Carbon dioxide laser debridement positioning device and control method

Abstract

The invention provides a carbon dioxide laser debridement positioning device and a control method, and the method comprises the steps: determining a self-adaptive gray scale base of a gray scale pixel point according to a target detection gray scale image, determining a gray scale modulation factor through the self-adaptive gray scale base, determining a gray scale modulation characteristic quantity corresponding to the gray scale pixel point according to the gray scale modulation factor, and repeating the above steps. Determining gray level modulation characteristic quantities corresponding to residual gray level pixel points in the target detection gray level image, determining adjacent pixel modulation entropies of each gray level modulation characteristic quantity, determining a plurality of debridement strategy domains through all adjacent pixel modulation entropies, determining a debridement positioning decision value of each debridement strategy domain, and determining a debridement positioning decision value of each debridement strategy domain. The debridement positioning boundary of the debridement target is determined according to all the debridement positioning decision values, and the debridement operation position of the carbon dioxide laser debridement equipment is marked through the debridement positioning boundary. The accuracy of intelligent positioning of the debridement target can be improved.

Description

Technical Field

[0001] This application relates to the field of intelligent positioning technology, and in particular to a carbon dioxide laser debridement positioning device and control method. Background Technology

[0002] Intelligent positioning typically refers to the process of achieving precise or real-time positioning through the use of intelligent technologies such as artificial intelligence, machine learning, and sensor technology. This can be included in various applications across different fields, such as navigation systems, logistics management, indoor positioning, and intelligent transportation. The development of intelligent positioning has not only improved the accuracy of location perception but also brought more efficient operations and services to many industries.

[0003] Carbon dioxide laser debridement is a method of target cleaning using laser technology. This method uses a carbon dioxide laser to generate a laser beam that acts on the target tissue in a highly focused and precise manner. Carbon dioxide laser debridement is widely used in the medical field. However, current technology often requires professionals to manually determine the location of the debridement target. If professionals are fatigued and make misjudgments, it is easy to damage tissues outside the debridement target. Therefore, how to intelligently locate the debridement target has become a challenge for the industry. Summary of the Invention

[0004] Based on this, it is necessary to address the aforementioned technical problems by providing a carbon dioxide laser debridement positioning device and control method for intelligent positioning of the debridement target.

[0005] To solve the above-mentioned technical problems, this application adopts the following technical solution:

[0006] In a first aspect, this application provides a control method for a carbon dioxide laser debridement and positioning device, comprising the following steps:

[0007] Activate the carbon dioxide laser debridement and positioning device to acquire a target detection grayscale image of the debridement target;

[0008] Select a grayscale pixel in the target detection grayscale image, determine the adaptive grayscale base of the grayscale pixel based on the target detection grayscale image, determine the grayscale modulation factor through the adaptive grayscale base, determine the grayscale modulation feature corresponding to the grayscale pixel based on the grayscale modulation factor, repeat the above steps to determine the grayscale modulation feature corresponding to the remaining grayscale pixels in the target detection grayscale image;

[0009] Determine the modulation entropy of adjacent pixels for each grayscale modulation feature, and determine multiple cleanup strategy localizations using all adjacent pixel modulation entropies;

[0010] Determine the debridement positioning decision value for each debridement strategy domain, and determine the debridement positioning boundary of the debridement target based on all debridement positioning decision values;

[0011] The location of the debridement operation using the carbon dioxide laser debridement device is marked using the debridement positioning boundary.

[0012] In some embodiments, determining the adaptive grayscale base of a grayscale pixel based on the target detection grayscale image specifically includes:

[0013] The pixel assimilation sliding window for the grayscale pixel is determined based on the target detection grayscale image;

[0014] The pixel assimilation judgment value of each grayscale pixel is determined by the pixel assimilation sliding window.

[0015] The adaptive grayscale base of a grayscale pixel is determined based on all pixel assimilation values.

[0016] In some embodiments, determining the neighboring pixel modulation entropy of each grayscale modulation feature specifically includes:

[0017] Sort all grayscale modulation features to obtain the grayscale modulation feature sequence;

[0018] Select one grayscale modulation feature from the grayscale modulation feature sequence;

[0019] Determine the grayscale modulation feature difference between this grayscale modulation feature and the grayscale modulation features on the left and right sides;

[0020] The modulation entropy of the adjacent pixels of the obtained gray-scale modulation feature is determined based on the difference between the two gray-scale modulation features.

[0021] In some embodiments, determining the localization of multiple debridement strategies using the modulation entropy of all adjacent pixels is achieved through the following steps:

[0022] The target detection pixel modulation image corresponding to the target detection grayscale image is determined based on the modulation entropy of all adjacent pixels;

[0023] Select the modulation entropy of an adjacent pixel in the target detection pixel modulation image, and determine the pixel modulation entropy difference of that adjacent pixel modulation entropy;

[0024] The modulation entropy of each adjacent pixel is divided according to the modulation entropy difference of all pixels to obtain the debridement strategy localization corresponding to the modulation entropy of the adjacent pixel. The above steps are repeated to divide the modulation entropy of the remaining adjacent pixels in the target detection pixel modulation image to obtain multiple debridement strategy localizations.

