CN114968952B - Medical image data compression method, rendering method, device and medium - Google Patents
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Abstract
The disclosure relates to a medical image data compression method, a rendering method, a device and a medium. The medical image data compression method comprises the following steps: acquiring a first voxel data set, wherein the first voxel data set comprises voxel data corresponding to each voxel point, and the storage space occupied by each voxel data is a first storage size; carrying out data processing on each voxel data according to the target voxel data range to obtain first gray data corresponding to each voxel data; determining a gray value interval corresponding to each gray level in the total number of target gray levels; determining a gray value interval to which the first gray data belongs, and updating the first gray data according to the gray scale number corresponding to the gray value interval to obtain second gray data; and sending the second gray data so that the receiving end performs image rendering according to the second gray data. Therefore, the occupation of medical image data to the memory can be effectively reduced, and the transmission rate of medical images is improved, so that the image reconstruction speed of a receiving end is improved.
Description
Technical Field
The present disclosure relates to the field of medical image processing, and in particular, to a medical image data compression method, a medical image rendering method, a device, and a medium.
Background
With the development of computer technology, more and more software applications are transplanted to a webpage (Web) end, the software applications based on the webpage end do not need to be installed locally, complicated configuration is not needed, and the software applications are convenient to use and fetch. Medical imaging tools software also begin to appear in succession on the web page side.
Medical image three-dimensional reconstruction technology based on webpage ends is currently in development stage. One of the unavoidable problems is that the data amount of a single group of medical images is generally large, the value range of voxel data corresponding to each voxel of each medical image is between plus or minus thousands, and the storage space occupied by each voxel is 16 bits, so that the medical images occupy large storage space, the transmission rate of the medical images is influenced, the image reconstruction speed of a receiving end is reduced, and even the browser is crashed.
Disclosure of Invention
The invention aims to provide a medical image data compression method, a medical image rendering method, a device and a medium, which can effectively reduce the occupation of medical image data to a memory, and improve the transmission rate of medical images under the condition of ensuring the definition of a rendering image of a receiving end so as to improve the image reconstruction speed of the receiving end and prevent the browser of the receiving end from collapsing.
To achieve the above object, a first aspect of the present disclosure provides a medical image data compression method, the method comprising:
acquiring a first voxel data set, wherein the first voxel data set comprises voxel data corresponding to each of a plurality of voxel points, and the storage space occupied by each voxel data is a first storage size;
performing data processing on each voxel data according to a target voxel data range to obtain first gray data corresponding to each voxel data;
determining a gray value interval corresponding to each gray level in a target gray level total number, wherein the target gray level total number is smaller than the gray level total number of a gray level graph;
determining a gray value interval to which the first gray data belongs, and updating the first gray data according to the gray scale number corresponding to the gray value interval to obtain second gray data, wherein a storage space occupied by the second gray data is a second storage size, and the second storage size is smaller than the first storage size;
and sending the second gray data so that the receiving end performs image rendering according to the second gray data.
Optionally, the processing the data of each voxel data according to the target voxel data range to obtain first gray data corresponding to each voxel data includes:
Updating the voxel data in the first voxel data set, which is larger than the upper limit of the target voxel data range, to be the upper limit of the target voxel data range, and updating the voxel data in the first voxel data set, which is smaller than the lower limit of the target voxel data range, to be the lower limit of the target voxel data range, so as to obtain a second voxel data set;
and carrying out gray level conversion on the second voxel data set to obtain the first gray level data, wherein the storage space occupied by the first gray level data is of a third storage size, and the third storage size is smaller than the first storage size and larger than the second storage size.
Optionally, the performing gray scale transformation on the second voxel data set to obtain the first gray scale data includes:
and determining a first gray scale reference value and a second gray scale reference value corresponding to each voxel data in the second voxel data set, and determining the larger one of the first gray scale reference value and the second gray scale reference value as the first gray scale data corresponding to the voxel data, wherein the first gray scale reference value and the second gray scale reference value are used for representing gray scale enhancement of different degrees.
Optionally, the determining the first gray scale reference value and the second gray scale reference value corresponding to each body of data in the second body of data includes:
determining a first gray reference value corresponding to the mth individual data in the second individual data set by the following formula:
wherein ,
for the first of the second set of volumetric datamIndividual voxel data>
Is the firstmFirst gray reference value corresponding to individual data,/->
For the lower limit of the target voxel data range, +.>
For the upper limit of the target voxel data range, < > for>
For the first parameter, ++>
The value range of (2) is 0 to 1;
determining the first of said second set of volumetric data by the formulamSecond gray reference values corresponding to the respective voxel data:
wherein ,
is the firstmSecond gray reference value corresponding to each voxel data, ">
For the second parameter, ++>
The value of (2) is in the range of 0 to 1.
Optionally, the upper limit of the target voxel data range is the smaller of a preset upper voxel data limit and a maximum value in the first voxel data set, and the lower limit of the target voxel data range is the larger of a preset lower voxel data limit and a minimum value in the first voxel data set.
Optionally, the acquiring the first body prime data set includes:
acquiring original voxel data corresponding to each voxel point included in the medical image;
when the data volume of the original voxel data is larger than the target data volume, determining a compression ratio according to the data volume of the original voxel data and the target data volume;
and carrying out compression processing on the original voxel data according to the compression ratio to obtain the first voxel data set.
Optionally, the compressing the original voxel data according to the compression ratio to obtain the first voxel data set includes:
determining a plurality of target voxel points from all voxel points included in the medical image according to the compression ratio;
for each target voxel point, determining at least one reference voxel point corresponding to the target voxel point according to the target voxel point and the compression ratio, and determining target voxel data corresponding to the target voxel point according to original voxel data corresponding to each of the target voxel point and the reference voxel point;
and generating the first voxel data set based on the target voxel data corresponding to the target voxel point.
Optionally, the medical image is a CT image.
A second aspect of the present disclosure provides a medical image rendering method, the method including:
receiving gray data, wherein the gray data is generated and transmitted by a transmitting end according to the method provided by the first aspect of the disclosure;
determining a gray scale distance according to a target gray scale total number, wherein the target gray scale total number is smaller than the gray scale total number of a gray scale map;
determining target gray data according to the product of the gray data and the gray interval;
and performing image rendering based on the target gray data.
