Blood detection method, blood detection system, and image processing apparatus

Abstract

The invention relates to a bleeding detection method, a bleeding detection system and image processing equipment, and the bleeding detection method comprises the steps: obtaining a first to-be-detected image, collected by an endoscope, of a target tissue; determining the bleeding amount condition of the target tissue according to the image gradient of the first to-be-detected image; when the bleeding amount condition of the target tissue is a target bleeding amount, acquiring a plurality of frames of second to-be-detected images of the target tissue acquired by the endoscope before and / or after the first to-be-detected image; determining the bleeding severity of the target tissue according to the change information of the color contrast between the at least two frames of first target images; the first target image is an image in the first to-be-detected image and the plurality of frames of second to-be-detected images. According to the application, the problems of low real-time performance and low accuracy of bleeding condition detection are solved, and a timely and efficient bleeding spot detection method is realized.

Description

Technical Field

[0001] The present application relates to the field of image processing technology, and in particular to a bleeding detection method, a bleeding detection system and an image processing device. Background Art

[0002] An endoscope is a commonly used medical device. After the insertion portion of the endoscope enters the target tissue of the object to be inspected, the endoscope can be used to collect the original image of the target tissue, and the object to be inspected can be inspected or treated based on the original image of the target tissue. However, during the operation, it is very likely that the deep blood vessels under the mucosa will be injured, resulting in severe bleeding in the target tissue. At this time, if the bleeding cannot be detected and stopped in time, it may cause certain harm to the user. In related technologies, although intraoperative bleeding will be detected, there is a general problem that it is difficult to detect the presence of bleeding in a timely manner, resulting in low real-time and accuracy of bleeding detection.

[0003] Currently, no effective solution has been proposed for the problems of low real-time and low accuracy of bleeding detection in related technologies. Summary of the invention

[0004] The embodiments of the present application provide a bleeding detection method, a bleeding detection system and an image processing device to at least solve the problems of low real-time and accuracy of bleeding detection in the related art.

[0005] In a first aspect, an embodiment of the present application provides a bleeding detection method, the method comprising:

[0006] Acquire a first image to be detected of the target tissue collected by the endoscope;

[0007] determining the amount of bleeding of the target tissue according to the image gradient of the first image to be detected;

[0008] When the bleeding amount of the target tissue is the target bleeding amount, acquiring several frames of second images to be detected of the target tissue collected by the endoscope before and / or after the first image to be detected;

[0009] The bleeding severity of the target tissue is determined according to the color contrast change information between at least two frames of first target images; the first target image is an image among the first image to be detected and the plurality of frames of second images to be detected.

[0010] In some embodiments, the method further comprises:

[0011] When the image gradient is less than a preset gradient change threshold, the bleeding condition of the target tissue is determined to be the target bleeding amount condition.

[0012] In some embodiments, determining the severity of bleeding of the target tissue according to the change information of the color contrast between at least two frames of the first target image includes:

[0013] Performing color space conversion processing on each frame of the first target image to obtain a LAB image in a LAB color space and characteristic color dimension information of the LAB image;

[0014] The change information of the color contrast between each frame of the LAB images is determined according to the characteristic color dimension information, and the severity of the bleeding is determined according to the change information of the color contrast between the LAB images.

[0015] In some embodiments, determining the severity of bleeding of the target tissue according to the change information of the color contrast between at least two frames of the first target image includes:

[0016] generating first color change trend information corresponding to the second target image according to the color contrast between the first image to be detected and the second target image in the plurality of frames of the second image to be detected;

[0017] determining the bleeding volume information of the target tissue according to the first color change trend information;

[0018] The severity of bleeding of the target tissue is determined according to the bleeding volume information and the change information of the color contrast between the at least two frames of the first target image.

[0019] In some embodiments, determining the bleeding volume information of the target tissue according to the first color change trend information includes:

[0020] Determine, according to the first color change trend information, a first color contrast to be measured corresponding to the number of target pixels in the second target image;

[0021] When it is detected that the first color contrast to be measured is within a preset color area, first concentration information between all the first color contrasts to be measured is calculated, and the bleeding volume information of the target tissue is determined according to the first concentration information.

[0022] In some embodiments, the second target image is the latest frame image among the first image to be detected and the plurality of frames of second images to be detected.

[0023] In some embodiments, determining the severity of bleeding of the target tissue according to the change information of the color contrast between at least two frames of the first target image includes:

[0024] generating second color change trend information corresponding to the first target image according to the color contrast of each frame of the first target image;

[0025] Determine the color contrast change information according to the change information between all the second color change trend information, and determine the bleeding speed information of the target tissue according to the color contrast change information;

[0026] The bleeding severity of the target tissue is determined according to the bleeding rate information.

[0027] In some embodiments, determining the color contrast change information according to the change information between all the second color change trend information includes:

[0028] Determine, according to the second color change trend information, a second color contrast to be measured corresponding to the number of target pixels in each frame of the first target image;

[0029] When it is detected that the second color contrast to be tested is within a preset color region, calculating second concentration degree information between all the second color contrasts to be tested in each frame of the first target image;

[0030] The change information of the color contrast is determined according to the change information of the second concentration degree information corresponding to each frame of the first target image.

[0031] In some embodiments, the image acquisition mode of the endoscope includes a white light mode and a non-white light mode, and after determining the severity of bleeding of the target tissue, the method further includes:

[0032] When the severity of bleeding is the target severity, the image acquisition mode is switched from the white light mode to the non-white light mode, and a non-white light image of the endoscope in the non-white light mode is acquired; wherein the light irradiated by the endoscope to the target tissue in the white light mode is white light, and the light irradiated to the target tissue in the non-white light mode is non-white light;

[0033] A bleeding position detection result for the target tissue is generated according to the non-white light image.

[0034] In some embodiments, after determining the severity of bleeding in the target tissue, the method further comprises:

[0035] Based on the bleeding position detection result, generating bleeding position marking information for the target tissue;

[0036] An image to be displayed is generated according to the bleeding position mark information and the white light image.

[0037] In a second aspect, an embodiment of the present application provides a bleeding detection system, the system comprising: a first acquisition unit, a first determination unit, a second acquisition unit, and a second determination unit;

[0038] The first acquisition unit is used to acquire a first image to be detected of the target tissue collected by the endoscope;

[0039] The first determining unit is used to determine the amount of bleeding of the target tissue according to the image gradient of the first image to be detected;

[0040] The second acquisition unit is used to acquire a plurality of frames of second images to be detected of the target tissue collected by the endoscope before and / or after the first image to be detected when the bleeding amount of the target tissue is the target bleeding amount;

[0041] The second determination unit is used to determine the bleeding severity of the target tissue based on the change information of the color contrast between at least two frames of the first target image; the first target image is an image in the first image to be detected and the plurality of frames of the second images to be detected.

