CN112233072A - Focus detection method and device - Google Patents
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- 238000007781 pre-processing Methods 0.000 claims description 6
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Abstract
The invention relates to the technical field of focus detection, in particular to a focus detection method and a device, comprising the following steps: the method comprises the following steps: acquiring a medical scanning image to be detected; step two: presetting a normal medical scanning image, and determining a contrast model; step three: introducing the medical image obtained by scanning into a comparison model for comparison, and performing framing marking on the focus in an image processing mode; step four: establishing a coordinate system on the medical image, marking the position of the focus through the coordinate system, and estimating the size of the focus; step five: deriving a final result; step six: large data detect lesions. The invention can be used for detecting the focus, can accurately detect the focus conditions of a plurality of parts in the body of a patient, accurately master the position, the size and the shape of the focus, and detect and evaluate the focus through big data.
Description
Technical Field
The invention relates to the technical field of focus detection, in particular to a focus detection method and device.
Background
Computer-aided diagnosis refers to automatically detecting a lesion in a reconstructed image by means of imaging, medical image analysis technology and other possible physiological and biochemical means in combination with analysis and calculation of a computer. The focus refers to a lesion site caused by the action of pathogenic factors on tissues or organs, and is a lesion site on the body. The focus is diagnosed with the assistance of a computer, so that doctors can conveniently find the focus in time and detect and evaluate the focus, the treatment and examination efficiency is improved, and the life of a patient is saved. Meanwhile, the existing big data is developed quickly, the big data can be used for storing focus data, and the big data is applied to focus detection and diagnosis, so that a more accurate focus detection result can be obtained, and meanwhile, various information of the focus can be compared and extracted, so that reference is provided for doctors, and the doctors can conveniently detect and evaluate the focus.
Disclosure of Invention
The invention aims to provide a focus detection method and a focus detection device.
In order to achieve the purpose, the invention adopts the following technical scheme:
provided is a lesion detection method including:
the method comprises the following steps: acquiring a medical scanning image to be detected, scanning to obtain the size of the medical scanning image, scaling the medical scanning image to a preset size according to a proportion, and performing definition adjustment processing on the amplified medical scanning image;
step two: presetting a normal medical scanning image, adjusting the size of the normal medical scanning image according to the medical scanning image of a normal human body, and determining a comparison model;
step three: introducing a medical image obtained by scanning into a comparison model, comparing, setting a difference threshold value for a specific repeated value, extracting a difference value of the repeated value which is higher than the threshold value position, determining the position of a focus, performing frame selection marking on the focus in an image processing mode, and synchronously exporting a marked image;
step four: establishing a coordinate system on the medical image, setting the origin of coordinates of the coordinate system as a constant position, selecting the middle point of a necessary organ structure clearly displayed on the medical image by the human body at the position, marking the position of a focus by the coordinate system to be (x, y), roughly assigning values to the coordinate system according to the proportion, thereby obtaining the area aiming at the medical image, determining the size of a unit length, marking four points marked by a frame shape to be (xn, yn), and calculating and estimating the size of the focus according to the unit length;
step five: exporting the obtained focus position and focus size, marking the result and the medical scanning image, and exporting a final result;
step six: and acquiring big data according to the obtained lesion position and the lesion size, and detecting and reading according to the vgg-19 network so as to acquire a lesion detection result.
Further, the specific working steps in the first step are as follows:
(1) obtaining a medical scanning image to be detected, detecting the length, the width and the height of the medical scanning image, and adjusting according to the preset length, the width and the height so as to obtain a proper medical scanning image;
(2) opening the medical scanning image processed in the step (1) in an image processing module, analyzing the main body type of the original image, setting the original definition according to the main body type, correcting the original image, acquiring the breadth size and resolution ratio parameters of the corrected original image, and selecting a radius value according to the breadth size and resolution ratio parameters of the original image to perform definition enhancement processing;
(3) and acquiring the processed medical scanning image, performing noise reduction processing, and reconstructing image signals of a high-frequency image layer and a low-frequency image layer by using multi-scale two-dimensional wavelets to obtain a new clear image.
