CN111751263A - Mark-free cell two-dimensional scattering image inversion method based on gray level co-occurrence matrix - Google Patents
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Abstract
The invention discloses a label-free cell two-dimensional scattering image inversion method based on a gray level co-occurrence matrix. The method is based on a forward two-dimensional scattering image of the cell, and the quantitative rule between the cell volume and the forward scattering light is inverted; in order to search the quantitative rule between the cell volume and the forward scattering light of the cell volume more simply and accurately, the scheme provides a gray level co-occurrence matrix and a calculation method of an angle second moment thereof based on the global surface texture characteristics of a cell two-dimensional forward scattering image; and the angle second moment is used as an indirect representation of the cell volume, a mathematical model between the cell volume and the angle second moment is constructed, and the cell volume is inverted more accurately and more quickly.
Description
Technical Field
The invention belongs to the field of unmarked cell scattering detection research, and relates to an unmarked cell two-dimensional scattering image inversion method based on a gray level co-occurrence matrix.
Background
Cellular light scattering is an important non-invasive means of detection. Light scattering results from the interaction of electromagnetic waves with a medium, and the scattered waves carry a great deal of information about the properties of the medium. The optical scattering information of the cell not only contains the common characteristics of the cells of the same kind, but also contains the individual characteristics of the cell, and can accurately reflect the physical characteristics of the biological cell in a non-intervention state, so that the cell is known as 'cell fingerprint' information.
The method of detecting human peripheral blood cells (circulating tumor cells, white blood cells, red blood cells, etc.) by using fluorescence labeling, radioactive isotope labeling and other methods is a widely used detection method in clinical diagnosis and biological research. The cellular immune marker detection technology is an intervention indirect detection technology based on biochemical means, and has the problems of biotoxicity and inactivation hazard, complex operation process, expensive target probe, serious environmental pollution and the like. The cytopathic process is obviously changed along with the physical characteristics such as form, structure, components and the like, so the optical scattering method known as 'cell fingerprint' realizes non-contact, mark-free and non-intervention measurement on cells, is undoubtedly a more direct, more effective and more green medical diagnosis technology, and has important research significance and potential clinical application value in the fields of cell canceration state tracking, cell immunotherapy, clinical accurate medical treatment and the like.
The biological intrinsic Light Scattering measurement method goes from a complicated one-dimensional Scattering Angle spectrum method (Angle dependent Light Scattering Pattern) of multi-Angle measurement to a two-dimensional Scattering image method rich in biological characteristic information. In the aspect of inversion research of two-dimensional scattering images and biological characteristics, SuXuanTao et al propose a method (Automatic classification of both channel and nuclear magnetic free cells with a wide-angle morphological cell metric) for extracting a series of characteristic values of two-dimensional scattering images by using gray difference statistics and training a Support Vector Machine (SVM) by using the obtained characteristic values, thereby realizing cell classification. The method has more processing steps and large calculation amount, and a machine learning model needs to be established, so that the rule information corresponding to the cell volume cannot be found. More importantly, although the machine learning method has good active intelligent discrimination capability, the discrimination criterion and condition is an unopened 'black box', that is, the inversion rule of the two-dimensional scattering image information and the biological intrinsic physical characteristics can not be obtained. However, the research of inversion rules is a very important link in scientific research, and has a great significance for predicting and tracking the occurrence-development changes of the explained phenomenon. Therefore, the research provides a non-labeling cell scattering inversion method based on a gray level co-occurrence matrix, which can definitely acquire the inversion rule of two-dimensional scattering image information and biological intrinsic physical characteristics, aiming at the problems existing in the current method.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a marker-free cell scattering inversion method based on a gray level co-occurrence matrix with statistical properties, and inverts the cell volume characteristics under a non-intervention and marker-free state through cell intrinsic optical information carried in a two-dimensional light scattering image of the marker-free cell and the method of the gray level co-occurrence matrix.