[0042] In a third aspect, an embodiment of the present application provides an image processing device, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the bleeding detection method described in the first aspect.

[0043] In a fourth aspect, an embodiment of the present application provides an endoscope system, the system comprising: a display, an endoscope, a light source device, and an image processing device as described in the third aspect above.

[0044] Compared with the related art, the bleeding detection method, bleeding detection system and image processing device provided in the embodiments of the present application obtain a first image of the target tissue to be detected collected by an endoscope; determine the bleeding amount of the target tissue according to the image gradient of the first image to be detected; when the bleeding amount of the target tissue is the target bleeding amount, obtain several frames of second images of the target tissue to be detected collected by the endoscope before and / or after the first image to be detected; determine the bleeding severity of the target tissue according to the change information of the color contrast between at least two frames of the first target image; the first target image is an image in the first image to be detected and the several frames of the second image to be detected, so that bleeding points can be detected in time when they appear, and the bleeding situation of the bleeding points can be detected in real time, avoiding the phenomenon that the bleeding points cannot be detected in time or the bleeding detection accuracy is low due to the untimely processing of the images collected by the endoscope, solving the problems of real-time and low accuracy of bleeding situation detection, and realizing a timely and efficient bleeding point detection method.

[0045] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0047] Figure 1 is a flow chart of a bleeding detection method according to an embodiment of the present application;

[0048] Figure 2 is a schematic diagram of pixel point statistics of a first target image or a second target image according to an embodiment of the present application;

[0049] Figure 3 is a flow chart of a bleeding detection method according to a preferred embodiment of the present application;

[0050] Figure 4 is a structural block diagram of a bleeding detection system according to an embodiment of the present application;

[0051] Figure 5 is a schematic structural diagram of an endoscope system according to an embodiment of the present application;

[0052] Figure 6 It is a structural diagram of the inside of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application. In addition, it can also be understood that although the efforts made in this development process may be complex and lengthy, for ordinary technicians in the field related to the contents disclosed in the present application, some changes such as design, manufacturing or production based on the technical contents disclosed in the present application are only conventional technical means, and should not be understood as insufficient contents disclosed in the present application.

[0054] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0055] Unless otherwise defined, the technical terms or scientific terms involved in this application should be understood by people with ordinary skills in the technical field to which this application belongs. The words "one", "a", "a", "the" and the like involved in this application do not indicate a quantitative limitation, and may represent the singular or plural. The terms "include", "comprise", "have" and any of their variations involved in this application are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units that are not listed, or may also include other steps or units inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "multiple" involved in this application refers to greater than or equal to two. "And / or" describes the association relationship of associated objects, indicating that there can be three relationships, for example, "A and / or B" can represent: A exists alone, A and B exist at the same time, and B exists alone. The terms "first", "second", "third" and the like involved in the present application are merely used to distinguish similar objects and do not represent a specific ordering of the objects.

[0056] An endoscope is an optical device that enters the human body through a natural orifice or a small incision made during surgery to observe relevant areas. Endoscopes of different lengths and diameters can be used to detect different areas of the human body. According to the different detection areas, endoscopes can be divided into laparoscopes, neuroendoscopes, arthroscopy, esophagoscopes and other types. During surgery, the submucosal blood vessels of the target tissue to be examined may be injured due to the operation, resulting in heavy bleeding. The blood that flows out covers more and more of the endoscope's image acquisition area, thereby affecting the operator's observation of the target tissue during surgery from the screen.

[0057] In an optional embodiment, the operator can observe the bleeding situation and the location of the bleeding point by observing the situation in the endoscopic image. However, for the operator, the submucosal layer of the target tissue is rich in blood vessels and difficult to detect, especially when the bleeding point bleeds quickly, the blood may not cover a large area of ​​the endoscopic image at the current moment, and it will not be determined that the bleeding point of the target tissue needs to be hemostatically controlled. It is often not detected until the blood begins to fill a large area of ​​the endoscopic image, which makes it difficult to detect the bleeding situation of the target tissue in a timely and accurate manner, thereby affecting the surgical operation.

[0058] In order to improve the real-time and low accuracy issues of bleeding situation detection, the bleeding detection method provided in the present application uses artificial intelligence computer vision technology to perform image recognition on the endoscopic image obtained by inserting the endoscope into the target tissue, so as to determine the severity of bleeding in the target tissue based on the image color information corresponding to the endoscopic image. Among them, the method is applicable to the application environment of various medical endoscope systems, for example, a medical endoscope system for a gastroscope or an endoscope system for a colonoscope, etc. Moreover, the method can be applied to the host of the endoscope system, that is, the image processing device. Based on this, the embodiments of the present application do not limit the application environment and execution subject of the method. For example, please refer to Figure 5 The application environment includes an endoscope system including a display 52, an endoscope 54, a light source device 56 and an image processing device 58. On this basis, the image processing device 58 is used to execute the first image to be detected and the second image to be detected taken by the endoscope in this application, so as to realize timely and accurate detection of the bleeding condition and bleeding severity of the target tissue.

[0059] This embodiment provides a bleeding detection method. Figure 1 is a flow chart of a bleeding detection method according to an embodiment of the present application. Figure 1 As shown, the process includes the following steps:

[0060] Step S110, obtaining a first image to be detected of the target tissue collected by the endoscope.

[0061] When using an endoscope to examine the digestive tract of an object to be examined, the insertion part of the endoscope can be inserted into the digestive tract to be examined. In the embodiment of the present application, the part of the digestive tract to be examined by the endoscope is referred to as the target tissue. For example, in the process of using a colonoscope to examine the intestine, the insertion part of the colonoscope is inserted into the intestine of the object to be examined, and the target tissue includes the intestine. For another example, in the process of using a gastroscope to examine the stomach, the insertion part of the gastroscope is inserted into the stomach, and the target tissue includes the stomach.

[0062] The first image to be detected refers to a single frame endoscopic image of the target tissue collected at a certain moment during the process of endoscope inspection of the target tissue. After the endoscope insertion portion penetrates the target tissue, the endoscope head end collects the internal information of the target tissue, thereby obtaining the first image to be detected.

[0063] Step S120: determining the amount of bleeding of the target tissue according to the image gradient of the first image to be detected.