Further, the third step includes the following steps:
1) extracting medical images obtained by scanning, simultaneously extracting preset normal medical scanning images, and performing overlapping covering treatment on the two images according to the structure of a human body;
2) regionalization is carried out according to the human body structure, the detection regions are divided into a plurality of detection regions, images in the regions are respectively compared, a difference value is determined in an image processing mode, a difference value threshold value a is preset, the difference values of different regions are extracted to obtain a difference value a1, when a1 is smaller than a, a focus is determined to be absent, otherwise, the focus is determined to be present;
3) and determining the position of the focus to obtain the rough number of the focus and the position of the focus.
Further, the third step further includes the following steps:
1) acquiring superposed images, determining repeated deviation by a parallel scanning mode, and scanning four times from the upper direction, the lower direction, the left direction and the right direction;
2) scanning from the upper side, establishing a repeated deviation threshold value, stopping scanning when the repeated deviation value of the scanning position is larger than the threshold value, and simultaneously setting a stop line K1 at the stop position;
3) scanning from the left side, establishing a repeated deviation threshold value, stopping scanning when the repeated deviation value of the scanning position is larger than the threshold value, and simultaneously setting a stop line K2 at the stop position;
4) scanning is carried out from the right side, a repeated deviation threshold value is established, when the repeated deviation value of the scanning position is larger than the threshold value, the scanning is stopped, and a stop line K3 is arranged at the stop position;
5) scanning is performed from the lower side, a repeated deviation threshold value is established, when the repeated deviation value of the scanning position is larger than the threshold value, the scanning is stopped, and a stop line K4 is arranged at the stop position;
6) and marking the mutual crossing points of K1, K2, K3 and K4 as a, b, c and d respectively, and connecting a, b, c and d adjacently to obtain the frame selection frame.
Further, the fourth step includes the following steps:
1) setting a coordinate system, presetting the origin of coordinates, selecting the middle point of a necessary organ structure of which the human body is clearly displayed on the medical image at the position, synchronously carrying out rough estimation on the unit length in the coordinate system, and then carrying out assignment;
2) determining coordinate system positions of a, b, c and d, namely (xa, ya), (xb, yb), (xc, yc) and (xd, yd), respectively, according to the coordinate system principle, wherein xa is equal to xd, ya is equal to yb, xb is equal to xc, yc is equal to yd, and obtaining the distance among a, b, c and d, thereby obtaining the length, width and height of the frame;
3) extracting the ratio of the focus in the frame-shaped block so as to estimate the size of the focus;
4) meanwhile, the position of the focus can be further accurately determined according to the coordinate system positions of a, b, c and d.
A lesion detection apparatus:
the image acquisition module is used for acquiring a medical scanning image to be detected;
the preprocessing module is used for preprocessing the medical scanning image and adjusting the size of the medical scanning image;
the contrast module is used for inputting the medical scanning image into a preset normal image superposition contrast model, carrying out regional processing on the superposed image, and detecting the difference value of different regions, so as to roughly detect the position and the size of a focus and mark the focus by using a frame type frame;
the processing module is used for setting a coordinate system to further accurately detect the position and the size of the focus, comparing and checking the position and the size of the focus with a rough result, and finally deriving the position and the size of the focus;
and the big data detection module is used for comparing the position, the size and the shape of the focus with the big data so as to realize the detection effect on the focus.
The invention has the beneficial effects that:
1. according to the invention, the medical scanning image is obtained, the size and definition of the medical scanning image are adjusted, so that the subsequent coincidence comparison with the normal scanning image is facilitated, the focus position, the focus size and the focus shape can be preliminarily confirmed by means of coincidence comparison detection difference, meanwhile, the coincidence image is obtained, and the coincidence image is subjected to subsequent processing;
2. the invention detects the focus by establishing a coordinate system, adds a frame-shaped frame mark to the focus by a frame-selecting mark mode, realizes the determination of the position and the size by utilizing the coordinate system, can estimate the size of the focus by utilizing the proportion of the focus in the frame-shaped frame, realizes the accurate detection of the focus information, and simultaneously carries out the comparison calibration of the accurate detection result and the rough detection result or the final result;
3. the invention also utilizes big data to detect and evaluate the focus, repeatedly compares, can provide doctors with similar types of the focus, and leads the doctors to quickly diagnose the focus.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments of the present invention will be briefly described below.
