CN112991325B - Intelligent coding-based speckled red-emitting image acquisition and processing method and system - Google Patents
Intelligent coding-based speckled red-emitting image acquisition and processing method and system Download PDFInfo
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B1/00—Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection, e.g. endoscopes; Illuminating arrangements therefor
- A61B1/00002—Operational features of endoscopes
- A61B1/00004—Operational features of endoscopes characterised by electronic signal processing
- A61B1/00009—Operational features of endoscopes characterised by electronic signal processing of image signals during a use of endoscope
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B1/00—Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection, e.g. endoscopes; Illuminating arrangements therefor
- A61B1/04—Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection, e.g. endoscopes; Illuminating arrangements therefor combined with photographic or television appliances
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B1/00—Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection, e.g. endoscopes; Illuminating arrangements therefor
- A61B1/273—Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection, e.g. endoscopes; Illuminating arrangements therefor for the upper alimentary canal, e.g. oesophagoscopes, gastroscopes
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B1/00—Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection, e.g. endoscopes; Illuminating arrangements therefor
- A61B1/273—Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection, e.g. endoscopes; Illuminating arrangements therefor for the upper alimentary canal, e.g. oesophagoscopes, gastroscopes
- A61B1/2736—Gastroscopes
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H30/00—ICT specially adapted for the handling or processing of medical images
- G16H30/20—ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10068—Endoscopic image
Abstract
The invention discloses a method and a system for acquiring and processing a spot red image based on intelligent coding, which are used for acquiring first image information; carrying out segmentation processing on the first image information according to the first segmentation instruction, and obtaining a first coding result; obtaining a first position image, carrying out initial analysis, and carrying out image adjustment on the first position image when a first analysis result meets a first preset threshold; obtaining a lesion degree estimation result, and matching a first color depth; respectively executing the steps to other position images to obtain a color depth set; inputting the color depth set and the first coding result into a region evaluation model to obtain a first output result, wherein the first output result comprises a first position region; and acquiring a magnified image of the first position area. The technical problems that in the prior art, the image acquisition of a patient is not intelligent enough, and the acquired image cannot be combined in real time to carry out intelligent further image acquisition so as to help a doctor to carry out better disease observation are solved.
Description
Technical Field
The invention relates to the field related to image acquisition and processing of endoscopes, in particular to a method and a system for acquiring and processing a speckled red image based on intelligent coding.
Background
The endoscope is a detection instrument integrating traditional optics, ergonomics, precision machinery, modern electronics, mathematics and software into a whole. One has an image sensor, optical lens, light source illumination, mechanical device, etc. that can enter the stomach orally or through other natural orifices. Fiberoptic gastroscopes, duodenoscopes and gastroscopes are the most widely used endoscopes in clinic, and can help doctors to directly observe the conditions in internal organs of a human body from the outside of the human body, so that doctors are assisted in diagnosing the conditions of patients better.
However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the inventors of the present application find that the above-mentioned technology has at least the following technical problems:
the technical problems that the image acquisition of a patient is not intelligent enough, and the acquired image cannot be combined in real time to carry out intelligent further image acquisition to help a doctor to carry out better disease observation exist in the prior art.
Disclosure of Invention
The embodiment of the application provides a method and a system for acquiring and processing a speckled red image based on intelligent coding, solves the technical problems that in the prior art, the image acquisition of a patient is not intelligent enough, and the acquired image cannot be combined in real time to perform intelligent further image acquisition, so that a doctor is helped to perform better disease condition observation, and achieves the technical effects of intelligently combining acquisition information, assisting the doctor in performing intelligent image acquisition and further assisting the doctor in performing more accurate disease condition observation.
In view of the foregoing problems, embodiments of the present application provide a method and a system for acquiring and processing a speckle-like red-emitting image based on intelligent coding.
In a first aspect, the present application further provides a speckle redness image acquiring and processing method based on intelligent coding, where the method is applied to an intelligent image acquiring and processing system, the system is communicatively connected to a first image acquiring device, and the method includes: step S100: obtaining first image information through the first image acquisition device; step S200: obtaining a first segmentation instruction, performing segmentation processing on the first image information according to the first segmentation instruction, and performing position coding on the segmented image to obtain a first coding result; step S300: obtaining a first position image in the first coding result, and performing initial image analysis on the first position image according to the intelligent image acquisition processing system to obtain a first analysis result; step S400: judging whether the first analysis result meets a first preset threshold value or not; step S500: when the first analysis result meets the first preset threshold value, obtaining a first adjusting instruction; step S600: performing image adjustment on the first position image according to the first adjustment instruction; step S700: analyzing the image of the first position image through the intelligent image acquisition and processing system to obtain a lesion degree estimation result of the first position image, and matching a first color depth according to the lesion degree estimation result; step S800: performing the steps S300 to S700 on the other position images in the first coding result, respectively, to obtain color depths corresponding to the position images in the first coding result, and obtaining a color depth set according to all the color depths; step S900: inputting the color depth set and the first encoding result into a region evaluation model to obtain a first output result, wherein the first output result comprises a first position region; step S1000: and carrying out amplification image acquisition on the first position area through the first image acquisition device.
