Dynamic developing method and device based on intelligent image optimization, equipment and medium

Abstract

The invention discloses a dynamic developing method, device and equipment based on intelligent image optimization and a medium, and the method comprises the steps: receiving a developing instruction, collecting a visible light image and an isotope imaging image at the same time, extracting contour information from the visible light image, and carrying out the region segmentation of the isotope imaging image to obtain a region segmentation image, and further performing background segmentation on the region segmentation image to obtain a foreground image and a background image, performing image optimization on the foreground image to obtain a development image, and circularly executing the steps and obtaining a continuous development image for dynamic development. According to the dynamic developing method, the visible light image and the isotope imaging image can be collected at the same time, region segmentation and background segmentation are carried out on the isotope imaging image according to the visible light image, image optimization is carried out on the foreground image to obtain the developing image, and the continuous developing image is obtained to achieve dynamic developing based on the radioactive isotope. And the dynamic linear image quality based on the radioactive isotope is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent image processing, and in particular to a dynamic development method, device, equipment and medium based on intelligent image optimization. Background Art

[0002] In modern medical technology, radioisotope imaging is widely used in brain angiography, cardiovascular angiography, and local tumor angiography. However, existing methods typically involve the introduction of positron-emitting radionuclides through injection, which accumulate in the target tissue. When the emitting radionuclide decays, it emits a positron, which rapidly combines with nearby electrons, resulting in the simultaneous emission of two identifiable gamma rays in opposite directions. This technique uses radiation emitted by internal radioactive sources for imaging. Although this method can obtain local dynamic images by detecting radiation from internal radioactive sources, injecting radioactive isotope materials into the body can cause damage. X-ray imaging, on the other hand, uses an external radiation source for imaging and is typically only capable of obtaining local static images, not high-quality dynamic images. Therefore, the isotope imaging methods in existing methods suffer from the inability to obtain high-quality dynamic images. Summary of the Invention

[0003] The embodiments of the present invention provide a dynamic development method, apparatus, device and medium based on intelligent image optimization, aiming to solve the problem of isotope development methods in the prior art that high-quality dynamic images cannot be obtained.

[0004] In a first aspect, an embodiment of the present invention provides a dynamic development method based on intelligent image optimization, wherein the method is applied to a controller, wherein the controller establishes communication connections with an infrared sensor, a visible light imaging device, and an isotope imaging device respectively to realize data information transmission, and the method includes:

[0005] If the input development instruction is received, simultaneously acquiring the visible light image acquired by the visible light imaging device and the isotope imaging image acquired by the isotope imaging device;

[0006] Extracting corresponding contour information from the visible light image according to a preset contour extraction model;

[0007] Performing regional segmentation on the isotope imaging image according to the contour information to obtain a corresponding regional segmentation image;

[0008] Performing background segmentation on the region segmented image according to a preset background segmentation rule and the radioactive material information set in the development instruction to obtain a foreground image and a background image;

[0009] Optimizing the foreground image according to a preset image optimization strategy, the distance information sensed by the infrared sensor, and the background image to obtain a corresponding developed image;

[0010] If the interval time point set in the development instruction is reached, the process returns to the step of simultaneously acquiring the visible light image acquired by the visible light imaging device and the isotope imaging image acquired by the isotope imaging device, and the above steps are executed in a loop to obtain continuous development images sorted in time sequence for dynamic development.

[0011] In a second aspect, an embodiment of the present invention further provides a dynamic development device based on intelligent image optimization, wherein the device is configured in a controller, and the controller establishes communication connections with an infrared sensor, a visible light imaging device, and an isotope imaging device respectively to realize data information transmission. The device is used to perform the dynamic development method based on intelligent image optimization as described in the first aspect above, and the device includes:

[0012] an image acquisition unit, configured to simultaneously acquire the visible light image acquired by the visible light imaging device and the isotope imaging image acquired by the isotope imaging device upon receiving the input development instruction;

[0013] A contour information acquisition unit, configured to extract corresponding contour information from the visible light image according to a preset contour extraction model;

[0014] A region segmentation image acquisition unit, configured to perform region segmentation on the isotope imaging image according to the contour information to obtain a corresponding region segmentation image;