A third aspect of the present disclosure provides a medical image data compression apparatus, the apparatus comprising:
the acquisition module is used for acquiring a first voxel data set, wherein the first voxel data set comprises voxel data corresponding to each of a plurality of voxel points, and the storage space occupied by each voxel data is a first storage size;
the processing module is used for carrying out data processing on each voxel data according to the target voxel data range to obtain first gray data corresponding to each voxel data;
the first determining module is used for determining a gray value interval corresponding to each gray level in the total number of target gray levels, wherein the total number of the target gray levels is smaller than the total number of the gray levels of the gray level graph;
The second determining module is used for determining a gray value interval to which the first gray data belongs, and updating the first gray data according to the gray scale number corresponding to the gray value interval to obtain second gray data, wherein a storage space occupied by the second gray data is a second storage size, and the second storage size is smaller than the first storage size;
and the sending module is used for sending the second gray data so that the receiving end performs image rendering according to the second gray data.
A fourth aspect of the present disclosure provides a medical image rendering apparatus, the apparatus comprising:
the receiving module is used for receiving gray data, and the gray data are generated and transmitted by the transmitting end according to the method provided by the first aspect of the disclosure;
the third determining module is used for determining gray scale intervals according to the total number of target gray scales, wherein the total number of the target gray scales is smaller than the total number of the gray scales of the gray scale map;
a fourth determining module, configured to determine target gray data according to a product of the gray data and the gray pitch;
and the rendering module is used for performing image rendering based on the target gray data.
A fifth aspect of the present disclosure provides a non-transitory computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of the method provided by the first or second aspects of the present disclosure.
A sixth aspect of the present disclosure provides a medical image data compression apparatus, comprising:
a memory having a computer program stored thereon;
and a controller, the computer program implementing the steps of the method provided in the first aspect of the disclosure when executed by the controller.
A seventh aspect of the present disclosure provides a medical image rendering apparatus, comprising:
a memory having a computer program stored thereon;
and a controller, the computer program implementing the steps of the method provided in the second aspect of the disclosure when executed by the controller.
According to the technical scheme, data processing is carried out on each voxel data according to the target voxel data range, and the voxel data of each voxel point are converted into first gray data. Because the range of gray data is 0-255, compared with voxel data with larger numerical range, the gray data occupies smaller storage space. And then, determining a gray value interval corresponding to each gray level in the total number of target gray levels, and updating the first gray level data according to the gray level number corresponding to the gray value interval to which the first gray level data belongs to obtain second gray level data. Because the total number of the target gray levels is smaller than that of the gray level map, the occupation of the storage space can be further reduced. Compared with voxel data in the first body data set, the storage space occupied by the second gray data is smaller, so that the occupation of the memory by the medical image data can be effectively reduced, the transmission rate of the medical image is improved under the condition that the definition of the image rendered by the receiving end is ensured, the image reconstruction speed of the receiving end is improved, and the browser of the receiving end is prevented from collapsing.
Additional features and advantages of the present disclosure will be set forth in the detailed description which follows.
Drawings
The accompanying drawings are included to provide a further understanding of the disclosure, and are incorporated in and constitute a part of this specification, illustrate the disclosure and together with the description serve to explain, but do not limit the disclosure. In the drawings:
FIG. 1 is a flow chart of a medical image data compression method according to an exemplary embodiment of the present disclosure;
FIG. 2 is a schematic diagram illustrating voxel point selection at compression ratio intervals provided by an exemplary embodiment;
FIG. 3 is a flowchart of a medical image rendering method according to an exemplary embodiment of the present disclosure;
FIG. 4 is a block diagram of a medical image data compression device according to an exemplary embodiment of the present disclosure;
FIG. 5 is a block diagram of a medical image rendering apparatus provided by an exemplary embodiment of the present disclosure;
FIG. 6 is a block diagram of a medical image data compression device according to an exemplary embodiment of the present disclosure;
fig. 7 is a block diagram of a medical image rendering apparatus according to an exemplary embodiment of the present disclosure.
Detailed Description
Specific embodiments of the present disclosure are described in detail below with reference to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the disclosure, are not intended to limit the disclosure.
It should be noted that, all actions for acquiring signals, information or data in the present disclosure are performed under the condition of conforming to the corresponding data protection rule policy of the country of the location and obtaining the authorization given by the owner of the corresponding device.
Fig. 1 is a flowchart of a medical image data compression method according to an exemplary embodiment of the present disclosure. It should be noted that the medical images described in the present disclosure may include CT (Computed Tomography ) images, MRI (Magnetic Resonance Imaging, magnetic resonance imaging) images, PET (Positron Emission Computed Tomography, positron emission tomography) images, and the like. For a clearer understanding of the present disclosure, a CT image will be exemplified below. It should be understood, however, that the illustration of CT images is not to be taken as limiting the scope and application of the present disclosure. The method can be applied to the sending end of the medical image. As shown in fig. 1, the method may include S101 to S105.
S101, acquiring a first prime data set, wherein the first prime data set comprises voxel data corresponding to each of a plurality of voxel points, and the storage space occupied by each prime data set is a first storage size.
For example, if the medical image is a CT image, the voxel data may be a CT value, which is expressed in Hu.
The size of voxel data corresponding to each voxel point of the medical image is often between plus and minus thousands. In order to ensure the uniformity of the size of the storage space occupied during data storage and the integrity of the stored data, the maximum value of the storage space occupied in each voxel data is often used as the corresponding storage space of each voxel data. For example, at the web page end, the storage space occupied by each voxel data is 16 bits, that is, the first storage size is 16 bits, so that the storage space occupied by the first voxel data set in the initial state is: data volume of voxel data
16 bits. In summary, the first voxel data set occupies a larger memory space. If the first voxel data set is directly transmitted and image rendering is performed based on the first voxel data set, the data transmission speed may be slow, and even a breakdown occurs in browser rendering. Accordingly, the present disclosure performs a compression process on a first voxel data set by the following steps.
