CN110928469B - Method for setting optimal display window width and window level - Google Patents
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- G06F3/04845—Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range for image manipulation, e.g. dragging, rotation, expansion or change of colour
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Abstract
The invention discloses a method for setting the optimal display window width and window level, which comprises the following steps: displaying an image representing the physical quantity CT by using gray scale GB according to a default window width WW and a default window level WL; receiving a selection of a target object point and a background point on the image; generating a target object region and a background region, wherein the target object region is related to the target object point, and the background region is related to the background point; calculating a mean CT of the physical quantity of the target object region o And a mean CT of the physical quantity of the background region b The method comprises the steps of carrying out a first treatment on the surface of the An optimal window width WW and window level WL are determined that maximize a gray-scale based contrast-to-noise ratio GBCNR of the image. The method can provide the optimal display window setting, so that the observer can obtain the optimal image display effect. Compared to manually adjusting window width WW and window level WL, the present method is time-saving and labor-saving and largely eliminates the bias between different readers having personal experiences or preferences.
Description
Technical Field
The present invention relates to the display of medical images.
Background
Subjective film reading is still a main approach of medical image diagnosis at present, and therefore, the visual effect of an image is an important factor affecting film reading performance or diagnosis quality.
In CT applications, the CT image is converted to a gray scale image by a window setting of the display. CT values of CT images range from-1024 to +3071, with values exceeding 4000, while common displays have gray scales ranging from 0 to 255. The CT value needs to be mapped to the gray scale of the display by Window Width (WW) and Window Level (WL). Thus, the display window width and level settings significantly affect the display of the CT image. CT systems typically have preset display window settings for different clinical applications and diagnostic purposes.
In many cases, the reader frequently manually adjusts preset window settings for the particular area of interest to the reader. Subjectively, manual operation not only results in bias between different readers having their own experiences and preferences, but also is laborious and time consuming, increasing unnecessary diagnostic time. More importantly, it is difficult for the reader to manually adjust the window setting to obtain the best display effect of the CT image.
Therefore, the optimal display window setting is provided for the reader, so that the reader can quickly obtain the optimal image display effect, and the method has strong necessity and important significance.
Disclosure of Invention
In view of this, the present invention proposes a method of setting an optimal display window width and level.
The invention provides a method for setting an optimal window width WW and a window level WL, which comprises the following steps: displaying an image representing the physical quantity CT by using gray scale GB according to a default window width WW and a default window level WL; receiving a selection of a target object point and a background point on the image; generating a target object region and a background region, wherein the target object region is related to the target object point, and the background region is related to the background point; calculating a mean CT of the physical quantity of the target object region o And a mean CT of the physical quantity of the background region b The method comprises the steps of carrying out a first treatment on the surface of the An optimal window width WW and window level WL are determined that maximize a gray-scale based contrast-to-noise ratio GBCNR of the image.
In one embodiment, the gray-scale based contrast-to-noise ratio GBCNR is calculated according to the following equation:
wherein mu o_GB Sum mu b_GB The average value and sigma of gray scales GB of the target object area and the background area under the current window and window level are respectively o_GB Sum sigma b_GB The standard deviation of gray scale GB of the target object area and the background area under the current window and window level are respectively.
In one embodiment, the gray level GB is calculated according to the following equation:
in one embodiment, the window width WW is calculated according to the following formula:
wherein Δct= |ct b -CT o |，ΔGB∈[1，255]。
In one embodiment, the window level WL is calculated according to:
wherein GB is b ∈[1，255]。
In an embodiment, the determining the optimal window width WW and window level WL comprises determining the optimal window width WW and window level WL by an iterative algorithm.
In an embodiment, the determining the optimal window width WW and window level WL includes:
step S114, calculating the window width WW by:
wherein Δct= |ct b -CT o |，ΔGB∈[1，255]The initial value is 1;
step S116, calculating the window level WL by:
wherein GB is b ∈[1，255]The initial value is 1;
step S118, GBCNR is calculated;
step S120, judging GB b Whether or not it is not less than 255: if yes, step S124 is skipped; if not, jumping to step S122;
step S122, GB b Increasing the first step length, and after the execution is finished, turning to step S116;
step S124, determining whether Δgb is not less than 255: if yes, go to step S128; if not, jumping to step S126;
step S126, delta GB is increased by a second step, and after execution is completed, the step S114 is carried out;
step S128, selecting the largest GBCNR;
step S130, the optimal window width WW and window level WL corresponding to the maximum GBCNR are acquired.
