High frame rate imaging method for boosting image vertical resolution

Abstract

The present application relates to a kind of high frame rate imaging methods for improving image vertical resolution, which comprises: in the process of blowing gas to patient cornea, the dynamic deformation image of corneal section in the process of patient corneal deformation is collected;Determine the target area of dynamic deformation image, wherein the target area is the minimum area including cornea;The target area in each frame of dynamic deformation image is intercepted, and the continuous frame image of target area is formed;The continuous frame image of target area is carried out high-resolution processing;The continuous frame image after high-resolution processing is carried out corneal biomechanical property evaluation.The present application determines the target area of dynamic deformation image in the process of patient corneal deformation, and the target area is the minimum area including cornea, by carrying out high-resolution processing to the continuous frame image of target area, the data precision of corneal area in continuous frame image is improved, and the accuracy of corneal biomechanical property evaluation result to the continuous frame image after high-resolution processing is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of image processing technology, and in particular to a high frame rate imaging method for improving the vertical resolution of images. Background Technology

[0002] Accurate measurement of corneal biomechanical properties is crucial for ophthalmic examination and treatment. Current methods for measuring corneal biomechanical properties are based on sequential image analysis. For example, Corvis ST applies an air pulse to the center of the patient's cornea while Scheimpflug high-speed imaging technology captures sequential images of the entire process of corneal deformation caused by the air pulse. Analysis of these sequential images yields the patient's corneal biomechanical parameters (such as deformation amplitude, deformation speed, and curvature).

[0003] The vertical resolution of the Corvis ST is affected by light diffraction, resulting in poor image resolution, which can affect the accuracy of corneal biomechanical property analysis. Summary of the Invention

[0004] To address the aforementioned problems, this invention provides a high frame rate imaging method for improving the vertical resolution of images, the method comprising:

[0005] During the process of blowing air into the patient's cornea, dynamic deformation images of the corneal cross-section were acquired during the deformation process; the dynamic deformation images were grayscale images.

[0006] Determine the target region of the dynamically deformed image, where the target region is the smallest region including the cornea;

[0007] Extract the target region from each frame of the dynamic deformation image to form a continuous frame image of the target region;

[0008] Perform high-resolution processing on consecutive frames of images of the target region;

[0009] The corneal biomechanical properties were evaluated from consecutive frames of images processed at high resolution.

[0010] Optionally, determining the target region of the dynamic deformation image includes:

[0011] Acquire the first frame of the dynamic deformation image;

[0012] Obtain the labeled edges of the cornea in the first frame of the dynamic deformation image;

[0013] Identify the corneal recognition edge in the first frame of the dynamic deformation image;

[0014] A set of labeled pixels is formed based on the labeled edges. A set of recognition pixels is formed based on the recognition edges. ;

[0015] Difference pixel set ;

[0016] according to , and The grayscale value of each element determines the threshold for change;

[0017] The target region of the dynamically deformed image is determined based on the change threshold.

[0018] Optionally, according to , and The grayscale values ​​of each element in the image determine the threshold for change, including:

[0019] Sure The first mean of the gray values ​​of all elements in the array. and the first standard deviation ;

[0020] Sure The second mean of the gray values ​​of all elements in the array. Second standard deviation ;

[0021] like If it is an empty set, then the change threshold is determined as follows: ;

[0022] like If it is a non-empty set, then it is determined. The third mean of the gray values ​​of all elements in the data. and the third standard deviation ;according to and The relationship between them, and and Determine the threshold for change.

[0023] Optionally, according to and The relationship between them, and and Determine the change threshold, including:

[0024] like Then the threshold for change is determined as ;

[0025] like Then the threshold for change is determined as .

[0026] Optionally, the target region of the dynamically deformed image is determined based on a change threshold, including:

[0027] Root Each element is categorized by column, forming a set of the first labeled pixels for each column. ;

[0028] Determine each column The minimum and maximum number of rows for all pixels in the array;

[0029] The pixel with the smallest row number moving up one row and the pixel with the largest row number moving down one row in each column are defined as the incrementing pixels. Form the second set of labeled pixels for each column ;

[0030] According to each column , And change thresholds, to determine the changed pixel points in each column;

[0031] The smallest rectangle containing the changing pixels of all columns is defined as the target region.

[0032] Optionally, based on each column , And a change threshold, to determine the changed pixel points in each column, including:

[0033] For any column, determine the column's... The fourth mean of the gray values ​​of all elements in the array. ;

[0034] Determine any column The fifth mean of the gray values ​​of all elements in the array. ;

[0035] like and Then determine the column. All pixels in the array are the changing pixels in any column; where, The preset grayscale threshold, ;

[0036] like ,or, but Then, based on any column , The changing pixel point is determined by the change threshold.

[0037] Optionally, based on either column , The change threshold is used to determine the changed pixel points, including:

[0038] Determine the set of changes ;

[0039] Sure The sixth mean of the gray values ​​of all elements in the array. and the sixth standard deviation ;

[0040] like Then determine the column. All pixels in the array are the changing pixels in any column; where, For the preset change precision, For the change threshold, for The first mean of the grayscale values ​​of all elements in the array. for The first standard deviation of the grayscale values ​​of all elements in the dataset;

[0041] like Then any column will be Update all pixels in the array to any column. For all pixels in the array, repeatedly perform the process of identifying the pixel with the smallest row number moving up one row and the pixel with the largest row number moving down one row as the added pixels. Then, use these added pixels and... Form the second set of labeled pixels for each column The steps and subsequent steps.

