CN114663653A - Window level and window width calculation method for medical image region of interest - Google Patents
Window level and window width calculation method for medical image region of interest Download PDFInfo
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- 238000004364 calculation method Methods 0.000 title claims abstract description 14
- 230000011218 segmentation Effects 0.000 claims abstract description 5
- 210000004072 lung Anatomy 0.000 claims description 19
- 238000000034 method Methods 0.000 claims description 16
- 238000013507 mapping Methods 0.000 claims description 8
- 230000003321 amplification Effects 0.000 claims description 2
- 238000003199 nucleic acid amplification method Methods 0.000 claims description 2
- 230000001131 transforming effect Effects 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 5
- 210000001519 tissue Anatomy 0.000 description 5
- 238000001914 filtration Methods 0.000 description 3
- 210000000988 bone and bone Anatomy 0.000 description 2
- 230000003902 lesion Effects 0.000 description 2
- 230000007547 defect Effects 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 239000002184 metal Substances 0.000 description 1
- 210000000056 organ Anatomy 0.000 description 1
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- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
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- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10081—Computed x-ray tomography [CT]
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30061—Lung
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Abstract
The invention discloses a window level and window width calculation method for a medical image region of interest, which comprises the following steps: converting voxel gray values of medical image data into Hu values; constructing a frequency histogram of the Hu value, and rejecting invalid groups in the frequency histogram according to a set threshold value proportion; and calculating the Hu value range of the region of interest according to the frequency histogram after the invalid group is removed, and calculating the window level width according to the Hu value range. The invention can obtain the window width of the focus part by segmentation calculation aiming at the medical images collected by different image devices or medical images after other processing, and the window width is mapped to the display screen for display.
Description
Technical Field
The invention relates to the technical field of medical images, in particular to a window level and window width calculation method for a region of interest of a medical image.
Background
Most medical images come from CT, DR, US and other image equipment, the bit depth is usually 12-16 bit, and if doctors need to diagnose, the proper window level and window width need to be adjusted according to different parts, and the gray level of a specific color gamut range can be displayed on a display screen (most 8 bit).
In the aspects of diagnosis and treatment of a focus of a patient, particularly a pulmonary nodule, doctors pay attention to the fact that the texture of the focus part and peripheral organs and the like can be clearly and accurately displayed, and medical images acquired by different imaging devices or medical images processed by other software cannot well display the characteristics of the focus part on a display screen.
Disclosure of Invention
The invention aims to: aiming at the defects, the invention provides a self-adaptive window method of a medical image region of interest, which can be used for obtaining the window level window width of a focus part by dividing and calculating aiming at medical images acquired by different image equipment or medical images processed by other software and mapping the window level window width to a display screen for displaying.
The technical scheme is as follows:
a window level window width calculation method for a region of interest of a medical image comprises the following steps:
converting voxel gray values of the medical image data into Hu values;
constructing a frequency histogram of the Hu value, and rejecting invalid groups in the frequency histogram according to a set threshold proportion;
and calculating the Hu value range of the region of interest according to the frequency histogram after the invalid group is removed, and calculating the window level width according to the Hu value range.
The removing of the invalid group in the frequency histogram according to the set threshold proportion specifically comprises:
dividing the number of wave crests in the frequency histogram;
if the number of the maximum wave crests is not less than the maximum number of the wave crests set according to the region of interest, eliminating the interference wave crests at the two ends of the frequency histogram according to a set threshold proportion, and taking a group where the first wave trough is located and a group where the last wave trough is located in the effective group obtained after the interference wave crests are eliminated as groups where the two ends of the effective group are located;
and if the number of the wave crests is less than the maximum number of the wave crests set according to the region of interest, eliminating interference groups at two ends of the frequency histogram according to a set threshold value proportion, and taking a starting group and an ending group in the effective group obtained after the interference groups are eliminated as groups at two ends of the effective group.