In one embodiment, the gray-scale based contrast-to-noise ratio GBCNR is calculated according to the following equation:
in an embodiment, the generating the target object region and the background region includes:
obtaining a target object seed point with a median related to the target object point and a background seed point with a median related to the background point through median filtering based on the target object point and the background point respectively;
determining stopping conditions of a region growing algorithm of a target object seed point and a background seed point respectively;
and respectively carrying out region growing from the target object seed point and the background seed point until the stopping condition of the corresponding region growing algorithm is met, thereby obtaining a target object region and a background region.
In an embodiment, the stopping condition is that a difference between a physical quantity CT of the new pixel and a mean value of the physical quantity CT of the region is greater than a first preset threshold, or a distance between the new pixel and the target object seed point or the background seed point is greater than a second preset threshold.
The method for setting the optimal display window width WW and the window level WL of the CT image considers the influence of window setting on the visual effect of the image, and enhances the contrast-to-noise ratio of the image based on gray scales by optimizing WW and WL, thereby maximizing the visibility of an observed object. Compared to manually adjusting window width WW and window level WL, the present method is time-saving and labor-saving and largely eliminates the bias between different readers having personal experiences or preferences. More importantly, the method can provide optimal display window setting, so that an observer can obtain optimal image display effect. The method provides an interactive mode of clicking a mouse on a graphical user interface, and can automatically and accurately capture the object image interested region really focused by the reader. The method is necessary supplement to the preset window setting of the current CT system, and can realize the local optimal display of the specific region really concerned by the reader. .
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The above and other features and advantages of the present invention will become more apparent to those of ordinary skill in the art by describing in detail preferred embodiments thereof with reference to the attached drawings in which:
fig. 1 is a flowchart of a method of setting an optimal display window width level according to an embodiment of the present invention.
Fig. 2 is a flowchart for determining the optimal window width WW and window level WL.
Detailed Description
The present invention will be further described in detail with reference to the following examples, in order to make the objects, technical solutions and advantages of the present invention more apparent.
In the present invention, a method for setting an optimal display window width and window level Based on GBCNR (Grey scale-Based Contrast-to-Noise Ratio) is provided. In the embodiment of the present invention, the CT values of the computed tomography image are taken as an example, but the principles of the present invention can also be applied to other types of medical images.
CNR (Contrast-to-Noise Ratio) is one of the most important physical parameters in CT image quality study and evaluation. CNR takes into account not only the differences in average attenuation coefficients between different tissues, but also the effects of correlated pixel noise, both of which directly affect the image impression and diagnostic reliability of the reader. CNR is defined as:
wherein mu o_CT Is the average CT value, mu, of a defined target region of interest (target object) b_CT Is the average CT value of the background surrounding the target object. And sigma (sigma) o_CT Sum sigma b_CT Is the total target object noise and background noise, and represents the targets of the pixel CT values from the target object target area and the background area, respectivelyAnd (5) accuracy difference.
CNR is calculated based on CT values. However, in CT diagnosis, the reader evaluates an image by visual impression, which is a gray-scale image output on the display that converts the original CT value into 8-bit gray-scale representation (i.e., 256 different gray-scales in the display device).
The conversion relationship between the pixel display gray scale and the pixel original CT value is given by:
wherein GB represents the display gray scale of the gray scale image, CT represents the original CT value of the pixel, and WW and WL represent the display window width and the display window level respectively.
Obviously, the CT value is not directly reflected on the display image, which means that the CNR calculated based on the CT value cannot always accurately reflect the characteristics of the display image. Thus, CNR in general cannot provide a comprehensive physical evaluation for gray-scale based display images.
The invention starts from the fact that a reader really sees a gray image on a display, fully considers the influence of display window setting on the image display quality, and provides a new image quality physical evaluation parameter, namely GBCNR.