[0042] Optionally, the target region is determined by the smallest rectangle containing the changed pixels of all columns, including:

[0043] Expand the changed pixels in each column upwards and downwards respectively. 100 pixels; among which... For the pre-set redundancy value, Determined based on patient attributes , This is the maximum redundancy value. , This represents the minimum number of pixels between the center point of the changed pixels in each column and the top edge of the first frame of the dynamic deformation image. This represents the minimum number of pixels between the center point of the changed pixels in each column and the bottom edge of the first frame of the dynamic deformation image. The maximum value is half the total number of changing pixels in each column;

[0044] The target region is determined by the smallest rectangle that contains the expanded, changed pixels of all columns.

[0045] Optionally, high-resolution processing is performed on consecutive frames of images of the target region, including:

[0046] For any frame in a series of consecutive frames of the target region, the following function is used: Perform high-resolution processing;

[0047] in, For frame image identifiers in a series of consecutive frame images of the target region, For frame images The pixel markers in the text For frame images The Middle The value of each pixel. For frame images The third high-resolution processed The value of each pixel. This is the downsampling matrix. For fuzzy matrices, For penalty parameters, This is a regular function.

[0048] Optionally, ;

[0049] in, For normalization parameters, These are the coordinates of pixels in the image. To based on frame image The obtained variance estimate.

[0050] This invention relates to a high frame rate imaging method for improving the vertical resolution of images. The method includes: acquiring dynamic deformation images of the corneal cross-section during corneal deformation while inflating the patient's cornea; determining the target region of the dynamic deformation images, wherein the target region is the smallest region including the cornea; cropping the target region from each frame of the dynamic deformation images to form a series of frame images of the target region; performing high-resolution processing on the series of frame images of the target region; and evaluating the corneal biomechanical properties of the high-resolution processed series of frame images. This method determines the target region of the dynamic deformation images during corneal deformation, which is the smallest region including the cornea. By performing high-resolution processing on the series of frame images of the target region, the data accuracy of the corneal region in the series of frame images is improved, ensuring the accuracy of the corneal biomechanical property evaluation results from the high-resolution processed series of frame images. Attached Figure Description

[0051] Figure 1 A flowchart illustrating a high frame rate imaging method for improving the vertical resolution of an image, provided in an embodiment of this application;

[0052] Figure 2 A schematic diagram of a dynamic deformation image provided in an embodiment of this application;

[0053] Figure 3 A schematic diagram of rows and columns in the first frame of a dynamically deformed image provided in an embodiment of this application;

[0054] Figure 4 This is a schematic diagram of rows and columns in another first frame of a dynamically deformed image provided in an embodiment of this application. Detailed Implementation

[0055] To better explain and facilitate understanding of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0056] When image acquisition devices acquire images, they capture images of the patient's eye area. However, the cornea is only a small part of the eye, which means that each frame of the image acquisition device includes a large number of images of non-corneal areas. This increases the amount of noise data in each frame, which not only increases the computational resource requirements for subsequent corneal biomechanical characteristic analysis, but also affects the accuracy of corneal area data accuracy caused by noise data.

[0057] To improve analytical accuracy, this invention provides a high frame rate imaging method for enhancing image vertical resolution. The method includes: acquiring dynamic deformation images of the corneal cross-section during corneal deformation while inflating the patient's cornea; determining the target region of the dynamic deformation images, wherein the target region is the smallest region including the cornea; extracting the target region from each frame of the dynamic deformation images to form a series of frame images of the target region; performing high-resolution processing on the series of frame images of the target region; and evaluating the corneal biomechanical properties of the high-resolution processed series of frame images. This method determines the target region of the dynamic deformation images during corneal deformation, which is the smallest region including the cornea. By performing high-resolution processing on the series of frame images of the target region, the data accuracy of the corneal region in the series of frame images is improved, ensuring the accuracy of the corneal biomechanical property evaluation results from the high-resolution processed series of frame images.

[0058] See Figure 1 This embodiment provides a high frame rate imaging method for improving the vertical resolution of images, and its implementation process is as follows:

[0059] 101. During the process of blowing air into the patient's cornea, dynamic deformation images of the corneal cross-section were acquired during the deformation of the patient's cornea.

[0060] Among them, the dynamic deformation image is a grayscale image.

[0061] Dynamic deformation images of the corneal cross-section can be acquired using the Corvis ST. The Corvis ST applies an air pulse to the center of the patient's cornea while a high-speed Scheimpflug camera captures continuous frames of the entire process of corneal deformation caused by the air pulse. In other words, the method provided in this embodiment performs high-resolution imaging on images captured by the Corvis ST using an ultra-high-speed Scheimpflug camera. Since the images captured by the Corvis ST are grayscale images, the dynamic deformation images are also grayscale images. For example, any frame of the dynamic deformation image is as follows: Figure 2 As shown.

[0062] 102. Determine the target region of the dynamically deformed image.

[0063] The target area is the smallest area including the cornea.

[0064] After obtaining the dynamic deformation image in step 101, the acquiring doctor will mark the corneal region in the first frame of the dynamic deformation image to determine the accurate location of the corneal region. At the same time, the corneal region in the first frame of the dynamic deformation image will be identified using existing image recognition technology, and then the target region will be determined in step 102 based on the relationship between the identified corneal region and the marked corneal region.

