CN110956107B - Three-dimensional blood vessel type distinguishing method based on OCT scanning system - Google Patents
Three-dimensional blood vessel type distinguishing method based on OCT scanning system Download PDFInfo
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Abstract
The invention discloses a three-dimensional blood vessel type distinguishing method based on an OCT scanning system, which comprises the steps of obtaining a three-dimensional stereogram group of a blood vessel in the time direction through the OCT scanning system; extracting a two-dimensional graph corresponding to the unit depth from the three-dimensional stereograph group, converting the two-dimensional graph into a two-dimensional matrix, and forming a three-dimensional matrix T by the two-dimensional matrix and the time corresponding to the two-dimensional matrix; carrying out noise reduction treatment on the three-dimensional matrix T to obtain a matrix G; performing Fourier transform on the matrix G to convert the matrix G from a time domain space into a frequency domain space to obtain a matrix K; carrying out quantization treatment on the matrix K through a parameter IR to obtain a matrix A, wherein an image represented by the matrix A is an arterial distribution diagram; subtracting the matrix A from the matrix T to obtain a matrix B; convolving the matrix B with a Gaussian function as a convolution kernel to obtain a matrix C; determining a radial search direction of a blood vessel; and searching in the radial searching direction of the blood vessel to obtain the diameter of the blood vessel and obtain a vein distribution diagram and a capillary distribution diagram. The method is mainly used for biological imaging.
Description
Technical Field
The invention relates to the technical field of medical detection, in particular to a three-dimensional blood vessel type distinguishing method based on an OCT scanning system.
Background
Before the method for automatically distinguishing the blood vessel types is proposed, the blood vessel types are distinguished mainly by doctors or biological laboratory staff according to own experience. With the development of optical imaging technology, some methods for automatically distinguishing blood vessel types based on optical imaging are also continuously proposed. In 1982 and 1998, akita et al and JussepphJy-Haw Yu et al set thresholds to distinguish fundus retinal vessel types based on the differences in arteriovenous reflected light intensities. However, in some cases of single wavelength illumination, the intensity of more arterial and venous regions is similar, and is insufficient to distinguish vessel types based on the intensity difference. In 2007, doctor et al, university of texas a & M Zhang Hao used a photoacoustic microscope to observe changes in hemoglobin oxygen saturation (SO 2) and to distinguish the arteriovenous type from the measured blood oxygen saturation. In 2007, the indian scientist narshimhaiyer et al identified vessel types by structural and functional features of fundus retinal images at 570nm wavelength and 600nm wavelength. Furthermore, single wavelength based imaging methods are also used to distinguish vessel types. In 2010 and 2011, shanghai university of transportation nerve engineering laboratory Miao Peng doctor et al and China university of science and technology Feng Nengyun doctor successively realize cerebral cortex vessel type identification through blood vessel anatomical features and light intensity distribution in single wavelength LSCI (SW-LSCI) images. With the application and development of MRA and CT techniques, they are also used for three-dimensional blood vessel classification for distinguishing head, lung and heart.
At present, two-dimensional blood vessel classification techniques mainly comprise a multi-wavelength arteriovenous classification method and a single-wavelength blood vessel classification method, but the two methods are complex and are not beneficial to rapidly distinguishing the types of blood vessels.
Disclosure of Invention
The present invention is directed to a three-dimensional blood vessel type distinguishing method based on an OCT scanning system, which solves one or more of the technical problems of the prior art, and at least provides an advantageous choice or creation condition.
The invention solves the technical problems as follows: a three-dimensional blood vessel type distinguishing method based on an OCT scanning system, comprising:
step 1, continuously C-scanning a region where a blood vessel is located through an OCT scanning system to obtain a three-dimensional stereogram group of the blood vessel in the time direction;
step 2, extracting a two-dimensional image corresponding to the unit depth from the three-dimensional stereogram group, converting the two-dimensional image into a two-dimensional matrix, and forming a three-dimensional matrix T by the two-dimensional matrix and the time corresponding to the two-dimensional matrix;
step 3, carrying out noise reduction treatment on the three-dimensional matrix T to obtain a matrix G;
step 4, performing Fourier transform on the matrix G to convert the matrix G from a time domain space into a frequency domain space to obtain a matrix K;
step 5, carrying out quantization processing on the matrix K through a parameter IR to obtain a matrix A, wherein an image represented by the matrix A is an arterial distribution diagram;
step 6, subtracting the matrix A from the matrix T to obtain a matrix B;
step 7, convolving the matrix B by taking a Gaussian function as a convolution kernel to obtain a matrix C;
step 8, calculating a Hessian matrix H (x, y) of the matrix C;
step 9, calculating the eigenvalue and the corresponding eigenvector of the Hessian matrix H (x, y), taking the direction of the included angle of the two eigenvectors as the direction of the blood vessel, and rotating for 90 degrees to obtain the radial search direction of the blood vessel;
step 10, searching in a radial searching direction of a blood vessel to obtain the diameter of the blood vessel;
step 11, obtaining a vein distribution diagram and a capillary distribution diagram through the diameters of blood vessels;
wherein, the expression of the parameter IR is:
Further, in step 3, the noise reduction process is an eight-domain noise reduction process.