Based on the foregoing display gray level GB, GBCNR can be expressed by the following equation.
Wherein mu o_GB Sum sigma o_GB Respectively representing the average value and standard deviation of the pixel display gray scale of the target object region under the current window width and window level; mu (mu) b_GB Sum sigma b_GB Respectively displaying the average value and standard deviation of gray scales of pixels of the background area under the current window setting; GB is calculated from equation (2).
Fig. 1 is a flow chart of a method 100 of setting an optimal display according to an embodiment of the present invention. As shown in fig. 1, the method 100 of setting the optimum window width WW and window level WL that can display the physical quantity CT includes steps S102, S104, S106, S108, and S110.
In step S102, the CT value image is displayed on the gray-scale display with a default window setting. The pixel display gray level GB is calculated from equation (2).
In step S104, a selection of a target object point object and a background point background on the display image is received. The reader can select his/her target object point object and its background point background by clicking on the image with a mouse.
In step S106, a target object region roi_object and a background region roi_background are generated. The target object region roi_object and the background region roi_background are related to the target object point object and the background point background, respectively, clicked by the mouse.
In step S108, a CT value mean CT of the target object region ROI_object is calculated o CT value mean CT of background region ROI_background b 。
In step S110, an optimal window width WW and a window level WL are determined, which maximize a gray-scale-based contrast-to-noise ratio GBCNR of the image, which is calculated according to equation (3).
As shown in fig. 1, the method 100 of setting the optimal window width WW and window level WL that can display the physical quantity CT may further include step S112. In step S112, an image is displayed in gray scale GB with the optimal window width WW and window level WL.
As shown in fig. 1, in the present embodiment, step S106 for generating the target object region roi_object and the background region roi_background includes step S1062, step S1064, and step S1066.
In step S1062, median filtering is applied to the target object point object and the background point background clicked by the reader mouse to obtain a target object seed point seed_object and a background seed point seed_background related to the target object point object and the background point background, respectively. For example, an n×n window may be applied to the target object point object and the background point background for median filtering. The purpose of this step is to eliminate salt and pepper noise and to some extent to reduce manual operation errors.
In step S1064, a growth algorithm stop condition for the target object region of interest and the background region is determined. The method utilizes a region growing algorithm to obtain a region of interest. The region growing algorithm iteratively grows the entire region from the seed points by comparing all unassigned neighboring pixels to the region. In this embodiment, the region growing algorithm is stopped when one of the following conditions is satisfied:
the difference between the CT values of the new pixel and the target object seed point seed_object or background seed point seed_background is greater than a first preset threshold;
the distance between the new pixel and the target object seed point seed_object or the background seed point seed_background is greater than a second preset threshold.
How to specify the stop condition has a great influence on the result of the region growing algorithm. The stop conditions depend on several factors such as the current displays WW and WL, the target object characteristics and the clinical application purpose.
In step S1066, region growing is performed from the target object seed point seed_object and the background seed point seed_background, respectively, until the stop condition of the corresponding region growing algorithm is satisfied, thereby obtaining the target object region roi_object and the background region roi_background.
In an embodiment of the invention, determining the optimal window width WW and window level WL comprises determining the optimal window width WW and window level WL by an iterative algorithm. Fig. 2 is a flowchart of step S110 of determining the optimal window width WW and window level WL.
In step S114, the window width WW is calculated by:
wherein Δct= |ct b -CT o |，ΔGB∈[1，255]Δgb is a gray-scale difference between the target object region roi_object and the background region roi_background, and its initial value is 1.
In step S116, the window level WL is calculated by:
wherein GB is b ∈[1，255]，GB b Is the gray level of the background region ROI_background and CT average value CT of the background region ROI_background b Correspondingly, the initial value is 1.
In step S118, GBCNR is calculated according to equations (2) and (3).
In step S120, GB is determined b Whether or not it is not less than 255: if yes, step S124 is skipped; if not, step S122 is skipped.
In step S122, GB b The first step size is increased. After the execution is completed, the process goes to step S116.
In step S124, it is determined whether Δgb is not less than 255: if yes, go to step S128; if not, step S126 is skipped.