A two-dimensional scattering image inversion method of label-free cells based on a gray level co-occurrence matrix is characterized in that an optical detection system is used for collecting a two-dimensional forward scattering image of cells, after image processing methods such as denoising and gray level processing are carried out, the gray level co-occurrence matrix of the two-dimensional forward scattering image is calculated to obtain an angle second moment of the two-dimensional forward scattering image, a mathematical model between a cell volume V and the angle second moment k is established through a data fitting method, the value V (k) is-6554.4 k +1752.2, the cell volume can be inverted by adopting the mathematical model, and therefore lesions of peripheral blood cells of a human body caused by volume changes are judged.
The gray level co-occurrence matrix is a method using probability statistics, all pixel points in the two-dimensional scattering image are subjected to statistical utilization, the number of scattering signals used for calculating the inversion rule is large, and the inverted cell volume is more accurate.
In the process of calculating the gray level co-occurrence matrix of the scattering image, the direction is taken as the horizontal direction, the offset value is 1, and the order number of the gray level co-occurrence matrix is 16, so that the linear function relationship between the obtained angular second moment and the cell volume is better.
The specific processes of extracting the angle second moment and establishing a mathematical model by the gray level co-occurrence matrix of the two-dimensional forward scattering image are as follows:
A. through the system for acquiring the forward scattering images, a plurality of groups of cell forward scattering images F1, F2, … and Fn with the volumes of V1, V2, … and Vn sequentially increasing are acquired;
B. respectively calculating gray level co-occurrence matrixes P1, P2, … and Pn of the forward two-dimensional scattering map;
C. and calculating angular second moments K1, K2, …, Kn based on gray level co-occurrence matrixes P1, P2, … and Pn;
D. performing data fitting on cell volumes V1, V2, … and Vn and angular second moments K1, K2, … and Kn through matlab software to obtain a function model V (K) with the cell volume V and the angular second moments K decreasing linearly;
and (3) measuring a forward scattering image Fx with an unknown volume by using the same method in the step A, obtaining an angular second moment Kx of the image according to the same method, and substituting the Kx into the function model V (K) to obtain the volume Vx of the cell with the unknown volume.
The invention can realize effective inversion of cell volume by adopting the intrinsic scattering physical property of the cell under the condition of not performing any intervention, contact and damage on the cell, through a two-dimensional intrinsic scattering image method that the intrinsic scattering physical property is superior to one-dimensional space scattering by the intrinsic scattering physical property, and by adopting a single characteristic value angle second moment technology with statistical property, and can finish accurate identification of the cell (fixed or living) in the green environment.
Drawings
FIG. 1 is a flow chart of feature inversion of a label-free cell two-dimensional scattering image based on a gray level co-occurrence matrix.
FIG. 2 is a diagram of a system for acquiring a forward scatter plot of a cell. The system comprises a Laser 1, a neutral density filter ND 2, an objective lens 3 with the numerical aperture of 10 times of 0.25, an optical fiber coupler 4, a three-coordinate displacement table 5, a 6-stage depth-of-field microscope, an optical fiber 7, a glass slide 8 and a computer 9.
FIG. 3 is a two-dimensional forward scattering plot, size, angular second moment of the five groups of polystyrene pellets tested
FIG. 4 is a linear function image of the angular second moment and cell volume of the two-dimensional forward scatter image of the five sets of polystyrene spheres of FIG. 3.
FIG. 5 is a flow chart of gray level co-occurrence matrix extraction.
Detailed Description
One laser beam of the present invention is irradiated on white blood cells in peripheral blood of a human body, and the laser beam is scattered after passing through the cells, and physical information such as the form and size of the cells is carried in the scattered light. The invention obtains a forward two-dimensional scattering image by measuring the forward scattering light of the cell and collecting the forward scattering light in a two-dimensional scattering spectrum intensity mode. And filtering and denoising the collected forward scattering image by a digital image processing method, then calculating a gray level co-occurrence matrix of the image and solving an angular second moment. Finally, it can be obtained that the angular second moment of the cell forward scattering image has a linear inverse relationship with the cell volume.
