CN111915523A - Self-adaptive adjustment method and system for DR image brightness - Google Patents
Self-adaptive adjustment method and system for DR image brightness Download PDFInfo
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Abstract
The embodiment of the invention discloses a DR image brightness self-adaptive adjusting method and a system, wherein the method comprises the following steps: extracting a tissue region: segmenting an image I to be adjusted, and extracting a binary image I of a tissue regionb(ii) a And a brightness calculation step: calculating the brightness of the tissue region; and an adjustment coefficient calculation step: calculating a brightness adjustment coefficient s; a mapping table calculation step: calculating a brightness adjustment mapping table according to the brightness adjustment coefficient s; and the brightness adjusting step is used for carrying out brightness adjusting mapping transformation on the image I to be adjusted and adjusting the brightness of the image. The method comprises the steps of firstly, automatically extracting an organization area to calculate and obtain the brightness of the organization area, then, adaptively calculating a corresponding brightness adjustment coefficient according to the brightness of the organization, and finally, executing brightness adjustment to realize the appropriate and stable image brightness; the invention does not need to additionally increase hardware components and has good economical efficiency; meanwhile, the whole brightness adjustment process is carried out in a self-adaptive mode, manual intervention is not needed, and the workload of doctors is saved.
Description
Technical Field
The invention relates to the technical field of image diagnosis, in particular to a DR image brightness self-adaptive adjusting method and system.
Background
The DR (Digital Radiography) system uses X-rays as an information carrier, and obtains human tissue state information by using physical characteristics of the X-rays, namely attenuation of different intensities caused by tissue density difference when penetrating a human body, so as to provide medical diagnosis. As a medical image diagnosis apparatus, the quality of an image output from a DR system objectively determines the accuracy of medical diagnosis. Therefore, any factor that may affect the image quality must be carefully considered. For medical images, the quality is usually expressed in terms of brightness, contrast, sharpness, signal-to-noise ratio, etc. The image brightness is an important response of image information output, is one of the most direct and sensitive aspects of human eyes, and whether the image brightness is suitable or not only affects the viewing experience of a user, but also has a critical influence on a diagnosis result.
In clinical photography, the amount of exposure radiation and the attenuation intensity of X-rays of different patient body types directly determine the intensity of an original signal received by a DR system detector, thereby indirectly determining the brightness of an obtained image. Therefore, to acquire a DR image with appropriate brightness, a photographer is often required to be able to accurately grasp the exposure dose according to the body type of the patient. In practice, however, such control is difficult to grasp. Either the dosage is too high and the image is dark, or the dosage is too low and the image is bright, so that repeated exposure is often needed, the workload of a doctor is increased, and the absorption of the patient to rays is increased invisibly, and the secondary injury of the patient is caused.
For DR image brightness control, two types of schemes are currently in common use. One is to realize image brightness stabilization by automatically controlling Exposure with the aid of an AEC (Automatic Exposure Control) hardware module. The other is to apply image processing technology to adjust the image brightness, such as the commonly used Gamma brightness correction. From the practical application, the former scheme basically does not need additional manual intervention, and has the advantages of simple operation and good image effect, but the former scheme is not friendly in economy due to the additional hardware component. The latter scheme is a pure image processing technology, does not depend on additional hardware, and is good in economy, however, in terms of the current common implementation mode, an equal-strength brightness adjustment mode is generally adopted, so that processing parameters are frequently and manually adjusted, and the workload of doctors is increased.
The invention is a deep neural network scheme, which is called 'a medical image brightness homogenization correction method' with the application number of 201910683490.2, and is characterized in that a countermeasure network is generated by constructing a GAN, a representative labeled brightness defect data set is used for training, the characteristic mapping from defect data to normal data is obtained, and finally the brightness correction of the brightness nonuniform image is realized. The scheme has large dependence on the data set, and the data set is good and bad in manufacturing, including the marking accuracy, the data volume and the like, which directly influence the precision performance of the model. On the other hand, the neural network model is usually large in calculation amount, high in requirement on hardware resources and not beneficial to clinical application.
Disclosure of Invention
The technical problem to be solved by the embodiments of the present invention is to provide a method and a system for adaptively adjusting the brightness of a DR image, so as to achieve a suitable and stable image brightness.