Further, the eight-domain noise reduction process specifically includes: a window of 3*3, all 1 values, is used to convolve the matrix T in turn, and then the data is smoothed using a Savitzky-Golay filter operator for a single column of eight neighbors in turn in the time direction.
Further, in step 10, the method for determining the diameter of the blood vessel includes: dividing blood vessels, binarizing, searching blood vessel boundaries along the radial search direction and the opposite direction of the blood vessels respectively, wherein the distance between adjacent blood vessel boundaries is the diameter of the blood vessels, and the diameter of the blood vessels is recorded as a first diameter; calculating the gradient value change conditions along the radial search direction of the blood vessel and the opposite direction of the blood vessel by adopting a gradient extremum method, determining the edge of the blood vessel, increasing the gradient value of the edge of the blood vessel from zero to gradient, taking the starting end and the ending end of the mutation as two edge points of the blood vessel, taking the Euclidean distance of the two edge points as the diameter of the blood vessel, and recording the diameter of the blood vessel as a second diameter; the vessel diameter is obtained by averaging the first diameter and the second diameter.
Further, in step 11, the acquisition modes of the venous distribution map and the capillary distribution map include: marking the blood vessels with the diameters of more than 15 mu m to obtain a vein distribution diagram, and marking the blood vessels with the diameters of less than or equal to 15 mu m to obtain a capillary distribution diagram.
The beneficial effects of the invention are as follows: the three-dimensional stereogram group is obtained by utilizing the OCT scanning system, then the artery is separated by utilizing the difference of vibration signals, and then the vein and the capillary vessel are separated by utilizing the difference between the diameters of the vein and the capillary vessel, so that the three-dimensional blood vessel type can be distinguished with high precision.
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In order to more clearly illustrate the technical solutions in the inventive embodiments of the present invention, the drawings that are used in the description of the embodiments will be briefly described below. It is evident that the drawings described are only some embodiments of the invention and not all embodiments, and that other designs and drawings can be obtained from these drawings by a person skilled in the art without the inventive effort.
Fig. 1 is a flow chart of the steps of a three-dimensional blood vessel type distinguishing method based on an OCT scanning system.
Detailed Description
Reference will now be made in detail to the present embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein the drawings are for purposes of supplementing the description of the written portion of the specification with graphics so that a person may intuitively and intuitively understand each and every technical feature and overall solution of the present invention, but should not be construed as limiting the scope of the present invention.
In the description of the invention, it should be understood that references to orientation descriptions such as upper, lower, front, rear, left, right, etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience in describing the invention and to simplify the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the invention.
In the description of the invention, if there is a word description such as "a number", it means one or more, and a plurality means two or more, and more, than, less than, exceeding, etc. are understood to not include the present number, and more, less, etc. are understood to include the present number.
In the description of the present invention, unless explicitly defined otherwise, terms such as arrangement, installation, connection, etc. should be construed broadly and the specific meaning of the terms in the present invention can be reasonably determined by a person skilled in the art in combination with the specific contents of the technical scheme.