In another aspect, the present application further provides a speckle redness image acquisition and processing system based on intelligent coding, the system includes: a first obtaining unit for obtaining first image information by the first image acquisition device; a second obtaining unit, configured to obtain a first segmentation instruction, perform segmentation processing on the first image information according to the first segmentation instruction, and perform position coding on the image after the segmentation processing to obtain a first coding result; a third obtaining unit, configured to obtain a first position image in the first encoding result, and perform initial image analysis on the first position image according to the intelligent image acquisition and processing system to obtain a first analysis result; the first judging unit is used for judging whether the first analysis result meets a first preset threshold value or not; a fourth obtaining unit, configured to obtain a first adjustment instruction when the first analysis result satisfies the first preset threshold; a first adjusting unit, configured to perform image adjustment on the first position image according to the first adjusting instruction; the first matching unit is used for analyzing the image of the first position image through the intelligent image acquisition and processing system to obtain a lesion degree estimation result of the first position image and matching a first color depth according to the lesion degree estimation result; a fifth obtaining unit, configured to perform the third obtaining unit to the first matching unit on the other position images in the first coding result, respectively, obtain color depths corresponding to the position images in the first coding result, and obtain a color depth set according to all the color depths; a sixth obtaining unit, configured to input the color depth set and the first encoding result into a region evaluation model, and obtain a first output result, where the first output result includes a first position region; the first acquisition unit is used for acquiring the amplified image of the first position area through the first image acquisition device.
In a third aspect, the present invention provides a speckle redness image acquisition and processing system based on intelligent coding, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method of the first aspect when executing the program.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
the method comprises the steps of obtaining first image information through the first image acquisition device, carrying out image segmentation on the first image information, coding the segmented image to obtain a first coding result, carrying out preliminary image analysis on a position image in the coding result to obtain a first analysis result, judging whether the first analysis result meets a preset threshold value, when the analysis result meets the preset threshold value, carrying out primary screening, carrying out image adjustment on the position image passing the primary screening, analyzing the adjustment result of the image, matching different position images according to the analysis result with different color depths, wherein the color depths are matched based on the estimation result of lesion degrees, when the lesion is more serious, the matched color is darker, and after the results meeting the primary screening in the first coding result are all subjected to color matching, the method comprises the steps of obtaining a color depth set, inputting the color depth set and a first coding result into a region evaluation model, obtaining a first position region based on color depth and coding result matching, and carrying out amplification image acquisition based on the first position region, so that the technical effects of intelligently combining acquisition information, assisting a doctor in carrying out intelligent image acquisition and further assisting the doctor in carrying out more accurate disease observation are achieved.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
Fig. 1 is a schematic flowchart of a method for acquiring and processing a speckle-like red-emitting image based on intelligent coding according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a speckle-like redness image acquisition and processing method based on intelligent coding according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.
Description of reference numerals: the device comprises a first obtaining unit 11, a second obtaining unit 12, a third obtaining unit 13, a first judging unit 14, a fourth obtaining unit 15, a first adjusting unit 16, a first matching unit 17, a fifth obtaining unit 18, a sixth obtaining unit 19, a first acquiring unit 20, a bus 300, a receiver 301, a processor 302, a transmitter 303, a memory 304 and a bus interface 305.
Detailed Description
The embodiment of the application provides a method and a system for acquiring and processing a speckled red image based on intelligent coding, solves the technical problems that in the prior art, the image acquisition of a patient is not intelligent enough, and the acquired image cannot be combined in real time to perform intelligent further image acquisition, so that a doctor is helped to perform better disease condition observation, and achieves the technical effects of intelligently combining acquisition information, assisting the doctor in performing intelligent image acquisition and further assisting the doctor in performing more accurate disease condition observation. Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are merely some embodiments of the present application and not all embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein.