In order to solve the above technical problem, an embodiment of the present invention provides a method for adaptively adjusting brightness of a DR image, including:
extracting a tissue region: utilizing a maximum inter-class variance method to segment the image I to be adjusted and extracting a binary image I of the tissue regionb；
And a brightness calculation step: binary image I from tissue regionsbCalculating a tissue area normalization gray level mean value b, and taking the gray level mean value b as tissue area brightness;
and an adjustment coefficient calculation step: calculating a brightness adjustment coefficient s according to the brightness of the tissue region;
a mapping table calculation step: calculating a brightness adjustment mapping table according to the brightness adjustment coefficient s;
and a brightness adjusting step: and according to the brightness adjustment mapping table, performing brightness adjustment mapping transformation on the image I to be adjusted to adjust the brightness of the image.
Further, in the luminance calculating step, the gray level mean value b is calculated by using the following formula:
wherein, H and W are respectively the height and width of the image to be adjusted, and a is a scale factor corresponding to the image gray scale.
Further, a takes the following formula:
further, in the adjustment coefficient calculating step, the luminance adjustment coefficient s is calculated by the following formula:
s＝Cgain·b+Coffset；
wherein, CgainAnd CoffsetRespectively a luminance gain factor and a luminance offset factor.
Further, in the map calculation step, the brightness adjustment map is calculated by the following formula:
where k is the input gray level value, T (k) is the output gray level value, ImaxAnd IminRespectively the maximum value and the minimum value of the image I to be adjusted.
Correspondingly, the embodiment of the invention also provides a DR image brightness self-adaptive adjusting system, which comprises:
an extract tissue region module: utilizing a maximum inter-class variance method to segment the image I to be adjusted and extracting a binary image I of the tissue regionb；
A brightness calculation module: binary image I from tissue regionsbCalculating a tissue area normalization gray level mean value b, and taking the gray level mean value b as tissue area brightness;
an adjustment coefficient calculation module: calculating a brightness adjustment coefficient s according to the brightness of the tissue region;
a mapping table calculation module: calculating a brightness adjustment mapping table according to the brightness adjustment coefficient s;
a brightness adjusting module: and according to the brightness adjustment mapping table, performing brightness adjustment mapping transformation on the image I to be adjusted to adjust the brightness of the image.
Further, in the luminance calculating module, the gray level mean value b is calculated by using the following formula:
wherein, H and W are respectively the height and width of the image to be adjusted, and a is a scale factor corresponding to the image gray scale.
Further, a takes the following formula:
further, in the adjustment coefficient calculating module, the brightness adjustment coefficient s is calculated by the following formula:
s＝Cgain·b+Coffset；
wherein, CgainAnd CoffsetRespectively a luminance gain factor and a luminance offset factor.
Further, in the mapping table calculating module, the brightness adjustment mapping table is calculated according to the following formula:
where k is the input gray level value, T (k) is the output gray level value, ImaxAnd IminRespectively the maximum value and the minimum value of the image I to be adjusted.
The invention has the beneficial effects that: the invention firstly calculates and acquires the brightness of the tissue area by automatically extracting the tissue area, then adaptively calculates the corresponding brightness adjustment coefficient according to the tissue brightness, and finally executes brightness adjustment to realize the proper and stable image brightness. As a pure image processing technical scheme, the method does not need to additionally increase hardware components, and is good in economy. Meanwhile, the whole brightness adjustment process is carried out in a self-adaptive mode, manual intervention is not needed, and the workload of doctors is saved.
Drawings
Fig. 1 is a schematic flow chart of a DR image brightness adaptive adjustment method according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of a DR image brightness adaptive adjustment system according to an embodiment of the present invention.
FIG. 3 is a comparison diagram of the adjustment effect of the DR image according to the embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application can be combined with each other without conflict, and the present invention is further described in detail with reference to the drawings and specific embodiments.
If directional indications (such as up, down, left, right, front, and rear … …) are provided in the embodiment of the present invention, the directional indications are only used to explain the relative position relationship between the components, the movement, etc. in a specific posture (as shown in the drawing), and if the specific posture is changed, the directional indications are changed accordingly.
In addition, the descriptions related to "first", "second", etc. in the present invention are only used for descriptive purposes and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature.
Referring to fig. 1, the method for adaptively adjusting the brightness of a DR image according to an embodiment of the present invention includes a tissue region extracting step, a brightness calculating step, an adjustment coefficient calculating step, a mapping table calculating step, and a brightness adjusting step.
Extracting a tissue region: utilizing a maximum inter-class variance method to segment the image I to be adjusted and extracting a binary image I of the tissue regionb：
Wherein omegatAnd ΩbRepresenting the tissue region and the background region, respectively.
And a brightness calculation step: binary image I from tissue regionsbAnd calculating a tissue area normalized gray level mean value b, and taking the gray level mean value b as the tissue area brightness.