[0064] Among them, the above-mentioned bleeding volume situation refers to whether the detected target tissue is bleeding or whether the bleeding is serious. For example, the bleeding volume situation that may occur in the target tissue can be divided in advance, and the divided bleeding volume situation includes a first-level bleeding volume situation, a second-level bleeding volume situation, and a third-level bleeding volume situation. The first-level bleeding volume situation is used to indicate moderate bleeding in the target tissue; the second-level bleeding volume situation is used to indicate mild bleeding in the target tissue; and the third-level bleeding volume situation is used to indicate a small amount of bleeding or no bleeding in the target tissue. Among them, the division thresholds of the three types of bleeding volume situations can be determined according to the situation of the target tissue in actual application, and are not limited here. It can be understood that the above-mentioned pre-divided bleeding volume situation can also be added according to the actual application to indicate the situation of severe bleeding of the target tissue, or the range of the above-mentioned bleeding volume situations can be further divided more finely, or it can be divided into two categories such as bleeding and no bleeding, which will not be repeated here. Then, according to the image gradient of the first image to be detected detected above, the range of the preset bleeding volume situation into which the actual bleeding volume situation of the target tissue at the corresponding moment falls is determined.

[0065] Specifically, the grayscale value of each pixel in the first image to be detected is counted, and the change speed of the grayscale value of the entire image in the frame of the first image to be detected is counted, so that the image gradient of the first image to be detected can be obtained. Next, based on the change speed of the image grayscale value of the first image to be detected, it can be detected whether the target tissue has bleeding such as vascular rupture at the corresponding moment when the first image to be detected is collected; for example, when it is detected that the image gradient difference in the first image to be detected is small, it means that there is more blood in the frame image, so that the grayscale values ​​of each pixel tend to be consistent, or when the grayscale gradient value of the first image to be detected in the corresponding color channel, such as the R channel, is small, it means that the corresponding moment of collecting the first image to be detected may cause vascular rupture, that is, blood traces appear in the first image to be detected at this time, and the amount of blood traces filled in the picture is positively correlated with the above-mentioned image gradient information, so that the corresponding bleeding amount can be determined.

[0066] It can be understood that, in this embodiment, the image gradient of at least one frame of the first image to be detected can be detected to determine the amount of bleeding in the corresponding frame. If the image gradient of the first image to be detected indicates a normal situation, for example, the amount of bleeding detected corresponding to the first image to be detected is in the above-mentioned first-level bleeding situation, each frame of image can be continuously collected and real-time monitoring can be performed through the above-mentioned steps; if the image gradient of the first image to be detected indicates an abnormal situation, for example, the amount of bleeding detected corresponding to the first image to be detected is in the above-mentioned second-level bleeding situation or third-level bleeding situation, the bleeding situation of the target tissue is further detected in subsequent steps based on the first detection image of the frame.

[0067] Step S130, when the bleeding amount of the target tissue is the target bleeding amount, obtaining several frames of second images to be detected of the target tissue collected by the endoscope before and / or after the first image to be detected.

[0068] The target bleeding volume situation refers to the situation that at the corresponding moment, the target tissue has a ruptured blood vessel and the bleeding volume is large, which needs to be processed through subsequent steps. Taking the bleeding volume situation of the target tissue as an example, the bleeding volume situation of the target tissue has been pre-divided into the above-mentioned first-level bleeding volume situation, second-level bleeding volume situation and third-level bleeding volume situation. Since the first-level bleeding volume situation and the second-level bleeding volume situation are used to indicate that bleeding points have appeared in the target tissue, and the target tissue is in a situation of moderate bleeding or mild bleeding, it may be necessary to attract the attention of the operator to pay attention to the real-time situation of the bleeding points of the target tissue during the operation. Therefore, the first-level bleeding volume situation and the second-level bleeding volume situation can be preset as the above-mentioned target bleeding volume situation. Then, when the target tissue bleeding volume is detected to be in the first-level bleeding volume situation and the second-level bleeding volume situation based on the first image to be detected through the above steps, the corresponding bleeding volume situation is determined to be the above-mentioned target bleeding volume situation; accordingly, the third-level bleeding volume situation is preset as a non-target bleeding volume situation. Alternatively, bleeding volume situations of other bleeding volume levels, such as bleeding volume situations indicating severe bleeding of the target tissue, can also be preset as the above-mentioned target bleeding volume situation according to actual applications. Alternatively, the above-mentioned three levels of bleeding volume situations may be further divided, and the bleeding volume situation in the three levels of bleeding volume situation indicating that the target tissue is in the range of 5% to 10% of the total blood volume of the body may also be preset as the above-mentioned target bleeding volume situation; correspondingly, the situation in the three levels of bleeding volume situation indicating that the target tissue is not bleeding may be preset as a non-target bleeding volume situation.

[0069] When the target tissue bleeding volume is detected to be in the target bleeding volume situation, it means that the bleeding volume at the corresponding moment is large, and further more accurate detection is required for its specific situation. Specifically, in practical applications, bleeding is a phenomenon that lasts for a period of time, so the specific situation of bleeding, such as bleeding speed and bleeding volume, can be analyzed based on the blood change information in the multi-frame image. Based on this, when the embodiment of the present application determines that there is a target bleeding volume situation in the first image to be detected, the image collected near the first image to be detected can be obtained to perform information analysis in the time dimension, so as to determine the required bleeding situation data. More specifically, multiple frames of endoscopic images can be obtained in the time period before the moment corresponding to the acquisition of the first image to be detected, that is, the second image to be detected before the first image to be detected is obtained; or, multiple frames of endoscopic images obtained in a period of time after the moment corresponding to the acquisition of the first image to be detected can be used as the above-mentioned second image to be detected; or, each frame of endoscopic image within a certain time period including the moment of acquiring the first image to be detected can be used as the above-mentioned second image to be detected. It can be seen that there is a temporal correlation between the above-mentioned first image to be detected and each frame of the second image to be detected, so as to further follow up the precise detection process.

[0070] Step S140, determining the bleeding severity of the target tissue according to the color contrast change information between at least two frames of first target images; the first target image is an image in the first image to be detected and the plurality of frames of second images to be detected.

[0071] Specifically, multiple frames of images for further detecting the severity of bleeding of the target tissue can be determined from the first image to be detected and the second image to be detected, and used as the first target image. For example, the first image to be detected and at least one of the plurality of second images to be detected can be used as the first target image, or at least two frames of images can be determined from the plurality of second images to be detected as the first image to be detected. It can be understood that among the at least two first target images, the first target images can be adjacent frames, or the first target images can be images with a determined interval. For example, the first target image and the second target image can be separated by two frames. Or among the at least two first target images, some of the first target images can be adjacent frames, and some of the first target images can be images with a determined interval.