S102, carrying out data processing on each piece of data according to the target voxel data range to obtain first gray data corresponding to each piece of data.
For example, the target voxel data range may be preset according to actual requirements. The data processing performed on each piece of data may be normalization processing for gradation conversion to obtain first gradation data corresponding to each piece of data. The gray scale map includes 256 gray scales, and the gray scale values are represented by 0 to 255, so that the data range corresponding to the first gray scale data is 0 to 255. I.e. by data processing, each body of data can be mapped into a range of 0 to 255. Thus, no data exceeding the range of 0 to 255 exists in all the first gradation data. Compared with voxel data with larger numerical range, the voxel data is converted into the first gray data, so that the occupation of storage space can be reduced. For example, at the web page end, the storage space occupied by each first gray data is 8 bits, and compared with the 16bit storage space occupied by the data of the previous voxel, the occupation of the memory is greatly reduced.
S103, determining a gray value interval corresponding to each gray level in the total number of target gray levels.
Wherein the total number of target gray levels is smaller than the total number of gray levels of the gray level map.
Illustratively, the target gray-scale total number may be set in advance, for example, may be set to 10. The value ranges (0 to 255) corresponding to 256 gray scales can be equally spaced according to the total number of target gray scales. The gray scale spacing between adjacent gray scales within the target gray scale total number can be calculated by the formula 
Determining, wherein->
Is gray scale interval>
Is the target gray scale total number.
The value range of the gray level n in the total number of the target gray levels is 0 toN-1, when n is equal to 0, the gray value interval corresponding to gray level 0 in the total number of target gray levels is [0,0]The method comprises the steps of carrying out a first treatment on the surface of the When n is greater than 0, the gray value interval corresponding to the gray level n in the total number of target gray levels is [ ]
，/>
]. For example, the total number of target gray levels is 10, calculated +.>
28, the gray value interval corresponding to gray level 1 is (0, 28)]The gray value interval corresponding to gray level 2 is (28, 56]The gray value interval corresponding to gray level 3 is (56, 84]… …, and so on, the gray-scale value interval corresponding to gray-scale 9 is (224, 252)]. In the practical application process, the gray value does not exceed 255, so the value range of the last gray value interval can be written as (/ -)>
，255]Continuing the above example, the gray value interval corresponding to gray level 9 is actually (224, 255]。
S104, determining a gray value interval to which the first gray data belongs, and updating the first gray data according to the gray scale number corresponding to the gray value interval to obtain second gray data.
The storage space occupied by the second gray level data is a second storage size, and the second storage size is smaller than the first storage size.
Illustratively, according to a specific value of the first gradation data, a gradation value section corresponding thereto may be determined. In this way, the data amount of the second gradation data is smaller than that of the first gradation data, so that the occupation of the memory space can be further reduced.
S105, sending the second gray data so that the receiving end performs image rendering according to the second gray data.
According to the technical scheme, data processing is carried out on each voxel data according to the target voxel data range, and the voxel data of each voxel point are converted into first gray data. Because the range of gray data is 0-255, compared with voxel data with larger numerical range, the gray data occupies smaller storage space. And then, determining a gray value interval corresponding to each gray level in the total number of target gray levels, and updating the first gray level data according to the gray level number corresponding to the gray value interval to which the first gray level data belongs to obtain second gray level data. Because the total number of the target gray levels is smaller than that of the gray level map, the occupation of the storage space can be further reduced. Compared with voxel data in the first body data set, the storage space occupied by the second gray data is smaller, so that the occupation of the memory by the medical image data can be effectively reduced, the transmission rate of the medical image is improved under the condition that the definition of the image rendered by the receiving end is ensured, the image reconstruction speed of the receiving end is improved, and the browser of the receiving end is prevented from collapsing.
Preferably, in S103, the target gray scale total number may be a positive integer not greater than 16.
In the process of observing an image, the number of gray scales that can be resolved by the human eye is generally not more than 16. Even if the total number of target gray levels is set to a value greater than 16, for example 48, the image rendered by the receiving end can be more accurate, but the human eye is not sensitive to excessive gray levels, and more accurate judgment is difficult to be made by the image with more gray levels (greater than 16 gray levels). Therefore, the total number of target gray scales can be set to be a positive integer not greater than 16, and the occupation of the data to be transmitted to the storage space can be effectively reduced under the condition that the definition of the image rendered by the receiving end is ensured. For example, if the total number of target gray levels is set to 16, at the web page end, the storage space occupied by a single data can be reduced to 4 bits, while in the computer field, the minimum storage unit is 1 byte, and the space size is 8 bits, so that two adjacent second gray level data can be stored in 1 byte by means of displacement and assignment. As described above, if the second gradation data is 0,3,1,7,6,4, if each of them occupies 8 bits of memory space, it is represented in binary form as 0000 0000, 0000 0011, 0000 0001, 0000 0111, 0000 0110, 0000 0100, which occupies 6 bytes in total. Each second gradation data can be represented by 4 bits by an assignment and displacement operation, for example, shifting left by 4 bits, so that the above contents can be stored in 3 bytes in a compressed manner, which can be 0000 0011, 0001 0111, 0110 0100 in particular.
To ensure the sharpness of the receiving-end rendered image, the target gray-scale total number may be set to 16 or an integer close thereto.
Alternatively, the upper limit of the target voxel data range may be the smaller of the preset upper voxel data limit and the maximum value in the first voxel data set, and the lower limit of the target voxel data range may be the larger of the preset lower voxel data limit and the minimum value in the first voxel data set.
Illustratively, the preset upper voxel data limit and the preset lower voxel data limit may be set according to actual requirements. Often, the medical image at the same location contains a plurality of human tissues, for example, blood vessels and muscle tissues may exist in one medical image at the same time. However, the voxel data ranges of different human tissues are not the same, so that the human tissues needing to be emphasized can be highlighted by the preset upper voxel data limit and the preset lower voxel data limit. For example, if the human tissue displayed by the medical image is bone tissue, the preset upper voxel data limit may be set to 1000Hu and the preset lower voxel data limit may be set to 400Hu. If the maximum value in the voxel data in the actually acquired first voxel data set is 800Hu and the minimum value is 500Hu, setting the target voxel data range to be 500Hu to 800Hu; if the maximum value of the voxel data in the actually acquired first voxel data set is 1100Hu and the minimum value is 500Hu, the target voxel data range may be set to 500Hu to 1000Hu. Therefore, the target voxel data range can be more accurate, and the human tissues to be observed can be highlighted.