And an adjustment coefficient calculation step: and calculating a brightness adjusting coefficient s according to the brightness of the tissue region.
A mapping table calculation step: and calculating a brightness adjustment mapping table according to the brightness adjustment coefficient s.
And a brightness adjusting step: and according to the brightness adjustment mapping table, performing brightness adjustment mapping transformation on the image I to be adjusted, and adjusting the brightness of the image to enable the brightness of the image to reach a proper level.
The embodiment of the invention is based on tissue region extraction, automatically calculates tissue brightness, replaces human eye observation, and is more objective and accurate; the brightness adjustment coefficient is calculated in a self-adaptive manner through the acquired tissue brightness, manual intervention is not needed, and labor is saved; the brightness adjustment mapping is carried out in a mapping table mode, and the execution efficiency is high.
In one embodiment, in the luminance calculating step, the gray-scale average value b is calculated by using the following formula:
h and W are respectively the height and width of the image I to be adjusted, and a is a scale factor corresponding to the image gray scale.
As an embodiment, a takes the following value:
in one embodiment, the adjustment coefficient calculating step calculates the luminance adjustment coefficient s by the following equation:
s＝Cgain·b+Coffset；
wherein, CgainAnd CoffsetRespectively a luminance gain factor and a luminance offset factor. CgainAnd CoffsetThe value of (A) generally needs to be determined by a large number of experiments, such as [ C ] as a referencegain,Coffset]＝[0.6329,-0.5759]。
In one embodiment, in the map calculation step, the brightness adjustment map is calculated by the following formula:
where k is the input gray level value, T (k) is the output gray level value, Imax、IminRespectively, the maximum value and the minimum value of the image I to be adjusted.
Referring to fig. 2, the DR image brightness adaptive adjustment system according to the embodiment of the present invention includes an organization region extracting module, a brightness calculating module, an adjustment coefficient calculating module, a mapping table calculating module, and a brightness adjustment module.
An extract tissue region module: utilizing a maximum inter-class variance method to segment the image I to be adjusted and extracting a binary image I of the tissue regionb：
Wherein omegatAnd ΩbRepresenting the tissue region and the background region, respectively.
A brightness calculation module: binary image I from tissue regionsbAnd calculating a tissue area normalized gray level mean value b, and taking the gray level mean value b as the tissue area brightness.
An adjustment coefficient calculation module: and calculating a brightness adjusting coefficient s according to the brightness of the tissue region.
A mapping table calculation module: and calculating a brightness adjustment mapping table according to the brightness adjustment coefficient s.
A brightness adjusting module: and according to the brightness adjustment mapping table, performing brightness adjustment mapping transformation on the image I to be adjusted to adjust the brightness of the image.
As an embodiment, in the luminance calculating module, the gray level average value b is calculated by using the following formula:
wherein, H and W are respectively the height and width of the image to be adjusted, and a is a scale factor corresponding to the image gray scale.
As an embodiment, a takes the following value:
in one embodiment, the adjustment coefficient calculating module calculates the brightness adjustment coefficient s by the following formula:
s＝Cgain·b+Coffset；
wherein, CgainAnd CoffsetRespectively a luminance gain factor and a luminance offset factor.
As an embodiment, in the mapping table calculating module, the brightness adjustment mapping table is calculated by the following formula:
where k is the input gray level value, T (k) is the output gray level value, ImaxAnd IminRespectively the maximum value and the minimum value of the image I to be adjusted.
The DR image has the characteristic of high gray scale, namely the gray scale is generally above 4096(12 bits), even 65536(16 bits), while the gray scale displayed by a common display is generally only 256(8 bits), and the gray scale displayed by a medical professional display can reach 65536(16 bits). Referring to fig. 3, when a common display is used, image gray scales need to be compressed, including linear compression and non-linear compression, and whatever compression method is used, the relative brightness of the image rather than the absolute brightness of the image is dominant to the brightness of the image displayed on the final display. For the medical professional display, the display gray scale of the display can reach the image gray scale, so that the image gray scale does not need to be compressed and generally needs to be subjected to nonlinear transformation, and as a result, the relative brightness of the image is still the main factor for determining the final display brightness.
Next, the absolute brightness of an image generally refers to the gray scale size of the image, i.e., if the gray scale is large, the absolute brightness is high, and if the gray scale is small, the absolute brightness is low. The relative brightness refers to the relative distribution of different gray levels of an image, and in the same gray level interval, the higher the high gray level ratio is, the higher the overall brightness of the image is, otherwise, the lower the overall brightness of the image is. The absolute brightness of the image is affected by the display gray scale compression, while the relative brightness is relatively stable. Therefore, adjusting the brightness of an image is essentially adjusting the relative distribution of high and low gray levels of the image, and this adjustment, in itself, means a non-linear transformation process.