Application overview: the endoscope is a detection instrument integrating traditional optics, ergonomics, precision machinery, modern electronics, mathematics and software into a whole. One has an image sensor, optical lens, light source illumination, mechanical device, etc. that can enter the stomach orally or through other natural orifices. Fiberoptic gastroscopes, duodenoscopes and gastroscopes are the most widely used endoscopes in clinic, and can help doctors to directly observe the conditions in internal organs of a human body from the outside of the human body, so that doctors are assisted in diagnosing the conditions of patients better. The technical problems that the image acquisition of a patient is not intelligent enough, and the acquired image cannot be combined in real time to carry out intelligent further image acquisition to help a doctor to carry out better disease observation exist in the prior art.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
the embodiment of the application provides a speckled red image acquisition and processing method based on intelligent coding, which is applied to an intelligent image acquisition and processing system, wherein the system is in communication connection with a first image acquisition device, and the method comprises the following steps: step S100: obtaining first image information through the first image acquisition device; step S200: obtaining a first segmentation instruction, performing segmentation processing on the first image information according to the first segmentation instruction, and performing position coding on the segmented image to obtain a first coding result; step S300: obtaining a first position image in the first coding result, and performing initial image analysis on the first position image according to the intelligent image acquisition processing system to obtain a first analysis result; step S400: judging whether the first analysis result meets a first preset threshold value or not; step S500: when the first analysis result meets the first preset threshold value, obtaining a first adjusting instruction; step S600: performing image adjustment on the first position image according to the first adjustment instruction; step S700: analyzing the image of the first position image through the intelligent image acquisition and processing system to obtain a lesion degree estimation result of the first position image, and matching a first color depth according to the lesion degree estimation result; step S800: performing the steps S300 to S700 on the other position images in the first coding result, respectively, to obtain color depths corresponding to the position images in the first coding result, and obtaining a color depth set according to all the color depths; step S900: inputting the color depth set and the first encoding result into a region evaluation model to obtain a first output result, wherein the first output result comprises a first position region; step S1000: and carrying out amplification image acquisition on the first position area through the first image acquisition device.
Having thus described the general principles of the present application, various non-limiting embodiments thereof will now be described in detail with reference to the accompanying drawings.
The first embodiment is as follows:
as shown in fig. 1, an embodiment of the present application provides a speckle redness image acquiring and processing method based on intelligent coding, wherein the method is applied to an intelligent image acquiring and processing system, the system is communicatively connected to a first image acquiring device, and the method includes:
step S100: obtaining first image information through the first image acquisition device;
specifically, the macular redness is an abnormal phenomenon of a gastroscopic image. The intelligent image acquisition and processing system is a system capable of analyzing and processing image data, is in communication connection with a hospital database, can call images and compare the images, is a part of the endoscope for image acquisition, is image acquisition equipment comprising an image sensor, an optical lens, a light source lighting device and the like, can upload acquired images to the intelligent image processing system in real time, and can control the first image acquisition device to acquire the images. And acquiring first image information of the endoscope through the first image acquisition device.
Step S200: obtaining a first segmentation instruction, performing segmentation processing on the first image information according to the first segmentation instruction, and performing position coding on the segmented image to obtain a first coding result;
specifically, the first segmentation instruction is an instruction for preprocessing the obtained first image information, and based on the first segmentation instruction, the first image is segmented, that is, the first image information is proportionally segmented into different image regions, and the segmented image regions are encoded according to a certain sequence, so as to obtain encoded results of the segmented and encoded first image information, wherein each code uniquely corresponds to one segmented image region, that is, one position point representing a patient sample.
Step S300: obtaining a first position image in the first coding result, and performing initial image analysis on the first position image according to the intelligent image acquisition processing system to obtain a first analysis result;
step S400: judging whether the first analysis result meets a first preset threshold value or not;
specifically, the first position image is an image of a first position where the first image information is encoded, where the first position image is a first encoded image, the first position image is subjected to preliminary image screening according to the intelligent image acquisition and processing system, and the first position image is subjected to preliminary screening based on the relevant data of the hospital acquired by the intelligent image acquisition and processing system to obtain a first analysis result, where the first analysis result includes at least two types of results, that is, an analysis result that satisfies the first preset threshold and an analysis result that does not satisfy the first preset threshold, and it is determined whether the first analysis result is an analysis result that satisfies the first preset threshold.
Step S500: when the first analysis result meets the first preset threshold value, obtaining a first adjusting instruction;
step S600: performing image adjustment on the first position image according to the first adjustment instruction;
specifically, when the first analysis result does not satisfy the first preset threshold, the position image corresponding to the first analysis result is not further analyzed, when the first analysis result satisfies the first preset threshold, it is indicated that the first analysis result may be abnormal at this time, the position image corresponding to the first analysis result is further processed, that is, the first position image is processed according to a first adjustment instruction, the processing process includes image noise point removal, image enhancement, and the like, so that the first position image is clearer and more accurate, and a basis is tamped for more accurate determination of the first position image.
Step S700: analyzing the image of the first position image through the intelligent image acquisition and processing system to obtain a lesion degree estimation result of the first position image, and matching a first color depth according to the lesion degree estimation result;
specifically, the adjusted image is further determined, that is, depth analysis of the image is performed according to the processed first position image, a lesion degree estimation result of the first position image is obtained, that is, the severity of a lesion at a position corresponding to the first position image is obtained, and the color depth corresponding to the severity is matched based on the severity. Further, each lesion severity level has a color depth uniquely corresponding thereto, wherein a more white color indicates a lighter lesion, i.e., a more normal lesion, and a more black color indicates a more severe lesion, and a first color depth matching the first position image is obtained according to the lesion severity level. And the severity of the lesion is characterized through the color depth, so that the severity of the lesion is clear at a glance, and the lesion severity at different positions is more clearly and vividly shown.