Optionally, in S102, performing data processing on each voxel data according to the target voxel data range to obtain first gray data corresponding to each voxel data, which may include:
s1021, updating the voxel data in the first voxel data set, which is larger than the upper limit of the target voxel data range, as the upper limit of the target voxel data range, and updating the voxel data in the first voxel data set, which is smaller than the lower limit of the target voxel data range, as the lower limit of the target voxel data range, so as to obtain a second voxel data set;
and S1022, carrying out gray scale conversion on the second voxel data set to obtain first gray scale data.
The storage space occupied by the first gray data is a third storage size, and the third storage size is smaller than the first storage size and larger than the second storage size.
For example, a medical image at the same location often contains a plurality of human tissues, and therefore, the voxel data range corresponding to the human tissues to be highlighted actually does not contain voxel data corresponding to all voxel points on the medical image, although the value range of the voxel data is between plus or minus several thousands. Voxel data in the first voxel data set, which is larger than the upper limit of the target voxel data range, can be updated as the upper limit of the target voxel data range, voxel data in the first voxel data set, which is smaller than the lower limit of the target voxel data range, can be updated as the lower limit of the target voxel data range, and a second voxel data set is obtained so as to highlight human tissues to be observed. For example, the target voxel data range is 200Hu to 500Hu, and the voxel data corresponding to each voxel point includes data exceeding the target voxel range, such as 100Hu and 600Hu, and the 100Hu can be updated to 200Hu and 600Hu and 500Hu.
In S1022, the second voxel data set is subjected to gray scale conversion, i.e. each voxel data in the second voxel data set is mapped into a range of 0 to 255. In this way, the first gray data occupies less memory space than the voxel data in the first set of voxel data.
For example, in S1022, the voxel data in the second voxel data set may be subjected to gray conversion by a Min-Max normalization method, to obtain the first gray data.
Wherein, the Min-Max standardization method is used for determining the first data set of the second bodymIndividual voxel data pairsThe formula of the corresponding first gradation data is as follows:
wherein ,
for the first of the second set of volumetric datamIndividual voxel data>
Is the firstmCorresponding first gray data, determined by Min-Max normalization method, of each voxel data,/for each voxel data>
For the lower limit of the target voxel data range, +.>
Is the upper limit of the target voxel data range.
When mapping is performed by a Min-Max standardization method, the voxel data in the second voxel data set and the corresponding first gray data are in a linear relation (namely, linear normalization), so that the voxel data in the second voxel data set can be mapped in the range of 0 to 255 on average.
For another example, in S1022, performing gray-scale transformation on the second voxel data set to obtain first gray-scale data may include:
and determining a first gray reference value and a second gray reference value corresponding to each voxel data in the second voxel data set, and determining the larger one of the first gray reference value and the second gray reference value as the first gray data corresponding to the voxel data, wherein the first gray reference value and the second gray reference value are used for representing gray enhancement of different degrees.
Illustratively, the first of the second set of volumetric data can be determined by the following formulamFirst gray reference value corresponding to individual data:
wherein ,
for the first of the second set of volumetric datamIndividual voxel data>
Is the firstmFirst gray reference value corresponding to individual data,/->
For the lower limit of the target voxel data range, +.>
For the upper limit of the target voxel data range, +.>
For the first parameter, ++>
The value of (2) is in the range of 0 to 1.
The first of the second set of volumetric data can be determined by the following formulamSecond gray reference values corresponding to the respective voxel data:
wherein ,
is the firstmSecond gray reference value corresponding to each voxel data, ">
For the second parameter, ++>
The value of (2) is in the range of 0 to 1.
The first gray scale reference value and the second gray scale reference value each exhibit a nonlinear relationship (i.e., nonlinear normalization) with the voxel data.
And (2) is (are) of>
The first gray reference value and the second gray reference value determined by the formulas (1), (2) are not smaller than the first gray data determined by the linear normalization method. Therefore, the first gray reference value and the second gray reference value are larger, so that the image can be enhanced to a certain extent, the brightness of the image can be integrally improved, and especially, the brightness of a dark color part (namely, a voxel point with smaller voxel data) on the image is obviously improved, and the dark color part on the medical image can be clearer. And the larger one of the first gray reference value and the second gray reference value is determined to be the first gray data corresponding to the voxel data, so that the dark part detail on the medical image can be further ensured to be clearer.
Optionally, in S101, acquiring the first body data set may include:
acquiring original voxel data corresponding to each voxel point included in the medical image;
when the data volume of the original voxel data is larger than the target data volume, determining a compression ratio according to the data volume of the original voxel data and the target data volume;
And compressing the original voxel data according to the compression ratio to obtain a first voxel data set.
Therefore, the data volume of the original voxel data can be effectively compressed under the condition of ensuring the definition of the compressed image, the occupation of memory and computing resources is reduced, the image reconstruction speed of the receiving end is improved, and the browser of the receiving end is prevented from collapsing.
The data volume of the raw voxel data may be the product of the actual data length of the medical image in each dimension. Taking a medical image as a three-dimensional image as an example, the medical image is as followsx、y、zThe actual data length in three dimensions is respectively
、/>
、/>
The data amount of the original voxel data is +.>
. The target data amount may be a product of maximum allowable data lengths of the medical image in each dimension, wherein the maximum allowable data length in each dimension may be preset. Taking a medical image as a three-dimensional image as an example, the medical image is as followsx、y、zMaximum allowable data length in three dimensions is +.>
、/>
、/>
The target data amount is +.>
。
If the data volume of the original voxel data is larger than the target data volume, the data volume of the original voxel data can be determined to be overlarge, and the medical image needs to be compressed. In the compression, the dimension to be compressed may be determined first according to the actual data length and the maximum allowable data length of the medical image in each dimension, where when the actual data length in a certain dimension is greater than the maximum allowable data length in that dimension, the dimension is determined as the dimension to be compressed. And then, determining the compression ratio in the dimension to be compressed according to the ratio of the actual data length to the maximum allowable data length in the dimension to be compressed. Taking a medical image as a three-dimensional image as an example, the medical image is as follows x、y、zThe actual data length in the three dimensions is larger than the maximum allowable data length in each dimension, and the dimension to be compressed is determined to bex、y、zThree dimensionsAnd the compression ratio in each dimension is respectively:
，
，/>
, wherein ,/>
Is thatxCompression ratio in direction, +.>
Is thatyCompression ratio in direction, +.>
Is thatzCompression ratio in the direction. If the calculated compression ratio is not an integer, the calculated compression ratio may be further rounded down to obtain an integer compression ratio.
