CN108961177B - OCT image scatter noise suppression method - Google Patents
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Abstract
The invention discloses a method for suppressing scattered point noise of an OCT image. The method comprises the steps of collecting an OCT image of a target object through an OCT system, performing curve smoothing on line data, performing wavelet transformation on the line data after the curve smoothing, constructing window functions with four different window widths, performing windowing processing on the line data after the wavelet transformation, performing weighted averaging on the line data after the four windows are added, performing wavelet inverse transformation again to obtain line data after noise suppression, and performing splicing processing on each line of data to further obtain the OCT image after the noise suppression. The invention furthest reserves the detail characteristics, improves the detail quality of OCT imaging, is beneficial to improving the noise reduction speed, and can be used for online noise reduction application.
Description
Technical Field
The invention belongs to the field of biological image processing, relates to an OCT image processing method, and particularly relates to a method for suppressing speckle noise of an OCT image.
Background
Optical Coherence Tomography (OCT) is a high resolution cross-sectional imaging technique that achieves imaging by collecting photons of the microstructural back-reflected light from the surface region of a sample and interfering with the reference path light. OCT devices were first applied to the in vivo analysis of biological tissues, mainly in the field of ophthalmology; the OCT can be used for high-resolution imaging of structures such as cornea, retina, crystalline lens and the like, photographing of macular diseases, measuring of optic nerve fiber thickness, monitoring and diagnosis of retinal diseases, measuring of retinal structures and the like, so that eye symptoms such as macular degeneration, glaucoma and the like can be diagnosed accurately at an early stage, and the eye is hardly damaged. Then OCT is applied to dentistry to obtain clear OCT image of separated enamel cementum, successfully finish the imaging of the tooth with carious lesion, and can quantitatively and qualitatively analyze the health condition of the oral cavity, estimate the thickness of the enamel, and have certain reports on the pre-diagnosis of tooth diseases. The OCT imaging system not only plays a great role in the medical field, but also has many successful applications in the industrial field. For example, in the field of production of filter membrane/nano-membrane materials, the quality of the membrane can be evaluated in the production process, the local appearance characteristic parameters of the membrane can be measured, and meanwhile, the decontamination and filtration capacity of the membrane can be objectively and accurately evaluated. In the field of printed circuit board manufacturing, particularly for a radio frequency circuit with high requirement on manufacturing precision, the appearance parameters of the radio frequency circuit can be rapidly measured, and the electrical performance and the wave performance can be evaluated. In the field of cultural relic protection and identification, the three-dimensional imaging of OCT is adopted, the organization structure evaluation can be carried out on the paint surfaces of oil paintings and porcelain, an effective observation tool is provided for cleaning and repairing the cultural relics, and meanwhile, partial jades and glassware can also be identified. With the development of the technology, the application field of OCT is being continuously expanded, and the industrial value thereof is being gradually increased.
However, due to the nature of interferometric imaging, speckle noise is inevitably introduced into the image, and this type of noise greatly reduces image contrast. Speckle suppression techniques typically use the acquisition of multiple images of the same object volume, relying on the irrelevancy of these several images to achieve noise suppression. These include spatial, phase, angle and frequency irrelevancy. These techniques, however, require multiple acquisitions at the same location in order to eliminate irrelevant scatter later using averaging or other algorithms.
Disclosure of Invention
In view of the problems in the background art, the present invention is directed to a method for suppressing speckle noise in an OCT image. The method combines frequency and multi-window processing and weighted averaging, can obviously reduce scattered point noise in the OCT image through the uncorrelated characteristic of the frequency, can consider a plurality of windows in the frequency window selection process, improves the frequency resolution ratio, simultaneously furthest retains the detail characteristics, finally improves the detail quality of the OCT imaging, and lays a technical foundation for improving the performance of the OCT technology in a plurality of detection applications by matching with other image processing methods of appearance detection such as imaging and the like.