Step S800: performing the steps S300 to S700 on the other position images in the first coding result, respectively, to obtain color depths corresponding to the position images in the first coding result, and obtaining a color depth set according to all the color depths;
specifically, the steps S300 to S700 are also repeated for the images at other positions in the first coding result, that is, the other coded images in the first coding result are subjected to preliminary screening of the analysis result, the coded images corresponding to the first preset threshold are subjected to image adjustment, color depth matching is performed according to the adjusted images, and a color depth set is obtained according to the result of the matched color depth.
Step S900: inputting the color depth set and the first encoding result into a region evaluation model to obtain a first output result, wherein the first output result comprises a first position region;
step S1000: and carrying out amplification image acquisition on the first position area through the first image acquisition device.
Specifically, the first position area is a position area for image acquisition in the next step, and further, the first position area is larger than an area formed by coded positions corresponding to the color depth set. Inputting the color depth set and the first coding result into a region evaluation model, and performing region prediction on the region to be subjected to further image acquisition based on the region evaluation model, wherein the region prediction result is larger than a position region formed by an actual color depth set, so as to prevent the position region adjacent or close to the color depth set from being judged wrongly and further cause abnormal diagnosis. And when the color corresponding to the position of the expanded position is darker, the expanded distance is larger, and the color is lighter, the expanded range is smaller. And obtaining the position area subjected to the selection, namely the first position area. And further acquiring an image of the first position area by the first image acquisition device, wherein the image acquisition is the acquisition of magnification compared with the acquisition of the first image, storing the acquired image, and using the stored image as a reference image for diagnosis of a subsequent doctor. Through intelligent image analysis and collection, the further collection of image is carried out to the intellectuality that comes to the anomaly of analysis image, for guaranteeing doctor's final diagnosis quality, supplementary doctor diagnoses, reduces the possibility that the doctor is omitted because of the information that the negligence caused, and then reaches the technological effect that can assist the doctor to carry out more accurate state of an illness and observe.
Further, the embodiment of the present application further includes:
step S1010: obtaining a first color depth threshold;
step S1020: judging whether color depths in the color depth set have color depths which do not meet the first color depth threshold value;
step S1030: obtaining a first color depth set that does not satisfy the first color depth threshold when there is a color depth in the color depth set that does not satisfy the first color depth threshold;
step S1040: obtaining a coding set corresponding to the first color depth set according to the first color depth set;
step S1050: and carrying out multi-angle image acquisition on the position corresponding to the coding set.
Specifically, the first color depth threshold is a color threshold for distinguishing a lesion degree, the threshold may be set by itself, and the colors of the color depth set are classified according to the first color depth threshold, that is, the colors are classified into a color set satisfying the first color depth threshold and a color set not satisfying the first color depth threshold, where the color set not satisfying the first color depth threshold is a set having a lesion degree higher than a set value, that is, a first color depth set, and based on the first color depth set, a coding set corresponding to the first color depth set is obtained. Further, based on the encoding set, a position region corresponding to the encoding set is obtained, and further multi-angle image acquisition is performed on the position region, where the multi-angle image acquisition is image acquisition at least including a first angle and a second angle, and further the number of angles of the multi-angle image acquisition is further related to the color depth, that is, the deeper the color is, the more the image angles are acquired. And carrying out multi-angle image acquisition on the position of which the color depth does not meet the first color depth threshold value, and tamping a foundation for observing the position in a subsequent further process.
Further, the embodiment of the present application further includes:
step S1060: according to the encoding set, obtaining a second position area formed by the encoding, and obtaining first graphic information formed by the second position area;
step S1070: acquiring a geometric center of the first graphic information according to the first graphic information;
step S1080: obtaining first distance information, and drawing a standard circle by taking the geometric center as a circle center and the first distance information as a radius, wherein the first distance information is the farthest distance between the geometric center and the position of the codes in the code set;
step S1090: and determining the number of multi-angle image acquisition according to the area ratio of the first graphic information in the standard circle.
Specifically, the second location area is a location area formed by the coding set, that is, an area formed by the coding corresponding to the color that does not satisfy the first color depth threshold, first graph information is obtained through the location area, the first graph information is a graph obtained through the area formed by the coding, a geometric center of prime number first graph information is obtained, if all segments that enclose the geometric graph (that is, when no circular arc exists), a minimum common divisor (that is, a minimum average partition interval) of all segment lengths can be obtained according to the length of each segment, then an average value of all points is counted, and finally the obtained centroid is a centroid of the geometric graph, and the centroid is the center; if a curve exists, the curve fitting needs to be decomposed into small line segments, and then the calculation is carried out through the method to obtain the center of the first graph. After finding the center of the first geometric figure, drawing a standard circle by taking the center as a circle center and taking the farthest distance between the center and a coded position as a radius, determining the number of multi-angle image acquisition according to the area ratio of the first figure information in the standard circle, namely, when the ratio is higher, indicating that the total density degree of the positions with the color depth exceeding a preset threshold value is higher, at the moment, increasing the number of multi-angle image acquisition by a proper amount, making basic image acquisition for subsequent accurate evaluation, when the ratio is lower, indicating that the total density degree of the positions with the color depth exceeding the preset threshold value is lower, at the moment, reducing the number of image acquisition by a proper amount, so as to ensure the total acquisition time of the images, and save resources and space.