In step S126, Δgb is increased by a second step size. After the execution is completed, the process goes to step S114.
In step S128, the largest GBCNR is selected.
In step S130, the window setting corresponding to the maximum GBCNR, that is, the optimal display window width WW and window level WL, is acquired.
The invention provides an automatic adjustment method for an optimal window width and a window level. The method is based on the proposed image display quality assessment index GBCNR, and the visibility of the target object is maximized by optimizing the display window setting. Compared with the common manual window width and window level adjustment, the method saves time and labor, and largely eliminates image display deviation caused by different personal experiences and preferences. More importantly, the present method provides for proper and even optimal display window settings, thereby providing the reader with an optimal visual impression of the image. The method also provides an interactive mode for accurately capturing the real focused target object area of the reader by clicking a mouse on the graphical user interface. The method can realize the local optimal display of the specific region really concerned by the reader, and is a necessary supplement to the window setting aiming at the whole image preset in the current CT application.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (10)
1. A method of setting an optimal display window width WW and window level WL, comprising:
displaying an image representing the physical quantity CT by using gray scale GB according to a default window width WW and a default window level WL;
receiving a selection of a target object point and a background point on the image;
generating a target object region and a background region, wherein the target object region is related to the target object point, and the background region is related to the background point;
calculating a mean CT of the physical quantity of the target object region o And a mean CT of the physical quantity of the background region b ；
An optimal window width WW and window level WL are determined that maximize a gray-scale based contrast-to-noise ratio GBCNR of the image.
2. The method of claim 1, wherein the gray-scale based contrast-to-noise ratio GBCNR is calculated according to the following equation:
wherein mu o_GB Sum mu b_GB The average value and sigma of gray scales GB of the target object area and the background area under the current window and window level are respectively o_GB Sum sigma b_GB The standard deviation of gray scale GB of the target object area and the background area under the current window and window level are respectively.
3. The method of claim 1, wherein the gray level GB is calculated according to the following equation:
4. the method of claim 1, wherein the window width WW is calculated according to the formula:
wherein Δct= |ct b -CT o |，ΔGB∈[1,255]。
5. The method of claim 4, wherein the window level WL is calculated according to:
wherein GB is b ∈[1,255]。
6. The method of claim 1, wherein determining the optimal window width WW and window level WL comprises determining the optimal window width WW and window level WL by an iterative algorithm.
7. The method of claim 6, wherein said determining the optimal window width WW and window level WL comprises:
step S114, calculating the window width WW by:
wherein Δct= |ct b -CT o |，ΔGB∈[1,255]The initial value is 1;
step S116, calculating the window level WL by:
wherein GB is b ∈[1,255]The initial value is 1;
step S118, GBCNR is calculated;
step S120, judging GB b Whether or not it is not less than 255: if yes, step S124 is skipped; if not, jumping to step S122;
step S122, GB b Increasing the first step length, and after the execution is finished, turning to step S116;
step S124, determining whether Δgb is not less than 255: if yes, go to step S128; if not, jumping to step S126;
step S126, delta GB is increased by a second step, and after execution is completed, the step S114 is carried out;
step S128, selecting the largest GBCNR;
step S130, the optimal window width WW and window level WL corresponding to the maximum GBCNR are acquired.
8. The method of claim 7, wherein the gray-scale based contrast-to-noise ratio GBCNR is calculated according to the following equation:
9. the method of claim 1, wherein the generating the target object region and the background region comprises:
obtaining a target object seed point with a median related to the target object point and a background seed point with a median related to the background point through median filtering based on the target object point and the background point respectively;
determining stopping conditions of a region growing algorithm of a target object seed point and a background seed point respectively;
and respectively carrying out region growing from the target object seed point and the background seed point until the stopping condition of the corresponding region growing algorithm is met, thereby obtaining a target object region and a background region.
10. The method of claim 9, wherein the stop condition is that a difference between a physical quantity CT of a new pixel and a mean value of the physical quantity CT of the region is greater than a first preset threshold, or a distance between the new pixel and the target object seed point or the background seed point is greater than a second preset threshold.
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