Intelligent medical optimization system

Abstract

The invention relates to an intelligent medical optimization system which comprises a retina imaging device, a signal enhancement device, a filtering sharpening device, a morphological processing device, a dynamic analysis device and a smoothing execution device. The present invention can prioritize a regimen based on a diseased retina of a patient.

Description

Technical Field

[0001] The present invention relates to the field of smart medical care, and in particular to a smart medical care optimization system. Background Art

[0002] Common medical devices include the following categories: Physical diagnostic instruments: such as thermometers, blood pressure meters, microscopes, audiometers, various physiological recorders, etc. Imaging instruments: such as X-ray machines, CT scans, magnetic resonance imaging, B-ultrasound, etc. Analytical instruments: such as various types of counters, biochemical and immunoassay instruments, etc. Electrophysiological instruments: such as electrocardiographs, electroencephalographs, electromyograms, etc. Ordinary surgical instruments and optical surgical instruments (such as fiber endoscopes, laser therapy machines, etc.). Other similar or related items.

[0003] In addition to specific applications in the two fields of medical equipment and medical consumables, medical devices also include instruments, equipment, appliances, in vitro diagnostic reagents and calibrants, materials, etc. for human body. For example, commonly used trauma treatment vehicles, operating tables, operating lights, monitors, anesthesia machines, ventilators, blood cell analyzers, differentiation analyzers, microplate readers, plate washers, urine analyzers, ultrasound machines (color ultrasound, B-ultrasound, etc.), X-ray machines, and nuclear magnetic resonance imaging machines in hospitals.

[0004] It can be seen that the quality of medical imaging has a huge impact on the diagnostic results of medical devices that use images as the basic data for diagnosis, such as the membrane imaging screen obtained by performing imaging operations on the patient's diseased retina. However, the various membrane imaging screens corresponding to different patients at different times have different contents, and the smoothing algorithms required to obtain the best image quality are also different. How to match the optimal smoothing algorithm for different video screen contents is one of the technical problems that need to be solved at present. Summary of the invention

[0005] In order to solve the technical problems in the prior art, the present invention provides a smart medical optimization system, the system comprising: A retinal imaging device, located in an ophthalmic diagnostic instrument, is used to perform imaging operations on the patient's diseased retina to obtain and output corresponding membrane imaging images; A signal enhancement device, located in the ophthalmic diagnostic instrument and connected to the retinal imaging device, is used to perform a histogram equalization process based on a distribution function on the received membrane imaging picture to obtain and output a corresponding current enhanced image; A filtering and sharpening device, connected to the signal enhancement device, for performing a high-pass filtering and sharpening process on the received current enhanced image to obtain and output a corresponding filtering and sharpening image; A morphological processing device, connected to the filter sharpening device, for sequentially performing a morphological processing of first dilation and then erosion on the received filter sharpening image, so as to obtain and output a corresponding morphologically processed image; A dynamic analysis device is located in the ophthalmic diagnostic instrument and connected to the morphological processing device, and is used to extract multiple information parameters from the morphologically processed image, wherein the multiple information parameters include the number of pixel rows, pixel column values, maximum noise amplitudes corresponding to various noise types, and grayscale values ​​corresponding to each pixel point of the morphologically processed image, perform multiple learning on the convolutional neural network to obtain an AI prediction model, and based on the AI ​​prediction model, intelligently predict the maximum noise amplitude of the picture obtained after smoothing the morphologically processed image using the certain smoothing algorithm according to the binary representation data stream corresponding to a certain set smoothing algorithm, the contrast of the morphologically processed image, and the multiple information parameters, and determine the type of effective smoothing algorithm based on the maximum noise amplitudes of the pictures corresponding to various smoothing algorithms, wherein the number of times the convolutional neural network is learned is proportional to the number of various noise types, and the determined effective smoothing algorithm type is the type of smoothing algorithm corresponding to the maximum noise amplitude of the picture with the smallest value among the maximum noise amplitudes of the pictures; A smoothing execution device, connected to the dynamic analysis device, for smoothing the morphologically processed image using a smoothing algorithm corresponding to the type of effective smoothing algorithm to obtain a smoothed image to be displayed; Among them, various smoothing algorithms include a non-scaling transformation fuzzy algorithm, a median fuzzy algorithm, a Gaussian fuzzy algorithm, and a bilateral smoothing fuzzy algorithm.