The present invention proposes to suppress speckle noise using a frequency compounding technique, but this technique is divided into narrow bands by spectral bandwidth to produce uncorrelated speckle patterns, resulting in reduced image axial resolution. The method of the invention independently determines different frequency spectrums and time resolutions by using four orthogonal frequency spectrum windows, and retains the original information of the image to the maximum extent.
The method of the invention directly completes the non-correlated weighting of the image in the frequency domain by utilizing the time-frequency irrelevance under different window functions, has less quantity of images needing to be simultaneously acquired, is favorable for improving the noise reduction speed, and can be used for online imaging.
The technical scheme adopted by the invention comprises the following steps:
firstly, an OCT image of a target object is acquired through an OCT system, and the following modes are adopted for processing aiming at each column of data in the image:
1) performing curve smoothing on each line of data of the image;
2) performing wavelet transformation on a column of data after curve smoothing;
3) selecting four window widths and constructing respective window functions to construct four different window functions, marking as windows 1-4, and respectively windowing the column data after wavelet transformation in the step 2) by using the four different window functions to respectively obtain four windowed column data;
4) and taking weighted average of the four windowed column data, performing wavelet inverse transformation to obtain noise-suppressed column data, and performing splicing processing on each column data to obtain a noise-suppressed OCT image.
The line data is pixel points of one line of the image.
The target object is a biological tissue or a sample, and specifically can be a pearl sample, a human skin tissue, a fruit tissue and the like.
The step 1) is specifically as follows:
1.1) taking a sliding window with a fixed size and taking the maximum value of gray values of all pixel points in the sliding window as Zmax and the minimum value as Zmin, wherein the line data is X;
1.2) judging the gray value of a central pixel point in the sliding window, and if the gray value is in a range of [ Zmin x 1.05, Zmax x 0.95], reserving the gray value of the central pixel point; otherwise, expanding the sliding window and carrying out the next step;
1.3) taking the maximum value of the gray value of the pixel point in the expanded sliding window as Zmax and the minimum value as Zmin, and repeating the step 1.2) to judge whether the gray value of the central pixel point in the expanded sliding window is in the interval of [ Zmin x 1.05, Zmax x 0.95] or not again, and if the gray value is in the interval of [ Zmin x 1.05, Zmax x 0.95], keeping the gray value of the central pixel point;
otherwise, replacing the gray value of the central pixel point in the sliding window by using the median value Zmed of all the gray values of the pixel points in the expanded sliding window, expanding the sliding window again and repeating the steps until the gray value of the pixel point in the current sliding window is in the interval of [ Zmin x 1.05, Zmax x 0.95 ].
In specific implementation, the neighborhood of one pixel point is enlarged each time the sliding window is enlarged.
In the step 2), the data column is X, and the following formula discretization processing is adopted for wavelet transformation:
wherein, WTx(m, n) is the result after wavelet transform, xiIs the ith pixel point in the data column X, k isTotal number of pixels in a data column, tiThe wavelet time variable of the ith pixel point in the data column is represented, m and n respectively represent a time domain position parameter and a frequency domain component, and the time domain position and the frequency domain resolution are adjusted by adjusting the values of m and n; Ψ () represents a discrete wavelet basis function, typically an optional Harr wavelet, Daubechies wavelet, Symlet wavelet, or the like.
In step 3), four different window functions are constructed as follows:
wherein n is a frequency domain resolution parameter, k represents a light source wave number bandwidth of the OCT system, Win1 (n)1)～Win4(n2) Respectively representing four different window functions;
the windowing process with four different window functions is calculated as:
DW1(m,n1)＝∫∫WTx(m,n1)·cos(k1·l)·cos(k2·l)·Win1(k1-n1)·Win2(k2-n1)·dk1dk2
DW2(m,n2)＝∫∫WTx(m,n2)·cos(k1·l)·cos(k3·l)·Win1(k1-n2)·Win3(k3-n2)·dk1dk3
DW3(m,n3)＝∫∫WTx(m,n3)·cos(k1·l)·cos(k3·l)·Win1(k1-n3)·Win4(k4-n3)·dk1dk4
DW4(m,n4)＝∫∫WTx(m,n4)·cos(k3·l)·cos(k4·l)·Win1(k3-n4)·Win4(k4-n4)·dk3dk4
wherein, DW1(m,n1)～DW4(m,n4) The results after windowing of four different window functions are respectively shown, i is the optical path difference of a reference arm and a sample arm in the OCT system, and k 1-k 4 respectively show the frequency sliding components in the windowing process.