FIG. 1 is a block diagram of the steps of the present invention;
fig. 2 is a schematic view of a lesion detection apparatus according to the present invention.
Detailed Description
The technical scheme of the invention is further explained by the specific implementation mode in combination with the attached drawings.
The drawings are only for purposes of illustration and are not intended to be limiting, and are merely schematic and non-limiting.
A lesion detection method comprising the steps of:
the method comprises the following steps: acquiring a medical scanning image to be detected, scanning to obtain the size of the medical scanning image, scaling the medical scanning image to a preset size according to a proportion, and performing definition adjustment processing on the amplified medical scanning image;
step two: presetting a normal medical scanning image, adjusting the size of the normal medical scanning image according to the medical scanning image of a normal human body, and determining a comparison model;
step three: introducing a medical image obtained by scanning into a comparison model, comparing, setting a difference threshold value for a specific repeated value, extracting a difference value of the repeated value which is higher than the threshold value position, determining the position of a focus, performing frame selection marking on the focus in an image processing mode, and synchronously exporting a marked image;
step four: establishing a coordinate system on the medical image, setting the origin of coordinates of the coordinate system as a constant position, selecting the middle point of a necessary organ structure clearly displayed on the medical image by the human body at the position, marking the position of a focus by the coordinate system to be (x, y), roughly assigning values to the coordinate system according to the proportion, thereby obtaining the area aiming at the medical image, determining the size of a unit length, marking four points marked by a frame shape to be (xn, yn), and calculating and estimating the size of the focus according to the unit length;
step five: exporting the obtained focus position and focus size, marking the result and the medical scanning image, and exporting a final result;
step six: and acquiring big data according to the obtained lesion position and the lesion size, and detecting and reading according to the vgg-19 network so as to acquire a lesion detection result.
The specific working steps in the step one are as follows:
(1) obtaining a medical scanning image to be detected, detecting the length, the width and the height of the medical scanning image, and adjusting according to the preset length, the width and the height so as to obtain a proper medical scanning image;
(2) opening the medical scanning image processed in the step (1) in an image processing module, analyzing the main body type of the original image, setting the original definition according to the main body type, correcting the original image, acquiring the breadth size and resolution ratio parameters of the corrected original image, and selecting a radius value according to the breadth size and resolution ratio parameters of the original image to perform definition enhancement processing;
(3) and acquiring the processed medical scanning image, performing noise reduction processing, and reconstructing image signals of a high-frequency image layer and a low-frequency image layer by using multi-scale two-dimensional wavelets to obtain a new clear image.
Wherein, the third step comprises the following steps:
1) extracting medical images obtained by scanning, simultaneously extracting preset normal medical scanning images, and performing overlapping covering treatment on the two images according to the structure of a human body;
2) regionalization is carried out according to the human body structure, the detection regions are divided into a plurality of detection regions, images in the regions are respectively compared, a difference value is determined in an image processing mode, a difference value threshold value a is preset, the difference values of different regions are extracted to obtain a difference value a1, when a1 is smaller than a, a focus is determined to be absent, otherwise, the focus is determined to be present;
3) and determining the position of the focus to obtain the rough number of the focus and the position of the focus.
Wherein, the third step also comprises the following steps:
1) acquiring superposed images, determining repeated deviation by a parallel scanning mode, and scanning four times from the upper direction, the lower direction, the left direction and the right direction;
2) scanning from the upper side, establishing a repeated deviation threshold value, stopping scanning when the repeated deviation value of the scanning position is larger than the threshold value, and simultaneously setting a stop line K1 at the stop position;
3) scanning from the left side, establishing a repeated deviation threshold value, stopping scanning when the repeated deviation value of the scanning position is larger than the threshold value, and simultaneously setting a stop line K2 at the stop position;
4) scanning is carried out from the right side, a repeated deviation threshold value is established, when the repeated deviation value of the scanning position is larger than the threshold value, the scanning is stopped, and a stop line K3 is arranged at the stop position;
5) scanning is performed from the lower side, a repeated deviation threshold value is established, when the repeated deviation value of the scanning position is larger than the threshold value, the scanning is stopped, and a stop line K4 is arranged at the stop position;
6) and marking the mutual crossing points of K1, K2, K3 and K4 as a, b, c and d respectively, and connecting a, b, c and d adjacently to obtain the frame selection frame.