[0065] The implementation process of step 102 is as follows:

[0066] 102-1, Obtain the first frame of dynamic deformation image.

[0067] 102-2, Obtain the labeled edges of the cornea in the first frame of the dynamic deformation image.

[0068] The marked edge is marked by the acquiring doctor. That is, after obtaining the dynamic deformation image in step 101, the acquiring doctor marks the corneal region in the first frame of the dynamic deformation image, and the mark can be obtained in step 102-2.

[0069] Since the marked edges are marked by the collecting doctor, the area included by the marked edges can be considered as the accurate corneal area.

[0070] However, doctors' annotations may have deviations due to hand tremors or other reasons. Therefore, the area outside the annotation area may include part of the corneal area. Thus, the area included by the annotation edge is the accurate corneal area, but not necessarily the entire corneal area. The complete corneal area will be determined later based on the annotation edge and the corneal area identified by existing image recognition technology.

[0071] 102-3, Identify the corneal recognition edge in the first frame of dynamic deformation image.

[0072] The edge is identified by existing image recognition technology. That is, after obtaining the dynamic deformation image in step 101, the corneal region in the initial image is identified by existing image recognition technology, and then the edge of the region is obtained. Therefore, the edge can be obtained in steps 102-3.

[0073] Since the edge recognition is automatically determined by image recognition technology, errors may occur due to the accuracy of the recognition. Therefore, the recognized edge is used as a reference to adjust the marked edge, ultimately obtaining an accurate corneal area.

[0074] 102-4, Form a set of labeled pixels based on the labeled edges. A set of recognition pixels is formed based on the recognition edges. .

[0075] like Figure 3 As shown, the pixel in the upper left corner of the first frame of the dynamic deformation image can be taken as the pixel in row 0 and column 0. The number of columns increases to the right, and the number of rows increases downwards, thus obtaining the number of rows and columns of all pixels in the first frame of the dynamic deformation image.

[0076] It should be noted that, Figure 3 The rows and columns shown are for illustrative purposes only. In actual applications, they should be determined based on the actual pixel count in the image.

[0077] Because the corneal region in the first frame of the dynamic deformation image is an irregular curve with a certain width, the labeled edge and the identified edge are obtained only by different methods, which are the edges of the corneal region, such as... Figure 3 As shown, we obtain pixels in columns 7 and 5 and columns 7 and 7 respectively. However, pixels between two pixels (such as those in column 7 and 6) are also pixels in the corneal region. Therefore, in step 102-4, the coordinates of all pixels for the labeled edges and identified edges are determined (e.g., coordinates are (column number, row number)). Then, column by column, the coordinates corresponding to all row numbers in each column (i.e., the column coordinates remain unchanged, only the row coordinates are supplemented) are used as fill coordinates (e.g., the coordinates of the labeled edge pixels in column 7 are (7,5) and (7,7), and the coordinates of all row numbers in columns 5 and 7 are (7,6) as supplementary coordinates). In this way, the coordinates of all labeled edge identifications and their corresponding supplementary coordinates form a set of labeled pixels. The coordinates of all edge detections and their corresponding supplementary coordinates are used to form a set of detection pixels. For example, for the 7th column in the labeled edge, it is in the set of labeled pixels. There are three coordinates, namely (7,5), (7,6) and (7,7).

[0078] This is how pixel sets are labeled. It contains the coordinates of all pixels within the region enclosed by the labeled edges, identifying the pixel set. It contains all the coordinates of the region enclosed by the identified edges.

[0079] 102-5, set of differing pixels .

[0080] Because the labeled edges and the recognized edges are obtained in different ways—labeled edges are obtained by doctors, and recognized edges are obtained by image recognition technology—different methods may produce errors. Therefore, the area enclosed by the labeled edges and the area enclosed by the recognized edges are not necessarily the same area. That is, the area enclosed by the labeled edges and the area enclosed by the recognized edges may have non-overlapping parts.

[0081] thus, This refers to all pixels within the region enclosed by the labeled edge and the region enclosed by the identified edge. The pixels shared by the region enclosed by the labeled edge and the region enclosed by the identified edge. This refers to the pixels within the region enclosed by the labeled edge and the pixels in the non-overlapping portion of the region enclosed by the identified edge.

[0082] In practical applications, the relationship between the region enclosed by the labeled edge and the region enclosed by the recognized edge can be threefold. The first is that they are completely identical (in which case the set of differing pixels is...). The first case is an empty set, indicating that the region enclosed by the labeled edges and the region enclosed by the recognized edges are the same, meaning that the edges of both the labeled and recognized corneal regions are very accurate, which is normal. The second case is partially the same (in this case, the set of differing pixels is empty). Non-empty set, but Less than or This indicates a discrepancy between the labeled edge and the recognized edge. The overlapping portion can be considered the main part of the patient's cornea, while the non-overlapping portion can be considered the edge of the corneal region. This discrepancy may be due to insufficient accuracy in edge recognition, which is normal. The third type is completely different (in this case, the set of differing pixels). Non-empty set, but This indicates that the labeled edge and the recognized edge are two independent regions. In this case, either the labeling is incorrect, the recognition is incorrect, or both labeling and recognition are incorrect, which is abnormal. Therefore, if the third case occurs, the process can be exited and the high frame rate imaging method for improving the vertical resolution of images provided in this embodiment can be re-executed.