[0072] After determining the above-mentioned multiple frames of first target images, the color contrast information in each frame of the first target image is counted, and then the change rate or change degree of the color contrast between the multiple frames of first target images and other information are calculated, and the numerical information is determined as the change information of the color contrast between the above-mentioned frames of the first target images.

[0073] It should be noted that when bleeding points have appeared in the target tissue during the operation, if the operator has not stopped bleeding in time, the target tissue may continue to bleed for a period of time, resulting in changes in the color contrast between the multiple frames of the first target image during the period. When the rate of change of the color contrast between the multiple frames is fast or the degree of change is high, such as the rate of change of the color contrast between the two frames reaches more than 50%, and / or the degree of change value reaches more than 50%, it means that the blood spreads faster at the bleeding point of the target tissue in the corresponding time period, so that the change of the color contrast between the two frames before and after is large, that is, the bleeding speed of the target tissue in the time period is fast; on the contrary, when the rate of change of the color contrast between the multiple frames is slow or the degree of change is low, it means that the blood spreads slower at the bleeding point of the target tissue in the corresponding time period, that is, the bleeding speed of the target tissue in the time period is slow. Therefore, the bleeding speed between the multiple frames can be determined according to the speed of the change of the color contrast between the multiple frames. Specifically, when a faster bleeding rate is detected, it means that the wound area of ​​the bleeding point of the target tissue is larger and the bleeding of the target tissue is more serious; when a slower bleeding rate is detected, the corresponding bleeding severity of the target tissue is lower, thereby ultimately determining the bleeding severity of the above-mentioned target tissue.

[0074] Through the above steps S110 to S140, the image gradient of the first image to be detected is used to preliminarily determine whether the target tissue has a bleeding amount, and the bleeding situation can be detected in time when a bleeding point appears. When the target bleeding amount is determined, the bleeding speed of the bleeding point is detected in real time through the change information of the color contrast between the first image to be detected and the first target image in several frames of the second image to be detected, so as to avoid the situation that when the bleeding amount is not large but the blood spreads very fast, it cannot be detected in time, so that the blood fills a large area of ​​the endoscope image and affects the surgical operation, so that the timeliness of bleeding detection can be improved by real-time detection of the bleeding speed. In addition, the data processing of the above bleeding detection method is relatively simple, and the number of image frames required is also small, so the processing load is low and the speed is fast, which can also effectively improve the real-time nature of bleeding detection. At the same time, by accurately quantitatively analyzing the image information collected by the endoscope, that is, determining the bleeding amount through the image gradient, and quantitatively detecting the bleeding speed through the change information of the color contrast between each frame of the image, the phenomenon of low accuracy of bleeding detection caused by manually observing the image and judging whether hemostasis is needed based on experience is avoided, which is conducive to improving the accuracy of bleeding detection of the target tissue. It can be seen that this embodiment solves the problems of low real-time and low accuracy of bleeding situation detection through the above steps, and realizes a timely and efficient bleeding point detection method.

[0075] In some embodiments, the bleeding detection method further comprises the following steps: when the image gradient is less than a preset gradient change threshold, the bleeding condition of the target tissue is judged to be the target bleeding amount condition. The image gradient average value of the first image to be detected is counted, and when it is detected that the gradient drops to the preset gradient change threshold, it is judged that the blood vessel of the target tissue may be ruptured during the operation at the current moment, so that there are traces of blood in the first image to be detected, that is, the bleeding condition of the target tissue is judged to be the target bleeding amount condition.

[0076] It should be noted that the target bleeding volume can also be adjusted based on the preset gradient change threshold in combination with the actual application. For example, when it is actually applied, it is necessary to detect the bleeding point just appearing in the target tissue through the preliminary detection step, that is, when the bleeding volume is relatively small, the subsequent fine detection step is performed, and the gradient change threshold can be set to the first threshold. It should be further noted that in the related art, during the operation of using an endoscope to probe into the target tissue, since the submucosal blood vessels of the target tissue are rich and difficult to find, it is usually difficult to detect the current bleeding point in time; and in this embodiment, the gradient of the entire image of the first image to be detected is monitored in real time in combination with the first threshold, which is conducive to timely detection of the bleeding point appearing in the target tissue at the current moment, so that corresponding measures can be taken in time. Alternatively, when it is necessary to detect that the bleeding volume of the target tissue in the first image to be detected reaches a certain amount in the above step, and then perform subsequent fine detection processing, the gradient change threshold can be set to the second threshold, and the second threshold is greater than the first threshold. It can be understood that in this embodiment, the gradient change threshold of different gears can be set according to the actual situation, which will not be repeated here.

[0077] Through the above-mentioned embodiment, the bleeding condition of the target tissue is determined by a preset gradient change threshold, which is conducive to improving the accuracy of bleeding detection of the target tissue. At the same time, by setting different gradient change thresholds, different bleeding volume situation division ranges can also be determined, thereby adjusting the sensitivity of the preliminary detection results of the target tissue during the bleeding detection method, making the bleeding detection method more flexible.

[0078] In some embodiments, the method of determining the bleeding severity of the target tissue according to the change information of the color contrast between at least two frames of the first target image comprises the following steps:

[0079] First, a color space conversion process is performed for each frame of the first target image to obtain a LAB image in the LAB color space and the characteristic color dimension information of the LAB image. It is necessary to explain that through the above steps, the first image to be detected or the second image to be detected collected by the endoscope is usually an original image in RGB format. Since the pixel value range in the R color channel is small in the RGB color space, it is not conducive to ensuring the accuracy of image-based bleeding detection; therefore, in this embodiment, in the process of performing a precise detection of the bleeding situation of the target tissue, the first target image originally in the RGB color space can be subjected to a color gamut conversion process to convert the first target image from the RGB color space to the LAB color space with a relatively wider red channel range, so that the detection result can be more obvious. Next, for the LAB image in the LAB color space, the color contrast in the red channel is detected, for example, the a value of all pixels in each frame of the LAB image is detected, and the a value is used to indicate the degree of color from red to yellow, thereby determining the characteristic color dimension information corresponding to each frame of the LAB image.

[0080] Then, the color contrast change information between each frame of the LAB image is determined according to the characteristic color dimension information, and the bleeding severity is determined according to the color contrast change information between the LAB images. Since the color change degree of the image in the red channel can be more clearly determined in the LAB color space, it is helpful to improve the accuracy of the bleeding severity determined based on the color contrast information between the LAB images. Therefore, through the above embodiment, the first target image is converted into a LAB image by color gamut conversion, and the bleeding severity is determined based on the LAB image, which effectively improves the accuracy of bleeding detection.

