabScope system is used as the physical resolution Pr of each Pixel in the SD-OCT image, a system self-provided caliper tool is used for measuring the thickness of the celadon glaze layer to adjust the parameter of 'Depth Per Pixel', and then the physical resolution Pr is adjusted until the thickness of the celadon glaze layer measured by the system self-provided caliper tool is the same as the actual thickness of the celadon glaze layer.
1.3) recording the physical resolution Pr of each pixel of the SD-OCT image of the celadon glaze layer, and calculating the refractive index n of each celadon glaze layer according to the optical resolution Or and the physical resolution Pr.
1.4) sequentially scanning all types of celadon glaze layers, calculating the refractive indexes of all types of celadon glaze layers according to the step 1.3) and establishing a celadon glaze layer refractive index database.
2) And collecting an SD-OCT image of the celadon glaze layer. An OQ LabScope system produced by Lumedica is used for collecting SD-OCT images of celadon with the thickness of a glaze layer to be measured, and a 3 x 5 size template is used for carrying out median filtering on the images.
Fig. 2 is a sample drawing of a celadon sample with a glaze layer to be measured, wherein (a) - (d) correspond to the sample, fig. 3 is a collected SD-OCT image of the celadon glaze layer, wherein (a) - (d) correspond to (a) - (d) in fig. 2 in sequence, fig. 4 is a SD-OCT image after noise reduction, and wherein (a) - (d) correspond to (a) - (d) in fig. 3 in sequence.
3) Extracting the upper boundary of the celadon glaze layer;
3.1) carrying out binarization processing on the filtered image, wherein the threshold value of the binarization of the image is set to be 0.1;
3.2) searching each row of pixel points in the binarized image from top to bottom to present a pixel point with a first gray value of 1, recording the point as a pixel point to be fitted, and recording the number of rows Top (i) of the pixel point to be fitted in each row, wherein i represents the number of rows of the pixel point to be fitted;
3.3) recording the median of the number of rows top (i) where the to-be-fitted pixel points of all columns are located as m, and starting to check and process the value of the number of rows top (i) where the to-be-fitted pixel points of each column are located from i ═ 1:
if the absolute value of the difference between the number of rows Top (1) where the pixel point to be fitted in the first row is located and the median m is larger than 30, updating and replacing the number of rows Top (1) with the median between the number of rows Top (2) where the pixel point to be fitted in the second row is located and the number of rows Top (I) where the pixel point to be fitted in the last row is located, wherein I represents the number of columns in the last row; if not, not processing;
if the absolute value of the difference between the number of rows Top (2) where the pixel points to be fitted in the second row are located and the median m is greater than 30, updating the value of the number of rows Top (2) into the number of rows Top (1); if not, not processing;
and if i is greater than 2 and the absolute value of the difference between the row number Top (i) of the pixel point to be fitted in the ith column and the row number Top (i-1) of the pixel point to be fitted in the (i-1) th column is greater than 30, updating the value of the row number Top (i) by using a Lagrange interpolation method. If not, not processing;
3.4) after updating the number of rows Top (i) where the pixel points to be fitted of all columns are located, forming an upper boundary pixel point by all the updated pixel points to be fitted, and calculating to obtain the mean value Top of the number of rows Top (i) where the pixel points to be fitted of all columns are locatedAverage。
In this embodiment, fig. 5 is a graph showing the fitting effect of the upper boundary of the celadon glaze layer, where (a) - (d) correspond to (a) - (d) in fig. 2, respectively.