Further, the number of multi-angle image acquisitions is determined according to the area ratio of the first graphic information in the standard circle, in step S1090 of the embodiment of the present application, the method further includes:
step S1091: constructing a database with matched area ratio and image acquisition amount;
step S1092: inputting the area ratio into the database to obtain a first image acquisition quantity matched with the area ratio;
step S1093: obtaining a first coding density according to the coding distribution of the coding set;
step S1094: and correcting and adjusting the first image acquisition quantity through the first coding density to obtain a second image acquisition quantity.
Specifically, the database is a database with matching area ratio and image acquisition quantity, wherein the matching is that the area ratio is matched with the image acquisition quantity, but the area ratio is not in one-to-one correspondence at all. For example, when the area ratio is 1/2, the corresponding number of image acquisitions is 6, when the area ratio is 2/5, the corresponding number of image acquisitions is 6, and when the area ratio is 1/3, the corresponding number of acquisitions is 5. And inputting the area ratio into a database to obtain the quantity information of the collected images matched with the area ratio. According to the code distribution condition of the code set, a first code density is obtained, the number of image acquisition is increased properly for positions with high code density, namely the number of first image acquisition of prime numbers is adjusted according to the density of the first code to obtain the number of second image acquisition, and the image acquisition is carried out based on the number of second image acquisition, so that the number of acquisition is more reasonable, and a foundation is laid for subsequent auxiliary analysis.
Further, step S1050 in the embodiment of the present application further includes:
step S1051: obtaining the number of codes in the code set through the code set;
step S1052: judging whether the code quantity exceeds the first code quantity preset threshold value or not;
step S1053: and when the code number exceeds the first code number preset threshold value, obtaining first marking information, and marking the user acquiring the image according to the marking information.
Specifically, the number of codes in the code set is obtained based on the code set, and the condition of the physical state of the user corresponding to the first image information is marked by the number information of the codes. Further, the marking process includes obtaining a threshold of a first predetermined number of codes, where the number is number information that color depths reflecting positions corresponding to the codes satisfy the first color depth threshold, distinguishing the number based on the threshold of the first predetermined number of codes, and marking users corresponding to the number satisfying the first predetermined number threshold by users. Through the mark, the foundation is tamped for the prediction of the number of people who subsequently carry out the severity of the illness and the prediction of the development trend.
Further, the embodiment of the present application further includes:
step S1054: obtaining first summary information;
step S1055: counting the marked users according to the first summary information to obtain a first counting result;
step S1056: estimating the number trend of the marked users according to the first statistical result to obtain a first estimation result;
step S1057: prompting a caregiver to prepare a resource based on the first estimate.
Specifically, the first summary information is a process of summarizing the number of the marked users, that is, the number of the users is summarized based on the marked information of the marked users to obtain a first statistical result, and the trend of the number in the next time period is predicted according to the length of the time period of the statistical result. Further, the prediction process includes a process of performing quantity statistical analysis with a day as a unit and a month as a period, drawing a quantity change curve based on a certain amount of sample quantity, performing people quantity prediction based on the trend of the curve to obtain a first prediction solution result, prompting nursing staff according to the first prediction result, and preparing related resources including detected equipment, medicines, other nursing devices, equipment and the like in a corresponding quantity.
Further, step S900 in the embodiment of the present application further includes:
step S910: inputting the color depth set and the first encoding result into a region evaluation model, wherein the region evaluation model is a model obtained by training multiple sets of training data, and each set of the multiple sets of data includes: the color depth set and the first coding result and identification information identifying the region expansion result;
step S920: obtaining an output of the region assessment model, the output including the first location region.
Specifically, the region evaluation model is a model for evaluating and selecting a region, the evaluation process is a process for selecting a region according to the distribution of the color shades, the model is obtained through training of a large amount of basic data, the training process includes a process for performing supervised learning, and the supervised learning data includes: the color depth set and the first coding result and identification information identifying the region expansion result; the supervision data at least comprises a plurality of groups of data, and the model has stronger processing capacity based on supervision and learning of the data, so that a more accurate expansion result can be obtained, the obtained result of the first position area is more accurate, the final diagnosis quality of a doctor is ensured in the follow-up process, the doctor is assisted in diagnosing, and the foundation is laid for reducing the possibility of information omission caused by negligence of the doctor.