[0025] In some embodiments, the division of the modulation entropy of an adjacent pixel based on the modulation entropy difference of all pixels to obtain the localization of the de-icing strategy corresponding to the modulation entropy of that adjacent pixel is achieved by the following steps:

[0026] The minimum difference in the modulation entropy of adjacent pixels is determined based on the difference in modulation entropy of all pixels.

[0027] The pixel modulation entropy minimum difference is divided and determined. If the pixel modulation entropy minimum difference is greater than the preset division threshold, the gray-level pixel corresponding to the modulation entropy of the adjacent pixels in the target detection gray-level image is marked as a division abnormality, and the corresponding gray-level pixel is deleted in the target detection gray-level image.

[0028] If the minimum difference of pixel modulation entropy is less than or equal to a preset division threshold, then the adjacent pixel modulation entropy and the grayscale pixel corresponding to the minimum difference of pixel modulation entropy in the target detection grayscale image are combined to form the debridement strategy region corresponding to the adjacent pixel modulation entropy.

[0029] In some embodiments, the debridement localization decision value for each debridement strategy region is determined by the following steps:

[0030] Obtain the grayscale pixel value of the Wth grayscale pixel in the vth cleaning strategy localization area.

[0031] Determine the total number W of grayscale pixels in the v-th cleanup strategy domain;

[0032] Determine the color correction factor θ for the y-th cleaning strategy localization. v ;

[0033] Standard grayscale pixel value X for determining the debridement target * ;

[0034] Based on the grayscale pixel value of the Wth grayscale pixel in the domain of the vth debridement strategy The total number W of grayscale pixels in the vth debridement strategy region, and the standard grayscale pixel value X of the debridement target. * and the color correction factor θ of the vth cleaning strategy localization v Determine the debridement positioning decision value for each debridement strategy region, wherein the debridement positioning decision value is determined using the following formula:

[0035]

[0036] Among them, Z v Let w represent the debridement positioning decision value of the v-th debridement strategy region, where w = 1, 2, ..., W.

[0037] In some embodiments, determining the debridement target debridement boundary based on all debridement positioning decision values ​​is achieved through the following steps:

[0038] Select one of the debridement positioning decision values;

[0039] When the debridement positioning decision value is less than or equal to the preset debridement positioning decision threshold, the debridement policy region corresponding to the debridement positioning decision value is marked as the debridement area;

[0040] When the debridement positioning decision value is greater than the preset debridement positioning decision threshold, the debridement policy region corresponding to the debridement positioning decision value is marked as a normal region and no further processing is performed.

[0041] Repeat the above steps to mark the debridement policy domains corresponding to the remaining debridement location decision values, thereby obtaining multiple debridement regions. All debridement regions are then combined to form the debridement location boundary of the debridement target.

[0042] Secondly, this application provides a carbon dioxide laser debridement and positioning device, which includes an intelligent control unit, the intelligent control unit comprising:

[0043] The target detection grayscale image acquisition module is used to acquire the target detection grayscale image of the debridement target after the carbon dioxide laser debridement positioning device is started.

[0044] The grayscale modulation feature determination module is used to select a grayscale pixel in the target detection grayscale image, determine the adaptive grayscale basis of the grayscale pixel based on the target detection grayscale image, determine the grayscale modulation factor through the adaptive grayscale basis, determine the grayscale modulation feature corresponding to the grayscale pixel based on the grayscale modulation factor, and repeat the above steps to determine the grayscale modulation feature corresponding to the remaining grayscale pixels in the target detection grayscale image;

[0045] The cleanup strategy localization determination module is used to determine the modulation entropy of adjacent pixels for each grayscale modulation feature, and to determine multiple cleanup strategy localizations through all adjacent pixel modulation entropies;

[0046] The debridement positioning boundary determination module is used to determine the debridement positioning decision value for each debridement strategy region, and to determine the debridement positioning boundary of the debridement target based on all debridement positioning decision values;

[0047] The positioning marking module is used to mark the position of the debridement operation of the carbon dioxide laser debridement device through the debridement positioning boundary.

[0048] Thirdly, this application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the control method of the above-described carbon dioxide laser debridement and positioning device.

[0049] Fourthly, this application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the control method for the carbon dioxide laser debridement and positioning device described above.

[0050] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0051] The carbon dioxide laser debridement positioning device and control method provided in this application reduces the color complexity of the target detection image by acquiring a target detection grayscale image of the debridement target. Then, an adaptive grayscale base is determined based on the target detection grayscale image. The adaptive grayscale base reflects the similarity between each grayscale pixel in the target detection grayscale image and the target grayscale pixel. A grayscale modulation factor is then determined using the adaptive grayscale base. The grayscale modulation factor reflects the correction degree of the grayscale pixel value of the corresponding grayscale pixel. A grayscale modulation feature quantity is then determined based on the grayscale modulation factor, thereby determining the debridement positioning decision value. This debridement positioning decision value reflects the similarity between the grayscale pixel value in the corresponding debridement decision domain and the standard grayscale pixel value of the debridement target. Finally, the debridement positioning boundary of the debridement target is determined based on all the debridement positioning decision values. The debridement positioning boundary is used to mark the position of the carbon dioxide laser debridement operation. Compared with the prior art, which requires professional personnel to judge the position of the debridement target, this method helps to improve the accuracy of intelligent positioning of the debridement target. Attached Figure Description