[0081] In some embodiments, determining the severity of bleeding of the target tissue according to the change information of the color contrast between at least two frames of the first target image includes:

[0082] Step S141 , generating first color change trend information corresponding to the second target image according to the color contrast between the first image to be detected and the second target image in the plurality of frames of second images to be detected.

[0083] The second target image may be one of the frames of the first target image, or the second target image may be a frame of image different from the first target image determined from the first image to be detected and the plurality of frames of the second image to be detected. In another embodiment, the second target image is the latest frame of image among the first image to be detected and the plurality of frames of the second image to be detected, so as to improve the accuracy of the precise detection process of the target tissue bleeding condition based on the second target image.

[0084] Next, the color contrast trend information is determined based on the correlation relationship between each pixel in the entire image of the second target image and the color contrast of each pixel, and the first color change trend information is obtained; that is, the first color change trend information is used to indicate the trend information of the color contrast of all pixels in the entire second target image. The first color change trend information can be determined by histogram statistics, fitting curve or fitting formula. For example, the histogram of the color contrast of all pixels in the second target image can be statistically calculated, and the trend information of the color contrast changing with the pixel under a certain color channel can be obtained based on the histogram, that is, the first color change trend information is obtained.

[0085] Step S142: determining the bleeding volume information of the target tissue according to the first color change trend information.

[0086] Since the first color change trend information carries the color contrast change trend of each pixel in the entire second target image, the concentration of the color contrast of each pixel in the red chromaticity area can be determined based on the first color change trend, and the area occupied by the blood in the second target image at the corresponding moment can be calculated. Specifically, the first mapping relationship between the color contrast value and the area occupied by the blood in the image can be pre-set based on historical data or prior knowledge to determine the actual area occupied by the blood in the image; for example, when the color contrast concentration is statistically 60% of all color contrast values, the corresponding actual area occupied by the blood in the image is found to be 10% based on the first mapping relationship.

[0087] Then, the above-mentioned bleeding volume information is determined according to the area occupied by the detected blood in the image, that is, the larger the area occupied by the blood in the image, the larger the bleeding volume of the target tissue bleeding point. It should be supplemented that, compared with the bleeding volume obtained by qualitative analysis in the above-mentioned step S120, in this embodiment, the color contrast of the image is statistically analyzed in the above-mentioned steps to obtain the quantitative result of the image area occupied by the blood, and then the corresponding bleeding volume can be quantitatively obtained. For example, the second mapping relationship between the area occupied by the blood in the image and the bleeding volume can be pre-set according to historical data or prior knowledge to determine the actual bleeding volume value. For example, when it is determined that the area occupied by the blood in the image is 10%, the corresponding actual bleeding volume value is found to be 5ml of bleeding volume through the second mapping relationship. In another embodiment, the mapping relationship between the color contrast concentration degree and the bleeding volume value can also be determined based on the above-mentioned first mapping relationship and the above-mentioned second mapping relationship, and the above-mentioned actual bleeding volume is calculated based on this mapping relationship.

[0088] Step S143, determining the severity of bleeding of the target tissue according to the bleeding volume information and the color contrast change information between the at least two frames of the first target image.

[0089] When the amount of bleeding of the target tissue is larger, that is, the area occupied by the blood in the image is larger, or when the color contrast between the multiple frames of the first target image changes faster, that is, the blood area in the image expands faster, it means that the bleeding severity of the target tissue is higher at this time. Therefore, the bleeding severity can be obtained by comprehensive analysis based on the bleeding amount detection result and the change information of the color contrast between the above-mentioned frames of images. Exemplarily, when the actual bleeding amount value is within the bleeding amount range of 30ml to 50ml, the change information of the color contrast between the frames indicates that the bleeding speed is within the bleeding speed range of A1 ml / min to A2 ml / min, and the corresponding bleeding severity is a first-level severity for indicating that the bleeding amount of the target tissue is large and the bleeding speed is fast, that is, the bleeding is severe; when the actual bleeding amount value is within the bleeding amount range of 15ml to 30ml, the change information of the color contrast between the frames indicates that the bleeding speed is within the bleeding speed range of A3ml / min to A1 ml / min, and the corresponding bleeding severity is a second-level severity for indicating that the bleeding amount of the target tissue is large and the bleeding speed is general.

[0090] Through the above embodiment, the amount of bleeding is further precisely detected through the change information of the color contrast between the first image to be detected and the second target image in the second image to be detected, so that a more accurate bleeding amount detection result can be obtained, which effectively improves the accuracy of bleeding detection.

[0091] In an exemplary embodiment, the method of determining the bleeding volume information of the target tissue according to the first color change trend information further includes the following steps:

[0092] According to the first color change trend information, the first color contrast to be measured corresponding to the number of target pixels in the second target image is determined. According to the first color change trend information, the number of pixels in the second target image with the same color contrast is counted. Since the larger the number of pixels, the more image pixels have this color contrast, that is, the larger the area occupied by the color corresponding to this color contrast in the second target image. Therefore, the number of pixels with the highest values ​​among all the numbers of pixels can be used as the above-mentioned target number of pixels, and the first color contrast to be measured corresponding to the target number of pixels is preferentially detected.

[0093] Next, when it is detected that the first color contrast to be tested is within the preset color area range, the first concentration information between all the first color contrasts to be tested is calculated, and the bleeding amount information of the target tissue is determined according to the first concentration information. The above-mentioned color area range refers to the color value range corresponding to hemoglobin determined according to the color domain where the current image is located. When it is detected that the above-mentioned first color contrast to be tested is within the color area range, it means that the image area with the highest color contrast in the current image is an area filled with blood, and then the first concentration information between the first color contrast to be tested can be further detected, and then the quantitative result of the bleeding area of ​​the above-mentioned target tissue can be calculated.

[0094] Specifically, taking the LAB color space as an example, Figure 2 is a schematic diagram of pixel point statistics of a first target image or a second target image according to an embodiment of the present application. Figure 2 As shown, according to the pixel values ​​of each point in the second target image and the a value corresponding to each pixel value, a fitting curve for representing the first color change trend is obtained by statistics, wherein a represents the degree of color from red to yellow, and the peak in the fitting curve is used to represent the number of pixels with the largest value under the same color contrast in the second target image, indicating that the color represented by the color contrast at the peak occupies the largest area in the image. Since hemoglobin is red, when it is detected that the chromaticity value of the color contrast at the peak is within the above-mentioned color area range, that is, the red chromaticity value range, the first integration degree information of the above-mentioned first color contrast to be measured can be determined according to the peak width Δa value near the peak. When the Δa value is smaller, it means that the integration degree of the first color contrast to be measured is higher, that is, the more pixel color contrasts are close to the first color contrast to be measured, the higher the area occupied by blood in the second target image, and the higher the actual bleeding amount, and then the specific actual bleeding amount value can be determined based on the Δa value obtained by statistics through the above-mentioned association relationship. It can be understood that the peak width Δa value can be calculated from the peak width at the 1 / n peak, and n is a positive number greater than 1.