[0015] a background segmentation unit, configured to perform background segmentation on the region segmented image according to a preset background segmentation rule and the radioactive material information set in the development instruction, so as to obtain a foreground image and a background image;

[0016] An image optimization unit, configured to optimize the foreground image according to a preset image optimization strategy, the distance information sensed by the infrared sensor, and the background image to obtain a corresponding developed image;

[0017] The dynamic development unit is used to return to the execution of the simultaneous acquisition of the visible light image acquired by the visible light imaging device and the isotope imaging image acquired by the isotope imaging device if the interval time point set in the development instruction is reached, and to loop through the above steps to obtain continuous development images sorted in time sequence for dynamic development.

[0018] In a third aspect, an embodiment of the present invention further provides a computer device, wherein the device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus;

[0019] Memory for storing computer programs;

[0020] The processor is used to implement the steps of the dynamic development method based on intelligent image optimization described in the first aspect when executing the program stored in the memory.

[0021] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the dynamic development method based on intelligent image optimization as described in the first aspect above are implemented.

[0022] Embodiments of the present invention provide a dynamic development method, apparatus, device, and medium based on intelligent image optimization. The method includes: upon receiving a development instruction, simultaneously acquiring a visible light image and an isotope imaging image; extracting contour information from the visible light image to perform region segmentation on the isotope imaging image to obtain a region segmentation image; further performing background segmentation on the region segmentation image to obtain a foreground image and a background image; performing image optimization on the foreground image to obtain a developed image; and looping through the above steps to obtain continuous developed images for dynamic development. The above dynamic development method can simultaneously acquire a visible light image and an isotope imaging image; perform region segmentation and background segmentation on the isotope imaging image based on the visible light image; and perform image optimization on the foreground image to obtain a developed image. The continuous developed images are obtained to achieve dynamic development based on radioactive isotopes, thereby improving the image quality of dynamic linear development based on radioactive isotopes. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0024] Figure 1 A flow chart of a method for a dynamic development method based on intelligent image optimization provided by an embodiment of the present invention;

[0025] Figure 2 A schematic diagram of an application scenario of a dynamic development method based on intelligent image optimization provided by an embodiment of the present invention;

[0026] Figure 3A schematic diagram of another application scenario of the dynamic development method based on intelligent image optimization provided by an embodiment of the present invention;

[0027] Figure 4 A schematic block diagram of a dynamic development device based on intelligent image optimization provided by an embodiment of the present invention;

[0028] Figure 5 It is a schematic block diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0030] It will be understood that when used in this specification and the appended claims, the terms “comprises” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0031] It should also be understood that the terms used in the present specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0032] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0033] See also Figure 1 As shown in the figure, an embodiment of the present invention provides a dynamic development method based on intelligent image optimization, which is applied to a controller and executed by an application installed in the controller. The controller can be a device such as a laptop, desktop computer, tablet computer or mobile phone; Figure 2As shown, the controller 10 establishes communication connections with the infrared sensor 20, the visible light imaging device 30, and the isotope imaging device 40 respectively to realize data information transmission; the radiation source 50 emits radiation and partially passes through the human body. The infrared sensor 20, the visible light imaging device 30, and the isotope imaging device 40 are all set at positions opposite to the radiation source 50. The infrared sensor 20 can sense the position of the human body 60. The visible light imaging device 30 is used to collect human body images in the visible light band, and the isotope imaging device 40 is used to collect the radiation emitted by the radiation source 50 and obtain isotope imaging images. Figure 1 As shown, the method includes steps S110 to S160.

[0034] S110: If a development instruction is received, simultaneously acquire a visible light image captured by the visible light imaging device and an isotope imaging image captured by the isotope imaging device. An operator (e.g., a physician) may input a development instruction into a controller. Upon receiving the development instruction, the controller may issue corresponding image acquisition instructions to the visible light imaging device and the isotope imaging device. The visible light imaging device may acquire a corresponding visible light image, and the isotope imaging device may acquire a corresponding isotope imaging image. The controller may then acquire the visible light image and the isotope imaging image captured at the same time.