[0052] Figure 1 This is a flowchart illustrating the control method of the carbon dioxide laser debridement and positioning device in some embodiments of this application;

[0053] Figure 2 This is a flowchart illustrating the process of determining the debridement and debridement boundaries in some embodiments of this application;

[0054] Figure 3 This is a structural block diagram of the intelligent control unit in some embodiments of this application;

[0055] Figure 4 This is a diagram showing the internal structure of a computer device in some embodiments of this application. Detailed Implementation

[0056] The core of this application is to activate a carbon dioxide laser debridement and positioning device, acquire a target detection grayscale image of the debridement target, select a grayscale pixel in the target detection grayscale image, determine an adaptive grayscale basis for the grayscale pixel based on the target detection grayscale image, determine a grayscale modulation factor through the adaptive grayscale basis, determine the grayscale modulation feature quantity corresponding to the grayscale pixel based on the grayscale modulation factor, repeat the above steps to determine the grayscale modulation feature quantity corresponding to the remaining grayscale pixels in the target detection grayscale image, determine the adjacent pixel modulation entropy of each grayscale modulation feature quantity, determine multiple debridement policy regions through all adjacent pixel modulation entropies, determine the debridement positioning decision value for each debridement policy region, determine the debridement positioning boundary of the debridement target based on all debridement positioning decision values, and mark the position of the debridement operation of the carbon dioxide laser debridement device through the debridement positioning boundary. Compared with the prior art, which requires professional personnel to judge the position of the debridement target, this scheme helps to improve the accuracy of intelligent positioning of the debridement target.

[0057] To better understand the above technical solutions, a detailed description of the solutions will be provided below in conjunction with the accompanying drawings and specific implementation methods. (Reference) Figure 1 The figure is an exemplary flowchart of a control method for a carbon dioxide laser debridement and positioning device according to some embodiments of this application. The control method 100 of the carbon dioxide laser debridement and positioning device mainly includes the following steps:

[0058] In step 101, the carbon dioxide laser debridement and positioning device is activated to acquire a target detection grayscale image of the debridement target.

[0059] In practice, after activating the carbon dioxide laser debridement and positioning device, a high-definition camera can be used to capture images of the debridement target. The captured images are then used as the target detection images of the debridement target. Image processing software can be used to convert the target detection images to grayscale, and the converted images are used as the target detection grayscale images of the debridement target. This will not be elaborated further here.

[0060] In step 102, a grayscale pixel in the target detection grayscale image is selected, an adaptive grayscale base for the grayscale pixel is determined based on the target detection grayscale image, a grayscale modulation factor is determined based on the adaptive grayscale base, and a grayscale modulation feature corresponding to the grayscale pixel is determined based on the grayscale modulation factor. The above steps are repeated to determine the grayscale modulation feature corresponding to the remaining grayscale pixels in the target detection grayscale image.

[0061] In some embodiments, determining the adaptive grayscale basis of a grayscale pixel based on the target detection grayscale image can be achieved using the following steps:

[0062] The pixel assimilation sliding window for the grayscale pixel is determined based on the target detection grayscale image;

[0063] The pixel assimilation judgment value of each grayscale pixel is determined by the pixel assimilation sliding window.

[0064] The adaptive grayscale base of a grayscale pixel is determined based on all pixel assimilation values.

[0065] In specific implementation, a pixel assimilation sliding window is determined based on the target detection grayscale image. Specifically, the set of the four adjacent grayscale pixels (top, bottom, left, right) of the grayscale pixel in the target detection grayscale image can be used as the pixel assimilation sliding window for that grayscale pixel. Each pixel assimilation judgment value for that grayscale pixel is determined through the pixel assimilation sliding window. Specifically, a grayscale pixel is selected from the pixel assimilation sliding window, and the difference in grayscale value between the selected grayscale pixel and the grayscale pixel is calculated. This difference is used as the pixel assimilation judgment value between the grayscale pixel and the selected grayscale pixel. The above steps are repeated to obtain the pixel assimilation judgment values ​​between the remaining grayscale pixels in the pixel assimilation sliding window and the grayscale pixel. An adaptive grayscale base for that grayscale pixel is determined based on all the pixel assimilation judgment values. Specifically, a pixel assimilation judgment value is selected, and if the pixel... When the assimilation judgment value is less than the preset pixel assimilation judgment threshold, the grayscale pixel corresponding to the pixel assimilation judgment value is added to the pixel assimilation sliding window of the grayscale image of the target detection. The newly added grayscale pixel and the pixel assimilation judgment value of the grayscale pixel are calculated. The above steps are repeated to judge the remaining pixel assimilation judgment values ​​of the original pixel assimilation sliding window and the new pixel assimilation judgment values. The grayscale pixels corresponding to the pixel assimilation judgment threshold are added to the pixel assimilation sliding window of the grayscale image of the target detection. This process continues until the pixel assimilation judgment value of each grayscale pixel in the pixel assimilation sliding window is greater than or equal to the preset pixel assimilation judgment threshold. The final pixel assimilation sliding window is used as the adaptive grayscale base of the grayscale pixel.