After the compression ratio is determined, the original voxel data is compressed according to the compression ratio, and a first voxel data set is obtained. Illustratively, voxel points are selected at intervals according to compression ratios in each dimension. Wherein the selected voxel point may be determined as the target voxel point. For example, taking a two-dimensional medical image as an example, fig. 2 is a schematic diagram disclosing voxel point selection according to a compression ratio interval provided by an exemplary embodiment. As shown in FIG. 2, the size of the image 200 is 4×4, and 0-15 represents the index of each voxel point, determinedx、yAnd selecting voxel points according to the compression ratio interval, wherein the voxel points 0, 2, 8 and 10 are selected as target voxel points.
In one embodiment, after determining the target voxel point, the first voxel data set may be formed directly according to the original voxel data corresponding to the target voxel point. Along with the above example, the first voxel data set comprises raw voxel data for each of voxel points 0, 2, 8, 10.
In another embodiment, after determining the target voxel point, at least one reference voxel point corresponding to the target voxel point may be determined according to the target voxel point and the compression ratio, and the target voxel data corresponding to the target voxel point may be determined according to the original voxel data corresponding to each of the target voxel point and the reference voxel point. Then, a first voxel data set is generated based on target voxel data corresponding to the target voxel point.
For example, a neighborhood of the target voxel point may be determined according to the compression ratio, and other voxel points in the neighborhood may be determined as reference voxel points corresponding to the target voxel point. In the example shown in fig. 2, since the region of the target voxel point determined based on the compression ratio is indicated by a broken line box 201 for the target voxel point 0, the voxel points 1, 4, 5 are regarded as reference voxel points corresponding to the target voxel point 0. The region of the target voxel point 10 determined based on the compression ratio is indicated by a dashed box 202, and therefore, the voxel points 5, 6, 7, 9, 11, 13, 14, 15 are reference voxel points corresponding to the target voxel point 10.
After determining the reference voxel point corresponding to the target voxel point, determining the target voxel data corresponding to the target voxel point according to the original voxel data corresponding to the target voxel point and the reference voxel point. For example, an average value of original voxel data corresponding to each of the target voxel point and the reference voxel point may be determined as target voxel data corresponding to the target voxel point. For another example, the raw voxel data corresponding to each of the target voxel point and the reference voxel point is weighted-averaged, and the obtained data is taken as the target voxel data corresponding to the target voxel point, and so on. Thus, the determined target voxel data can reflect the original voxel data of the target voxel point and the corresponding reference voxel point, and the data quantity of the original voxel data is effectively compressed, and meanwhile, the definition of the compressed image can be ensured.
Fig. 3 is a flowchart of a medical image rendering method according to an exemplary embodiment of the present disclosure. The method can be applied to a receiving end of the medical image. As shown in fig. 3, the method may include S301 to S304.
S301, receiving gray data, wherein the gray data is generated and transmitted by a transmitting end according to the medical image data compression method according to any one of the embodiments.
S302, determining the gray scale distance according to the total number of the target gray scales.
Wherein the total number of target gray levels is smaller than the total number of gray levels of the gray level map.
For example, the target gray-scale total may be preset in the receiving end, and is consistent with the target gray-scale total used by the transmitting end. Alternatively, the target total number of gray levels may be received from the transmitting end. The determination of the gray scale interval may be performed according to the method described in S103, which is not described herein.
S303, determining target gray data according to the product of gray data and gray scale spacing.
Illustratively, the received gray data may be split by a shift, assign operation opposite to when two adjacent second gray data are stored to 1 byte. For example, if the received 3 bytes of stored content is 0000 0011, 0001 0111, 0110 0100, it can be re-split into 0000 0000, 0000 0011, 0000 0001, 0000 0111, 0000 0110, 0000 0100 by shifting 4 bits to the right and assigning operation, i.e. determining that the second gray level data is 0,3,1,7,6,4 respectively. If the gray scale pitch is 28, then the target gray scale data may be determined to be 0, 84, 28, 196, 168, 112, respectively.
S304, performing image rendering based on the target gray data.
Therefore, the occupation of medical image data to the memory can be effectively reduced, the image reconstruction speed of the receiving end can be improved under the condition that the definition of the image rendered by the receiving end is ensured, and the browser of the receiving end is prevented from collapsing.
Based on the same inventive concept, the present disclosure also provides a medical image data compression device. Fig. 4 is a block diagram of a medical image data compression apparatus 400 provided in an exemplary embodiment of the present disclosure. Referring to fig. 4, the medical image data compression apparatus 400 may include:
an obtaining module 401, configured to obtain a first voxel data set, where the first voxel data set includes voxel data corresponding to each of a plurality of voxel points, and a storage space occupied by each of the voxel data is a first storage size;
a processing module 402, configured to perform data processing on each voxel data according to a target voxel data range, so as to obtain first gray data corresponding to each voxel data;
a first determining module 403, configured to determine a gray value interval corresponding to each gray level in a target gray level total number, where the target gray level total number is smaller than a gray level total number of the gray level map;
a second determining module 404, configured to determine a gray value interval to which the first gray data belongs, and update the first gray data according to a gray level number corresponding to the gray value interval to obtain second gray data, where a storage space occupied by the second gray data is a second storage size, and the second storage size is smaller than the first storage size;
And the sending module 405 is configured to send the second gray data, so that the receiving end performs image rendering according to the second gray data.