[0095] In some embodiments, the above-mentioned determination of the severity of bleeding of the target tissue based on the transformation information of the color contrast between at least two frames of the first target image further includes the following step: generating the second color change trend information corresponding to the first target image based on the color contrast of each frame of the first target image. That is, the color contrast trend information is statistically determined based on the correlation relationship between each pixel point in the entire image of each frame of the first target image and the color contrast of each pixel point, and the second color change trend information corresponding to each frame of the first target image is obtained; that is, the second color change trend information is used to indicate the trend information of the color contrast of all pixel points in the entire first target image.

[0096] Then, the color contrast change information is determined based on the change information between all the second color change trend information, and the bleeding speed information of the target tissue is determined based on the color contrast change information; and the bleeding severity of the target tissue is determined based on the bleeding speed information. Specifically, the faster the change between the second color change trend information of each frame of the first target image is, the faster the blood spreads at the bleeding point of the target tissue during this period of time, and the faster the bleeding speed of the target tissue is.

[0097] In an exemplary embodiment, the above-mentioned determination of the change information of the color contrast according to the change information between all the second color change trend information also includes the following steps: determining the second color contrast to be tested corresponding to the number of target pixels in each frame of the first target image according to the second color change trend information; the number of target pixels in the first target image can be the number of pixels with the highest values ​​among all the pixel numbers of the first target image. When it is detected that the second color contrast to be tested is within the preset color area, calculating the second concentration degree information between all the second color contrast to be tested in each frame of the first target image; determining the change information of the color contrast according to the change information between the second concentration degree information corresponding to each frame of the first target image.

[0098] Specifically, see Figure 2, a fitting curve for representing the second color change trend is obtained according to the pixel values ​​of each point in the first target image and the a value corresponding to each pixel value, and the peak in the fitting curve is used to represent the number of pixels with the largest value under the same color contrast in the first target image. When the chromaticity value of the color contrast at the peak is within the above-mentioned color region, and the change rate of the peak width Δa value at the 1 / n peak between each frame of the first target image is getting faster and faster, that is, the change rate between the second integration degree information corresponding to each frame of the first target image is getting faster and faster, it can be proved that the bleeding speed in the picture is getting faster and faster, and then the above-mentioned change rate information can be determined through the above-mentioned association relationship. It should be supplemented that when it is detected that the change amount of Δa between each frame reaches the preset width change amount threshold, the image processing device can determine that the cleaning and hemostasis operation must be performed, and remind the operator through the display screen display or voice prompt.

[0099] Through the above embodiment, by monitoring the second color change trend information of the first target image, the change rate information between each frame is calculated, so that the severity of bleeding can be determined by specific calculated values, thereby realizing a quantitative detection method for the bleeding speed of the target tissue, which is beneficial to improving the accuracy of bleeding detection.

[0100] In some embodiments, the image acquisition mode of the endoscope includes a white light mode and a non-white light mode. After determining the severity of bleeding of the target tissue, the bleeding detection method further includes the following steps:

[0101] Step S151, when the bleeding severity is the target severity, switching the image acquisition mode from the white light mode to the non-white light mode, and acquiring a non-white light image of the endoscope in the non-white light mode; wherein the light irradiated by the endoscope to the target tissue in the white light mode is white light, and the light irradiated to the target tissue in the non-white light mode is non-white light.

[0102] Among them, the severity of bleeding of the target tissue can be divided by setting different preset thresholds such as bleeding speed threshold or bleeding volume threshold; when the bleeding volume corresponding to the target image is detected to be large and / or the bleeding speed is fast through the above embodiment, it means that the current target tissue bleeding is more serious, that is, it is determined to be the above target severity. Under normal circumstances, the above endoscope generally collects images in white light mode to obtain the above first image to be detected and the above second image to be detected. When the bleeding severity of the above target tissue is determined to be the target severity, in order for the operator to stop bleeding in time, the image processing device can control the light source device of the endoscope to switch from white light to non-white light, so as to obtain the non-white light image collected in the non-white light mode at the current moment to detect the bleeding point position. It can be understood that in the above non-white light mode, the wavelength band of the irradiation light switched to by the light source device can generally be set to, for example, a near-infrared wavelength range of 600nm to 630nm.

[0103] It should be noted that when the bleeding of the target tissue is detected to be at the target severity, the switching method for the image acquisition mode of the above-mentioned endoscope may include a manual switching method and an automatic switching method. For example, when the above-mentioned measurement values ​​are monitored in real time to reach the preset threshold, a text message or voice prompt may be provided by the display interface to remind the operator to clean the blood in the screen and stop the bleeding as soon as possible, and the operator manually switches the illumination light source device of the endoscope to a non-white light source device to realize the image acquisition mode switching; or, when the current bleeding is detected to be severe, the image processing device may automatically switch the image acquisition mode and control the endoscope to irradiate non-white light to the target tissue to obtain the above-mentioned non-white light image.

[0104] Step S152: generating a bleeding position detection result for the target tissue according to the non-white light image.

[0105] The above-mentioned bleeding position detection method based on non-white light images can be performed by manual detection or automatic detection by algorithms. For example, after the endoscope collects the non-white light image, the image processing device can send the collected non-white light image to the display, and the operator can perform manual detection based on the non-white light image currently switched and displayed on the display to determine the specific position of the bleeding point of the target tissue at the current moment, that is, to obtain the above-mentioned bleeding position detection result.

[0106] Alternatively, the image processing device can also directly use the absorption rate characteristics of hemoglobin for non-white light in this band in the back end to automatically detect the bleeding point position through an algorithm to reduce the operating steps required by the operator and improve the efficiency of the operation. Specifically, when it is found that the blood area and area expansion speed in the picture reach the threshold, the operator is prompted to clean the blood stains by voice or interface, and the bleeding position is found by switching the image observation mode back and forth every other frame, and the display screen is still a white light image, that is, the previous frame is an image collected in the white light mode, and the next frame is an image collected in the non-white light mode, where the white light image is displayed in the display. Next, the background automatically determines the bleeding position through a pattern matching method, that is, in the color space, the orange-yellow area is identified as the blood color in the special mode, and the position closest to red is found in the area in the image to determine the specific bleeding position; or the bleeding point is automatically found through a neural network, that is, through a neural network trained for bleeding point recognition, and the above-mentioned non-white light image is input into the neural network, and the bleeding point position is finally output. Or other target detection algorithms are also used to realize the automatic detection of the specific position of the bleeding point of the target tissue in the background, which will not be repeated here.