[0066] It should be noted that if a grayscale pixel has been added to the pixel assimilation sliding window in this application, it will not be added again. The adaptive grayscale base is the set of all grayscale pixels in the target detection grayscale image that are adjacent to the current grayscale pixel and have similar grayscale pixel values. The adaptive grayscale base reflects the similarity between each grayscale pixel in the target detection grayscale image and the target grayscale pixel. All grayscale pixels in the pixel assimilation sliding window are adjacent to the current grayscale pixel. All grayscale pixel values ​​corresponding to the pixel assimilation judgment value of the current grayscale pixel that are less than the preset pixel assimilation judgment threshold are similar to the grayscale pixel value of the current grayscale pixel. All grayscale pixels in the adaptive grayscale base are used as adaptive grayscale pixels, and the grayscale pixel values ​​corresponding to all grayscale pixels are used as adaptive grayscale pixel values. The pixel assimilation judgment threshold can be set to the average value of all historical pixel assimilation judgment values. Other methods can also be used to set it in other embodiments, which are not limited here.

[0067] In some embodiments, determining the grayscale modulation factor using the adaptive grayscale basis can be achieved through the following steps:

[0068] Determine the grayscale difference X between the q-th adaptive grayscale pixel and the corresponding grayscale pixel in the adaptive grayscale basis. q ;

[0069] Determine the pixel fluctuation coefficient α of the adaptive grayscale base;

[0070] Determine the pixel position difference L between the q-th adaptive grayscale pixel and the corresponding grayscale pixel in the adaptive grayscale base. q ;

[0071] Determine the position fluctuation coefficient β of the adaptive grayscale base;

[0072] Determine the pixel activity P of the i-th different adaptive grayscale pixel value in the adaptive grayscale base. i ;

[0073] Determine the pixel activity P of the j-th different grayscale pixel value in the target detection grayscale image. j ;

[0074] The grayscale pixel difference X between the q-th adaptive grayscale pixel and the corresponding grayscale pixel in the adaptive grayscale base. q The pixel fluctuation coefficient α of the adaptive grayscale base, and the pixel position difference L between the q-th adaptive grayscale pixel and the corresponding grayscale pixel in the adaptive grayscale base. q The position fluctuation coefficient β of the adaptive grayscale base, and the pixel activity P of the i-th different adaptive grayscale pixel value in the adaptive grayscale base. iThe pixel activity P of the j-th different grayscale pixel value in the target detection grayscale image j The grayscale modulation factor is determined, wherein the grayscale modulation factor can be determined using the following formula:

[0075]

[0076] Where ξ represents the grayscale modulation factor, Q represents the total number of adaptive grayscale pixels in the adaptive grayscale base, I represents the total number of different adaptive grayscale pixel values ​​in the adaptive grayscale base, J represents the total number of different grayscale pixel values ​​in the target detection grayscale image, In represents the logarithmic function with base e, i = 1, 2, ..., I, j = 1, 2, ..., J, q = 1, 2, ..., Q.

[0077] In a specific implementation, the adaptive grayscale value of the q-th adaptive grayscale pixel in the adaptive grayscale base can be subtracted from the grayscale value of that grayscale pixel, and this difference can be used as the grayscale difference X between the q-th adaptive grayscale pixel and that grayscale pixel in the adaptive grayscale base. q A smart algorithm can be used to determine the spatial distance between the q-th adaptive grayscale pixel in the adaptive grayscale base and the corresponding grayscale pixel in the target detection grayscale image. This spatial distance is then used as the pixel position difference L between the q-th adaptive grayscale pixel in the adaptive grayscale base and the corresponding grayscale pixel. q The pixel activity P of the i-th different adaptive grayscale pixel value in the adaptive grayscale base can be calculated by dividing the total number of the i-th different adaptive grayscale pixel values ​​by the total number of all adaptive grayscale pixel values ​​in the adaptive grayscale base. i The pixel activity P of the j-th different grayscale pixel value in the target detection grayscale image can be calculated as the ratio of the total number of the j-th different grayscale pixel values ​​in the target detection grayscale image to the total number of all grayscale pixel values ​​in the target detection grayscale image. j The different adaptive grayscale pixel values ​​refer to: classifying pixels with the same adaptive grayscale pixel value as the same adaptive grayscale pixel value, and classifying pixels with different adaptive grayscale pixel values ​​as different adaptive grayscale pixel values. Similarly, classifying pixels with the same grayscale pixel value as the same grayscale pixel value, and classifying pixels with different grayscale pixel values ​​as different grayscale pixel values.

[0078] It should be noted that the pixel fluctuation coefficient in this application reflects the degree of fluctuation of all adaptive grayscale pixel values ​​in the adaptive grayscale base. The larger the pixel fluctuation coefficient, the greater the fluctuation of all adaptive grayscale pixel values ​​in the adaptive grayscale base; the smaller the pixel fluctuation coefficient, the smaller the fluctuation of all adaptive grayscale pixel values ​​in the adaptive grayscale base. In some embodiments, the standard deviation of all adaptive grayscale pixel values ​​in the adaptive grayscale base can be used to represent this. The positional fluctuation coefficient reflects the degree of fluctuation of the spatial distance between all adaptive grayscale pixels in the adaptive grayscale base and the current grayscale pixel in the target detection grayscale image. The larger the positional fluctuation coefficient, the greater the fluctuation of the spatial distance between all adaptive grayscale pixels in the adaptive grayscale base and the current grayscale pixel in the target detection grayscale image; the smaller the positional fluctuation coefficient, the smaller the fluctuation of the spatial distance between all adaptive grayscale pixels in the adaptive grayscale base and the current grayscale pixel in the target detection grayscale image. The grayscale modulation factor reflects the degree of correction of the grayscale pixel value of the corresponding grayscale pixel.