According to the technical scheme, data processing is carried out on each voxel data according to the target voxel data range, and the voxel data of each voxel point are converted into first gray data. Because the range of gray data is 0-255, compared with voxel data with larger numerical range, the gray data occupies smaller storage space. And then, determining a gray value interval corresponding to each gray level in the total number of target gray levels, and updating the first gray level data according to the gray level number corresponding to the gray value interval to which the first gray level data belongs to obtain second gray level data. Because the total number of the target gray levels is smaller than that of the gray level map, the occupation of the storage space can be further reduced. Compared with the original voxel data in the first voxel data set, the storage space occupied by the second gray level data is smaller, so that the occupation of the memory by the medical image data can be effectively reduced, the transmission rate of the medical image is improved under the condition that the definition of the image rendered by the receiving end is ensured, the image reconstruction speed of the receiving end is improved, and the browser of the receiving end is prevented from collapsing.
Optionally, the processing module 402 includes:
an updating sub-module, configured to update voxel data in the first voxel data set that is greater than an upper limit of the target voxel data range to an upper limit of the target voxel data range, and update voxel data in the first voxel data set that is less than a lower limit of the target voxel data range to a lower limit of the target voxel data range, so as to obtain a second voxel data set;
and the conversion sub-module is used for carrying out gray conversion on the second voxel data set to obtain the first gray data, wherein the storage space occupied by the first gray data is of a third storage size, and the third storage size is smaller than the first storage size and larger than the second storage size.
Optionally, the conversion sub-module is configured to perform gray conversion on the second voxel data set to obtain the first gray data by:
and determining a first gray scale reference value and a second gray scale reference value corresponding to each voxel data in the second voxel data set, and determining the larger one of the first gray scale reference value and the second gray scale reference value as the first gray scale data corresponding to the voxel data, wherein the first gray scale reference value and the second gray scale reference value are used for representing gray scale enhancement of different degrees.
Wherein the conversion sub-module is configured to determine a first of the second set of volumetric data by the following formulamFirst gray reference value corresponding to individual data:
wherein ,
for the first of the second set of volumetric datamIndividual voxel data>
Is the firstmFirst gray reference value corresponding to individual data,/->
For the lower limit of the target voxel data range, < > for>
For the upper limit of the target voxel data range, < > for>
For the first parameter, ++>
The value range of (2) is 0 to 1;
the conversion sub-module is used for determining a first of the second set of volumetric data by the following formulamSecond gray reference values corresponding to the respective voxel data:
wherein ,
is the firstmSecond gray reference value corresponding to each voxel data, ">
For the second parameter, ++>
The value of (2) is in the range of 0 to 1.
Optionally, the upper limit of the target voxel data range is the smaller of a preset upper voxel data limit and a maximum value in the first voxel data set, and the lower limit of the target voxel data range is the larger of a preset lower voxel data limit and a minimum value in the first voxel data set.
Optionally, the acquiring module 401 includes:
the acquisition sub-module is used for acquiring original voxel data corresponding to each voxel point included in the medical image;
A first determining submodule, configured to determine a compression ratio according to the data amount of the original voxel data and the target data amount when the data amount of the original voxel data is greater than a preset value;
and the compression sub-module is used for carrying out compression processing on the original voxel data according to the compression ratio to obtain the first voxel data set.
Optionally, the compression sub-module includes:
a second determining sub-module, configured to determine a plurality of target voxel points from the voxel points included in the medical image according to the compression ratio;
a third determining sub-module, configured to determine, for each target voxel point, at least one reference voxel point corresponding to the target voxel point according to the target voxel point and the compression ratio, and determine target voxel data corresponding to the target voxel point according to original voxel data corresponding to each of the target voxel point and the reference voxel point;
and the generation sub-module is used for generating the first voxel data set based on the target voxel data corresponding to the target voxel point.
Optionally, the medical image is a CT image.
Based on the same inventive concept, the present disclosure also provides a medical image rendering device. Fig. 5 is a block diagram of a medical image rendering apparatus 500 provided in an exemplary embodiment of the present disclosure. Referring to fig. 5, the medical image rendering apparatus 500 may include:
A receiving module 501, configured to receive gray data, where the gray data is generated and sent by a sending end according to the medical image data compression method described in any one of the foregoing embodiments;
a third determining module 502, configured to determine a gray scale distance according to a target gray scale total number, where the target gray scale total number is smaller than a gray scale total number of the gray scale map;
a fourth determining module 503, configured to determine target gray data according to a product of the gray data and the gray pitch;
and a rendering module 504, configured to perform image rendering based on the target gray data.
Therefore, the occupation of medical image data to the memory can be effectively reduced, and the image reconstruction speed of the receiving end is improved under the condition that the definition of the image rendered by the receiving end is ensured, so that the browser of the receiving end is prevented from collapsing.
The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
Fig. 6 is a block diagram illustrating a medical image data compression apparatus 600 according to an exemplary embodiment. As shown in fig. 6, the medical image data compression apparatus 600 may include: a processor 601, a memory 602. The medical image data compression apparatus 600 may further include one or more of a multimedia component 603, an input/output (I/O) interface 604, and a communication component 605.
The processor 601 is configured to control the overall operation of the medical image data compression apparatus 600 to perform all or part of the steps in the medical image data compression method. The memory 602 is used to store various types of data to support operation of the medical image data compression device 600, which may include, for example, instructions for any application or method operating on the medical image data compression device 600, as well as application-related data, such as contact data, messages, pictures, audio, video, and the like. The Memory 602 may be implemented by any type or combination of volatile or nonvolatile Memory devices, such as static random access Memory (Static Random Access Memory, SRAM for short), electrically erasable programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory, EEPROM for short), erasable programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM for short), programmable Read-Only Memory (Programmable Read-Only Memory, PROM for short), read-Only Memory (ROM for short), magnetic Memory, flash Memory, magnetic disk, or optical disk. The multimedia component 603 may include a screen and an audio component. Wherein the screen may be, for example, a touch screen, the audio component being for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 602 or transmitted through the communication component 605. The audio assembly further comprises at least one speaker for outputting audio signals. The I/O interface 604 provides an interface between the processor 601 and other interface modules, which may be a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 605 is used for wired or wireless communication between the medical image data compression apparatus 600 and other devices. Wireless communication, such as Wi-Fi, bluetooth, near field communication (Near Field Communication, NFC for short), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or one or a combination of more of them, is not limited herein. The corresponding communication component 605 may thus comprise: wi-Fi module, bluetooth module, NFC module, etc.