[0035] S120 : Extracting corresponding contour information from the visible light image according to a preset contour extraction model.

[0036] The corresponding contour information can be extracted from the visible light image through a preset contour extraction model, and the obtained contour information includes edge information of the body of the person being detected.

[0037] In a specific embodiment, step S120 includes sub-steps: calculating the contrast value of each pixel in the visible light image according to the contrast calculation formula in the contour extraction model; performing pixel dissolution on the pixel points in the visible light image according to the pixel dissolution ratio set in the contour extraction model and the contrast value to obtain a contour image containing contour pixels; and binarizing the contour image to obtain corresponding contour information.

[0038] Specifically, the contrast value of each pixel in the image can be calculated according to the contrast calculation formula in the contour extraction model. For example, the contrast calculation formula can be as shown in formula (1):

[0039]

[0040] Where D is the calculated contrast value corresponding to s0, s0 is the pixel value of the center pixel, s mis the pixel value of the mth pixel in the first circle around the s0 pixel point, and the value range of m is [1, M]. n is the pixel value of the nth pixel in the second circle around the pixel s0, where n is in the range [1, N]. The larger the calculated contrast value, the greater the difference in pixel value between the central pixel and the peripheral pixels.

[0041] Furthermore, the pixels in the visible light image are dissolved according to the pixel dissolution ratio set in the contour extraction model. Specifically, the pixels in the visible light image can be first sorted according to the contrast value. The larger the contrast value, the higher the ranking. The pixels at the front of the sorted pixels are intercepted according to the pixel dissolution ratio, and the other pixels at the back of the sort are dissolved. For example, if the pixel dissolution ratio is 0.15, the pixels at the front that account for 15% of the total number of pixels are intercepted according to the sorted pixels and retained, and the other pixels in the visible light image are dissolved, thereby obtaining a contour image containing contour pixels, that is, the pixels retained in the image are contour pixels.

[0042] The contour pixels in the contour image are further binarized to determine that the pixel value of the contour pixel is "1" and the pixel value of the remaining pixels in the contour image is "0", thereby obtaining an image containing only the two pixel values ​​of "0" and "1", in which the pixel points with the pixel value of "1" are combined into contour information.

[0043] S130 , performing region segmentation on the isotope imaging image according to the contour information to obtain a corresponding region segmentation image.

[0044] The isotope imaging image can be regionally segmented according to the contour information, thereby segmenting the isotope imaging image into multiple regional segmentation images reflecting local details. The multiple regional segmentation images can be spliced ​​and combined to restore the isotope imaging image.

[0045] In a specific embodiment, step S130 includes the sub-steps of: determining a closed area corresponding to the contour information; and performing region segmentation on the isotope imaging image according to the closed area to obtain a corresponding region segmentation image.

[0046] Specifically, the closed region corresponding to the contour information can be determined. Specifically, the contour line formed by connecting the pixels in the contour information is combined with the image border of the visible light image to form an independent closed frame. From this closed frame, the minimum closed region that cannot be further segmented can be extracted. Since the isotope imaging image and the visible light image are captured in the same direction, the isotope imaging image can be segmented based on the extracted minimum closed region to obtain a corresponding segmented image. In this case, the edge of each segmented image corresponds to a minimum closed region.

[0047] In a specific embodiment, before performing regional segmentation on the isotope imaging image based on the contour information to obtain the corresponding regional segmentation image, it also includes: performing pixel correction on the isotope imaging image based on the relative position relationship between the visible light imaging device and the isotope imaging device and the visible light image to obtain the corrected isotope imaging image.

[0048] Furthermore, in order to avoid the position deviation between the visible light imaging device and the isotope imaging device, which may cause the pixel positions in the visible light image and the isotope imaging image to not correspond, pixel correction can be performed on the isotope imaging image based on the relative position relationship between the visible light imaging device and the isotope imaging device and the visible light image. Figure 3 As shown, Figure 3 The middle dotted line represents the human standing surface, the angle between the line between the visible light imaging device and the center point of the visible light image and the horizontal line is δ, and the angle between the line between the isotope imaging device and the center point of the isotope imaging image and the horizontal line is θ.