The number of peaks in the segmentation frequency histogram is specifically as follows:
according to the maximum frequency F in the frequency histogrammaxSetting a frequency threshold F of a frequency histogrampDefinition of the cut-off frequency FCStarting from 0, continuously according to the iteration frequency step dpLifting FCDividing the frequency histogram according to the number of the wave troughs in the frequency histogram obtained by identification to obtain the isolated waves in the frequency histogramNumber of peaks, up to the number of peaks n in the obtained frequency histogrampeakThe maximum number N of wave crests greater than the setpeakOr the cut-off frequency FCExceeding a frequency threshold Fp。
The frequency threshold value FpIs set to 0.1Fmax。
The Hu value range of the region of interest calculated according to the frequency histogram after the invalid group is removed is specifically as follows:
defining minimum value Y of Hu values on frequency histogramminAnd maximum value YmaxThe number of groups of the frequency histogram is NhistThen the width of the histogram is g = (Y)max-Ymin)/Nhist；
Defining the group at two ends of the effective group of the frequency histogram as NminAnd NmaxThen N isminAnd NmaxThe corresponding Hu values are:
Pmin=Ymin+(Nmin-α)*g
Pmax=Ymax+(Nmax+β)*g
wherein, alpha and beta are respectively a reduction coefficient and an amplification coefficient;
so as to calculate the Hu value range [ P ] of the interested region of the medical image datamin, Pmax]。
And if the region of interest is a lung, the maximum number of peaks is set to 2.
The β > α.
The α =0 and the β = 1.
The set threshold ratio is set to 10%.
Further comprising the step of mapping to a display screen:
transforming Hu value range of region of interest into corresponding voxel gray value range [ pixel ]min, pixelmax]And mapping the image to the display range of the display screen by the following formula:
(pixel-pixelmin)/(pixelmax-pixelmin)*256
wherein pixels represent the gray value of a certain voxel in the medical image data.
Has the advantages that: the invention can obtain the window width of the focus part by segmentation calculation aiming at the medical images collected by different image devices or the medical images processed by other software, and the window width is mapped to the display screen to be displayed, thereby showing the substantial characteristics of the interested region to doctors more clearly.
Drawings
FIG. 1 is a flowchart illustrating a window level and window width calculation method for a region of interest of a medical image according to the present invention;
FIG. 2 is a diagram of a four-view of a default window width of a CT image;
FIG. 3 is a frequency histogram of medical images;
FIG. 4 is a histogram with multiple peaks;
FIG. 5 is a diagram showing the calculation of the groups N where the two ends of the concentrated peak are located when the number of the divided peaks exceeds the maximum number of the peaksminAnd NmaxA schematic diagram of (a);
FIG. 6 is a diagram showing the calculation of the groups N where the two ends of the concentrated peaks are located when the number of the divided peaks does not exceed the maximum number of the peaksminAnd NmaxA schematic diagram of (a);
fig. 7 is a result image after window level window width adjustment.
Detailed Description
The invention is further elucidated with reference to the drawings and the embodiments.
The window level window width calculation method of the medical image region of interest of the invention is shown in fig. 1, and comprises the following steps:
(1) converting voxel gray values of the medical image data into Hu values;
the method comprises the steps of converting voxel gray values of medical image data from different sources (such as medical image data acquired by CT, DR, US and other image equipment or medical image data processed by other software) into Hu values, taking DICOM image data as an example, as shown in FIG. 2, because a doctor focuses on Hu values in the operation process, the medical image data needs to be converted;
the method specifically comprises the following steps: reading Slope (Tag 0028| 1052) and Intercept (Tag 0028| 1053) from the medical image data, and converting voxel gray values in the medical image data into Hu values commonly used by doctors according to a formula Hu = pixel × Slope + Intercept, wherein pixel represents the gray value of a certain voxel in the medical image data;
(2) constructing a frequency histogram of Hu values in medical image data;
as shown in FIG. 3, the leftmost end and the rightmost end of the frequency histogram are the minimum value Y of the Hu values of the medical image dataminAnd maximum value YmaxThe number of groups of the frequency histogram is Nhist(recommended set to 256), the width of the histogram is g = (Y)max-Ymin)/Nhist；
(3) Eliminating invalid data;
in the invention, the Hu values converted from the voxel gray value in the lung image data in the step (1) show peak values in different regions in a frequency histogram, as shown in FIG. 4, the Hu values in the lung image data are mainly concentrated in a low-value region, the Hu values in the lung part account for a greater proportion in the low-value region relative to the Hu values of hard tissues such as bones, and troughs are inevitably separated between two adjacent peak values; in addition, there may be interference of air Hu values of alveoli, outside of the body, etc., but the overall specific gravity is not high; the Hu value of the lung part shows that concentrated peaks exist in the image data of the lung tissue in the frequency histogram, and invalid peaks are removed according to the concentrated peaks; the method specifically comprises the following steps:
number n of wave crests in iterative segmentation frequency histogrampeak：
According to the maximum frequency F in the frequency histogrammax(i.e. the percentage of voxels in the highest peak in the frequency histogram to all voxels), a frequency threshold F for the frequency histogram is setpIn the present invention, the frequency threshold FpIs set to 0.1Fmax(ii) a Defining the cut-off frequency as FCStarting from 0, continuously according to the iteration frequency step dpRaising the cut-off frequency FCAt a frequency of 0 to a cut-off frequency FCIn the interval, the number of isolated peaks in the frequency histogram is obtained by dividing according to the number of identified troughs in the frequency histogram until the number n of peaks in the frequency histogrampeakThe number of the wave crests N is larger than or equal to the preset maximum wave crest number NpeakOr the cut-off frequency FCExceeding a frequency threshold Fp(ii) a Wherein, the maximum number of wave crests NpeakDetermined according to the actual focus part, if the focus is lung, the maximum number of wave crests NpeakThe recommended setting is 2.