Wherein, the step four includes the following steps:
1) setting a coordinate system, presetting the origin of coordinates, selecting the middle point of a necessary organ structure of which the human body is clearly displayed on the medical image at the position, synchronously carrying out rough estimation on the unit length in the coordinate system, and then carrying out assignment;
2) determining coordinate system positions of a, b, c and d, namely (xa, ya), (xb, yb), (xc, yc) and (xd, yd), respectively, according to the coordinate system principle, wherein xa is equal to xd, ya is equal to yb, xb is equal to xc, yc is equal to yd, and obtaining the distance among a, b, c and d, thereby obtaining the length, width and height of the frame;
3) extracting the ratio of the focus in the frame-shaped block so as to estimate the size of the focus;
4) meanwhile, the position of the focus can be further accurately determined according to the coordinate system positions of a, b, c and d.
Referring to fig. 2, a lesion detection apparatus includes:
the image acquisition module is used for acquiring a medical scanning image to be detected;
the preprocessing module is used for preprocessing the medical scanning image and adjusting the size of the medical scanning image;
the contrast module is used for inputting the medical scanning image into a preset normal image superposition contrast model, carrying out regional processing on the superposed image, and detecting the difference value of different regions, so as to roughly detect the position and the size of a focus and mark the focus by using a frame type frame;
the processing module is used for setting a coordinate system to further accurately detect the position and the size of the focus, comparing and checking the position and the size of the focus with a rough result, and finally deriving the position and the size of the focus;
and the big data detection module is used for comparing the position, the size and the shape of the focus with the big data so as to realize the detection effect on the focus.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (6)
1. A lesion detection method is characterized by comprising the following steps:
the method comprises the following steps: acquiring a medical scanning image to be detected, scanning to obtain the size of the medical scanning image, scaling the medical scanning image to a preset size according to a proportion, and performing definition adjustment processing on the amplified medical scanning image;
step two: presetting a normal medical scanning image, adjusting the size of the normal medical scanning image according to the medical scanning image of a normal human body, and determining a comparison model;
step three: introducing a medical image obtained by scanning into a comparison model, comparing, setting a difference threshold value for a specific repeated value, extracting a difference value of the repeated value which is higher than the threshold value position, determining the position of a focus, performing frame selection marking on the focus in an image processing mode, and synchronously exporting a marked image;
step four: establishing a coordinate system on the medical image, setting the origin of coordinates of the coordinate system as a constant position, selecting the middle point of a necessary organ structure clearly displayed on the medical image by the human body at the position, marking the position of a focus by the coordinate system to be (x, y), roughly assigning values to the coordinate system according to the proportion, thereby obtaining the area aiming at the medical image, determining the size of a unit length, marking four points marked by a frame shape to be (xn, yn), and calculating and estimating the size of the focus according to the unit length;
step five: exporting the obtained focus position and focus size, marking the result and the medical scanning image, and exporting a final result;
step six: and acquiring big data according to the obtained lesion position and the lesion size, and detecting and reading according to the vgg-19 network so as to acquire a lesion detection result.
2. The method for detecting a lesion according to claim 1, wherein the specific working steps in the first step are as follows:
(1) obtaining a medical scanning image to be detected, detecting the length, the width and the height of the medical scanning image, and adjusting according to the preset length, the width and the height so as to obtain a proper medical scanning image;
(2) opening the medical scanning image processed in the step (1) in an image processing module, analyzing the main body type of the original image, setting the original definition according to the main body type, correcting the original image, acquiring the breadth size and resolution ratio parameters of the corrected original image, and selecting a radius value according to the breadth size and resolution ratio parameters of the original image to perform definition enhancement processing;
(3) and acquiring the processed medical scanning image, performing noise reduction processing, and reconstructing image signals of a high-frequency image layer and a low-frequency image layer by using multi-scale two-dimensional wavelets to obtain a new clear image.
3. The method for detecting a lesion according to claim 1, wherein the third step comprises the steps of:
1) extracting medical images obtained by scanning, simultaneously extracting preset normal medical scanning images, and performing overlapping covering treatment on the two images according to the structure of a human body;
2) regionalization is carried out according to the human body structure, the detection regions are divided into a plurality of detection regions, images in the regions are respectively compared, a difference value is determined in an image processing mode, a difference value threshold value a is preset, the difference values of different regions are extracted to obtain a difference value a1, when a1 is smaller than a, a focus is determined to be absent, otherwise, the focus is determined to be present;
3) and determining the position of the focus to obtain the rough number of the focus and the position of the focus.