[0049] First, calculate the projected lengths of the upper and lower borders of the visible light image onto the standing surface. Assume that the length between the upper and lower borders in the visible light image is L1. Then, the projected length of L1 onto the standing surface is L1 / cosδ. The length between the upper and lower borders in the isotope imaging image is L2. Calculate cosθ×L1 / cosδ. It can be determined that L2 is greater than cosθ×L1 / cosδ. Using cosθ×L1 / cosδ as the vertical length, the corresponding target region image is obtained from the isotope imaging image. The length between the upper and lower borders in the target region image is cosθ×L1 / cosδ. Divide the vertical length r between each pixel in the target region image and the center point of the isotope imaging image by cosθ to project the target region image onto the standing surface, thereby performing pixel deskew on the isotope imaging image. During this process, the horizontal spacing between pixels in the target region image remains unchanged; only the vertical position changes. The image obtained after deskew is the corrected isotope imaging image.

[0050] During this process, it can also be determined whether δ is zero. If so, pixel deskew of the visible light image is not required. If not, the vertical length t between each pixel in the visible light image and the center of the visible light image is divided by cosδ, thereby projecting the visible light image onto the standing surface of the human body. This allows both the visible light image and the isotope imaging image to be projected onto the same plane, ensuring a one-to-one correspondence between pixels representing the same information in the images. This significantly improves the accuracy of the subsequent segmentation of the isotope imaging image based on the corresponding contour information in the visible light image.

[0051] S140 , performing background segmentation on the region segmented image according to a preset background segmentation rule and the radioactive material information set in the development instruction to obtain a foreground image and a background image.

[0052] The development instructions also include radioactive material information, including the material type, such as technetium (Tc-99) and iodine (I-131). This information also includes radiation intensity, which represents the radiation intensity of the radioactive material and is measured in μSv / h (microsieverts / hour). The regional segmentation image can be segmented based on pre-set background segmentation rules and radiation intensity, generating corresponding foreground and background images. The controller controls the intensity of the radiation emitted by the radiation source based on the radiation intensity, ensuring that the intensity meets the actual development requirements.

[0053] In a specific embodiment, step S140 includes sub-steps: calculating a corresponding background brightness threshold based on the background segmentation rule and the radiation intensity of the radioactive material information; classifying the region segmentation images according to the background brightness threshold to determine the layer type of each region segmentation image; and classifying and combining the region segmentation images according to the layer type to obtain corresponding foreground images and background images.

[0054] The radiation intensity is calculated according to the intensity calculation formula in the background segmentation rule to obtain the corresponding background brightness threshold. Specifically, the intensity calculation formula can be as shown in formula (2):

[0055]

[0056] Among them, Y x is the calculated background brightness threshold, J x is the horizontal distance between the isotope imaging device and the radiation source, J0 is the basic distance value preset in the intensity calculation formula, q is the radiation intensity, y0 is the basic brightness value preset in the intensity calculation formula, and q0 is the basic intensity preset in the intensity calculation formula.

[0057] Furthermore, the segmented images are classified according to the background brightness threshold, and it is determined whether the average brightness value of the pixels in each segmented image is greater than the background brightness threshold. If so, the layer type of the segmented image is determined to be a background layer; if not, the layer type of the segmented image is determined to be a foreground layer. The above determination method can be used to determine whether the layer type of each segmented image is a foreground layer or a background layer.

[0058] The region segmentation images are classified and combined according to the layer type, and the region segmentation images of the same layer type are combined. The region segmentation images of the foreground layer are combined into the foreground image, and the region segmentation images of the background layer are combined into the background image. The foreground image is the local image obtained by the irradiation of the human body, and the background image is the local image obtained by the irradiation of the human body without passing through the irradiation.

[0059] S150 , performing image optimization on the foreground image according to a preset image optimization strategy, the distance information sensed by the infrared sensor, and the background image to obtain a corresponding developed image.

[0060] The controller is also pre-configured with an image optimization strategy. The infrared sensor senses distance information, which represents the horizontal distance between the subject and the isotope imaging device. Based on this image optimization strategy, distance information, and background image, the foreground image is optimized, resulting in the optimized image as the developed image.