4) Extracting the lower boundary of the celadon glaze layer;
4.1) carrying out edge detection on the filtered image by using a canny operator, and taking the generated image as an edge detection image Fb;
4.2) defining a circular structure element se with the radius of five pixel lengths, wherein the pixel point of a circular area in the circular structure element se is 1, the surrounding pixel points are 0, and performing a closing operation on the edge detection image Fb by using the structure element se to obtain an image Fc, wherein the calculation is as follows:
Fc＝Fb·se
wherein,
symbol
Representing dilation operations on images, symbols
Representing an erosion operation on the image;
4.3) taking connected pixel points as a target in the image Fc, wherein the connection means that eight neighborhoods of the Sudoku are connected around the pixel points; then counting the number of pixel points of the target, arranging all targets from small to large according to the number of the pixel points, taking the number of the pixel points positioned in the median as a point threshold, deleting the targets with the number of the pixel points smaller than the point threshold, and then dividing the targets into two types:
the first-class target is a target with the number of lines occupied by the contained pixel points larger than the number of columns, and is reserved;
the other type of target is a target that the number of lines occupied by the contained pixel points is less than the number of columns, and deletion is carried out;
4.4) counting the number of rows of the most-lower pixel points at the row positions in all the targets remained in the image Fc, and solving the average value Bot of the row positions of all the pixel pointsAverage；
4.5) in the SD-OCT image of the celadon glaze layer, the distance of D pixels between each column of the upper boundary and the lower boundary of the glaze layer is calculated.
4.6) translating the upper boundary pixel point obtained in the step 3) downwards by a distance of D pixels according to a row to serve as the position of a lower boundary pixel point of the celadon glaze layer, and recording the position as bot (i).
In this embodiment, fig. 6 is a fitting effect diagram of the lower boundary of the celadon glaze layer, where (a) - (d) correspond to (a) - (d) in fig. 2, respectively.
5) And calculating the thickness of the celadon glaze layer.
5.1) obtaining the refractive index n corresponding to the celadon according to the type x of the measured celadonxObtaining the physical resolution Pr of each pixel of the collected celadon SD-OCT image by the processing of the step 1.3) of the step 1)x；
5.2) converting the distance between the upper boundary and the lower boundary of the glaze layer on the celadon glaze layer SD-OCT image obtained in the step 4) into the actual glaze layer thickness T.
Claims (4)
1. A method for measuring the thickness of a celadon glaze layer of an SD-OCT image is characterized by comprising the following steps
1) Establishing a database of refractive indexes of different types of celadon glaze layers;
the step 1) is specifically as follows:
1.1) collecting celadon samples of different types and with known glaze layer thickness;
1.2) acquiring an SD-OCT image of the celadon glaze layer by using an OQ LabScope system, taking a 'Depth Per Pixel' parameter in the OQ LabScope system as the physical resolution Pr of each Pixel in the SD-OCT image, measuring the thickness of the celadon glaze layer by using a system self-provided caliper tool, adjusting the 'Depth Per Pixel' parameter, and further adjusting the physical resolution Pr until the thickness of the celadon glaze layer measured by the system self-provided caliper tool is the same as the actual thickness of the celadon glaze layer;
1.3) recording the physical resolution Pr of each pixel of the SD-OCT image of the celadon glaze layer, and calculating the refractive index n of each celadon glaze layer according to the optical resolution Or and the physical resolution Pr as follows:
1.4) sequentially scanning all types of celadon glaze layers, calculating the refractive indexes of all types of celadon glaze layers according to the step 1.3) and establishing a celadon glaze layer refractive index database;
2) collecting an SD-OCT image of the celadon glaze layer;
3) extracting the upper boundary of the celadon glaze layer;
the step 3) is specifically as follows:
3.1) carrying out binarization processing on the filtered image;
3.2) searching each row of pixel points in the binarized image from top to bottom to present a pixel point with a first gray value of 1, recording the point as a pixel point to be fitted, and recording the number of rows Top (i) of the pixel point to be fitted in each row, wherein i represents the number of rows of the pixel point to be fitted;
3.3) recording the median of the number of rows top (i) where the to-be-fitted pixel points of all columns are located as m, and starting to check and process the value of the number of rows top (i) where the to-be-fitted pixel points of each column are located from i ═ 1: if the absolute value of the difference between the number of rows Top (1) where the pixel point to be fitted in the first row is located and the median m is larger than 30, updating and replacing the number of rows Top (1) with the median between the number of rows Top (2) where the pixel point to be fitted in the second row is located and the number of rows Top (I) where the pixel point to be fitted in the last row is located, wherein I represents the number of columns in the last row; if the absolute value of the difference between the number of rows Top (2) where the pixel points to be fitted in the second row are located and the median m is greater than 30, updating the value of the number of rows Top (2) into the number of rows Top (1); if i is greater than 2 and the absolute value of the difference between the row number Top (i) of the pixel point to be fitted in the ith column and the row number Top (i-1) of the pixel point to be fitted in the i-1 column is greater than 30, updating the value of the row number Top (i) by using a Lagrange interpolation method, and calculating as follows:
Top(i)＝2×Top(i-1)-Top(i-2)
wherein i represents the row ordinal number of the pixel point to be fitted;
3.4) after updating the number of rows Top (i) where the pixel points to be fitted of all columns are located, forming an upper boundary pixel point by all the updated pixel points to be fitted, and calculating to obtain the mean value Top of the number of rows Top (i) where the pixel points to be fitted of all columns are locatedAverage；
4) Extracting the lower boundary of the celadon glaze layer;
5) and calculating the thickness of the celadon glaze layer.