In the step 4), the weighted average of the four windowed column data is specifically:
DW＝(8DW1+4DW2+2DW3+DW4)/15
DW＝(8DW1(m,n1)+4DW2(m,n2)+2DW3(m,n3)+DW4(m,n4))/15
wherein DW represents the weighted average of the column data, DW1(m,n1)～DW4(m,n2) Respectively representing the results of windowing processing of four different window functions.
The invention has the beneficial effects that:
the invention uses a frequency complex windowing method to control the scatter noise in the image. The novel noise reduction scheme only needs to acquire images for 4 times, can work on the existing OCT small-sized instrument, and does not need to further increase the computing power of the system.
The sizes of the four spectral windows related in the invention balance the scattered point inhibition capability, the resolution loss and the calculation speed, and an optimal balance point is found. More importantly, the method can be applied in deployment at a video rate, and through the parallel computing capability of the GPU of the modern computer, the method has less insertion computation amount and less resources required by signal processing and image rendering.
In contrast, conventional speckle suppression requires averaging over about 100 images with uncorrelated speckle, with a large normalized standard deviation. The composite processing mode of the invention can obviously improve the signal-to-noise ratio of the images by averaging 4 images with angle intervals. Although the signal-to-noise ratio improvement is low compared to other computationally intensive methods, these hybrid weighting processes do not physically modify the OCT system or require multiple image acquisitions, and the number of scatter points applicable in real-time imaging systems and post-processing is rapidly reduced.
The invention adopts the serialized preprocessing, has less loss of the original information of the image and has certain robustness of the detection effect.
Frequency compounding is achieved by processing each of the images with a plurality of narrow spectral windows, each having independent speckle removal, which are then combined to minimize axial resolution loss, equivalent to a low resolution windowed image multiplied by a full bandwidth image.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is an OCT image of a seawater nucleated pearl input for an embodiment.
FIG. 3 shows the effect of wavelet transform of the intermediate data column of the OCT image of the seawater nucleated pearl sample in FIG. 1; (a) original data; (b) approximation coefficients after wavelet transform; (c) detail coefficient after wavelet transform;
fig. 4 is a final effect diagram of the seawater nucleated pearl sample OCT image of fig. 1 after noise reduction.
Detailed Description
The present invention will be described in further detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the invention and the implementation process thereof are as follows:
1) using a common OCT system to acquire a batch of OCT images of pearl samples serving as target objects; FIG. 2 is an OCT image showing a seawater nucleated pearl.
2) Performing curve smoothing on each line of data of the image; the image resolution of the OCT system is 512 x 512, the length of an image data column is 512, and the bit width is 8 bits, so the range of the gray value is 0-255;
2) performing curve smoothing on each line of data of the image; the image resolution of the OCT system is 512 x 512, the length of an image data column is 512, and the bit width is 8 bits, so the range of the gray value is 0-255;
2.1) taking a sliding window with a fixed size, wherein the maximum value of gray values of all pixel points in the sliding window is Zmax, and the minimum value is Zmin;
2.2) judging the gray value of the central pixel point in the sliding window, and if the gray value is in the interval of [ Zmin x 1.05, Zmax x 0.95], reserving the gray value of the central pixel point; otherwise, expanding a sliding window of one pixel and carrying out the next step;
2.3) taking the maximum value of the gray value of the pixel point in the expanded sliding window as Zmax and the minimum value as Zmin, judging whether the gray value of the central pixel point in the expanded sliding window is in the interval of [ Zmin x 1.05, Zmax x 0.95], and if the gray value is in the interval of [ Zmin x 1.05, Zmax x 0.95], keeping the gray value of the central pixel point; otherwise, replacing the gray value of the central pixel point in the sliding window by the median value Zmed of all the gray values of the pixel points in the expanded sliding window, expanding the sliding window of one pixel again and repeating the steps until the gray value of the pixel point in the current sliding window is in the interval of [ zminx 1.05, Zmax x 0.95 ].