[0079] In a specific implementation, the grayscale modulation feature is the new grayscale pixel value of the corresponding grayscale pixel after the grayscale pixel value of the corresponding grayscale pixel is corrected. In some embodiments, the grayscale modulation feature can be obtained by multiplying the grayscale modulation factor by the grayscale pixel value of the corresponding grayscale pixel, which will not be elaborated here.

[0080] In step 103, the modulation entropy of adjacent pixels for each grayscale modulation feature is determined, and multiple cleaning strategies are localized using all the modulation entropy of adjacent pixels.

[0081] In some embodiments, determining the neighboring pixel modulation entropy of each grayscale modulation feature can be achieved using the following steps:

[0082] Sort all grayscale modulation features to obtain the grayscale modulation feature sequence;

[0083] Select one grayscale modulation feature from the grayscale modulation feature sequence;

[0084] Determine the grayscale modulation feature difference between this grayscale modulation feature and the grayscale modulation features on the left and right sides;

[0085] The modulation entropy of the adjacent pixels of the obtained gray-scale modulation feature is determined based on the difference between the two gray-scale modulation features.

[0086] In specific implementation, all grayscale modulation features are sorted to obtain a grayscale modulation feature sequence. That is, all grayscale modulation features are sorted from top to bottom and left to right according to the position of the corresponding grayscale pixel in the target detection grayscale image, and the sorted sequence is used as the grayscale modulation feature sequence. The grayscale modulation feature difference between the grayscale modulation feature and its two adjacent grayscale modulation features is determined. That is, the difference between the grayscale modulation feature and its two adjacent grayscale modulation features is calculated, and the calculated difference is used as the grayscale modulation feature difference between the grayscale modulation feature and its corresponding adjacent grayscale modulation features. The adjacent pixel modulation entropy of the grayscale modulation feature is determined based on the two obtained grayscale modulation feature differences. That is, the smallest obtained grayscale modulation feature difference is used as the adjacent pixel modulation entropy of the grayscale modulation feature.

[0087] In some embodiments, determining the localization of multiple debridement strategies using the modulation entropy of all adjacent pixels can be achieved through the following steps:

[0088] The target detection pixel modulation image corresponding to the target detection grayscale image is determined based on the modulation entropy of all adjacent pixels;

[0089] Select the modulation entropy of an adjacent pixel in the target detection pixel modulation image, and determine the pixel modulation entropy difference of that adjacent pixel modulation entropy;

[0090] The modulation entropy of each adjacent pixel is divided according to the modulation entropy difference of all pixels to obtain the debridement strategy localization corresponding to the modulation entropy of the adjacent pixel. The above steps are repeated to divide the modulation entropy of the remaining adjacent pixels in the target detection pixel modulation image to obtain multiple debridement strategy localizations.

[0091] In specific implementation, the target detection pixel modulation image corresponding to the target detection grayscale image is determined based on all adjacent pixel modulation entropies. That is, all adjacent pixel modulation entropies are used to replace the corresponding grayscale pixel values ​​in the target detection image, and the image obtained after replacement is used as the target detection pixel modulation image corresponding to the target detection grayscale image. The pixel modulation entropy difference of each adjacent pixel modulation entropy is determined. That is, the difference between the modulation entropy of the adjacent pixel and the modulation entropy of the four adjacent pixels in the target detection pixel modulation image is calculated, and the calculated difference is used as the pixel modulation entropy difference between the modulation entropy of the adjacent pixel and the modulation entropy of the corresponding adjacent pixels.

[0092] In some embodiments, the debridement strategy localization corresponding to the modulation entropy of a neighboring pixel is obtained by dividing the modulation entropy of the neighboring pixel based on the modulation entropy difference of all pixels, which can be achieved by the following steps:

[0093] The minimum difference in the modulation entropy of adjacent pixels is determined based on the difference in modulation entropy of all pixels.

[0094] The pixel modulation entropy minimum difference is divided and determined. If the pixel modulation entropy minimum difference is greater than the preset division threshold, the gray-level pixel corresponding to the modulation entropy of the adjacent pixels in the target detection gray-level image is marked as a division abnormality, and the corresponding gray-level pixel is deleted in the target detection gray-level image.

[0095] If the minimum difference of pixel modulation entropy is less than or equal to a preset division threshold, then the adjacent pixel modulation entropy and the grayscale pixel corresponding to the minimum difference of pixel modulation entropy in the target detection grayscale image are combined to form the debridement strategy region corresponding to the adjacent pixel modulation entropy.

[0096] It should be noted that, in the process of dividing gray-level pixels in the target detection grayscale image according to the minimum difference of pixel modulation entropy in this application, if gray-level pixel A can form a region of debridement strategy with gray-level pixel B, and gray-level pixel B can form a region of debridement strategy with gray-level pixel C, but gray-level pixel A and gray-level pixel C cannot form a region of debridement strategy, then the minimum difference of pixel modulation entropy S corresponding to gray-level pixel A and gray-level pixel B is set to... AB The minimum difference S between the pixel modulation entropy corresponding to grayscale pixel B and grayscale pixel C BC By comparing the grayscale pixel B with the grayscale pixel corresponding to the smallest difference in pixel modulation entropy between the two, a clearing strategy localization is formed, for example: S AB <S BC If grayscale pixel A can be combined with grayscale pixel B to form a cleaning policy region, then grayscale pixel B cannot be combined with grayscale pixel C to form a cleaning policy region.