[0061] In a specific embodiment, step S150 includes sub-steps: performing inversion processing on the background image and obtaining the background pixel brightness of the background image after the inversion processing; performing inversion processing on the foreground image and obtaining the foreground image brightness and brightness singularity of the foreground image after the inversion processing; obtaining an image optimization coefficient corresponding to the foreground image brightness and brightness singularity according to the image optimization strategy, the distance information and the background pixel brightness; performing image optimization on the foreground image according to the image optimization coefficient to obtain a corresponding developed image.

[0062] First, the background image can be inverted, and the background pixel brightness of the inverted background image can be obtained. Furthermore, the foreground image can be inverted, and the foreground image brightness of the inverted foreground image can be obtained. Furthermore, the brightness gradient of the pixels in the foreground image is calculated, and the corresponding brightness singular point is determined based on the brightness gradient of the pixels. The brightness singular point is also the center point of the brightness drop; the brightness gradient values ​​in each direction calculated based on the pixel corresponding to the brightness singular point are all greater than zero, that is, the brightness of the pixels radiating from the pixel corresponding to the brightness singular point in all directions is greater than the pixel corresponding to the brightness singular point.

[0063] An image optimization coefficient corresponding to the foreground image is obtained based on the image optimization strategy, distance information, and background pixel brightness. The foreground image is optimized based on the obtained image optimization coefficient, i.e., the brightness of each pixel in the foreground image is adjusted to obtain the corresponding developed image.

[0064] In a specific embodiment, the image optimization coefficient corresponding to the foreground image brightness and the brightness singularity is obtained according to the image optimization strategy, the distance information and the background pixel brightness, including: obtaining the brightness enhancement coefficient corresponding to the distance information, the background pixel brightness and the foreground image brightness according to the first coefficient calculation formula in the image optimization strategy; obtaining the local optimization coefficient corresponding to the background pixel brightness, the foreground image brightness and the brightness singularity according to the second coefficient calculation formula in the image optimization strategy; and combining the brightness enhancement coefficient and the local optimization coefficient into the corresponding image optimization coefficient.

[0065] Specifically, the brightness enhancement coefficient corresponding to the distance information, background pixel brightness, and foreground image brightness can be calculated based on the first coefficient calculation formula. Specifically, the first coefficient calculation formula can be shown as formula (3):

[0066]

[0067] Among them, F q is the foreground image brightness, F b is the background image brightness, H is the distance information, J x is the horizontal distance between the isotope imaging device and the radiation source, and x1 is the brightness enhancement coefficient.

[0068] The local optimization coefficients corresponding to the background pixel brightness, foreground image brightness, and brightness singularity can be calculated based on the second coefficient calculation formula. Specifically, the second coefficient calculation formula can be shown as formula (4):

[0069]

[0070] Among them, F q is the foreground image brightness, F b is the background image brightness, F r is the brightness value of the pixel corresponding to the brightness singular point, H is the distance information, and x2 is the local optimization coefficient.

[0071] The brightness enhancement coefficient and the local optimization coefficient are combined into a corresponding image optimization coefficient. When optimizing the foreground image according to the image optimization coefficient, the foreground image can be locally optimized according to the local optimization coefficient first. Specifically, the brightness value C of each pixel after local optimization can be calculated. s ×x2×(K / 3-K s ), C is the brightness value of the pixel after local optimization, c s is the initial brightness value of the pixel, K is the diagonal length of the isotope imaging image, K sis the distance between the pixel and the pixel corresponding to the brightness singular point. By the above method, each pixel in the foreground image can be locally optimized.

[0072] Furthermore, the brightness of each pixel in the locally optimized foreground image is multiplied by the brightness enhancement coefficient, thereby improving the brightness of each pixel in the locally optimized foreground image, and the optimized image is spliced ​​and combined with the background image to finally obtain the developed image for image optimization.

[0073] S160. If the interval time point set in the development instruction is reached, return to the step of simultaneously acquiring the visible light image acquired by the visible light imaging device and the isotope imaging image acquired by the isotope imaging device, and loop through the above steps to obtain continuous development images sorted in time sequence for dynamic development.