3) Performing wavelet transformation on each line of data after curve smoothing;
the wavelet transformation is carried out by adopting the following formula discretization treatment:
the wavelet basis function of an embodiment takes the sym4 wavelet.
Wherein, WTx(m, n) is the result after wavelet transform, xiIs the ith pixel in the data column X, k is the total number of pixels in the data column, tiRepresenting a column of dataThe wavelet time variable of the ith pixel point, m and n respectively represent a time domain position parameter and a frequency domain component,
FIG. 3 shows the results of wavelet transform of the intermediate data column of the OCT image of the seawater nucleated pearl sample of FIG. 1; the original data is shown in fig. 3(a), the approximation coefficients after wavelet transform are shown in fig. 3(b), and the detail coefficients after wavelet transform are shown in fig. 3 (c).
4) Selecting four window widths, constructing four different window functions, and recording the window functions as windows 1-4;
the wave number bandwidth kappa of the light source of the OCT system built in the implementation is 1/200nm-1Four different window functions are calculated and established as follows:
where n is the frequency domain resolution parameter, Win1 (n)1)～Win4(n2) Respectively representing four different window functions;
5) windowing 1-4 processing is carried out on the column data after the wavelet transformation in the step 3), and a window function is constructed in the step 4) to respectively obtain four windowed column data 1-4;
DW1(m,n1)＝∫∫WTx(m,n1)·cos(k1·l)·cos(k2·l)·Win1(k1-n1)·Win2(k2-n1)·dk1dk2
DW2(m,n2)＝∫∫WTx(m,n2)·cos(k1·l)·cos(k3·l)·Win1(k1-n2)·Win3(k3-n2)·dk1dk3
DW3(m,n3)＝∫∫WTx(m,n3)·cos(k1·l)·cos(k3·l)·Win1(k1-n3)·Win4(k4-n3)·dk1dk4
DW4(m,n4)＝∫∫WTx(m,n4)·cos(k3·l)·cos(k4·l)·Win1(k3-n4)·Win4(k4-n4)·dk3dk4
6) taking weighted average of the 1-4 windowed column data obtained in the step 5) to obtain windowed column data DW:
DW＝(8DW1(m,n1)+4DW2(m,n2)+2DW3(m,n3)+DW4(m,n4))/15
7) and (3) performing wavelet inverse transformation on each column of data processed in the step 6), and finally combining each column of data to obtain the OCT image after noise suppression.
According to the steps, each column of data is processed to obtain an OCT image after noise suppression.
FIG. 4 shows the final effect of noise reduction of the OCT image of the seawater nucleated pearl sample of FIG. 1;
in the present embodiment, the standard deviation σ of the entire OCT image gradation is calculated; manually selecting a pearl target area of an OCT image of a pearl sample, and calculating the average value beta of the gray scale of the target area; the noise reduction effect of different methods is measured by the uniform noise intensity alpha-sigma/beta; in this embodiment, 40 pearl images are counted, and if the conventional noise reduction method of averaging multiple images is adopted, the average value of α is 0.6, whereas the average value of α is 0.48 by adopting the method proposed by the present invention. Experimental results show that the method has a good noise reduction effect on 40 OCT images. Compared with the existing reported frequency, amplitude and phase averaging scheme, the method obviously improves the noise reduction speed and the number of acquired images, and shows the advantages of the method.