[0006] The technical solution of the present invention is to perform targeted optimization processing on the membrane imaging picture obtained by performing imaging operations on the patient's diseased retina to analyze the morphological processing image, and extract multiple information parameters from the morphological processing image, the multiple information parameters including the number of pixel rows, pixel column values, the maximum noise amplitudes corresponding to various noise types, and the grayscale values ​​corresponding to each pixel point of the morphological processing image; based on the AI ​​prediction model, according to the binary representation data stream corresponding to a certain set smoothing algorithm, the contrast of the morphological processing image and the multiple information parameters, the maximum noise amplitude of the picture obtained after smoothing the morphological processing image using the certain smoothing algorithm is intelligently predicted, and the type of effective smoothing algorithm is determined based on the maximum noise amplitudes of each picture corresponding to various smoothing algorithms, thereby solving the above-mentioned technical difficulties. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] The embodiments of the present invention will be described below with reference to the accompanying drawings.

[0008] Figure 1 Schematic diagram of the internal structure of the intelligent medical optimization system according to implementation A of the present invention.

[0009] Figure 2Schematic diagram of the internal structure of the intelligent medical optimization system according to implementation B of the present invention.

[0010] Figure 3 Schematic diagram of the internal structure of the intelligent medical optimization system according to implementation scheme C of the present invention. DETAILED DESCRIPTION

[0011] The implementation scheme of the intelligent medical optimization system of the present invention will be described in detail below with reference to the accompanying drawings.