Embodiment 1, referring to fig. 1, a three-dimensional blood vessel type distinguishing method based on an OCT scanning system includes:
step 1, continuously C-scanning a region where a blood vessel is located through an OCT scanning system to obtain a three-dimensional stereogram group of the blood vessel in the time direction;
step 2, extracting a two-dimensional image corresponding to the unit depth from the three-dimensional stereogram group, converting the two-dimensional image into a two-dimensional matrix, and forming a three-dimensional matrix T by the two-dimensional matrix and the time corresponding to the two-dimensional matrix;
step 3, carrying out noise reduction treatment on the three-dimensional matrix T to obtain a matrix G;
step 4, performing Fourier transform on the matrix G to convert the matrix G from a time domain space into a frequency domain space to obtain a matrix K;
step 5, carrying out quantization processing on the matrix K through a parameter IR to obtain a matrix A, wherein an image represented by the matrix A is an arterial distribution diagram;
step 6, subtracting the matrix A from the matrix T to obtain a matrix B;
step 7, convolving the matrix B by taking a Gaussian function as a convolution kernel to obtain a matrix C;
step 8, calculating a Hessian matrix H (x, y) of the matrix C;
step 9, calculating the eigenvalue and the corresponding eigenvector of the Hessian matrix H (x, y), taking the direction of the included angle of the two eigenvectors as the direction of the blood vessel, and rotating for 90 degrees to obtain the radial search direction of the blood vessel;
step 10, searching in a radial searching direction of a blood vessel to obtain the diameter of the blood vessel;
step 11, obtaining a vein distribution diagram and a capillary distribution diagram through the diameters of blood vessels;
wherein, the expression of the parameter IR is:
Wherein, in some optimized embodiments, in step 3, the noise reduction process is an eight domain noise reduction process. The eight-field noise reduction treatment specifically comprises: a window of 3*3, all 1 values, is used to convolve the matrix T in turn, and then the data is smoothed using a Savitzky-Golay filter operator for a single column of eight neighbors in turn in the time direction. The Savitzky-Golay filter is a special low-pass filter, and can directly smooth and denoise data in a time domain, and has the characteristic of high fidelity of abnormal signals. The idea of the filter is to slide filter the data with a window width of 2m+1, and fit the data in the window with an n-th order polynomial:
after the expression of the fitting polynomial P (x) is obtained based on the least square principle, the abscissa of the central point in the window is brought into P (x), and the obtained function value P (x) 0 ) The Savitzky-Golay filter value is used as the point, and the filtering of all data can be completed by sliding the window from beginning to end.
Since the vibrations of the artery originate from the beating of the animal's heart, the I (v) of the arterial location is mainly distributed at a specific frequency f 0 The method comprises the steps of carrying out a first treatment on the surface of the The vein and the capillary vessel do not have obvious and stable vibration signals, so that I (v) of the vein and the capillary vessel are randomly distributed in the whole frequency domain. Based on the above characteristics, the vibration noise at the non-artery is attenuated by the parametric IR, so that the artery position is highlighted. Thereby obtaining an arterial profile.
After obtaining the arterial distribution diagram, veins and capillaries need to be distinguished, a Gaussian function is used as a convolution kernel, the interference of random noise is reduced, and a matrix G is obtained, and the matrix G is represented by T (x, y; s), namely:
T(x,y；s)＝T(x,y)*G(x,y；s)
wherein is a rollProduct sign, variance s 2 The gaussian kernel function G (x, y; s) of (c) is defined as:
in order to accurately calculate the diameter of the blood vessel, it is necessary to calculate the direction of the blood vessel first, and take the vertical direction of the blood vessel as the search direction when calculating the diameter of the blood vessel. The invention calculates the blood vessel direction by adopting eigenvalue analysis based on a Hessian matrix. The Hessian matrix of a two-dimensional image is a symmetric square matrix consisting of the second partial derivatives of the real-valued function of the bivariate, defined as:
the primary image T (X, Y) is respectively convolved by calculating a first derivative and a second derivative of the Gaussian kernel function in the X direction and the Y direction, and finally a Hessian matrix of the matrix C at the point (X, Y) is obtained:
calculating the eigenvalue and the corresponding eigenvector of the Hessian matrix H (x, y), taking the direction of the included angle of the two eigenvectors as the direction of the blood vessel, and rotating by 90 degrees to obtain the radial search direction of the blood vessel. And searching in the radial search direction of the blood vessel to obtain the diameter of the blood vessel. The specific method comprises the following steps: dividing blood vessels, binarizing, searching blood vessel boundaries along the radial search direction and the opposite direction of the blood vessels respectively, wherein the distance between adjacent blood vessel boundaries is the diameter of the blood vessels, and the diameter of the blood vessels is recorded as a first diameter; calculating the gradient value change conditions along the radial search direction of the blood vessel and the opposite direction of the blood vessel by adopting a gradient extremum method, determining the edge of the blood vessel, increasing the gradient value of the edge of the blood vessel from zero to gradient, taking the starting end and the ending end of the mutation as two edge points of the blood vessel, taking the Euclidean distance of the two edge points as the diameter of the blood vessel, and recording the diameter of the blood vessel as a second diameter; the vessel diameter is obtained by averaging the first diameter and the second diameter. Among other methods, the vessel segmentation includes, but is not limited to: franki algorithm based on Hessian matrix, algorithm based on PCA, matched filtering algorithm, adaptive contrast enhancement algorithm. By averaging the first diameter and the second diameter, a more accurate determination of the vessel diameter at a location can be made, thereby better distinguishing between veins and capillaries. Among them, the method of distinguishing veins and capillaries is by comparing diameters of veins and capillaries. Specifically, a blood vessel with the diameter of more than 15 mu m is marked to obtain a vein distribution diagram, and a blood vessel with the diameter of less than or equal to 15 mu m is marked to obtain a capillary distribution diagram.