[0097] In step 104, the debridement positioning decision value for each debridement strategy region is determined, and the debridement positioning boundary of the debridement target is determined based on all the debridement positioning decision values.

[0098] In some embodiments, determining the debridement localization decision value for each debridement strategy region can be achieved through the following steps:

[0099] Obtain the grayscale pixel value of the w-th grayscale pixel in the v-th cleaning strategy localization area.

[0100] Determine the total number W of grayscale pixels in the v-th cleanup strategy domain;

[0101] Determine the color correction factor θ for the y-th cleaning strategy localization. v ;

[0102] Standard grayscale pixel value X for determining the debridement target * ;

[0103] Based on the grayscale pixel value of the w-th grayscale pixel in the domain of the v-th debridement strategy The total number W of grayscale pixels in the vth debridement strategy region, and the standard grayscale pixel value X of the debridement target. * and the color correction factor θ of the vth cleaning strategy localization v Determine the debridement positioning decision value for each debridement strategy region, wherein the debridement positioning decision value can be determined using the following formula:

[0104]

[0105] Among them, Z v Let w represent the debridement positioning decision value of the v-th debridement strategy region, where w = 1, 2, ..., W.

[0106] It should be noted that the debridement positioning decision value in this application reflects the similarity between the grayscale pixel value in the corresponding debridement strategy domain and the standard grayscale pixel value of the debridement target; the color correction factor represents the parameter of the degree of correction of the grayscale pixel value in the debridement strategy domain. The larger the color correction factor, the greater the degree of correction of the grayscale pixel value in the debridement strategy domain; the smaller the color correction factor, the smaller the degree of correction of the grayscale pixel value in the debridement strategy domain. In some embodiments, the color correction factor can be set by a robot algorithm according to the influencing factors of the external environment, and is generally taken as a value close to 1. In other embodiments, other methods can also be used to set the color correction factor, which is not limited here.

[0107] Additionally, in some embodiments, references Figure 2 As shown in the figure, this is a flowchart illustrating the process of determining the debridement and cleaning boundaries in some embodiments of this application. In this embodiment, the debridement and cleaning boundaries can be determined using the following steps:

[0108] First, in step 1041, one of the debridement positioning decision values ​​is selected;

[0109] Secondly, in step 1042, when the debridement positioning decision value is less than or equal to the preset debridement positioning decision threshold, the debridement policy region corresponding to the debridement positioning decision value is marked as the debridement area.

[0110] Then, in step 1043, when the debridement positioning decision value is greater than the preset debridement positioning decision threshold, the debridement policy region corresponding to the debridement positioning decision value is marked as a normal region and no processing is performed;

[0111] Finally, in step 1044, the above steps are repeated to mark the debridement policy regions corresponding to the remaining debridement positioning decision values, resulting in multiple debridement regions. All debridement regions are then combined to form the debridement positioning boundary of the debridement target.

[0112] It should be noted that the debridement and localization decision threshold in this application can be set based on historical experimental data, and the value is the average of all historical debridement and localization decision values. Other methods can also be used to set it in other embodiments, and no limitation is made here.

[0113] In step 105, the location of the debridement operation of the carbon dioxide laser debridement device is marked by the debridement positioning boundary.

[0114] In specific implementation, a three-dimensional coordinate system can be established using the target detection grayscale image and the spatial linear distance between the CO2 laser debridement device performing the debridement operation and the debridement target. Specifically, the plane formed by the target detection grayscale image is used as the plane formed by the X-axis and Y-axis of the three-dimensional coordinate system (x, y, z). The correspondence between the X-axis and Y-axis of the three-dimensional coordinate system and each grayscale pixel in the target detection grayscale image can be set according to specific needs and is not limited here. The spatial linear distance between the CO2 laser debridement device performing the debridement and the debridement target is used as the Z-axis of the three-dimensional coordinate system (x, y, z), thereby establishing a three-dimensional coordinate system. The three-dimensional coordinate system is then established based on the spatial linear distance between each grayscale pixel in the target detection grayscale image within the debridement positioning boundary. The location and the value of the straight-line distance between the CO2 laser debridement device performing the debridement and the debridement target determine the three-dimensional coordinates of the debridement location and the three-dimensional coordinates of the CO2 laser debridement device performing the debridement. For example, taking the CO2 laser debridement device performing the debridement as the origin in the three-dimensional coordinate system, if there is a gray-scale pixel in the debridement positioning boundary located in the 4th row and 7th column of the target detection gray-scale image, and the value of the straight-line distance between the debridement target and the debridement target is 5, then the three-dimensional coordinates of the CO2 laser debridement device performing the debridement are (0, 0, 0), and the three-dimensional coordinates of the location of the debridement corresponding to this gray-scale pixel are (4, 7, 5). In other embodiments, other methods can be used to determine the corresponding three-dimensional coordinates, which are not limited here.