[0012] Implementation A Figure 1 This is a schematic diagram of the internal structure of a smart medical optimization system according to Embodiment A of the present invention, wherein the system comprises: A retinal imaging device, located in an ophthalmic diagnostic instrument, is used to perform imaging operations on the patient's diseased retina to obtain and output corresponding membrane imaging images; A signal enhancement device, located in the ophthalmic diagnostic instrument and connected to the retinal imaging device, is used to perform a histogram equalization process based on a distribution function on the received membrane imaging picture to obtain and output a corresponding current enhanced image; Exemplarily, the signal enhancement device is located in the ophthalmic diagnostic instrument and connected to the retinal imaging device, and is used to perform a histogram equalization process based on a distribution function on the received film body imaging picture to obtain and output a corresponding current enhanced image, including: selecting to use a GAL device to implement the signal enhancement device, which is located in the ophthalmic diagnostic instrument and connected to the retinal imaging device, and is used to perform a histogram equalization process based on a distribution function on the received film body imaging picture to obtain and output a corresponding current enhanced image; A filtering and sharpening device, connected to the signal enhancement device, for performing a high-pass filtering and sharpening process on the received current enhanced image to obtain and output a corresponding filtering and sharpening image; A morphological processing device, connected to the filter sharpening device, for sequentially performing a morphological processing of first dilation and then erosion on the received filter sharpening image, so as to obtain and output a corresponding morphologically processed image; A dynamic analysis device is located in the ophthalmic diagnostic instrument and connected to the morphological processing device, and is used to extract multiple information parameters from the morphologically processed image, wherein the multiple information parameters include the number of pixel rows, pixel column values, maximum noise amplitudes corresponding to various noise types, and grayscale values ​​corresponding to each pixel point of the morphologically processed image, perform multiple learning on the convolutional neural network to obtain an AI prediction model, and based on the AI ​​prediction model, intelligently predict the maximum noise amplitude of the picture obtained after smoothing the morphologically processed image using the certain smoothing algorithm according to the binary representation data stream corresponding to a certain set smoothing algorithm, the contrast of the morphologically processed image, and the multiple information parameters, and determine the type of effective smoothing algorithm based on the maximum noise amplitudes of the pictures corresponding to various smoothing algorithms, wherein the number of times the convolutional neural network is learned is proportional to the number of various noise types, and the determined effective smoothing algorithm type is the type of smoothing algorithm corresponding to the maximum noise amplitude of the picture with the smallest value among the maximum noise amplitudes of the pictures; Specifically, based on the AI ​​prediction model, according to the binary representation data stream corresponding to a certain set smoothing algorithm, the contrast of the morphologically processed image, and the multiple information parameters, the maximum noise amplitude of the picture obtained after smoothing the morphologically processed image using the certain smoothing algorithm is intelligently predicted, and the type of effective smoothing algorithm is determined based on the maximum noise amplitudes of each picture corresponding to various smoothing algorithms, wherein the number of times the convolutional neural network learns is proportional to the number of various noise types, and the type of effective smoothing algorithm determined is the type of smoothing algorithm corresponding to the maximum noise amplitude of the picture with the smallest value among the maximum noise amplitudes of each picture, including: performing binary value conversion on the binary representation data stream corresponding to a certain set smoothing algorithm, the contrast of the morphologically processed image, and the multiple information parameters, and then inputting them into the AI ​​prediction model; A smoothing execution device, connected to the dynamic analysis device, for smoothing the morphologically processed image using a smoothing algorithm corresponding to the type of effective smoothing algorithm to obtain a smoothed image to be displayed; Among them, various smoothing algorithms include non-scaling transformation fuzzy algorithm, median fuzzy algorithm, Gaussian fuzzy algorithm and bilateral smoothing fuzzy algorithm; Among them, based on the AI ​​prediction model, according to the binary representation data stream corresponding to a set smoothing algorithm, the contrast of the morphologically processed image and the multiple information parameters, intelligently predicting the maximum noise amplitude of the picture obtained after smoothing the morphologically processed image using the certain smoothing algorithm includes: inputting the binary representation data stream corresponding to a set smoothing algorithm, the contrast of the morphologically processed image and the multiple information parameters into the AI ​​prediction model in parallel.