[0083] 102-6, according to , and The grayscale value of each element determines the threshold for change.

[0084] The implementation process of step 102-6 is as follows:

[0085] 201, confirmed The first mean of the gray values ​​of all elements in the array. and the first standard deviation .

[0086] In step 201, the pixels of the region enclosed by the labeled edges are determined (i.e., The mean of the gray values ​​of all elements in the dataset (i.e., the first mean). ) and the pixels in the area enclosed by the marked edges (i.e. The standard deviation (i.e., the first standard deviation) of the gray values ​​of all elements in the dataset. ).

[0087] First mean The first standard deviation represents the average gray value of the region enclosed by the labeled edges. It represents the degree of change in grayscale values ​​of the area enclosed by the labeled edge.

[0088] 202, confirmed The second mean of the gray values ​​of all elements in the array. Second standard deviation .

[0089] In step 202, the pixels in the region enclosed by the identification edge are determined (i.e., The mean of the gray values ​​of all elements in the dataset (i.e., the second mean). ) and the pixels in the region enclosed by the identification edge (i.e. The standard deviation (i.e., the second standard deviation) of the gray values ​​of all elements in the dataset. ).

[0090] Second mean The second standard deviation represents the average gray value of the region enclosed by the identification edge. It characterizes the degree of change in grayscale values ​​of the region enclosed by the identification edge.

[0091] 203, if If it is an empty set, then the change threshold is determined as follows: .

[0092] like If the set is empty, it means that both the labeled edges and the recognized edges are very accurate. Therefore, the average gray value of the region enclosed by the labeled edges is... The average gray value of the region enclosed by the recognition edge The grayscale values ​​of the area enclosed by the labeled edges should be the same, reflecting the degree of change. The degree of change in grayscale values ​​of the region enclosed by the identification edge It should be the same. Considering the uncontrollable errors that may exist in practical applications, this step will... The threshold of change when the set is empty .

[0093] This represents the average gray value of the corneal region (including the area enclosed by the labeled edges and the area enclosed by the identified edges). This refers to the degree of change in grayscale values ​​of the corneal region (including the area enclosed by the labeled edges and the area enclosed by the identified edges). This is the ratio of the change in grayscale value of the corneal region (including the area enclosed by the labeled edge and the area enclosed by the recognized edge) to the average grayscale value of the corneal region (including the area enclosed by the labeled edge and the recognized edge). This ratio characterizes the grayscale value fluctuation of the corneal region (including the area enclosed by the labeled edge and the recognized edge). The larger the value, the greater the grayscale value fluctuation of the corneal region (including the area enclosed by the labeled edge and the recognized edge). This fluctuation is the true fluctuation of the patient's cornea, and the threshold of change varies from patient to patient. They may be the same or different. Further adjustments will be based on a change threshold. Determining the target area allows for personalized target area selection, ensuring that each user's target area matches their specific circumstances and improving the accuracy of the target area selection.

[0094] 204, if If it is a non-empty set, then it is determined. The third mean of the gray values ​​of all elements in the data. and the third standard deviation .according to and The relationship between them, and and Determine the threshold for change.

[0095] This is and The case of the difference pixel set Non-empty set, but Less than or ,like Figure 3 The situation is shown below.

[0096] In step 204, the pixels that mark the smallest circle and identify the non-overlapping parts of the smallest circle are determined (i.e., The mean of the gray values ​​of all elements in the dataset (i.e., the third mean). ) and identify the pixels covered by the smallest circle (i.e. The standard deviation (i.e., the third standard deviation) of the gray values ​​of all elements in the dataset. ).according to and The relationship between them, and and Determine the threshold for change.

[0097] Third mean The third standard deviation represents the average gray value of the non-overlapping portion of the region enclosed by the labeled edge and the region enclosed by the recognized edge. It characterizes the degree of change in grayscale values ​​of the non-overlapping parts of the regions enclosed by the labeled edges and the regions enclosed by the identified edges.

[0098] in addition, and There are two kinds of relationships, one is... This relationship indicates that although the regions enclosed by the labeled edges and the regions enclosed by the recognized edges differ, the degree of grayscale value change of each pixel in both regions is the same. Another type is... This relationship indicates that there is a difference between the region enclosed by the labeled edge and the region enclosed by the recognized edge, and the degree of gray value change of each pixel in the region enclosed by the labeled edge and the region enclosed by the recognized edge is also different.

[0099] for In this case, the change threshold can be determined. .in, This represents the average gray value of the corneal region (including the area enclosed by the labeled edges and the area enclosed by the identified edges). The degree of change in grayscale values ​​of the corneal region (including the area enclosed by the labeled edge and the area enclosed by the identified edge) (in practical use) It characterizes the degree of change in grayscale values ​​of the corneal region (including the area enclosed by the labeled edge and the area enclosed by the identified edge), but because ,therefore Use directly here This characterizes the degree of change in grayscale values ​​of the corneal region (including the region enclosed by the labeled edge and the region enclosed by the identified edge). In practical applications, it can also be used... The degree of change in grayscale values ​​characterizes the corneal region (including the region enclosed by the labeled edge and the region enclosed by the identified edge), i.e. It does not affect the change threshold. ), It characterizes the gray value fluctuation of the corneal region (including the region enclosed by the labeled edge and the region enclosed by the identified edge).