4. The method for detecting lesions according to claim 1, wherein said step three further comprises the steps of:
1) acquiring superposed images, determining repeated deviation by a parallel scanning mode, and scanning four times from the upper direction, the lower direction, the left direction and the right direction;
2) scanning from the upper side, establishing a repeated deviation threshold value, stopping scanning when the repeated deviation value of the scanning position is larger than the threshold value, and simultaneously setting a stop line K1 at the stop position;
3) scanning from the left side, establishing a repeated deviation threshold value, stopping scanning when the repeated deviation value of the scanning position is larger than the threshold value, and simultaneously setting a stop line K2 at the stop position;
4) scanning is carried out from the right side, a repeated deviation threshold value is established, when the repeated deviation value of the scanning position is larger than the threshold value, the scanning is stopped, and a stop line K3 is arranged at the stop position;
5) scanning is performed from the lower side, a repeated deviation threshold value is established, when the repeated deviation value of the scanning position is larger than the threshold value, the scanning is stopped, and a stop line K4 is arranged at the stop position;
6) and marking the mutual crossing points of K1, K2, K3 and K4 as a, b, c and d respectively, and connecting a, b, c and d adjacently to obtain the frame selection frame.
5. The method for detecting lesions according to claim 1, wherein said step four comprises the steps of:
1) setting a coordinate system, presetting the origin of coordinates, selecting the middle point of a necessary organ structure of which the human body is clearly displayed on the medical image at the position, synchronously carrying out rough estimation on the unit length in the coordinate system, and then carrying out assignment;
2) determining coordinate system positions of a, b, c and d, namely (xa, ya), (xb, yb), (xc, yc) and (xd, yd), respectively, according to the coordinate system principle, wherein xa is equal to xd, ya is equal to yb, xb is equal to xc, yc is equal to yd, and obtaining the distance among a, b, c and d, thereby obtaining the length, width and height of the frame;
3) extracting the ratio of the focus in the frame-shaped block so as to estimate the size of the focus;
4) meanwhile, the position of the focus can be further accurately determined according to the coordinate system positions of a, b, c and d.
6. A lesion detection apparatus, comprising:
the image acquisition module is used for acquiring a medical scanning image to be detected;
the preprocessing module is used for preprocessing the medical scanning image and adjusting the size of the medical scanning image;
the contrast module is used for inputting the medical scanning image into a preset normal image superposition contrast model, carrying out regional processing on the superposed image, and detecting the difference value of different regions, so as to roughly detect the position and the size of a focus and mark the focus by using a frame type frame;
the processing module is used for setting a coordinate system to further accurately detect the position and the size of the focus, comparing and checking the position and the size of the focus with a rough result, and finally deriving the position and the size of the focus;
and the big data detection module is used for comparing the position, the size and the shape of the focus with the big data so as to realize the detection effect on the focus.
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| CN202011057924.7A CN112233072A (en) | 2020-09-30 | 2020-09-30 | Focus detection method and device |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113724243A (en) * | 2021-09-14 | 2021-11-30 | 首都医科大学附属北京天坛医院 | Image processing method, image processing device, electronic equipment and storage medium |
| CN114119491A (en) * | 2021-10-29 | 2022-03-01 | 吉林医药学院 | Data processing system based on medical image analysis |
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2020
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113724243A (en) * | 2021-09-14 | 2021-11-30 | 首都医科大学附属北京天坛医院 | Image processing method, image processing device, electronic equipment and storage medium |
| CN113724243B (en) * | 2021-09-14 | 2022-12-09 | 首都医科大学附属北京天坛医院 | Image processing method, image processing device, electronic equipment and storage medium |
| CN114119491A (en) * | 2021-10-29 | 2022-03-01 | 吉林医药学院 | Data processing system based on medical image analysis |
| CN114119491B (en) * | 2021-10-29 | 2022-09-13 | 吉林医药学院 | Data processing system based on medical image analysis |
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