[0013] Implementation Plan B Figure 2 This is a schematic diagram of the internal structure of the intelligent medical optimization system according to Embodiment B of the present invention, including the following components: A retinal imaging device, located in an ophthalmic diagnostic instrument, is used to perform imaging operations on the patient's diseased retina to obtain and output corresponding membrane imaging images; A signal enhancement device, located in the ophthalmic diagnostic instrument and connected to the retinal imaging device, is used to perform a histogram equalization process based on a distribution function on the received membrane imaging picture to obtain and output a corresponding current enhanced image; A filtering and sharpening device, connected to the signal enhancement device, for performing a high-pass filtering and sharpening process on the received current enhanced image to obtain and output a corresponding filtering and sharpening image; A morphological processing device, connected to the filter sharpening device, for sequentially performing a morphological processing of first dilation and then erosion on the received filter sharpening image, so as to obtain and output a corresponding morphologically processed image; A dynamic analysis device is located in the ophthalmic diagnostic instrument and connected to the morphological processing device, and is used to extract multiple information parameters from the morphologically processed image, wherein the multiple information parameters include the number of pixel rows, pixel column values, maximum noise amplitudes corresponding to various noise types, and grayscale values ​​corresponding to each pixel point of the morphologically processed image, perform multiple learning on the convolutional neural network to obtain an AI prediction model, and based on the AI ​​prediction model, intelligently predict the maximum noise amplitude of the picture obtained after smoothing the morphologically processed image using the certain smoothing algorithm according to the binary representation data stream corresponding to a certain set smoothing algorithm, the contrast of the morphologically processed image, and the multiple information parameters, and determine the type of effective smoothing algorithm based on the maximum noise amplitudes of the pictures corresponding to various smoothing algorithms, wherein the number of times the convolutional neural network is learned is proportional to the number of various noise types, and the determined effective smoothing algorithm type is the type of smoothing algorithm corresponding to the maximum noise amplitude of the picture with the smallest value among the maximum noise amplitudes of the pictures; A smoothing execution device, connected to the dynamic analysis device, for smoothing the morphologically processed image using a smoothing algorithm corresponding to the type of effective smoothing algorithm to obtain a smoothed image to be displayed; a thickness sensor device, connected to the signal enhancement device, the filter sharpening device, the morphological processing device and the dynamic analysis device, respectively, and used to measure the current real-time thickness values ​​of the signal enhancement device, the filter sharpening device, the morphological processing device and the dynamic analysis device respectively; Wherein, the thickness sensor device is respectively connected with the signal enhancement device, the filter sharpening device, the morphological processing device and the dynamic analysis device, and is used to respectively measure the current real-time thickness values ​​of the signal enhancement device, the filter sharpening device, the morphological processing device and the dynamic analysis device, including: the thickness sensor device includes a plurality of thickness measurement units, which are respectively connected with the signal enhancement device, the filter sharpening device, the morphological processing device and the dynamic analysis device, so as to complete the measurement of the current real-time thickness values ​​of the signal enhancement device, the filter sharpening device, the morphological processing device and the dynamic analysis device; Wherein, the thickness sensor device comprises a plurality of thickness measuring units, which are used to be respectively connected with the signal enhancement device, the filter sharpening device, the morphological processing device and the dynamic analysis device to complete the respective measurement of the current real-time thickness values ​​of the signal enhancement device, the filter sharpening device, the morphological processing device and the dynamic analysis device, including: the plurality of thickness measuring units are a plurality of thickness sensors, which are used to be respectively connected with the signal enhancement device, the filter sharpening device, the morphological processing device and the dynamic analysis device to complete the respective measurement of the current real-time thickness values ​​of the signal enhancement device, the filter sharpening device, the morphological processing device and the dynamic analysis device; And wherein the plurality of thickness measuring units are a plurality of thickness sensors, which are used to be connected to the signal enhancement device, the filter sharpening device, the morphological processing device and the dynamic analysis device respectively, so as to complete the respective measurement of the current real-time thickness values ​​of the signal enhancement device, the filter sharpening device, the morphological processing device and the dynamic analysis device respectively, including: the plurality of thickness sensors have the same structure; And wherein, the multiple thickness measurement units are multiple thickness sensors, which are used to be connected to the signal enhancement device, the filter sharpening device, the morphological processing device and the dynamic analysis device respectively, so as to complete the respective measurements of the current real-time thickness values ​​of the signal enhancement device, the filter sharpening device, the morphological processing device and the dynamic analysis device, and also include: the multiple thickness sensors have the same thickness measurement upper limit value and thickness measurement lower limit value.