The invention uses the OCT scanning system to obtain the three-dimensional stereogram group, then uses the difference of vibration signals to separate the artery, and then uses the difference between the diameters of vein and capillary to separate the vein from the capillary, thereby realizing the high-precision distinguishing of three-dimensional blood vessel types.
While the preferred embodiments of the present invention have been illustrated and described, the present invention is not limited to the embodiments, and various equivalent modifications and substitutions can be made by one skilled in the art without departing from the spirit of the present invention, and these are intended to be included within the scope of the present invention as defined in the appended claims.
Claims (4)
1. A three-dimensional blood vessel type distinguishing method based on an OCT scanning system, comprising:
step 1, continuously C-scanning a region where a blood vessel is located through an OCT scanning system to obtain a three-dimensional stereogram group of the blood vessel in the time direction;
step 2, extracting a two-dimensional image corresponding to the unit depth from the three-dimensional stereogram group, converting the two-dimensional image into a two-dimensional matrix, and forming a three-dimensional matrix T by the two-dimensional matrix and the time corresponding to the two-dimensional matrix;
step 3, carrying out noise reduction treatment on the three-dimensional matrix T to obtain a matrix G;
step 4, performing Fourier transform on the matrix G to convert the matrix G from a time domain space into a frequency domain space to obtain a matrix K;
step 5, carrying out quantization processing on the matrix K through a parameter IR to obtain a matrix A, wherein an image represented by the matrix A is an arterial distribution diagram;
step 6, subtracting the matrix A from the matrix T to obtain a matrix B;
step 7, convolving the matrix B by taking a Gaussian function as a convolution kernel to obtain a matrix C;
step 8, calculating a Hessian matrix H (x, y) of the matrix C;
step 9, calculating the eigenvalue and the corresponding eigenvector of the Hessian matrix H (x, y), taking the direction of the included angle of the two eigenvectors as the direction of the blood vessel, and rotating for 90 degrees to obtain the radial search direction of the blood vessel;
step 10, searching in a radial searching direction of a blood vessel to obtain the diameter of the blood vessel;
step 11, obtaining a vein distribution diagram and a capillary distribution diagram through the diameters of blood vessels;
wherein, the expression of the parameter IR is:
I(υ f0 ) For I (v) at a specific frequency f 0 Intensity value at I (v) 0 ) For the intensity value of I (v) at zero frequency, I (v) is expressed as the intensity value of each point of the matrix K, the specific frequency f 0 Is preset;
in step 10, the method for determining the diameter of a blood vessel includes: dividing blood vessels, binarizing, searching blood vessel boundaries along the radial search direction and the opposite direction of the blood vessels respectively, wherein the distance between adjacent blood vessel boundaries is the diameter of the blood vessels, and the diameter of the blood vessels is recorded as a first diameter; calculating the gradient value change conditions along the radial search direction of the blood vessel and the opposite direction of the blood vessel by adopting a gradient extremum method, determining the edge of the blood vessel, increasing the gradient value of the edge of the blood vessel from zero to gradient, taking the starting end and the ending end of the mutation as two edge points of the blood vessel, taking the Euclidean distance of the two edge points as the diameter of the blood vessel, and recording the diameter of the blood vessel as a second diameter; the vessel diameter is obtained by averaging the first diameter and the second diameter.
2. The method according to claim 1, wherein in step 3, the noise reduction process is an eight-domain noise reduction process.
3. The three-dimensional blood vessel type distinguishing method based on the OCT scanning system according to claim 2, wherein the eight-domain noise reduction process specifically includes: a window of 3*3, all 1 values, is used to convolve the matrix T in turn, and then the data is smoothed using a Savitzky-Golay filter operator for a single column of eight neighbors in turn in the time direction.
4. The method of claim 1, wherein in step 11, the acquiring means of the venous distribution map and the capillary distribution map includes: marking the blood vessels with the diameters of more than 15 mu m to obtain a vein distribution diagram, and marking the blood vessels with the diameters of less than or equal to 15 mu m to obtain a capillary distribution diagram.
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