The method mainly aims at the forward scattering image of the cell, and inverts the rule between the cell volume and the forward scattering image by a gray level co-occurrence matrix method. The gray level co-occurrence matrix is a common method for describing texture by studying the spatial correlation characteristics of gray levels, i.e. a set of image features (angular second moment, entropy, contrast, inverse difference moment) of an image about texture information can digitize the texture. The implementation process of the invention mainly comprises two parts, namely the acquisition of the forward scattering image of the cell, and the calculation of the gray level co-occurrence matrix of the scattering image so as to extract the characteristics and establish a mathematical model between the characteristic value and the cell volume.
Leukocytes in human peripheral blood are used as measurement targets. Removing red blood cells, platelets and serum by adopting a standard human body peripheral blood leukocyte separation method to obtain a plurality of leukocytes with similar spherical shapes, and then fixing by adopting formaldehyde to prepare a test sample.
In the acquisition of a forward two-dimensional scatter image of a cell. The forward scattering light measurement system comprises a laser light source with the wavelength of 632nm, a neutral density filter, an objective lens, an optical fiber coupler, a three-coordinate displacement platform, a single-mode optical fiber, a glass slide, a super-depth-of-field microscope and the like. A cell suspension of a certain concentration is prepared, the cell suspension is sucked onto a glass slide by using a pipette gun, and the glass slide is placed in the center of a microscope lens. And turning on a white light illumination source of the ultra-depth-of-field microscope, finding sample cells under white light illumination, and moving the sample cells to the central position of the visual field through a self-carried electric stage of the microscope. And turning off a white light illumination light source, turning on a 632nm laser light source, enabling the laser to pass through a neutral density filter to enable stray light in the laser, enabling a light beam after filtering to pass through an objective lens, adjusting a three-coordinate displacement table behind the objective lens, focusing the light beam into an optical fiber coupler, and enabling the laser to excite cells to be detected on the glass slide through an optical fiber. And finally, adjusting the focal length of the microscope to enable the microscope to be in an out-of-focus state, wherein the microscope can observe a clear scattering image in the out-of-focus state. And finally, collecting the forward scattering image of the cell by using a CCD (charge coupled device) of the super-field-depth microscope and storing the forward scattering image of the cell in a computer.
The specific processes of extracting the angular second moment from the gray level co-occurrence matrix of the forward scattering image and establishing a mathematical model are as follows:
E. by the system for acquiring the forward scattering images, a plurality of groups of cell forward scattering images F1, F2, … and Fn with the volumes of V1, V2, … and Vn sequentially increasing are acquired.
F. Gray level co-occurrence matrices P1, P2, …, Pn of the forward two-dimensional scattergram are calculated, respectively.
G. And calculates the angular second moments K1, K2, …, Kn based on the gray level co-occurrence matrices P1, P2, …, Pn.
H. And (3) performing data fitting on the cell volumes V1, V2, … and Vn and the angular second moments K1, K2, … and Kn through matlab software to obtain a function model V (K) with the linear decline of the cell volumes V and the angular second moments K.
I. And (3) measuring a forward scattering image Fx with an unknown volume by using the same method in the step A, obtaining an angular second moment Kx of the image according to the same method, and substituting the Kx into the function model V (K) to obtain the volume Vx of the unknown cell.
The method for extracting the gray level co-occurrence matrix and the eigenvalue K comprises the following steps:
(1) and carrying out gray level processing on the collected color picture to obtain a gray level image. And converting the color image acquired by the experiment into a gray image F, wherein the data form of the gray image F is a two-dimensional array, F (x, y) represents the gray value at the position (x, y) in the image F, and the gray value is an integer within the interval [0,255], wherein 0 corresponds to black and 255 corresponds to white.