[0014] Implementation Plan C Figure 3 This is a schematic diagram of the internal structure of the intelligent medical optimization system according to Embodiment C of the present invention, which includes the following components: A retinal imaging device, located in an ophthalmic diagnostic instrument, is used to perform imaging operations on the patient's diseased retina to obtain and output corresponding membrane imaging images; A signal enhancement device, located in the ophthalmic diagnostic instrument and connected to the retinal imaging device, is used to perform a histogram equalization process based on a distribution function on the received membrane imaging picture to obtain and output a corresponding current enhanced image; A filtering and sharpening device, connected to the signal enhancement device, for performing a high-pass filtering and sharpening process on the received current enhanced image to obtain and output a corresponding filtering and sharpening image; A morphological processing device, connected to the filter sharpening device, for sequentially performing a morphological processing of first dilation and then erosion on the received filter sharpening image, so as to obtain and output a corresponding morphologically processed image; A dynamic analysis device is located in the ophthalmic diagnostic instrument and connected to the morphological processing device, and is used to extract multiple information parameters from the morphologically processed image, wherein the multiple information parameters include the number of pixel rows, pixel column values, maximum noise amplitudes corresponding to various noise types, and grayscale values ​​corresponding to each pixel point of the morphologically processed image, perform multiple learning on the convolutional neural network to obtain an AI prediction model, and based on the AI ​​prediction model, intelligently predict the maximum noise amplitude of the picture obtained after smoothing the morphologically processed image using the certain smoothing algorithm according to the binary representation data stream corresponding to a certain set smoothing algorithm, the contrast of the morphologically processed image, and the multiple information parameters, and determine the type of effective smoothing algorithm based on the maximum noise amplitudes of the pictures corresponding to various smoothing algorithms, wherein the number of times the convolutional neural network is learned is proportional to the number of various noise types, and the determined effective smoothing algorithm type is the type of smoothing algorithm corresponding to the maximum noise amplitude of the picture with the smallest value among the maximum noise amplitudes of the pictures; A smoothing execution device, connected to the dynamic analysis device, for smoothing the morphologically processed image using a smoothing algorithm corresponding to the type of effective smoothing algorithm to obtain a smoothed image to be displayed; A temporary value storage mechanism, connected to the signal enhancement device, the filter sharpening device, the morphological processing device and the plurality of thickness measurement units of the dynamic analysis device, respectively, for temporarily storing the input data and output data of the signal enhancement device, the filter sharpening device, the morphological processing device and the dynamic analysis device respectively; Wherein, the temporary value storage mechanism is respectively connected to the signal enhancement device, the filter sharpening device, the morphological processing device and the plurality of thickness measurement units of the dynamic analysis device, and is used to temporarily store the input data and output data of the signal enhancement device, the filter sharpening device, the morphological processing device and the dynamic analysis device respectively, including: the temporary value storage mechanism is a FLASH flash memory; And wherein, the numerical temporary storage mechanism is respectively connected to the signal enhancement device, the filter sharpening device, the morphological processing device and multiple thickness measurement units of the dynamic analysis device, and is used to temporarily store the input data and output data of the signal enhancement device, the filter sharpening device, the morphological processing device and the dynamic analysis device respectively, including: the numerical temporary storage mechanism is an MMC storage chip.

[0015] In addition, in the smart medical optimization system, based on the AI ​​prediction model according to a binary representation data stream corresponding to a set smoothing algorithm, the contrast of the morphologically processed image and the multiple information parameters, intelligently predicting the maximum noise amplitude of the picture obtained after smoothing the morphologically processed image using the certain smoothing algorithm also includes: running the AI ​​prediction model to obtain the maximum noise amplitude of the picture obtained after smoothing the morphologically processed image using the certain smoothing algorithm output by the AI ​​prediction model.

[0016] The technical advantages of the present invention are as follows: Firstly, the membrane imaging picture obtained by performing imaging operation on the patient's diseased retina is subjected to targeted optimization processing to analyze the morphologically processed image, and multiple information parameters are extracted from the morphologically processed image, wherein the multiple information parameters include the number of pixel rows, pixel column values, maximum noise amplitudes corresponding to various noise types, and grayscale values ​​corresponding to each pixel point of the morphologically processed image; Secondly: performing multiple learning on the convolutional neural network to obtain an AI prediction model, wherein the number of times the convolutional neural network is learned is proportional to the number of various noise types, thereby completing the customization of the structure of the AI ​​prediction model; Again: based on the AI ​​prediction model, according to the binary representation data stream corresponding to a certain set smoothing algorithm, the contrast of the morphologically processed image and the multiple information parameters, the maximum noise amplitude of the picture obtained after smoothing the morphologically processed image using the certain smoothing algorithm is intelligently predicted, and the type of effective smoothing algorithm is determined based on the maximum noise amplitudes of the pictures corresponding to various smoothing algorithms, wherein the determined type of effective smoothing algorithm is the type of smoothing algorithm corresponding to the maximum noise amplitude of the picture with the smallest value among the maximum noise amplitudes of the pictures.