In summary, the method and system for acquiring and processing the speckle-shaped red-emitting image based on the intelligent coding provided by the embodiment of the present application have the following technical effects:
1. the method comprises the steps of obtaining first image information through the first image acquisition device, carrying out image segmentation on the first image information, coding the segmented image to obtain a first coding result, carrying out preliminary image analysis on a position image in the coding result to obtain a first analysis result, judging whether the first analysis result meets a preset threshold value, when the analysis result meets the preset threshold value, carrying out primary screening, carrying out image adjustment on the position image passing the primary screening, analyzing the adjustment result of the image, matching different position images according to the analysis result with different color depths, wherein the color depths are matched based on the estimation result of lesion degrees, when the lesion is more serious, the matched color is darker, and after the results meeting the primary screening in the first coding result are all subjected to color matching, the method comprises the steps of obtaining a color depth set, inputting the color depth set and a first coding result into a region evaluation model, obtaining a first position region based on color depth and coding result matching, and carrying out amplification image acquisition based on the first position region, so that the technical effects of intelligently combining acquisition information, assisting a doctor in carrying out intelligent image acquisition and further assisting the doctor in carrying out more accurate disease observation are achieved.
2. The positions with the color depths not meeting the first color depth threshold value are subjected to multi-angle image acquisition, and the positions are further observed and tamped.
Example two:
based on the same inventive concept as the method for acquiring and processing the speckle-shaped red-emitting image based on the intelligent coding in the foregoing embodiment, the present invention further provides a system for acquiring and processing the speckle-shaped red-emitting image based on the intelligent coding, as shown in fig. 2, the system includes:
a first obtaining unit 11, where the first obtaining unit 11 is configured to obtain first image information through the first image capturing device;
a second obtaining unit 12, where the second obtaining unit 12 is configured to obtain a first division instruction, perform division processing on the first image information according to the first division instruction, and perform position coding on the image after the division processing to obtain a first coding result;
a third obtaining unit 13, where the third obtaining unit 13 is configured to obtain a first position image in the first encoding result, and perform initial image analysis on the first position image according to the intelligent image acquisition and processing system to obtain a first analysis result;
a first judging unit 14, where the first judging unit 14 is configured to judge whether the first analysis result satisfies a first preset threshold;
a fourth obtaining unit 15, where the fourth obtaining unit 15 is configured to obtain a first adjusting instruction when the first analysis result satisfies the first preset threshold;
a first adjusting unit 16, wherein the first adjusting unit 16 is configured to perform image adjustment on the first position image according to the first adjusting instruction;
the first matching unit 17 is configured to analyze the image of the first position image through the intelligent image acquisition and processing system to obtain a lesion degree estimation result of the first position image, and match a first color depth according to the lesion degree estimation result;
a fifth obtaining unit 18, where the fifth obtaining unit 18 is configured to perform the third obtaining unit to the first matching unit on the other position images in the first encoding result, obtain color depths corresponding to the position images in the first encoding result, and obtain a color depth set according to all the color depths;
a sixth obtaining unit 19, configured to input the color depth set and the first encoding result into a region evaluation model, and obtain a first output result, where the first output result includes a first position region;
a first collecting unit 20, wherein the first collecting unit 20 is configured to collect an enlarged image of the first position area through the first image collecting device.
Further, the system further comprises:
a seventh obtaining unit configured to obtain a first color depth threshold;
a second determination unit configured to determine whether there is a color depth in the color depth set that does not satisfy the first color depth threshold;
an eighth obtaining unit configured to obtain a first color depth set that does not satisfy the first color depth threshold when there is a color depth in the color depth set that does not satisfy the first color depth threshold;
a ninth obtaining unit, configured to obtain, according to the first color depth set, an encoding set corresponding to the first color depth set;
and the second acquisition unit is used for acquiring multi-angle images of the positions corresponding to the code sets.
Further, the system further comprises:
a tenth obtaining unit configured to obtain, from the encoding set, a second position area composed of the encoding, and obtain first graphics information composed of the second position area;
an eleventh obtaining unit, configured to obtain a geometric center of the first graphic information according to the first graphic information;
a twelfth obtaining unit, configured to obtain first distance information, where the first distance information is a radius of a circle drawn by using the geometric center as a center of the circle, and the first distance information is a farthest distance between the geometric center and a position of a code in the code set;
a first determination unit for determining the number of multi-angle image acquisitions by an area ratio of the first graphic information in the standard circle.
Further, the system further comprises:
the first construction unit is used for constructing a database with matched area ratio and image acquisition amount;
a thirteenth obtaining unit, configured to input the area ratio into the database, and obtain a first image acquisition number that matches the area ratio;
a fourteenth obtaining unit, configured to obtain a first encoding density according to the encoding distribution of the encoding set;
a fifteenth obtaining unit, configured to perform modification adjustment on the first image acquisition number according to the first encoding density to obtain a second image acquisition number.