(2) And performing degradation processing on the gray level images of 0-255 levels to make the gray level image of 16 levels. As can be known from the definition of the gray level co-occurrence matrix, the order of the gray level co-occurrence matrix is the same as the order of the gray level image, that is, when the order of the gray level image is the same, the gray level co-occurrence matrix is the same. In order to reduce the calculation amount, the gray level images of [0,255] are compressed into the gray level images of [0,15], so that the information can be ensured not to be lost, and the calculation amount can be reduced.
(3) Respectively calculating gray level co-occurrence matrixes of the gray level image in four directions of 0 degree, 45 degrees, 90 degrees and 135 degrees, calculating angle second moments of the gray level co-occurrence matrixes in the four directions, and generating a function model V (K)0)、V(K45)、V(K90)、V(K135) The best linearity in the vertical direction can be seen by comparison;
(4) for the gray level co-occurrence matrix in the 0 ° direction, the step length d is 1, d is 2, and d is 3, the angular second moment of each step length is calculated, and the method is compared with the method in step (3), and it is known that when the step length d is 1, the linearity of the function model is the best.
The calculation formula for calculating the image characteristic value angle second moment based on the gray level co-occurrence matrix P is
Where P (i, j, θ) represents the value of the gray level co-occurrence matrix P at (i, j) over the angle θ.
(5) And calculating the mean angular second moment of the four angular second moments. Comparing the mean angular second moment with angular second moments in four directions, and analyzing the influence of five angular second moments on the size of the cell volume, wherein the calculation formula is as follows:
Claims (4)
1. the method is characterized in that an optical detection system is used for collecting a two-dimensional forward scattering image of a cell, the two-dimensional forward scattering image is subjected to image processing methods such as denoising and gray processing, a gray level co-occurrence matrix of the two-dimensional forward scattering image is calculated to obtain an angle second moment of the two-dimensional forward scattering image, a mathematical model between a cell volume V and the angle second moment k is established through a data fitting method, and the value of V (k) -6554.4k +1752.2 is obtained, and the size of the cell volume can be inverted by adopting the mathematical model, so that the lesion caused by the volume change of a human peripheral blood cell is judged.
2. The method for inverting the unmarked cell two-dimensional scattering image based on the gray level co-occurrence matrix according to claim 1, wherein the gray level co-occurrence matrix is a method using probability statistics, all pixel points in the two-dimensional scattering image are statistically utilized, and the volume of the inverted cell is more accurate if more scattering signals are used for calculating the inversion rule.
3. The grayscale symbiotic matrix-based label-free cell two-dimensional scattering image inversion method according to claim 1, wherein in the process of calculating the grayscale symbiotic matrix of the scattering image, the direction is taken as the horizontal direction, the offset value is 1, and the order of the grayscale symbiotic matrix is 16, so that the linear function relationship between the obtained angular second moment and the cell volume is better.
4. The method for inverting the unmarked cell two-dimensional scattering image based on the gray level co-occurrence matrix according to claim 1, wherein the specific processes of extracting the angle second moment and establishing the mathematical model of the gray level co-occurrence matrix of the two-dimensional forward scattering image are as follows:
A. through the system for acquiring the forward scattering images, a plurality of groups of cell forward scattering images F1, F2, … and Fn with the volumes of V1, V2, … and Vn sequentially increasing are acquired;
B. respectively calculating gray level co-occurrence matrixes P1, P2, … and Pn of the forward two-dimensional scattering map;
C. and calculating angular second moments K1, K2, …, Kn based on gray level co-occurrence matrixes P1, P2, … and Pn;
D. performing data fitting on cell volumes V1, V2, … and Vn and angular second moments K1, K2, … and Kn through matlab software to obtain a function model V (K) with the cell volume V and the angular second moments K decreasing linearly;
and (3) measuring a forward scattering image Fx with an unknown volume by using the same method in the step A, obtaining an angular second moment Kx of the image according to the same method, and substituting the Kx into the function model V (K) to obtain the volume Vx of the cell with the unknown volume.
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