[0017] The intelligent medical optimization system of the present invention solves the technical problem in the prior art that it is difficult to obtain the optimal smoothing algorithm for each membrane imaging image of different patients at different times. The system can use an AI prediction model to determine the maximum noise amplitude of the image obtained after each set smoothing algorithm is used to smooth the morphologically processed image based on the various image data of the membrane imaging image obtained by performing imaging operations on the patient's diseased retina, and determine the type of effective smoothing algorithm based on the maximum noise amplitudes of each image corresponding to each smoothing algorithm, thereby solving the above technical difficulties.

[0018] Although the present invention has been described with reference to the structures disclosed herein, it is not limited to the details shown, and this application is intended to cover such modifications or changes as fall within the scope of the following claims.

Claims

1. A smart medical optimization system, characterized in that: The system comprises: A retinal imaging device, located in an ophthalmic diagnostic instrument, is used to perform imaging operations on the patient's diseased retina to obtain and output corresponding membrane imaging images; A signal enhancement device, located in the ophthalmic diagnostic instrument and connected to the retinal imaging device, is used to perform a histogram equalization process based on a distribution function on the received membrane imaging picture to obtain and output a corresponding current enhanced image; A filtering and sharpening device, connected to the signal enhancement device, for performing a high-pass filtering and sharpening process on the received current enhanced image to obtain and output a corresponding filtering and sharpening image; A morphological processing device, connected to the filter sharpening device, for sequentially performing a morphological processing of first dilation and then erosion on the received filter sharpening image, so as to obtain and output a corresponding morphologically processed image; A dynamic analysis device is located in the ophthalmic diagnostic instrument and connected to the morphological processing device, and is used to extract multiple information parameters from the morphologically processed image, wherein the multiple information parameters include the number of pixel rows, pixel column values, maximum noise amplitudes corresponding to various noise types, and grayscale values ​​corresponding to each pixel point of the morphologically processed image, perform multiple learning on the convolutional neural network to obtain an AI prediction model, and based on the AI ​​prediction model, intelligently predict the maximum noise amplitude of the picture obtained after smoothing the morphologically processed image using the certain smoothing algorithm according to the binary representation data stream corresponding to a certain set smoothing algorithm, the contrast of the morphologically processed image, and the multiple information parameters, and determine the type of effective smoothing algorithm based on the maximum noise amplitudes of the pictures corresponding to various smoothing algorithms, wherein the number of times the convolutional neural network is learned is proportional to the number of various noise types, and the determined effective smoothing algorithm type is the type of smoothing algorithm corresponding to the maximum noise amplitude of the picture with the smallest value among the maximum noise amplitudes of the pictures; A smoothing execution device, connected to the dynamic analysis device, for smoothing the morphologically processed image using a smoothing algorithm corresponding to the type of effective smoothing algorithm to obtain a smoothed image to be displayed; Among them, various smoothing algorithms include a non-scaling transformation fuzzy algorithm, a median fuzzy algorithm, a Gaussian fuzzy algorithm, and a bilateral smoothing fuzzy algorithm.

2. The intelligent medical optimization system according to claim 1, characterized in that: Based on the AI ​​prediction model, according to the binary representation data stream corresponding to a certain set smoothing algorithm, the contrast of the morphologically processed image and the multiple information parameters, intelligently predicting the maximum noise amplitude of the picture obtained after smoothing the morphologically processed image using the certain smoothing algorithm includes: inputting the binary representation data stream corresponding to a certain set smoothing algorithm, the contrast of the morphologically processed image and the multiple information parameters into the AI ​​prediction model in parallel.