In an exemplary embodiment, the medical image data compression apparatus 600 may be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, abbreviated as ASIC), digital signal processors (Digital Signal Processor, abbreviated as DSP), digital signal processing devices (Digital Signal Processing Device, abbreviated as DSPD), programmable logic devices (Programmable Logic Device, abbreviated as PLD), field programmable gate arrays (Field Programmable Gate Array, abbreviated as FPGA), controllers, microcontrollers, microprocessors, or other electronic components for performing the medical image data compression method described above.
In another exemplary embodiment, a computer readable storage medium is also provided, comprising program instructions which, when executed by a processor, implement the steps of the medical image data compression method described above. For example, the computer readable storage medium may be the memory 602 including program instructions described above, which are executable by the processor 601 of the medical image data compression apparatus 600 to perform the medical image data compression method described above.
In another exemplary embodiment, a computer program product is also provided, comprising a computer program executable by a programmable apparatus, the computer program having code portions for performing the above-described medical image data compression method when executed by the programmable apparatus.
Fig. 7 is a block diagram illustrating a medical image rendering apparatus 700 according to an exemplary embodiment. As shown in fig. 7, the medical image rendering apparatus 700 may include: a processor 701, a memory 702. The medical image rendering device 700 may also include one or more of a multimedia component 703, an input/output (I/O) interface 704, and a communication component 705.
The processor 701 is configured to control the overall operation of the medical image rendering apparatus 700 to perform all or part of the above-mentioned medical image rendering method. The memory 702 is used to store various types of data to support operation at the medical image rendering device 700, which may include, for example, instructions for any application or method operating on the medical image rendering device 700, as well as application-related data, such as contact data, transceived messages, pictures, audio, video, and the like. The Memory 702 may be implemented by any type or combination of volatile or non-volatile Memory devices, such as static random access Memory (Static Random Access Memory, SRAM for short), electrically erasable programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory, EEPROM for short), erasable programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM for short), programmable Read-Only Memory (Programmable Read-Only Memory, PROM for short), read-Only Memory (ROM for short), magnetic Memory, flash Memory, magnetic disk, or optical disk. The multimedia component 703 can include a screen and an audio component. Wherein the screen may be, for example, a touch screen, the audio component being for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 702 or transmitted through the communication component 705. The audio assembly further comprises at least one speaker for outputting audio signals. The I/O interface 704 provides an interface between the processor 701 and other interface modules, which may be a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 705 is configured to perform wired or wireless communication between the medical image rendering apparatus 700 and other devices. Wireless communication, such as Wi-Fi, bluetooth, near field communication (Near Field Communication, NFC for short), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or one or a combination of more of them, is not limited herein. The corresponding communication component 705 may thus comprise: wi-Fi module, bluetooth module, NFC module, etc.
In an exemplary embodiment, the medical image rendering apparatus 700 may be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, abbreviated as ASIC), digital signal processor (Digital Signal Processor, abbreviated as DSP), digital signal processing device (Digital Signal Processing Device, abbreviated as DSPD), programmable logic device (Programmable Logic Device, abbreviated as PLD), field programmable gate array (Field Programmable Gate Array, abbreviated as FPGA), controller, microcontroller, microprocessor, or other electronic components for performing the medical image rendering method described above.
In another exemplary embodiment, a computer readable storage medium comprising program instructions which, when executed by a processor, implement the steps of the medical image rendering method described above is also provided. For example, the computer readable storage medium may be the memory 702 including the program instructions described above, which are executable by the processor 701 of the medical image rendering apparatus 700 to perform the medical image rendering method described above.
In another exemplary embodiment, a computer program product is also provided, comprising a computer program executable by a programmable apparatus, the computer program having code portions for performing the above-described medical image rendering method when executed by the programmable apparatus.
The preferred embodiments of the present disclosure have been described in detail above with reference to the accompanying drawings, but the present disclosure is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solutions of the present disclosure within the scope of the technical concept of the present disclosure, and all the simple modifications belong to the protection scope of the present disclosure.
In addition, the specific features described in the above embodiments may be combined in any suitable manner without contradiction. The various possible combinations are not described further in this disclosure in order to avoid unnecessary repetition.
Moreover, any combination between the various embodiments of the present disclosure is possible as long as it does not depart from the spirit of the present disclosure, which should also be construed as the disclosure of the present disclosure.
Claims (11)
1. A method of medical image data compression, the method comprising:
acquiring a first voxel data set, wherein the first voxel data set comprises voxel data corresponding to each of a plurality of voxel points, and the storage space occupied by each voxel data is a first storage size;
performing data processing on each voxel data according to a target voxel data range to obtain first gray data corresponding to each voxel data, wherein a storage space occupied by the first gray data is a third storage size, and the third storage size is smaller than the first storage size;
Determining a gray value interval corresponding to each gray level in a target gray level total number, wherein the target gray level total number is smaller than the gray level total number of a gray level graph, and the gray level graph comprises 256 gray levels;
determining a gray value interval to which the first gray data belongs, and updating the first gray data according to the gray scale number corresponding to the gray value interval to obtain second gray data, wherein a storage space occupied by the second gray data is a second storage size, and the second storage size is smaller than the third storage size;
transmitting the second gray data so that the receiving end performs image rendering according to the second gray data;
the data processing is performed on each voxel data according to the target voxel data range to obtain first gray data corresponding to each voxel data, including:
updating the voxel data in the first voxel data set, which is larger than the upper limit of the target voxel data range, to be the upper limit of the target voxel data range, and updating the voxel data in the first voxel data set, which is smaller than the lower limit of the target voxel data range, to be the lower limit of the target voxel data range, so as to obtain a second voxel data set;
Performing gray level conversion on the second voxel data set to obtain the first gray level data;
the performing gray conversion on the second voxel data set to obtain the first gray data includes:
determining a first gray reference value and a second gray reference value corresponding to each voxel data in the second voxel data set, and determining the larger one of the first gray reference value and the second gray reference value as the first gray data corresponding to the voxel data, wherein the first gray reference value and the second gray reference value are used for representing gray enhancement of different degrees;
wherein determining the first gray reference value and the second gray reference value corresponding to each body of data in the second body of data set includes:
determining the first of said second set of volumetric data by the formulamFirst gray reference value corresponding to individual data:
wherein ,
for the first of the second set of volumetric datamIndividual voxel data>
Is the firstmFirst gray reference value corresponding to individual data,/->
For the lower limit of the target voxel data range, < > for>
For the upper limit of the target voxel data range, < > for >
For the first parameter, ++>
The value range of (2) is 0 to 1;
determining the first of said second set of volumetric data by the formulamSecond gray reference values corresponding to the respective voxel data:
wherein ,
is the firstmSecond gray reference value corresponding to each voxel data, ">
For the second parameter, ++>
The value of (2) is in the range of 0 to 1.