The method comprises the steps of firstly calculating the tissue brightness, then determining whether adjustment is needed or not according to the obtained image brightness, and determining how much intensity adjustment is needed, namely, adaptively calculating an adjustment coefficient; and finally, according to the obtained brightness adjustment coefficient, carrying out nonlinear gray level conversion on the image, and adjusting the relative distribution of high and low gray levels of the image to fulfill the aim of adjusting the brightness of the image.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. A DR image brightness adaptive adjustment method is characterized by comprising the following steps:
extracting a tissue region: utilizing a maximum inter-class variance method to segment the image I to be adjusted and extracting a binary image I of the tissue regionb；
And a brightness calculation step: binary image I from tissue regionsbCalculating a tissue area normalization gray level mean value b, and taking the gray level mean value b as tissue area brightness;
and an adjustment coefficient calculation step: calculating a brightness adjustment coefficient s according to the brightness of the tissue region;
a mapping table calculation step: calculating a brightness adjustment mapping table according to the brightness adjustment coefficient s;
and a brightness adjusting step: and according to the brightness adjustment mapping table, performing brightness adjustment mapping transformation on the image I to be adjusted to adjust the brightness of the image.
2. The adaptive brightness adjustment method for DR images according to claim 1, wherein in the brightness calculation step, the gray level mean value b is calculated by using the following formula:
wherein, H and W are respectively the height and width of the image to be adjusted, and a is a scale factor corresponding to the image gray scale.
3. The adaptive method for adjusting the brightness of a DR image of claim 2, wherein a is expressed as follows:
4. the adaptive brightness adjustment method for DR image as claimed in claim 1, wherein the adjustment coefficient calculating step calculates the brightness adjustment coefficient s by the following formula:
s＝Cgain·b+Coffset；
wherein, CgainAnd CoffsetRespectively a luminance gain factor and a luminance offset factor.
5. The adaptive brightness adjustment method for DR image of claim 1 wherein the mapping table calculating step calculates the brightness adjustment mapping table by the following formula:
where k is the input gray level value, T (k) is the output gray level value, ImaxAnd IminRespectively the maximum value and the minimum value of the image I to be adjusted.
6. A DR image brightness adaptive adjustment system is characterized by comprising:
an extract tissue region module: utilizing a maximum inter-class variance method to segment the image I to be adjusted and extracting a binary image I of the tissue regionb；
A brightness calculation module: binary image I from tissue regionsbCalculating a tissue area normalization gray level mean value b, and taking the gray level mean value b as tissue area brightness;
an adjustment coefficient calculation module: calculating a brightness adjustment coefficient s according to the brightness of the tissue region;
a mapping table calculation module: calculating a brightness adjustment mapping table according to the brightness adjustment coefficient s;
a brightness adjusting module: and according to the brightness adjustment mapping table, performing brightness adjustment mapping transformation on the image I to be adjusted to adjust the brightness of the image.
7. The adaptive brightness adjustment system for DR images of claim 6, wherein the brightness calculation module calculates the average gray scale value b by using the following formula:
wherein, H and W are respectively the height and width of the image to be adjusted, and a is a scale factor corresponding to the image gray scale.
8. The adaptive brightness adjustment system for DR images of claim 7 wherein a is given by the following formula:
9. the adaptive brightness adjustment system for DR images according to claim 6, wherein the adjustment coefficient calculating module calculates the brightness adjustment coefficient s by the following formula:
s＝Cgain·b+Coffset；
wherein, CgainAnd CoffsetRespectively a luminance gain factor and a luminance offset factor.