[0100] It characterizes the gray value fluctuation of the non-overlapping parts of the region enclosed by the labeled edge and the region enclosed by the identified edge.

[0101] Change threshold This is defined as the ratio of gray value fluctuations in the overlapping portion of the region enclosed by the labeled edge to the gray value fluctuations in the non-overlapping portion of the region. This ratio characterizes the difference in gray value fluctuations between the main part and the edge part of the patient's cornea. This difference represents the true difference in the patient's cornea; the difference may be the same or different among different patients. This difference is used as a threshold for variation. This can also reflect the individualization of different patients. Further adjustments will be made based on change thresholds. Determining the target area allows for personalized target area selection, ensuring that each user's target area matches their specific circumstances and improving the accuracy of the target area selection.

[0102] for In this case, the change threshold can be determined. .in, The grayscale value fluctuation of the area enclosed by the marked edge is determined because the marked edge is marked by the collecting doctor and is considered accurate. Therefore, the area enclosed by the marked edge is used as the main corneal area to determine the change threshold. This characterizes the proportion of the difference in standard deviation between the region enclosed by the identified edge and the region enclosed by the labeled edge to the standard deviation of the region enclosed by the identified edge. A larger proportion indicates a greater difference in the standard deviations between the regions enclosed by the identified edge and the labeled edge. This characterizes the degree of grayscale value change between the region enclosed by the identified edge and the region enclosed by the labeled edge. Based on this, the degree of grayscale value change between the region enclosed by the identified edges and the region enclosed by the labeled edges is considered. The area enclosed by the marked edges is adjusted to obtain the gray value fluctuation of the adjusted corneal area (i.e., ).

[0103] It characterizes the gray value fluctuation of the non-overlapping parts of the region enclosed by the labeled edge and the region enclosed by the identified edge.

[0104] Change threshold The ratio of gray value fluctuation in the adjusted corneal region to the gray value fluctuation in the non-overlapping region is used, with the adjusted corneal region as the main part of the patient's cornea. This ratio characterizes the difference in gray value fluctuation between the main part and the peripheral part of the patient's cornea. This difference represents the true difference in the patient's cornea; the difference may be the same or different among different patients. This difference is used as a threshold for variation. This can also reflect the individualization of different patients. Further adjustments will be made based on change thresholds. Determining the target area allows for personalized target area selection, ensuring that each user's target area matches their specific circumstances and improving the accuracy of the target area selection.

[0105] Based on the above explanation, the change threshold can accurately reflect the current gray value changes in the corneal region of the patient.

[0106] 102-7. Determine the target region of the dynamic deformation image based on the change threshold.

[0107] The implementation process of step 102-7 is as follows:

[0108] 301, will Each element is categorized by column, forming a set of the first labeled pixels for each column. .

[0109] As can be seen from step 102-4, It is a set consisting of the coordinates of all identified edges and their corresponding supplementary coordinates. In step 301, it will be identified by columns. The coordinates of the same column form a .like Figure 4 As shown, for column 7, since The coordinates of the 7th column are (7,5), (7,6), and (7,7). Therefore, the coordinates of the 7th column are... Includes: (7,5) (i.e.) Figure 4 Points A1, (7,6) and (7,7) in the middle (i.e. Figure 4 Point A2 in the middle).

[0110] 302, determine the columns The minimum and maximum number of rows for all pixels in the array.

[0111] For example, column 7 The minimum number of rows for all pixels is 5 in A1 and the maximum number of rows is 7 in A2.

[0112] It should be noted that in this embodiment, each coordinate and pixel has a one-to-one correspondence; each coordinate corresponds to a unique pixel, and each pixel corresponds to a unique coordinate. Therefore, in step 302, each column... The minimum and maximum number of rows for all pixels are also the number of columns. The minimum and maximum values ​​of all coordinates in a row. Unless otherwise specified, a pixel can be understood as its coordinates, and a coordinate can be understood as the pixel corresponding to those coordinates; both actually correspond to the same object.

[0113] 303, the pixel with the smallest row number moving up one row and the pixel with the largest row number moving down one row in each column are defined as the increment pixels. The increment pixels and... Form the second set of labeled pixels for each column .

[0114] For example, move the smallest row number 5 in column 7 to the next row (i.e., the pixel in column 7, row 4, such as...). Figure 4 (Point A0 in the image) and the pixel in the next row below the maximum row number (i.e., column 7, row 8, such as...) Figure 4 Point A3 in the diagram is identified as the added pixel, and the added pixel and Form the second set of labeled pixels for each column .like Includes: (7,4) (i.e.) Figure 4 Points A0 and (7,5) in the middle (i.e.) Figure 4 Points A1, (7,6), and (7,7) in the middle (i.e.) Figure 4 Points A2 and (7,8) in the middle (i.e.) Figure 4 (Point A3 in the middle).

[0115] In other words, for any column, its Includes All elements in, and more than There are two additional elements, representing the pixel values ​​of the previous and next rows, respectively. That is... Expanding outward by one pixel forms .

[0116] 304, based on each column , And change thresholds are used to determine the changed pixel points in each column.