[0115] In another aspect, in some embodiments, this application provides a carbon dioxide laser debridement and positioning device, which includes an intelligent control unit, as referenced. Figure 3 The figure is a schematic diagram of exemplary hardware and / or software of an intelligent control unit according to some embodiments of this application. The intelligent control unit 300 includes: a target detection grayscale image acquisition module 301, a grayscale modulation feature quantity determination module 302, a wound cleaning strategy localization determination module 303, a wound cleaning localization boundary determination module 304, and a localization marker module 305, which are described below:

[0116] The target detection grayscale image acquisition module 301 in this application is mainly used to acquire the target detection grayscale image of the debridement target after the carbon dioxide laser debridement positioning device is started.

[0117] The grayscale modulation feature determination module 302 in this application is mainly used to select a grayscale pixel in the target detection grayscale image, determine the adaptive grayscale base of the grayscale pixel based on the target detection grayscale image, determine the grayscale modulation factor through the adaptive grayscale base, determine the grayscale modulation feature corresponding to the grayscale pixel based on the grayscale modulation factor, and repeat the above steps to determine the grayscale modulation feature corresponding to the remaining grayscale pixels in the target detection grayscale image;

[0118] The cleansing strategy localization determination module 303 in this application is mainly used to determine the modulation entropy of adjacent pixels for each grayscale modulation feature, and to determine multiple cleansing strategy localizations through all adjacent pixel modulation entropies.

[0119] The debridement positioning boundary determination module 304 in this application is mainly used to determine the debridement positioning decision value of each debridement policy domain, and to determine the debridement positioning boundary of the debridement target based on all the debridement positioning decision values;

[0120] The positioning marking module 305 in this application is mainly used to mark the position of the debridement operation of the carbon dioxide laser debridement device through the debridement positioning boundary.

[0121] Each module in the aforementioned intelligent control unit can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0122] It should be noted that the carbon dioxide laser debridement and positioning device in this application can be used as a functional unit of the carbon dioxide laser debridement equipment, that is, the carbon dioxide laser debridement and positioning device can be integrated into the carbon dioxide laser debridement equipment as a functional unit of the equipment, or other combinations can be used, which are not specifically limited here.

[0123] In another embodiment, this application provides a computer device, which may be a server, and its internal structure diagram may be as follows. Figure 4As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores carbon dioxide laser debridement and positioning data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements a control method for a carbon dioxide laser debridement and positioning device.

[0124] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0125] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the control method embodiment of the carbon dioxide laser debridement and positioning device described above.

[0126] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the control method embodiment of the carbon dioxide laser debridement and positioning device described above.

[0127] In one embodiment, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the steps described in the control method embodiment of the carbon dioxide laser debridement and positioning device.

[0128] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods.

[0129] Any references to memory, storage, database, or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0130] In summary, the carbon dioxide laser debridement positioning device and control method disclosed in the embodiments of this application,

[0131] First, the carbon dioxide laser debridement and positioning device is activated to acquire a target detection grayscale image of the debridement target. A grayscale pixel is selected from the target detection grayscale image. An adaptive grayscale basis is determined for this grayscale pixel based on the target detection grayscale image. A grayscale modulation factor is determined using the adaptive grayscale basis. Finally, the grayscale modulation feature quantity corresponding to this grayscale pixel is determined based on the grayscale modulation factor.

[0132] Repeat the above steps to determine the grayscale modulation feature corresponding to the remaining grayscale pixels in the target detection grayscale image, determine the adjacent pixel modulation entropy of each grayscale modulation feature, determine multiple debridement strategy regions through all adjacent pixel modulation entropies, determine the debridement positioning decision value for each debridement strategy region, determine the debridement positioning boundary of the debridement target based on all debridement positioning decision values, and mark the position of the debridement operation of the carbon dioxide laser debridement device through the debridement positioning boundary. Compared with the existing technology that requires professional personnel to manually judge the position of the debridement target, this scheme helps to improve the accuracy of intelligent positioning of the debridement target.

[0133] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0134] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A control method for a carbon dioxide laser debridement and positioning device, characterized in that, Includes the following steps: Activate the carbon dioxide laser debridement and positioning device to acquire a target detection grayscale image of the debridement target; Select a grayscale pixel in the target detection grayscale image, determine the adaptive grayscale base of the grayscale pixel based on the target detection grayscale image, determine the grayscale modulation factor through the adaptive grayscale base, determine the grayscale modulation feature corresponding to the grayscale pixel based on the grayscale modulation factor, repeat the above steps to determine the grayscale modulation feature corresponding to the remaining grayscale pixels in the target detection grayscale image; Determine the modulation entropy of adjacent pixels for each grayscale modulation feature, and determine multiple cleanup strategy localizations using all adjacent pixel modulation entropies; Determine the debridement positioning decision value for each debridement strategy domain, and determine the debridement positioning boundary of the debridement target based on all debridement positioning decision values; The location of the debridement operation using the carbon dioxide laser debridement device is marked using the debridement positioning boundary.

2. The method as described in claim 1, characterized in that, Determining the adaptive grayscale base of a grayscale pixel based on the target detection grayscale image specifically includes: The pixel assimilation sliding window for the grayscale pixel is determined based on the target detection grayscale image; The pixel assimilation judgment value of each grayscale pixel is determined by the pixel assimilation sliding window. The adaptive grayscale base of a grayscale pixel is determined based on all pixel assimilation values.