[0074] If the interval time point set in the imaging instruction is reached, the controller returns to step S110. Specifically, the interval time point can be determined based on the cycle time. For example, if the cycle time is set to 0.1s, the controller will return to step S110 every 0.1s and obtain a new imaging image. By combining multiple imaging images in chronological order, a continuous imaging image can be obtained. Dynamic display of the continuous imaging images can achieve dynamic imaging of the human body based on radioisotopes.

[0075] If the termination instruction is input, the controller terminates the step of acquiring the development image.

[0076] In the dynamic development method based on intelligent image optimization disclosed in the above embodiment, the method includes: upon receiving a development instruction, simultaneously acquiring a visible light image and an isotope imaging image, extracting contour information from the visible light image to perform region segmentation on the isotope imaging image to obtain a region segmentation image, further performing background segmentation on the region segmentation image to obtain a foreground image and a background image, performing image optimization on the foreground image to obtain a developed image, and looping through the above steps to obtain continuous developed images for dynamic development. The above dynamic development method can simultaneously acquire a visible light image and an isotope imaging image, perform region segmentation and background segmentation on the isotope imaging image based on the visible light image, and perform image optimization on the foreground image to obtain a developed image. Continuous developed images are obtained to achieve dynamic development based on radioactive isotopes, thereby improving the image quality of dynamic linear development based on radioactive isotopes.

[0077] The embodiment of the present invention further provides a dynamic development device based on intelligent image optimization, which can be configured in a controller and is used to execute any embodiment of the aforementioned dynamic development method based on intelligent image optimization. Figure 4 , Figure 4 A schematic block diagram of a dynamic development device based on intelligent image optimization provided by an embodiment of the present invention.

[0078] like Figure 4 As shown, the dynamic development device 100 based on intelligent image optimization includes an image acquisition unit 110 , a contour information acquisition unit 120 , a region segmentation image acquisition unit 130 , a background segmentation unit 140 , an image optimization unit 150 and a dynamic development unit 160 .

[0079] The image acquisition unit 110 is configured to simultaneously acquire the visible light image acquired by the visible light imaging device and the isotope imaging image acquired by the isotope imaging device upon receiving the input development instruction.

[0080] The contour information acquisition unit 120 is configured to extract corresponding contour information from the visible light image according to a preset contour extraction model.

[0081] The region segmentation image acquisition unit 130 is configured to perform region segmentation on the isotope imaging image according to the contour information to obtain a corresponding region segmentation image.

[0082] The background segmentation unit 140 is configured to perform background segmentation on the region segmented image according to a preset background segmentation rule and the radioactive material information set in the development instruction, so as to obtain a foreground image and a background image.

[0083] The image optimization unit 150 is configured to optimize the foreground image according to a preset image optimization strategy, the distance information sensed by the infrared sensor, and the background image to obtain a corresponding developed image.

[0084] The dynamic development unit 160 is used to return to the execution of the simultaneous acquisition of the visible light image acquired by the visible light imaging device and the isotope imaging image acquired by the isotope imaging device if the interval time point set in the development instruction is reached, and to loop through the above steps to obtain continuous development images sorted in time sequence for dynamic development.

[0085] The dynamic development device based on intelligent image optimization provided in an embodiment of the present invention applies the above-mentioned dynamic development method based on intelligent image optimization. Upon receiving a development instruction, it simultaneously acquires a visible light image and an isotope imaging image, extracts contour information from the visible light image to perform region segmentation on the isotope imaging image to obtain a region segmentation image, further performs background segmentation on the region segmentation image to obtain a foreground image and a background image, optimizes the foreground image to obtain a developed image, and repeats the above steps to obtain continuous developed images for dynamic development. The above-mentioned dynamic development method can simultaneously acquire a visible light image and an isotope imaging image, perform region segmentation and background segmentation on the isotope imaging image based on the visible light image, and optimize the foreground image to obtain a developed image. Continuous developed images are obtained to achieve dynamic development based on radioactive isotopes, thereby improving the image quality of dynamic linear development based on radioactive isotopes.