3. The intelligent medical optimization system according to claim 2, characterized in that: The system further comprises: a thickness sensor device, connected to the signal enhancement device, the filter sharpening device, the morphological processing device and the dynamic analysis device, respectively, and used to measure the current real-time thickness values ​​of the signal enhancement device, the filter sharpening device, the morphological processing device and the dynamic analysis device respectively; Among them, the thickness sensor device is respectively connected to the signal enhancement device, the filter sharpening device, the morphological processing device and the dynamic analysis device, and is used to respectively measure the current real-time thickness values ​​of the signal enhancement device, the filter sharpening device, the morphological processing device and the dynamic analysis device. The thickness sensor device includes multiple thickness measurement units, which are respectively connected to the signal enhancement device, the filter sharpening device, the morphological processing device and the dynamic analysis device to complete the respective measurements of the current real-time thickness values ​​of the signal enhancement device, the filter sharpening device, the morphological processing device and the dynamic analysis device.

4. The intelligent medical optimization system as claimed in claim 3, characterized in that: The thickness sensor device includes a plurality of thickness measuring units, which are used to be respectively connected to the signal enhancement device, the filter sharpening device, the morphological processing device and the dynamic analysis device to complete the respective measurement of the current real-time thickness values ​​of the signal enhancement device, the filter sharpening device, the morphological processing device and the dynamic analysis device, including: the plurality of thickness measuring units are a plurality of thickness sensors, which are used to be respectively connected to the signal enhancement device, the filter sharpening device, the morphological processing device and the dynamic analysis device to complete the respective measurement of the current real-time thickness values ​​of the signal enhancement device, the filter sharpening device, the morphological processing device and the dynamic analysis device.

5. The intelligent medical optimization system according to claim 4, characterized in that: The multiple thickness measurement units are multiple thickness sensors, which are used to be connected to the signal enhancement device, the filter sharpening device, the morphological processing device and the dynamic analysis device respectively, so as to complete the respective measurements of the current real-time thickness values ​​of the signal enhancement device, the filter sharpening device, the morphological processing device and the dynamic analysis device, including: the structures of the multiple thickness sensors are the same.

6. The intelligent medical optimization system according to claim 5, characterized in that: The multiple thickness measurement units are multiple thickness sensors, which are used to be connected to the signal enhancement device, the filter sharpening device, the morphological processing device and the dynamic analysis device respectively to complete the measurement of the current real-time thickness values ​​of the signal enhancement device, the filter sharpening device, the morphological processing device and the dynamic analysis device respectively, and also include: the multiple thickness sensors have the same thickness measurement upper limit value and thickness measurement lower limit value.

7. The intelligent medical optimization system according to any one of claims 3 to 6, characterized in that: The system further comprises: The numerical temporary storage mechanism is respectively connected to the signal enhancement device, the filter sharpening device, the morphological processing device and multiple thickness measurement units of the dynamic analysis device, and is used to temporarily store the input data and output data of the signal enhancement device, the filter sharpening device, the morphological processing device and the dynamic analysis device respectively.

8. The intelligent medical optimization system according to claim 7, characterized in that: The numerical temporary storage mechanism is respectively connected to the signal enhancement device, the filtering and sharpening device, the morphological processing device and multiple thickness measurement units of the dynamic analysis device, and is used to temporarily store the input data and output data of the signal enhancement device, the filtering and sharpening device, the morphological processing device and the dynamic analysis device respectively, including: the numerical temporary storage mechanism is a FLASH flash memory.

9. The intelligent medical optimization system according to claim 7, characterized in that: The numerical temporary storage mechanism is respectively connected to the signal enhancement device, the filter sharpening device, the morphological processing device and multiple thickness measurement units of the dynamic analysis device, and is used to temporarily store the input data and output data of the signal enhancement device, the filter sharpening device, the morphological processing device and the dynamic analysis device respectively. The numerical temporary storage mechanism includes: the MMC storage chip.

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