3. The method as described in claim 1, characterized in that, Determining the modulation entropy of adjacent pixels for each grayscale modulation feature specifically includes: Sort all grayscale modulation features to obtain the grayscale modulation feature sequence; Select one grayscale modulation feature from the grayscale modulation feature sequence; Determine the grayscale modulation feature difference between this grayscale modulation feature and the grayscale modulation features on the left and right sides; The modulation entropy of the adjacent pixels of the obtained gray-scale modulation feature is determined based on the difference between the two gray-scale modulation features.

4. The method as described in claim 1, characterized in that, Determining the localization of multiple debridement strategies by using the modulation entropy of all adjacent pixels is achieved through the following steps: The target detection pixel modulation image corresponding to the target detection grayscale image is determined based on the modulation entropy of all adjacent pixels; Select the modulation entropy of an adjacent pixel in the target detection pixel modulation image, and determine the pixel modulation entropy difference of that adjacent pixel modulation entropy; The modulation entropy of each adjacent pixel is divided according to the modulation entropy difference of all pixels to obtain the debridement strategy localization corresponding to the modulation entropy of the adjacent pixel. The above steps are repeated to divide the modulation entropy of the remaining adjacent pixels in the target detection pixel modulation image to obtain multiple debridement strategy localizations.

5. The method as described in claim 4, characterized in that, The following steps are used to divide the modulation entropy of an adjacent pixel based on the modulation entropy difference of all pixels, and to obtain the localization of the cleaning strategy corresponding to the modulation entropy of that adjacent pixel: The minimum difference in the modulation entropy of adjacent pixels is determined based on the difference in modulation entropy of all pixels. The pixel modulation entropy minimum difference is divided and determined. If the pixel modulation entropy minimum difference is greater than the preset division threshold, the gray-level pixel corresponding to the modulation entropy of the adjacent pixels in the target detection gray-level image is marked as a division abnormality, and the corresponding gray-level pixel is deleted in the target detection gray-level image. If the minimum difference of pixel modulation entropy is less than or equal to a preset division threshold, then the adjacent pixel modulation entropy and the grayscale pixel corresponding to the minimum difference of pixel modulation entropy in the target detection grayscale image are combined to form the debridement strategy region corresponding to the adjacent pixel modulation entropy.

6. The method as described in claim 1, characterized in that, The following steps are used to determine the debridement localization decision value for each debridement strategy region: Get the wth element in the vth cleansing strategy domain. grayscale pixel value of grayscale pixel Determine the total number W of grayscale pixels in the v-th cleanup strategy domain; Determine the color correction factor θ for the v-th cleansing strategy localization. v ; Standard grayscale pixel value X for determining the debridement target * ; Based on the grayscale pixel value of the Wth grayscale pixel in the domain of the vth debridement strategy The total number W of grayscale pixels in the vth debridement strategy region, and the standard grayscale pixel value X of the debridement target. * and the color correction factor θ of the vth cleaning strategy localization v Determine the debridement positioning decision value for each debridement strategy region, wherein the debridement positioning decision value is determined using the following formula: Among them, Z v Let w represent the debridement positioning decision value of the v-th debridement strategy region, where w = 1, 2, ..., W.

7. The method as described in claim 1, characterized in that, The following steps are used to determine the debridement target boundary based on all debridement positioning decision values: Select one of the debridement positioning decision values; When the debridement positioning decision value is less than or equal to the preset debridement positioning decision threshold, the debridement policy region corresponding to the debridement positioning decision value is marked as the debridement area; When the debridement positioning decision value is greater than the preset debridement positioning decision threshold, the debridement policy region corresponding to the debridement positioning decision value is marked as a normal region and no further processing is performed. Repeat the above steps to mark the debridement policy domains corresponding to the remaining debridement location decision values, thereby obtaining multiple debridement regions. All debridement regions are then combined to form the debridement location boundary of the debridement target.

8. A carbon dioxide laser debridement and positioning device, characterized in that, It includes an intelligent control unit, which includes: The target detection grayscale image acquisition module is used to acquire the target detection grayscale image of the debridement target after the carbon dioxide laser debridement positioning device is started. The grayscale modulation feature determination module is used to select a grayscale pixel in the target detection grayscale image, determine the adaptive grayscale basis of the grayscale pixel based on the target detection grayscale image, determine the grayscale modulation factor through the adaptive grayscale basis, determine the grayscale modulation feature corresponding to the grayscale pixel based on the grayscale modulation factor, and repeat the above steps to determine the grayscale modulation feature corresponding to the remaining grayscale pixels in the target detection grayscale image; The cleanup strategy localization determination module is used to determine the modulation entropy of adjacent pixels for each grayscale modulation feature, and to determine multiple cleanup strategy localizations through all adjacent pixel modulation entropies. The debridement positioning boundary determination module is used to determine the debridement positioning decision value for each debridement strategy region, and to determine the debridement positioning boundary of the debridement target based on all debridement positioning decision values; The positioning marking module is used to mark the position of the debridement operation of the carbon dioxide laser debridement device through the debridement positioning boundary.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the control method for the carbon dioxide laser debridement and positioning device according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the control method for the carbon dioxide laser debridement and positioning device as described in any one of claims 1 to 7.

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