[0117] For any column, in step 304, it will be based on the column's... , Based on the change threshold, the changed pixel points in any column are determined. The specific implementation process is as follows:

[0118] 304-1, determine the value of any column. The fourth mean of the gray values ​​of all elements in the array. .

[0119] In step 304-1, the number of pixels covered by any column of corneal regions (i.e. The mean of the gray values ​​of all elements in the dataset (i.e., the fourth mean). ).

[0120] Fourth mean It represents the average gray value of the area covered by any column of corneal regions.

[0121] 304-2, determine the value of any column. The fifth mean of the gray values ​​of all elements in the array. .

[0122] In step 304-2, the number of pixels covered by extending one row outward from any column of the corneal region (i.e., The mean of the gray values ​​of all elements in the dataset (i.e., the fifth mean). ).

[0123] Fifth Mean It represents the average gray value of the area covered by any column of corneal regions when extended outward by one row.

[0124] 304-3, if and Then determine the column. All pixels in the array are the changing pixels in any column. If ,or, but Then, based on any column , The changing pixel point is determined by the change threshold.

[0125] in, The preset grayscale threshold, .

[0126] This indicates that the average gray value of the area covered by any column of corneal region extending outward by one row is smaller than the average gray value of the area covered by any column of corneal region. This is because the gray values ​​of the additional pixels included in the extended row are larger. The gray values ​​outside the corneal region are significantly larger than the gray values ​​inside the corneal region. Therefore, this increase is due to the inclusion of pixels from non-corneal regions. This means that the average gray value of the area covered by any column of the corneal region extending outward by one row is smaller or equal to the average gray value of the area covered by any column of the corneal region. This is because the gray values ​​of the additional pixels included in the extended row become smaller or equal. The gray values ​​outside the corneal region will be significantly larger than the gray values ​​inside the corneal region. Therefore, this decrease or equality is considered to include pixels from the corneal region and not pixels from non-corneal regions.

[0127] This indicates the proportion of the increase in the average gray value of any column of corneal regions and the area covered by any column of corneal regions extending outward by one row to the average gray value of the area covered by any column of corneal regions. The larger this proportion is, the greater the extent to which the area covered by any column of corneal regions extending outward by one row includes non-corneal regions.

[0128] and This means that if any column of the corneal region expands outward by one row, the area covered includes non-corneal areas. Furthermore, if the proportion of non-corneal areas within this expanded area is relatively large, then this column of the corneal region is considered the smallest region encompassing the entire corneal area. Expanding it by even one more pixel would include even more non-corneal areas. Therefore, we can stop expanding any column of the corneal region and consider this column as the smallest region encompassing the entire corneal region. All pixels in the array are determined as the changing pixels in any column.

[0129] This means that the area covered by any column of corneal regions extending outward by one row does not include non-corneal areas. but This indicates that any column of corneal regions extending outwards by one row covers an area that includes non-corneal regions, but the proportion of non-corneal regions included is relatively small. Therefore, it is necessary to... , And the change threshold determines whether to stop expanding any column of corneal region, and then determines the changed pixel points, that is, based on the any column , The changing pixel point is determined by the change threshold.

[0130] Among them, according to , The process of determining the changed pixel points using the change threshold is as follows:

[0131] 1. Determine the set of changes .

[0132] That is, the two additional pixels added when any column of corneal region is expanded outward by one row.

[0133] 2. Determine The sixth mean of the gray values ​​of all elements in the array. and the sixth standard deviation .

[0134] Sixth Mean The mean of the gray values ​​of the two pixels added when any column of the corneal region is expanded outward by one row. Sixth standard deviation. The standard deviation of the gray values ​​of the two pixels added when any column of corneal region is expanded outward by one row.

[0135] 3. If Then determine the column. All pixels in the array are the changing pixels in any column. If Then any column will be Update all pixels in the array to any column. For all pixels in the array, repeatedly perform the process of identifying the pixel with the smallest row number moving up one row and the pixel with the largest row number moving down one row as the added pixels. Then, use these added pixels and... Form the second set of labeled pixels for each column The steps (i.e., repeating step 303) and subsequent steps.

[0136] in, For the preset change precision, The threshold value is the value obtained in step 102-6. for The first mean of the grayscale values ​​of all elements in the array. for The first standard deviation of the gray values ​​of all elements in the dataset.

[0137] To indicate the grayscale value fluctuations of the area enclosed by the annotation edge. The grayscale value fluctuation of two pixels added when any column of corneal region is expanded outward by one row. The ratio of the change in grayscale value of the area enclosed by the marked edge to the grayscale value fluctuation of the two additional pixels added when any column of the corneal region expands outward by one row. It represents the normal fluctuations in the current corneal and non-corneal regions of the patient.

[0138] like This indicates that the grayscale fluctuation caused by adding two pixels by expanding any column of the corneal region outward by one row is greater than the patient's normal fluctuation. Therefore, any column of the corneal region is considered the smallest region encompassing the entire corneal region. Expanding any column of the corneal region outward by one row will bring abnormal grayscale fluctuations. Therefore, the expansion of any column of the corneal region can be stopped, and any column of the corneal region (i.e., any column of...) can be... All pixels in the image are identified as changing pixels.