10. The adaptive brightness adjustment system for DR images according to claim 6, wherein said mapping table calculating module calculates the brightness adjustment mapping table by the following formula:
where k is the input gray level value, T (k) is the output gray level value, ImaxAnd IminRespectively the maximum value and the minimum value of the image I to be adjusted.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113448557A (en) * | 2021-06-30 | 2021-09-28 | 深圳市多米玩科技有限公司 | Game development method based on HTML5 |
| CN116777795A (en) * | 2023-08-21 | 2023-09-19 | 江苏游隼微电子有限公司 | Luminance mapping method suitable for vehicle-mounted image |
| CN117252783A (en) * | 2023-11-06 | 2023-12-19 | 上海为旌科技有限公司 | Definition computing method, device and equipment for defocus blurred image |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102169580A (en) * | 2011-04-08 | 2011-08-31 | 中国船舶重工集团公司第七○二研究所 | Self-adaptive image processing method utilizing image statistic characteristics |
| CN104243824A (en) * | 2014-09-19 | 2014-12-24 | 深圳市安健科技有限公司 | Method and system for nonlinearly adjusting image brightness |
| CN106683061A (en) * | 2017-01-05 | 2017-05-17 | 南京觅踪电子科技有限公司 | Method for enhancing medical image based on corrected multi-scale retinex algorithm |
| CN108447058A (en) * | 2018-03-30 | 2018-08-24 | 北京理工大学 | A kind of image quality evaluating method and system |
| CN108711140A (en) * | 2018-05-16 | 2018-10-26 | 广东欧谱曼迪科技有限公司 | A kind of image brightness uniformity real-time recovery method based on inter-class variance description |
| CN109410126A (en) * | 2017-08-30 | 2019-03-01 | 中山大学 | A kind of tone mapping method of details enhancing and the adaptive high dynamic range images of brightness |
| US20190237001A1 (en) * | 2018-01-30 | 2019-08-01 | Apple Inc. | Applying gain and offset correction factors for pixel uniformity compensation in display panels |
| CN110570384A (en) * | 2019-09-16 | 2019-12-13 | 西南科技大学 | method and device for carrying out illumination equalization processing on scene image, computer equipment and computer storage medium |
| CN111161185A (en) * | 2019-12-30 | 2020-05-15 | 深圳蓝韵医学影像有限公司 | Method and system for continuously adjusting X-ray image |
-
2020
- 2020-08-04 CN CN202010772404.8A patent/CN111915523B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102169580A (en) * | 2011-04-08 | 2011-08-31 | 中国船舶重工集团公司第七○二研究所 | Self-adaptive image processing method utilizing image statistic characteristics |
| CN104243824A (en) * | 2014-09-19 | 2014-12-24 | 深圳市安健科技有限公司 | Method and system for nonlinearly adjusting image brightness |
| CN106683061A (en) * | 2017-01-05 | 2017-05-17 | 南京觅踪电子科技有限公司 | Method for enhancing medical image based on corrected multi-scale retinex algorithm |
| CN109410126A (en) * | 2017-08-30 | 2019-03-01 | 中山大学 | A kind of tone mapping method of details enhancing and the adaptive high dynamic range images of brightness |
| US20190237001A1 (en) * | 2018-01-30 | 2019-08-01 | Apple Inc. | Applying gain and offset correction factors for pixel uniformity compensation in display panels |
| CN108447058A (en) * | 2018-03-30 | 2018-08-24 | 北京理工大学 | A kind of image quality evaluating method and system |
| CN108711140A (en) * | 2018-05-16 | 2018-10-26 | 广东欧谱曼迪科技有限公司 | A kind of image brightness uniformity real-time recovery method based on inter-class variance description |
| CN110570384A (en) * | 2019-09-16 | 2019-12-13 | 西南科技大学 | method and device for carrying out illumination equalization processing on scene image, computer equipment and computer storage medium |
| CN111161185A (en) * | 2019-12-30 | 2020-05-15 | 深圳蓝韵医学影像有限公司 | Method and system for continuously adjusting X-ray image |
Non-Patent Citations (1)
| Title |
|---|
| 刘红;乐建威;: "DR图像均衡处理的研究", 中国医疗器械杂志, no. 03, 30 May 2007 (2007-05-30) * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113448557A (en) * | 2021-06-30 | 2021-09-28 | 深圳市多米玩科技有限公司 | Game development method based on HTML5 |
| CN113448557B (en) * | 2021-06-30 | 2022-06-17 | 深圳淘乐网络科技有限公司 | Game development method based on HTML5 |
| CN116777795A (en) * | 2023-08-21 | 2023-09-19 | 江苏游隼微电子有限公司 | Luminance mapping method suitable for vehicle-mounted image |
| CN116777795B (en) * | 2023-08-21 | 2023-10-27 | 江苏游隼微电子有限公司 | Luminance mapping method suitable for vehicle-mounted image |
| CN117252783A (en) * | 2023-11-06 | 2023-12-19 | 上海为旌科技有限公司 | Definition computing method, device and equipment for defocus blurred image |
| CN117252783B (en) * | 2023-11-06 | 2024-05-17 | 上海为旌科技有限公司 | Definition computing method, device and equipment for defocus blurred image |
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