Further, the system further comprises:
a sixteenth obtaining unit, configured to obtain, through the code set, a number of codes in the code set;
a seventeenth obtaining unit configured to obtain a predetermined threshold for the first number of codes;
a third judging unit configured to judge whether the code number exceeds the first code number by a predetermined threshold;
an eighteenth obtaining unit, configured to obtain first marking information when the number of codes exceeds the first number of codes by a predetermined threshold, and mark a user who has acquired an image according to the marking information.
Further, the system further comprises:
a nineteenth obtaining unit configured to obtain the first summary information;
a twentieth obtaining unit, configured to count the marked users according to the first summary information, and obtain a first statistical result;
a twenty-first obtaining unit, configured to estimate, according to the first statistical result, a number trend of the marked users to obtain a first estimation result;
the first prompting unit is used for prompting a nursing staff to prepare resources based on the first estimation result.
Further, the system further comprises:
a first input unit, configured to input the color depth set and the first encoding result into a region evaluation model, where the region evaluation model is a model obtained by training multiple sets of training data, and each of the multiple sets of data includes: the color depth set and the first coding result and identification information identifying the region expansion result;
a twenty-second obtaining unit for obtaining an output result of the region evaluation model, the output result including the first position region.
Various changes and specific examples of the method for acquiring and processing the intelligent coding-based macular red emission image in the first embodiment of fig. 1 are also applicable to the system for acquiring and processing the intelligent coding-based macular red emission image in the present embodiment, and a person skilled in the art can clearly know the method for implementing the system for acquiring and processing the intelligent coding-based macular red emission image in the present embodiment through the foregoing detailed description of the method for acquiring and processing the intelligent coding-based macular red emission image, so for the brevity of the description, detailed description is not given here.
An exemplary electronic device:
the electronic device of the embodiment of the present application is described below with reference to fig. 3.
Fig. 3 illustrates a schematic structural diagram of an electronic device according to an embodiment of the present application.
Based on the inventive concept of the intelligent coding-based speckle redness image acquisition and processing method in the foregoing embodiments, the present invention further provides an intelligent coding-based speckle redness image acquisition and processing system, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps of any one of the foregoing intelligent coding-based speckle redness image acquisition and processing methods.
Where in fig. 3 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 305 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e., a transceiver, providing a means for communicating with various other systems over a transmission medium.
The processor 302 is responsible for managing the bus 300 and general processing, and the memory 304 may be used for storing data used by the processor 302 in performing operations.
The embodiment of the invention provides a speckle redness image acquisition and processing method based on intelligent coding, which is applied to an intelligent image acquisition and processing system, wherein the system is in communication connection with a first image acquisition device, and the method comprises the following steps: step S100: obtaining first image information through the first image acquisition device; step S200: obtaining a first segmentation instruction, performing segmentation processing on the first image information according to the first segmentation instruction, and performing position coding on the segmented image to obtain a first coding result; step S300: obtaining a first position image in the first coding result, and performing initial image analysis on the first position image according to the intelligent image acquisition processing system to obtain a first analysis result; step S400: judging whether the first analysis result meets a first preset threshold value or not; step S500: when the first analysis result meets the first preset threshold value, obtaining a first adjusting instruction; step S600: performing image adjustment on the first position image according to the first adjustment instruction; step S700: analyzing the image of the first position image through the intelligent image acquisition and processing system to obtain a lesion degree estimation result of the first position image, and matching a first color depth according to the lesion degree estimation result; step S800: performing the steps S300 to S700 on the other position images in the first coding result, respectively, to obtain color depths corresponding to the position images in the first coding result, and obtaining a color depth set according to all the color depths; step S900: inputting the color depth set and the first encoding result into a region evaluation model to obtain a first output result, wherein the first output result comprises a first position region; step S1000: and carrying out amplification image acquisition on the first position area through the first image acquisition device. The technical problem of exist among the prior art not intelligent enough to patient's image acquisition, can not combine the image of gathering in real time to carry out intelligent further image acquisition, help the doctor to carry out better state of an illness observation is solved, reach intelligent combination information of gathering, supplementary doctor carries out intelligent image acquisition, and then supplementary doctor carries out more accurate state of an illness observation's technological effect.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (8)
1. An intelligent coding-based speckle redness image acquisition and processing method, wherein the method is applied to an intelligent image acquisition and processing system, the system is connected with a first image acquisition device in a communication way, and the method comprises the following steps:
step S100: obtaining first image information through the first image acquisition device;
step S200: obtaining a first segmentation instruction, performing segmentation processing on the first image information according to the first segmentation instruction, and performing position coding on the segmented image to obtain a first coding result;
step S300: obtaining a first position image in the first coding result, and performing initial image analysis on the first position image according to the intelligent image acquisition processing system to obtain a first analysis result;
step S400: judging whether the first analysis result meets a first preset threshold value or not;
step S500: when the first analysis result meets the first preset threshold value, obtaining a first adjusting instruction;
step S600: performing image adjustment on the first position image according to the first adjustment instruction;
step S700: analyzing the image of the first position image through the intelligent image acquisition and processing system to obtain a lesion degree estimation result of the first position image, and matching a first color depth according to the lesion degree estimation result;
step S800: performing the steps S300 to S700 on the other position images in the first coding result, respectively, to obtain color depths corresponding to the position images in the first coding result, and obtaining a color depth set according to all the color depths;
step S900: inputting the color depth set and the first encoding result into a region evaluation model to obtain a first output result, wherein the first output result comprises a first position region;
step S1000: acquiring an enlarged image of the first position area through the first image acquisition device;
wherein the method further comprises:
obtaining a first color depth threshold;
judging whether color depths in the color depth set have color depths which do not meet the first color depth threshold value;
obtaining a first color depth set that does not satisfy the first color depth threshold when there is a color depth in the color depth set that does not satisfy the first color depth threshold;
obtaining a coding set corresponding to the first color depth set according to the first color depth set;
and carrying out multi-angle image acquisition on the position corresponding to the coding set.