In the embodiment of the present invention, it can be further understood by those skilled in the art that all or part of the steps in the method for implementing the above embodiment may be implemented by instructing the relevant hardware through a program, where the program may be stored in a computer-readable storage medium, where the storage medium includes a ROM/RAM, a magnetic disk, an optical disk, and the like.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (5)
1. A method for suppressing OCT image scatter noise is characterized by comprising the following steps: firstly, an OCT image of a target object is acquired through an OCT system, and the following modes are adopted for processing aiming at each column of data in the image:
1) performing curve smoothing on the column data;
2) performing wavelet transformation on a column of data after curve smoothing;
3) selecting four window widths and constructing respective window functions to construct four different window functions, and windowing the column data subjected to wavelet transform in the step 2) by using the four different window functions respectively to obtain four windowed column data respectively;
4) and taking weighted average of the four windowed column data, performing wavelet inverse transformation to obtain noise-suppressed column data, and performing splicing processing on each column data to obtain a noise-suppressed OCT image.
2. The method for suppressing speckle noise of an OCT image according to claim 1, wherein: the step 1) is specifically as follows:
1.1) taking a sliding window with a fixed size, wherein the maximum value of gray values of all pixel points in the sliding window is Zmax, and the minimum value is Zmin;
1.2) judging the gray value of a central pixel point in the sliding window, and if the gray value is in a range of [ Zmin x 1.05, Zmax x 0.95], reserving the gray value of the central pixel point; otherwise, expanding the sliding window and carrying out the next step;
1.3) taking the maximum value of the gray value of the pixel point in the expanded sliding window as Zmax and the minimum value as Zmin, judging whether the gray value of the central pixel point in the expanded sliding window is in the interval of [ Zmin x 1.05 and Zmax x 0.95], and if the gray value is in the interval of [ Zmin x 1.05 and Zmax x 0.95], keeping the gray value of the central pixel point;
otherwise, jumping to step 1.4);
1.4) replacing the gray value of the central pixel point in the sliding window by using the median value Zmed of all the gray values of the pixel points in the expanded sliding window, expanding the sliding window again and repeating the step 1.3) until the gray value of the pixel point in the current sliding window is in the interval of [ zminx 1.05, Zmax x 0.95 ].
3. The method for suppressing speckle noise of an OCT image according to claim 1, wherein: in the step 2), the following formula discretization processing is adopted for wavelet transformation:
wherein, WTx(m, n) is the result after wavelet transform, xiIs the ith pixel in the data column X, k is the total number of pixels in the data column, tiThe wavelet time variable of the ith pixel point in the data column is represented, and m and n respectively represent a time domain position parameter and a frequency domain component; Ψ () represents a discrete wavelet basis function.
4. The method for suppressing speckle noise of an OCT image according to claim 1, wherein: in step 3), four different window functions are constructed as follows:
wherein n is1～n4Is the frequency domain component of four window functions, k represents the wave number bandwidth of the light source of the OCT system, Win1 (n)1)～Win4(n2) Respectively representing four different window functions;
the windowing process with four different window functions is calculated as:
DW1(m,n1)＝∫∫WTx(m,n1)·cos(k1·l)·cos(k2·l)·Win1(k1-n1)·Win2(k2-n1)·dk1dk2
DW2(m,n2)＝∫∫WTx(m,n2)·cos(k1·l)·cos(k3·l)·Win1(k1-n2)·Win3(k3-n2)·dk1dk3
DW3(m,n3)＝∫∫WTx(m,n3)·cos(k1·l)·cos(k3·l)·Win1(k1-n3)·Win4(k4-n3)·dk1dk4
DW4(m,n4)＝∫∫WTx(m,n4)·cos(k3·l)·cos(k4·l)·Win1(k3-n4)·Win4(k4-n4)·dk3dk4
wherein, DW1(m,n1)～DW4(m,n4) The results after windowing of four different window functions are respectively shown, i is the optical path difference of a reference arm and a sample arm in the OCT system, and k 1-k 4 respectively show the frequency sliding components in the windowing process.
5. The method for suppressing speckle noise of an OCT image according to claim 1, wherein: in the step 4), the weighted average of the four windowed column data is specifically:
DW＝(8DW1(m,n1)+4DW2(m,n2)+2DW3(m,n3)+DW4(m,n4))/15
wherein DW represents the weighted average of the column data, DW1(m,n1)～DW4(m,n2) Respectively representing the results of windowing processing of four different window functions.
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