[0139] like This indicates that the grayscale fluctuation caused by adding two pixels by expanding any column of the corneal region outward by one row is not greater than the patient's normal fluctuation. Therefore, it is considered that any column of the corneal region is not the smallest region encompassing the entire corneal area, and expanding any column of the corneal region outward by one row will not have a significant impact on the grayscale fluctuation. Compared to any column of the corneal region, the area covered by expanding any column of the corneal region outward by one row includes a more complete corneal area. Therefore, based on the area covered by expanding any column of the corneal region outward by one row, another row will be added outward to find the smallest region that accurately includes the entire corneal area. Therefore, any column of the corneal region will be expanded outward by one row. Update all pixels in the array to any column. Repeat step 303 and subsequent steps for all pixels in the region until the smallest region that includes the entire corneal area is found.

[0140] in addition, These are pre-set empirical values, determined based on imaging accuracy. Higher imaging accuracy results in higher accuracy. The larger the value, the more it satisfies this condition. The possibility of this is reduced, and more pixels in non-corneal areas between any column of corneal regions and any column of corneal regions are needed to determine the changed pixel point. This makes the determined changed pixel point more accurate.

[0141] These are pre-set empirical values, determined based on imaging accuracy. Higher imaging accuracy results in higher accuracy. The larger the value, The larger it is, the more satisfied it will be. The possibility of this is reduced, and it is necessary to expand any column of corneal region outward by one row to cover an area that brings greater pixel value fluctuations in order to determine the changed pixel point. This makes the determined changed pixel point more accurate.

[0142] 305, the smallest rectangle containing the changing pixels of all columns is determined as the target region.

[0143] In step 305, the smallest rectangle containing all the changing pixels in all columns can be directly determined as the target region.

[0144] However, in practice, it is difficult to ensure that the patient remains absolutely still. Therefore, it is also possible to pre-estimate the redundancy value of pixel value changes caused by patient movement. (like ), expand the changed pixels in each column upwards and downwards respectively. Each pixel is considered. The smallest rectangle containing the expanded, changed pixels of all columns is defined as the target region.

[0145] in, For the pre-set redundancy value, It is determined based on the patient's attributes, such as the patient's self-control.

[0146] but, , This is the maximum redundancy value. , This represents the minimum number of pixels between the center point of the changed pixels in each column and the top edge of the first frame of the dynamic deformation image. This represents the minimum number of pixels between the center point of the changed pixels in each column and the bottom edge of the first frame of the dynamic deformation image. The maximum value is half of the total number of changing pixels in each column.

[0147] In other words, the redundancy value cannot be greater than the minimum number of redundant pixels that can be found above and below the center point of the corneal region from the first frame of the dynamically deformed image, ensuring that there are enough pixel values ​​for redundancy.

[0148] 103. Extract the target region from each frame of the dynamic deformation image to form a continuous frame image of the target region.

[0149] In step 103, the existing scheme will be used to extract the target region in each frame of dynamic deformation image, and the target regions will be sorted according to the frame number to obtain continuous frame images of the target region.

[0150] 104. High-resolution processing is performed on consecutive frames of images of the target region.

[0151] Although the consecutive frame images of the target area acquired in step 103 can reduce the area of ​​invalid regions in the images, it cannot guarantee the image resolution, and therefore cannot guarantee the data accuracy of the corneal region in the consecutive frame images. Step 104 can improve the resolution of each frame in the consecutive frame images, thereby improving the data accuracy of the corneal region in the consecutive frame images.

[0152] Step 104 performs high-resolution processing on each frame of the consecutive frames of the target region.

[0153] For any frame image in a series of consecutive frames of the target region Its high-resolution processing is based on a processing function, which is: .

[0154] in, The frame image identifier is used to identify consecutive frames within the target region.

[0155] For frame images The pixel identifier is determined by the pixel position in the image. For example, the pixel in the top left corner of the frame image of the target region is taken as the first pixel, and then all pixels in the grayscale image are numbered sequentially from left to right and from top to bottom.

[0156] For frame images The Middle The value of each pixel.

[0157] For frame images The third high-resolution processed The value of each pixel.

[0158] This is the downsampling matrix.

[0159] This is the penalty parameter.

[0160] This is a regular expression function used to incorporate regular expression rules into high-resolution processing.

[0161] It is a fuzzy matrix. .

[0162] in, For normalization parameters, These are the coordinates of pixels in the image. To based on frame image The obtained variance estimate.

[0163] like .

[0164] for The parameter form, for The parameter form, , For regularization parameters, Eigenvalues Circular matrix This represents the spatial frequency of Gaussian noise.

[0165] For non-descending iterative changes, the regularization parameter is... For frame images The maximum value of the pixels in the image.

[0166] 105. Evaluation of corneal biomechanical properties was performed on consecutive frames of images processed at high resolution.

[0167] This step uses existing corneal biomechanical property assessment protocols, such as assessing central corneal thickness, deformation amplitude, deformation rate, and curvature, which will not be described in detail here.

[0168] This embodiment provides a high frame rate imaging method for improving the vertical resolution of images. During the process of blowing air into the patient's cornea, dynamic deformation images of the corneal cross-section during corneal deformation are acquired. A target region is determined from the dynamic deformation images, wherein the target region is the smallest region including the cornea. The target region is extracted from each frame of the dynamic deformation images to form a series of frame images of the target region. High-resolution processing is performed on the series of frame images of the target region. The corneal biomechanical properties are then evaluated using the high-resolution processed series of frame images. This method determines the target region of the dynamic deformation images during the patient's corneal deformation process. This target region is the smallest region including the cornea. By performing high-resolution processing on the series of frame images of the target region, the data accuracy of the corneal region in the series of frame images is improved, ensuring the accuracy of the corneal biomechanical property evaluation results from the high-resolution processed series of frame images.