2. The method of claim 1, wherein an upper limit of the target voxel data range is the smaller of a preset upper voxel data limit and a maximum value in the first voxel data set, and a lower limit of the target voxel data range is the larger of a preset lower voxel data limit and a minimum value in the first voxel data set.
3. The method of claim 1, wherein the acquiring the first body data set comprises:
acquiring original voxel data corresponding to each voxel point included in the medical image;
when the data volume of the original voxel data is larger than the target data volume, determining a compression ratio according to the data volume of the original voxel data and the target data volume;
and carrying out compression processing on the original voxel data according to the compression ratio to obtain the first voxel data set.
4. A method according to claim 3, wherein said compressing said raw voxel data according to said compression ratio to obtain said first set of voxel data comprises:
determining a plurality of target voxel points from all voxel points included in the medical image according to the compression ratio;
for each target voxel point, determining at least one reference voxel point corresponding to the target voxel point according to the target voxel point and the compression ratio, and determining target voxel data corresponding to the target voxel point according to original voxel data corresponding to each of the target voxel point and the reference voxel point;
and generating the first voxel data set based on the target voxel data corresponding to the target voxel point.
5. The method of any one of claims 1-4, wherein the medical image is a CT image.
6. A medical image rendering method, the method comprising:
receiving gray data, wherein the gray data is generated and transmitted by a transmitting end according to the method of any one of claims 1 to 5;
determining a gray scale distance according to a target gray scale total number, wherein the target gray scale total number is smaller than the gray scale total number of a gray scale map;
Determining target gray data according to the product of the gray data and the gray interval;
and performing image rendering based on the target gray data.
7. A medical image data compression apparatus, the apparatus comprising:
the acquisition module is used for acquiring a first voxel data set, wherein the first voxel data set comprises voxel data corresponding to each of a plurality of voxel points, and the storage space occupied by each voxel data is a first storage size;
the processing module is used for carrying out data processing on each voxel data according to the target voxel data range to obtain first gray data corresponding to each voxel data, wherein the storage space occupied by the first gray data is a third storage size, and the third storage size is smaller than the first storage size;
the first determining module is used for determining a gray value interval corresponding to each gray level in the total number of target gray levels, wherein the total number of the target gray levels is smaller than the total number of gray levels of a gray level graph, and the gray level graph comprises 256 gray levels;
the second determining module is used for determining a gray value interval to which the first gray data belongs, and updating the first gray data according to the gray scale number corresponding to the gray value interval to obtain second gray data, wherein a storage space occupied by the second gray data is a second storage size, and the second storage size is smaller than the third storage size;
The sending module is used for sending the second gray data so that the receiving end performs image rendering according to the second gray data;
wherein, the processing module includes:
an updating sub-module, configured to update voxel data in the first voxel data set that is greater than an upper limit of the target voxel data range to an upper limit of the target voxel data range, update voxel data in the first voxel data set that is less than a lower limit of the target voxel data range to a lower limit of the target voxel data range, and obtain a second voxel data set;
the conversion sub-module is used for carrying out gray conversion on the second voxel data set to obtain the first gray data;
the conversion sub-module is used for carrying out gray conversion on the second voxel data set in the following manner to obtain the first gray data:
determining a first gray reference value and a second gray reference value corresponding to each voxel data in the second voxel data set, and determining the larger one of the first gray reference value and the second gray reference value as the first gray data corresponding to the voxel data, wherein the first gray reference value and the second gray reference value are used for representing gray enhancement of different degrees;
Wherein the conversion sub-module is configured to determine a first of the second set of volumetric data by the following formulamFirst gray reference value corresponding to individual data:
wherein ,
for the first of the second set of volumetric datamIndividual voxel data>
Is the firstmFirst gray reference value corresponding to individual data,/->
For the lower limit of the target voxel data range, < > for>
For the upper limit of the target voxel data range, < > for>
For the first parameter, ++>
The value range of (2) is 0 to 1;
the conversion sub-module is used for determining a first of the second set of volumetric data by the following formulamSecond gray reference values corresponding to the respective voxel data:
wherein ,
is the firstmSecond gray reference value corresponding to each voxel data, ">
For the second parameter, ++>
The value of (2) is in the range of 0 to 1.
8. A medical image rendering apparatus, the apparatus comprising:
a receiving module, configured to receive gray data, where the gray data is generated and sent by a sending end according to the method of any one of claims 1-5;
the third determining module is used for determining gray scale intervals according to the total number of target gray scales, wherein the total number of the target gray scales is smaller than the total number of the gray scales of the gray scale map;
A fourth determining module, configured to determine target gray data according to a product of the gray data and the gray pitch;
and the rendering module is used for performing image rendering based on the target gray data.
9. A medical image data compression apparatus, the apparatus comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to implement the steps of the method of any one of claims 1-5.
10. A medical image rendering apparatus, the apparatus comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to implement the steps of the method of claim 6.
11. A non-transitory computer readable storage medium, having stored thereon a computer program, characterized in that the program when executed by a processor realizes the steps of the method according to any of claims 1-5 or the steps of the method according to claim 6.
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