In the present invention, the iteration frequency step dpIs set to 0.1Fp；
All wave crests are segmented in an envelope curve of the frequency histogram, so that invalid data can be filtered subsequently to obtain medical image data of the region of interest, and the appropriate window level width can be calculated.
Aiming at an interested region (namely a focus part), because concentrated interference of high Hu value regions such as local bones and metal ornaments or low Hu value regions such as external air may exist, interference of wave crests exists at two ends of a frequency histogram and needs to be removed;
number n of wave crests obtained by divisionpeak ≥NpeakIf the local interference of the high gray value cluster or the low gray value cluster exists and is distributed at two ends, filtering the data lower than N according to the set threshold value proportion ThistT and greater than NhistInterference peaks of (1-T), i.e. if the group in which the peak is located is not in NhistT to NhistWithin the range of 1-T, removing to obtain a group with the first wave trough and a group with the last wave trough in the effective group left after filtering the interference wave crest, and obtaining a group N with the two ends of the effective groupminAnd NmaxAs shown in fig. 5; in the invention, the set threshold value proportion T is determined according to the actual lesion position, and if the lesion is a lung, the threshold value proportion T is set to be 10%.
Peak n obtained by dividingpeak < NpeakIf the background is interfered or the background is image data of lung textures, which indicates that the frequency histogram can not segment the Hu value of the lung medical image from other surrounding tissues, the histogram group in the concentrated wave crest is deleted in the same way, except that the filtering is lower than N according to the set threshold value proportion ThistT and higher than NhistThe interference group of (1-T) is filtered to obtain the beginning group and the ending group in the remaining effective groupGroup N at both ends of the active groupminAnd NmaxAs shown in FIG. 6, data with a ratio in the range of T and 1-T is retained;
(4) after eliminating invalid data, calculating the Hu value range of the region of interest of the medical image data;
calculating the group N at both ends of the effective groupminAnd NmaxCorresponding Hu values:
Pmin=Ymin+(Nmin-α)*g
Pmax=Ymax+(Nmax+β)*g
the focus is taken as a lung, the lung tissue comprises lung parenchyma, skin, heart and other tissues, the lung parenchyma part is more concentrated on the tail part of the rising edge of the peak, the tail part of the falling edge has a higher Hu value and can be the skin, the heart and the like, a doctor pays more attention to the lung parenchyma, and therefore the focus is set to be beta > alpha to keep more images of the lung parenchyma; preferably, α =0, β = 1;
so as to obtain Hu value range [ P ] of the interested region of the medical image datamin, Pmax]And calculating the window level wl and the window width ww of the region of interest according to the calculation:
wl=(Pmax+Pmin)/2
ww=Pmax–Pmin
then, according to the calculation of the step (1), the Hu value range of the interested region is transformed into a corresponding voxel gray value range [ pixel ]min, pixelmax]Linearly mapping the medical image data in the corresponding voxel gray value range to a display range (0-256) of a display screen, as shown in fig. 7, so that the lung parenchymal texture can be more clearly displayed to a doctor;
the specific mapping formula is as follows:
(pixel-pixelmin)/(pixelmax-pixelmin)*256。
the method comprises the steps of designing an algorithm through the characteristic that a frequency histogram corresponding to voxels of a lung is mainly concentrated in a low Hu value, converting an image from a voxel value to the Hu value, calculating the number of peaks of the frequency histogram, eliminating anomalies in the peaks, calculating end point values of concentrated peak groups to obtain a Hu range value of an interested area, and finally linearly mapping medical image data in a corresponding voxel gray value range to a display range of a display screen, so that the substantial features of the interested area can be more clearly shown to a doctor.