2. The method of claim 1, wherein the method further comprises:
according to the encoding set, obtaining a second position area formed by the encoding, and obtaining first graphic information formed by the second position area;
acquiring a geometric center of the first graphic information according to the first graphic information;
obtaining first distance information, and drawing a standard circle by taking the geometric center as a circle center and the first distance information as a radius, wherein the first distance information is the farthest distance between the geometric center and the position of the codes in the code set;
and determining the number of multi-angle image acquisition according to the area ratio of the first graphic information in the standard circle.
3. The method of claim 2, wherein the number of multi-angle image acquisitions is determined by an area ratio of the first graphic information in the standard circle, the method further comprising:
constructing a database with matched area ratio and image acquisition amount;
inputting the area ratio into the database to obtain a first image acquisition quantity matched with the area ratio;
obtaining a first coding density according to the coding distribution of the coding set;
and correcting and adjusting the first image acquisition quantity through the first coding density to obtain a second image acquisition quantity.
4. The method of claim 1, wherein the method further comprises:
obtaining the number of codes in the code set through the code set;
obtaining a first code quantity preset threshold value;
judging whether the code quantity exceeds the first code quantity preset threshold value or not;
and when the code number exceeds the first code number preset threshold value, obtaining first marking information, and marking the user acquiring the image according to the marking information.
5. The method of claim 4, wherein the method further comprises:
obtaining first summary information;
counting the marked users according to the first summary information to obtain a first counting result;
estimating the number trend of the marked users according to the first statistical result to obtain a first estimation result;
prompting a caregiver to prepare a resource based on the first estimate.
6. The method of claim 1, wherein the step S900 further comprises:
inputting the color depth set and the first coding result into a region evaluation model, wherein the region evaluation model is a model obtained by training multiple sets of training data, and each set of the multiple sets of training data comprises: the color depth set and the first coding result and identification information identifying the region expansion result;
obtaining an output of the region assessment model, the output including the first location region.
7. A speckle redness image acquisition and processing system based on smart coding, wherein the system comprises:
the first obtaining unit is used for obtaining first image information through a first image acquisition device;
a second obtaining unit, configured to obtain a first segmentation instruction, perform segmentation processing on the first image information according to the first segmentation instruction, and perform position coding on the image after the segmentation processing to obtain a first coding result;
a third obtaining unit, configured to obtain a first position image in the first encoding result, and perform initial image analysis on the first position image according to an intelligent image acquisition and processing system to obtain a first analysis result;
the first judging unit is used for judging whether the first analysis result meets a first preset threshold value or not;
a fourth obtaining unit, configured to obtain a first adjustment instruction when the first analysis result satisfies the first preset threshold;
a first adjusting unit, configured to perform image adjustment on the first position image according to the first adjusting instruction;
the first matching unit is used for analyzing the image of the first position image through the intelligent image acquisition and processing system to obtain a lesion degree estimation result of the first position image and matching a first color depth according to the lesion degree estimation result;
a fifth obtaining unit, configured to perform the third obtaining unit to the first matching unit on the other position images in the first coding result, respectively, obtain color depths corresponding to the position images in the first coding result, and obtain a color depth set according to all the color depths;
a sixth obtaining unit, configured to input the color depth set and the first encoding result into a region evaluation model, and obtain a first output result, where the first output result includes a first position region;
the first acquisition unit is used for acquiring an amplified image of the first position area through the first image acquisition device;
a seventh obtaining unit configured to obtain a first color depth threshold;
a second determination unit configured to determine whether there is a color depth in the color depth set that does not satisfy the first color depth threshold;
an eighth obtaining unit configured to obtain a first color depth set that does not satisfy the first color depth threshold when there is a color depth in the color depth set that does not satisfy the first color depth threshold;
a ninth obtaining unit, configured to obtain, according to the first color depth set, an encoding set corresponding to the first color depth set;
and the second acquisition unit is used for acquiring multi-angle images of the positions corresponding to the code sets.
8. A speckle redness image acquisition and processing system based on smart coding, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method of any one of claims 1-6 when executing the program.
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