2. The method for measuring the thickness of the celadon glaze layer of the SD-OCT image as claimed in claim 1, wherein: the step 2) is specifically as follows: and (3) acquiring an SD-OCT (secure digital-optical coherence tomography) image of the celadon with the glaze layer thickness to be measured by using an OQ LabScope system, and performing median filtering on the image by using a 3 multiplied by 5 size template.
3. The method for measuring the thickness of the celadon glaze layer of the SD-OCT image as claimed in claim 1, wherein: the step 4) is specifically as follows:
4.1) carrying out edge detection on the filtered image by using a canny operator, and taking the generated image as an edge detection image Fb;
4.2) defining a circular structural element se with the radius of five pixels, and performing a closing operation on the edge detection image Fb by using the structural element se to obtain an image Fc, wherein the closing operation is calculated as follows:
Fc＝Fb·se
wherein,
symbol
Representing dilation operations on imagesMake, symbol
Representing an erosion operation on the image;
4.3) taking connected pixel points as a target in the image Fc, wherein the connection means that eight neighborhoods of the Sudoku are connected around the pixel points; then counting the number of pixel points of the target, arranging all targets from small to large according to the number of the pixel points, taking the number of the pixel points positioned in the median as a point threshold, deleting the targets with the number of the pixel points smaller than the point threshold, and then dividing the targets into two types: the first-class target is a target with the number of lines occupied by the contained pixel points larger than the number of columns, and is reserved; the other type of target is a target that the number of lines occupied by the contained pixel points is less than the number of columns, and deletion is carried out;
4.4) counting the number of rows of the most-lower pixel points at the row positions in all the targets remained in the image Fc, and solving the average value Bot of the row positions of all the pixel pointsAverage；
4.5) in the SD-OCT image of the celadon glaze layer, the D pixel distance between each column of the upper glaze layer boundary and the lower glaze layer boundary is calculated as follows:
D＝BotAverage-TopAverage
wherein, TopAverageRepresenting the mean value of the number of rows top (i) where the pixel points to be fitted of all the columns after the updating is finished;
4.6) translating the upper boundary pixel point obtained in the step 3) downwards by a distance of D pixels according to a row to serve as the position of a lower boundary pixel point of the celadon glaze layer, and recording the position as bot (i).
4. The method for measuring the thickness of the celadon glaze layer of the SD-OCT image as claimed in claim 1, wherein: the step 5) is specifically as follows:
5.1) obtaining the refractive index n corresponding to the celadon according to the type x of the measured celadonxThe physical resolution Pr of each pixel of the collected celadon SD-OCT image is obtained by the processing of the step 1)x；
5.2) converting the distance between the upper boundary and the lower boundary of the glaze layer on the celadon glaze layer SD-OCT image obtained in the step 4) into the actual glaze layer thickness T, and calculating as follows:
T＝D×Prx
wherein D represents the distance between the upper and lower boundaries of the glaze layer on the celadon glaze layer SD-OCT image.
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