Although the preferred embodiments of the present invention have been described in detail, the present invention is not limited to the details of the foregoing embodiments, and various equivalent changes (such as number, shape, position, etc.) may be made to the technical solution of the present invention within the technical spirit of the present invention, and these equivalent changes are all within the protection scope of the present invention.
Claims (10)
1. A window level window width calculation method for a medical image region of interest is characterized by comprising the following steps: the method comprises the following steps:
converting voxel gray values of the medical image data into Hu values;
constructing a frequency histogram of the Hu value, and rejecting invalid groups in the frequency histogram according to a set threshold proportion;
and calculating the Hu value range of the region of interest according to the frequency histogram after the invalid group is removed, and calculating the window level width according to the Hu value range.
2. The method of claim 1, wherein the window level and width of the region of interest in the medical image is calculated by: the step of removing the invalid group in the frequency histogram according to the set threshold proportion specifically comprises the following steps:
dividing the number of wave crests in the frequency histogram;
if the number of the maximum wave crests is not less than the maximum number of the wave crests set according to the region of interest, eliminating the interference wave crests at the two ends of the frequency histogram according to a set threshold proportion, and taking a group where the first wave trough is located and a group where the last wave trough is located in the effective group obtained after the interference wave crests are eliminated as groups where the two ends of the effective group are located;
and if the number of the wave crests is less than the maximum number of the wave crests set according to the region of interest, eliminating interference groups at two ends of the frequency histogram according to a set threshold value proportion, and taking a starting group and an ending group in the effective group obtained after the interference groups are eliminated as groups at two ends of the effective group.
3. The method of claim 2, wherein the window width of the window of the region of interest in the medical image is calculated by: the number of peaks in the segmentation frequency histogram is specifically as follows:
according to the maximum frequency F in the frequency histogrammaxSetting a frequency threshold F of a frequency histogrampDefinition of the cut-off frequency FCStarting from 0, continuously according to the iteration frequency step dpLifting FCDividing the frequency histogram according to the number of the identified troughs in the frequency histogram to obtain isolated peaks in the frequency histogram until the number n of the peaks in the obtained frequency histogrampeakThe maximum number N of wave crests greater than the setpeakOr the cut-off frequency FCExceeding a frequency threshold Fp。
4. The method of claim 3, wherein the window level and width of the region of interest in the medical image is calculated by: the frequency threshold value FpIs set to 0.1Fmax。
5. The method of claim 2, wherein the window width of the window of the region of interest in the medical image is calculated by: the Hu value range of the region of interest calculated according to the frequency histogram after the invalid group is removed is specifically as follows:
defining minimum value Y of Hu values on frequency histogramminAnd maximum value YmaxThe number of groups of the frequency histogram is NhistThen the width of the histogram is g = (Y)max-Ymin)/Nhist；
Defining the group at two ends of the effective group of the frequency histogram as NminAnd NmaxThen N isminAnd NmaxThe corresponding Hu values are:
Pmin=Ymin+(Nmin-α)*g
Pmax=Ymax+(Nmax+β)*g
wherein, alpha and beta are respectively a reduction coefficient and an amplification coefficient;
so as to calculate the Hu value range [ P ] of the interested region of the medical image datamin, Pmax]。
6. The method of claim 5, wherein the window level and width of the region of interest in the medical image is calculated by: and if the region of interest is a lung, the maximum number of peaks is set to 2.
7. The method for calculating the window level and the window width of the region of interest in medical images as claimed in claim 6, wherein: the β > α.
8. The method of claim 7, wherein the window level and width of the region of interest in the medical image is calculated by: the α =0 and the β = 1.
9. The method for calculating the window level and the window width of the region of interest in medical images according to claim 2, wherein: the set threshold ratio is set to 10%.
10. The method for calculating the window level and the window width of the region of interest in medical images as claimed in claim 1, wherein: further comprising the step of mapping to a display screen:
transforming Hu value range of region of interest into corresponding voxel gray value range [ pixel ]min, pixelmax]And mapping the image to the display range of the display screen by the following formula:
(pixel-pixelmin)/(pixelmax-pixelmin)*256
wherein pixels represent the gray value of a certain voxel in the medical image data.
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