[0107] It should be noted that, when the bleeding point is detected through the above steps and the operator cleans and stops the bleeding point of the target tissue, the first target image and / or the second target image are continuously acquired and monitored in real time. When it is detected that the proportion of the blood area occupied by the screen area at the current moment drops below the threshold, the image acquisition mode of the endoscope is switched from the non-white light mode to the white light mode through manual control or automatic control, and the acquisition work and real-time monitoring of the endoscope are continued.

[0108] Through the above steps S151 to S152, when it is detected that the bleeding severity of the target tissue is the target severity, the image acquisition mode of the endoscope is switched from the white light mode to the non-white light mode, and the bleeding point is found by using the acquired non-white light image using the absorption rate characteristics of hemoglobin to light in this band, which is beneficial to improve the accuracy and efficiency of bleeding point position detection, and further effectively improves the accuracy and efficiency of bleeding detection.

[0109] In some embodiments, after determining the severity of bleeding of the target tissue, the bleeding detection method further includes the following steps: generating bleeding position marking information for the target tissue based on the bleeding position detection result; and generating an image to be displayed based on the bleeding position marking information and the white light image.

[0110] Specifically, after the bleeding position detection result for the target tissue is obtained through the above method embodiment, the specific bleeding position determined based on the non-white light image can be used to generate corresponding bleeding position marking information, and the bleeding position marking information can be information such as a prompt box or text identification information used to prompt the bleeding point position in the image. Next, the bleeding position marking information determined by the frame is superimposed on the same position of the non-white light image in the next frame of white light image collected to obtain the image to be displayed; since the time interval between each frame is extremely short, the position difference between the previous and next frame images of the same part is not large, so the bleeding position found on the previous frame of non-white light image is almost the same as the bleeding position on the next frame of white light image, and there is no need to worry about the error of the prompted bleeding position. Finally, the image to be displayed can be directly sent to the display for interface display, or the image to be displayed can be first stored in the image processing device, and when the operator inputs an interactive display instruction through the display, the stored image to be displayed is sent to the display for display in response to the detected display instruction. For example, taking the use of a prompt box to mark the bleeding position information as an example, in the image to be displayed, based on the phenomenon that the image brightness becomes darker as it approaches the center of the bleeding point, changing the brightness in the prompt box in the image can further help the operator locate the bleeding point as accurately as possible and use electrocoagulation to stop bleeding, thereby facilitating visual focus.

[0111] Therefore, through the above-mentioned embodiment, the display screen can continue to display the white light image collected in the white light mode, and at the same time, the bleeding position mark information detected by the background based on the non-white light image is superimposed on the white light image, so as to prompt the operator of the specific location of the bleeding point through the white light image, and the color of the displayed picture is consistent with the color of the organ cavity observed by the human body under normal white light, thereby avoiding affecting the operator's operation and effectively improving the accuracy and efficiency of bleeding detection.

[0112] The following describes the invention in conjunction with specific embodiments. Figure 3 is a flow chart of a bleeding detection method according to a preferred embodiment of the present application. Figure 3 As shown, the calibration process includes the following steps:

[0113] Step S301 : monitoring a first image to be detected and a second image to be detected collected by an endoscope.

[0114] Step S302, determining whether the image monitoring result obtained in the above steps reaches a threshold, if so, executing the subsequent step S303, if not, continuing to execute the above step S301.

[0115] Step S303: The display performs picture or sound prompts, and outputs white light images and non-white light images in alternate frames.

[0116] Step S304: the display shows the white light image, and the background automatically searches for bleeding points in the non-white light image.

[0117] Step S305: The operator cleans and stops bleeding at the target tissue bleeding point based on the monitoring result.

[0118] It should be noted that the steps shown in the above process or the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0119] This embodiment also provides a bleeding detection system, which is used to implement the above embodiments and preferred implementations, and the descriptions that have been made will not be repeated. As used below, the terms "part", "unit", "subunit", etc. can implement a combination of software and / or hardware for a predetermined function. Although the system described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0120] Figure 4 is a structural block diagram of a bleeding detection system according to an embodiment of the present application, such as Figure 4 As shown, the system includes: a first acquisition unit 42, a first determination unit 44, a second acquisition unit 46 and a second determination unit 48. The first acquisition unit 42 is used to acquire a first image to be detected of a target tissue acquired by an endoscope; the first determination unit 44 is used to determine the amount of bleeding of the target tissue according to the image gradient of the first image to be detected; the second acquisition unit 46 is used to acquire several frames of second images to be detected of the target tissue acquired by the endoscope before and / or after the first image to be detected when the amount of bleeding of the target tissue is the target amount of bleeding; the second determination unit 48 is used to determine the severity of bleeding of the target tissue according to the change information of the color contrast between at least two frames of the first target image; the first target image is an image of the first image to be detected and the several frames of the second images to be detected.

[0121] Through the above embodiment, the first determination unit 44 preliminarily determines whether the target tissue is bleeding by using the image gradient of the first image to be detected. When the target bleeding amount is determined, the second determination unit 48 detects the severity of bleeding by using the change information of the color contrast between the first image to be detected and the first target image in several frames of the second image to be detected. Thus, bleeding points can be detected in time when they appear, and the bleeding situation of the bleeding points can be detected in real time, thereby avoiding the phenomenon that the bleeding points cannot be detected in time or the bleeding detection accuracy is low due to the untimely processing of the endoscopic image acquisition, solving the problems of real-time and low accuracy of bleeding situation detection, and realizing a timely and efficient bleeding point detection system.

[0122] In some of the embodiments, the bleeding detection device further includes a mode switching unit; the mode switching unit is used to switch the image acquisition mode from the white light mode to the non-white light mode when the bleeding severity is the target severity, and obtain a non-white light image of the endoscope in the non-white light mode; wherein the light irradiated by the endoscope to the target tissue in the white light mode is white light, and the light irradiated to the target tissue in the non-white light mode is non-white light; the mode switching unit generates a bleeding position detection result for the target tissue based on the non-white light image.

[0123] In some embodiments, the bleeding detection device further includes a display unit; the display unit is used to generate bleeding position marking information for the target tissue based on the bleeding position detection result; the display unit generates an image to be displayed according to the bleeding position marking information and the white light image.

[0124] It should be noted that the above modules can be functional modules or program modules, and can be implemented by software or hardware. For modules implemented by hardware, the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0125] This embodiment further provides an image processing device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0126] Optionally, the image processing device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0127] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0128] S1, obtaining a first image of a target tissue to be detected collected by an endoscope.