[0086] The above-mentioned dynamic development device based on intelligent image optimization can be implemented in the form of a computer program. The computer program can be used in Figure 5 Runs on the computer equipment shown.

[0087] See also Figure 5 , Figure 5 1 is a schematic block diagram of a computer device provided by an embodiment of the present invention. The computer device may be a controller for executing a dynamic development method based on intelligent image optimization to achieve dynamic development based on radioactive isotopes.

[0088] See Figure 5 The computer device 500 includes a processor 502 , a memory, and a communication interface 505 connected via a communication bus 501 , wherein the memory may include a storage medium 503 and an internal memory 504 .

[0089] The storage medium 503 may store an operating system 5031 and a computer program 5032. When the computer program 5032 is executed, the processor 502 may execute a dynamic development method based on intelligent image optimization. The storage medium 503 may be a volatile storage medium or a non-volatile storage medium.

[0090] The processor 502 is used to provide computing and control capabilities to support the operation of the entire computer device 500.

[0091] The internal memory 504 provides an environment for the operation of the computer program 5032 in the storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute a dynamic development method based on intelligent image optimization.

[0092] The communication interface 505 is used for network communication, such as providing data information transmission. Those skilled in the art will understand that Figure 5 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention and does not constitute a limitation on the computer device 500 to which the solution of the present invention is applied. The specific computer device 500 may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0093] The processor 502 is configured to run a computer program 5032 stored in the memory to implement corresponding functions in the above-mentioned dynamic development method based on intelligent image optimization.

[0094] Those skilled in the art will understand that Figure 5 The embodiment of the computer device shown in the figure does not constitute a limitation on the specific composition of the computer device. In other embodiments, the computer device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. For example, in some embodiments, the computer device may only include a memory and a processor. In such an embodiment, the structure and function of the memory and processor are the same as those in the figure. Figure 5 The embodiments shown are consistent and will not be described again here.

[0095] It should be understood that in the embodiment of the present invention, the processor 502 may be a central processing unit (CPU), and the processor 502 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0096] In another embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium may be volatile or non-volatile. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps included in the above-described dynamic development method based on intelligent image optimization.

[0097] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0098] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, or units with the same function may be combined into one unit. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices or units, or may be an electrical, mechanical or other form of connection.

[0099] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the objectives of the embodiments of the present invention.

[0100] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0101] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a computer-readable storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned computer-readable storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk.

[0102] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and such modifications or substitutions are intended to be within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A dynamic development method based on intelligent image optimization, characterized in that: The method is applied to a controller, wherein the controller establishes communication connections with an infrared sensor, a visible light imaging device, and an isotope imaging device respectively to realize data information transmission, and the method includes: If the input development instruction is received, simultaneously acquiring the visible light image acquired by the visible light imaging device and the isotope imaging image acquired by the isotope imaging device; Extracting corresponding contour information from the visible light image according to a preset contour extraction model; Performing regional segmentation on the isotope imaging image according to the contour information to obtain a corresponding regional segmentation image; Performing background segmentation on the region segmented image according to a preset background segmentation rule and the radioactive material information set in the development instruction to obtain a foreground image and a background image; Optimizing the foreground image according to a preset image optimization strategy, the distance information sensed by the infrared sensor, and the background image to obtain a corresponding developed image; If the interval time point set in the development instruction is reached, the process returns to the step of simultaneously acquiring the visible light image acquired by the visible light imaging device and the isotope imaging image acquired by the isotope imaging device, and the above steps are executed in a loop to obtain continuous development images sorted in time sequence for dynamic development.

2. The dynamic development method based on intelligent image optimization according to claim 1, characterized in that: The extracting corresponding contour information from the visible light image according to a preset contour extraction model includes: Calculating the contrast value of each pixel in the visible light image according to the contrast calculation formula in the contour extraction model; Performing pixel dissolution on the pixels in the visible light image according to the pixel dissolution ratio set in the contour extraction model and the contrast value to obtain a contour image containing contour pixels; The contour image is binarized to obtain corresponding contour information.