[0169] It should be clarified that the present invention is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of the present invention.

[0170] It should also be noted that the exemplary embodiments mentioned in this invention describe methods or systems based on a series of steps or apparatus. However, this invention is not limited to the order of the steps described above; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0171] Finally, it should be noted that the above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A high frame rate imaging method for improving the vertical resolution of images, characterized in that, The method includes: During the process of blowing air into the patient's cornea, dynamic deformation images of the corneal cross-section during the deformation process are acquired; wherein, the dynamic deformation images are grayscale images; Determine the target region of the dynamically deformed image, wherein the target region is the smallest region including the cornea; Extract the target region from each frame of the dynamic deformation image to form a continuous frame image of the target region; Perform high-resolution processing on consecutive frames of images of the target region; Evaluation of corneal biomechanical properties of consecutive frames after high-resolution processing; Determining the target region of the dynamically deformed image includes: Acquire the first frame of dynamic deformation image; Obtain the marked edges of the cornea in the first frame of the dynamic deformation image; Identify the corneal edge in the first frame of the dynamic deformation image; A set of labeled pixels is formed based on the labeled edges. A set of identification pixels is formed based on the identified edges. ; Difference pixel set ; according to , and The grayscale value of each element determines the threshold for change; The target region of the dynamic deformation image is determined based on the change threshold. According to , and The grayscale values ​​of each element in the image determine the threshold for change, including: Sure The first mean of the gray values ​​of all elements in the array. and the first standard deviation ; Sure The second mean of the gray values ​​of all elements in the array. Second standard deviation ; like If it is an empty set, then the change threshold is determined as follows: ; like If it is a non-empty set, then it is determined. The third mean of the gray values ​​of all elements in the data. and the third standard deviation ;according to and The relationship between them, and and Determine the threshold for change; According to and The relationship between them, and and Determine the change threshold, including: like Then the threshold for change is determined as ; like Then the threshold for change is determined as .

2. The method according to claim 1, characterized in that, Determining the target region of the dynamic deformation image based on the change threshold includes: Will Each element is categorized by column, forming a set of the first labeled pixels for each column. ; Determine each column The minimum and maximum number of rows for all pixels in the array; The pixel with the smallest row number moving up one row and the pixel with the largest row number moving down one row in each column are defined as the incrementing pixels. Form the second set of labeled pixels for each column ; According to each column , Based on the change threshold, determine the changed pixel points in each column; The smallest rectangle containing the changing pixels of all columns is defined as the target region.

3. The method according to claim 2, characterized in that, The terms are based on each column. , Based on the change threshold, the changed pixel points in each column are determined, including: For any column, determine the column's... The fourth mean of the gray values ​​of all elements in the array. ; Determine any column The fifth mean of the gray values ​​of all elements in the array. ; like and Then determine the value of any column. All pixels in the array are the changing pixels in any column; where, The preset grayscale threshold, ; like ,or, but Then, based on any of the columns , The changed pixel points are determined by the change threshold.

4. The method according to claim 3, characterized in that, The according to any of the columns , Determining the changed pixel points using the change threshold includes: Determine the set of changes ; Sure The sixth mean of the gray values ​​of all elements in the array. and the sixth standard deviation ; like Then determine the value of any column. All pixels in the array are the changing pixels in any column; where, For the preset change precision, For the change threshold, for The first mean of the grayscale values ​​of all elements in the array. for The first standard deviation of the gray values ​​of all elements in the dataset; like Then any column of the above All pixels in the array are updated to the values ​​of any of the columns. For all pixels in the array, repeatedly perform the process of identifying the pixel with the smallest row number moving up one row and the pixel with the largest row number moving down one row as the added pixels. Then, use these added pixels and... Form the second set of labeled pixels for each column The steps and subsequent steps.

5. The method according to claim 2, characterized in that, The step of determining the target region as the smallest rectangle containing all the changing pixels in all columns includes: Expand the changed pixels in each column upwards and downwards respectively. 100 pixels; among which... For the pre-set redundancy value, Determined based on the patient attributes. , This is the maximum redundancy value. , This is the minimum number of pixels between the center point of each column's changing pixels and the top edge of the first frame's dynamic deformation image. This is the minimum number of pixels between the center point of each column's changing pixels and the bottom edge of the first frame's dynamic deformation image. The maximum value is half the total number of changing pixels in each column; The target region is determined by the smallest rectangle that contains the expanded, changed pixels of all columns.

6. The method according to claim 1, characterized in that, The high-resolution processing of consecutive frames of images of the target region includes: For any frame image in a series of consecutive frames of the target region, the following function is used: Perform high-resolution processing; in, The frame image identifiers are the consecutive frame images of the target region. For frame images The pixel markers in the text For frame images The Middle The value of each pixel. For frame images The third high-resolution processed The value of each pixel. This is the downsampling matrix. For fuzzy matrices, For penalty parameters, This is a regular function.

7. The method according to claim 6, characterized in that, The ; in, For normalization parameters, These are the coordinates of pixels in the image. To be based on frame image The obtained variance estimate.

Images