[0129] S2: Determine the amount of bleeding of the target tissue according to the image gradient of the first image to be detected.

[0130] S3, when the bleeding amount of the target tissue is the target bleeding amount, obtaining several frames of second images to be detected of the target tissue collected by the endoscope before and / or after the first image to be detected.

[0131] S4, determining the bleeding severity of the target tissue according to the color contrast change information between at least two frames of first target images; the first target image is an image among the first image to be detected and the plurality of frames of second images to be detected.

[0132] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0133] This embodiment also provides an endoscope system, Figure 5 is a schematic structural diagram of an endoscope system according to an embodiment of the present application, such as Figure 5 As shown, the system includes: a display 52, an endoscope 54, a light source device 56, and an image processing device 58 as described in the above embodiment. In another embodiment, the image processing device 58 can also be deployed in a cloud box connected to the endoscope system through a network.

[0134] This embodiment further provides a computer device, which may be a server device. Figure 6 is a structural diagram of the inside of a computer device according to an embodiment of the present application, such as Figure 6 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store a first image to be detected and a second image to be detected. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a bleeding detection method is implemented.

[0135] Those skilled in the art will understand that Figure 6The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0136] In addition, in combination with the bleeding detection method in the above embodiment, the embodiment of the present application can provide a storage medium for implementation. The storage medium stores a computer program; when the computer program is executed by a processor, any one of the bleeding detection methods in the above embodiment is implemented.

[0137] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0138] Those skilled in the art should understand that the technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described 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.

[0139] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for detecting bleeding, characterized in that: The method comprises: Acquire a first image to be detected of the target tissue collected by the endoscope; determining the amount of bleeding of the target tissue according to the image gradient of the first image to be detected; When the bleeding amount of the target tissue is the target bleeding amount, acquiring several frames of second images to be detected of the target tissue collected by the endoscope before and / or after the first image to be detected; The bleeding severity of the target tissue is determined according to the color contrast change information between at least two frames of first target images; the first target image is an image among the first image to be detected and the plurality of frames of second images to be detected.

2. The bleeding detection method according to claim 1, characterized in that: The method further comprises: When the image gradient is less than a preset gradient change threshold, the bleeding condition of the target tissue is determined to be the target bleeding amount condition.

3. The bleeding detection method according to claim 1, characterized in that: The step of determining the severity of bleeding of the target tissue according to the change information of the color contrast between at least two frames of the first target image comprises: Performing color space conversion processing on each frame of the first target image to obtain a LAB image in a LAB color space and characteristic color dimension information of the LAB image; The change information of the color contrast between each frame of the LAB images is determined according to the characteristic color dimension information, and the severity of the bleeding is determined according to the change information of the color contrast between the LAB images.

4. The bleeding detection method according to any one of claims 1 to 3, characterized in that: The step of determining the severity of bleeding of the target tissue according to the change information of the color contrast between at least two frames of the first target image comprises: generating first color change trend information corresponding to the second target image according to the color contrast between the first image to be detected and the second target image in the plurality of frames of the second image to be detected; determining the bleeding volume information of the target tissue according to the first color change trend information; The severity of bleeding of the target tissue is determined according to the bleeding volume information and the change information of the color contrast between the at least two frames of the first target image.

5. The bleeding detection method according to claim 4, characterized in that: The determining the bleeding volume information of the target tissue according to the first color change trend information includes: Determine, according to the first color change trend information, a first color contrast to be measured corresponding to the number of target pixels in the second target image; When it is detected that the first color contrast to be measured is within a preset color area, first concentration information between all the first color contrasts to be measured is calculated, and the bleeding volume information of the target tissue is determined according to the first concentration information.

6. The bleeding detection method according to claim 4, characterized in that: The second target image is the latest frame image among the first image to be detected and the plurality of frames of second images to be detected.

7. The bleeding detection method according to any one of claims 1 to 3, characterized in that: The step of determining the severity of bleeding of the target tissue according to the change information of the color contrast between at least two frames of the first target image comprises: generating second color change trend information corresponding to the first target image according to the color contrast of each frame of the first target image; Determine the color contrast change information according to the change information between all the second color change trend information, and determine the bleeding speed information of the target tissue according to the color contrast change information; The bleeding severity of the target tissue is determined according to the bleeding rate information.

8. The bleeding detection method according to claim 7, characterized in that: The step of determining the color contrast change information according to the change information among all the second color change trend information includes: Determine, according to the second color change trend information, a second color contrast to be measured corresponding to the number of target pixels in each frame of the first target image; When it is detected that the second color contrast to be tested is within a preset color region, calculating second concentration degree information between all the second color contrasts to be tested in each frame of the first target image; The change information of the color contrast is determined according to the change information of the second concentration degree information corresponding to each frame of the first target image.

9. The bleeding detection method according to any one of claims 1 to 3, characterized in that: The image acquisition mode of the endoscope includes a white light mode and a non-white light mode. After determining the bleeding severity of the target tissue, the method further includes: When the severity of bleeding is the target severity, the image acquisition mode is switched from the white light mode to the non-white light mode, and a non-white light image of the endoscope in the non-white light mode is acquired; wherein the light irradiated by the endoscope to the target tissue in the white light mode is white light, and the light irradiated to the target tissue in the non-white light mode is non-white light; A bleeding position detection result for the target tissue is generated according to the non-white light image.

10. The bleeding detection method according to claim 9, characterized in that: After determining the severity of bleeding in the target tissue, the method further comprises: Based on the bleeding position detection result, generating bleeding position marking information for the target tissue; An image to be displayed is generated according to the bleeding position mark information and the white light image.

11. A bleeding detection system, characterized in that: The system comprises: a first acquisition unit, a first determination unit, a second acquisition unit, and a second determination unit; The first acquisition unit is used to acquire a first image to be detected of the target tissue collected by the endoscope; The first determining unit is used to determine the amount of bleeding of the target tissue according to the image gradient of the first image to be detected; The second acquisition unit is used to acquire a plurality of frames of second images to be detected of the target tissue collected by the endoscope before and / or after the first image to be detected when the bleeding amount of the target tissue is the target bleeding amount; The second determination unit is used to determine the bleeding severity of the target tissue based on the change information of the color contrast between at least two frames of the first target image; the first target image is an image in the first image to be detected and the plurality of frames of the second images to be detected.

12. An image processing device, comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the bleeding detection method according to any one of claims 1 to 10.

13. An endoscope system, characterized in that: The system comprises: a display, an endoscope, a light source device, and the image processing device as claimed in claim 12.

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