3. The dynamic development method based on intelligent image optimization according to claim 1, characterized in that: The performing region segmentation on the isotope imaging image according to the contour information to obtain a corresponding region segmentation image includes: determining a closed area corresponding to the contour information; The isotope imaging image is segmented according to the closed area to obtain a corresponding segmented image.

4. The dynamic development method based on intelligent image optimization according to claim 3, characterized in that: Before performing region segmentation on the isotope imaging image according to the contour information to obtain a corresponding region segmented image, the method further includes: Pixel deflection correction is performed on the isotope imaging image according to the relative positional relationship between the visible light imaging device and the isotope imaging device and the visible light image to obtain a deflected isotope imaging image.

5. The dynamic development method based on intelligent image optimization according to claim 1, characterized in that: The performing background segmentation on the region segmented image according to the preset background segmentation rule and the radioactive material information set in the development instruction to obtain a foreground image and a background image includes: Calculating a corresponding background brightness threshold according to the background segmentation rule and the radiation intensity of the radioactive material information; Classifying the region segmented images respectively according to the background brightness threshold to determine the layer type of each region segmented image; The region segmented images are classified and combined according to the layer type to obtain corresponding foreground images and background images.

6. The dynamic development method based on intelligent image optimization according to claim 1, characterized in that: The step of optimizing the foreground image according to a preset image optimization strategy, the distance information sensed by the infrared sensor, and the background image to obtain a corresponding developed image includes: Performing inversion processing on the background image and obtaining background pixel brightness of the inverted background image; Performing color inversion processing on the foreground image and obtaining the foreground image brightness and brightness singularity of the foreground image after the color inversion processing; Obtaining an image optimization coefficient corresponding to the foreground image brightness and brightness singularity according to the image optimization strategy, the distance information, and the background pixel brightness; The foreground image is optimized according to the image optimization coefficient to obtain a corresponding developed image.

7. The dynamic development method based on intelligent image optimization according to claim 6, characterized in that: The obtaining of an image optimization coefficient corresponding to the foreground image brightness and the brightness singular point according to the image optimization strategy, the distance information, and the background pixel brightness includes: Obtaining a brightness enhancement coefficient corresponding to the distance information, the background pixel brightness, and the foreground image brightness according to the first coefficient calculation formula in the image optimization strategy; Obtaining a local optimization coefficient corresponding to the background pixel brightness, the foreground image brightness, and the brightness singular point according to the second coefficient calculation formula in the image optimization strategy; The brightness enhancement coefficient and the local optimization coefficient are combined into a corresponding image optimization coefficient.

8. A dynamic development device based on intelligent image optimization, characterized in that: The device is configured in a controller, and the controller establishes communication connections with the infrared sensor, the visible light imaging device, and the isotope imaging device respectively to realize data information transmission. The dynamic development device based on intelligent image optimization is used to perform the dynamic development method based on intelligent image optimization according to any one of claims 1 to 7, and the device includes: an image acquisition unit, configured to simultaneously acquire the visible light image acquired by the visible light imaging device and the isotope imaging image acquired by the isotope imaging device upon receiving the input development instruction; A contour information acquisition unit, configured to extract corresponding contour information from the visible light image according to a preset contour extraction model; A region segmentation image acquisition unit, configured to perform region segmentation on the isotope imaging image according to the contour information to obtain a corresponding region segmentation image; a background segmentation unit, configured to perform background segmentation on the region segmented image according to a preset background segmentation rule and the radioactive material information set in the development instruction, so as to obtain a foreground image and a background image; An image optimization unit, configured to optimize the foreground image according to a preset image optimization strategy, the distance information sensed by the infrared sensor, and the background image to obtain a corresponding developed image; The dynamic development unit is used to return to the execution of the simultaneous acquisition of the visible light image acquired by the visible light imaging device and the isotope imaging image acquired by the isotope imaging device if the interval time point set in the development instruction is reached, and to loop through the above steps to obtain continuous development images sorted in time sequence for dynamic development.

9. A computer device, characterized in that: The device includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory for storing computer programs; The processor is configured to implement the steps of the dynamic development method based on intelligent image optimization according to any one of claims 1 to 7 when executing the program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the dynamic development method based on intelligent image optimization are implemented as described in any one of claims 1 to 7.

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