CN114968952A - Medical image data compression method, rendering method, device and medium - Google Patents
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Abstract
The present disclosure relates to a medical image data compression method, a rendering method, an apparatus, 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 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 the target voxel data range to obtain first gray data corresponding to each voxel data; determining a gray value interval corresponding to each gray scale in the total number of the target gray scales; determining a gray value interval to which the first gray data belongs, and updating the first gray data according to a gray level order corresponding to the gray value interval to obtain second gray data; and sending the second gray scale data to enable the receiving end to render the image according to the second gray scale data. Therefore, the occupation of the medical image data on the memory can be effectively reduced, the transmission rate of the medical image is improved, and the image reconstruction speed of the 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 apparatus, and a medium.
Background
With the development of computer technology, more and more software applications are transplanted to a webpage (Web) end, and the software applications based on the webpage end do not need to be installed locally, complicated configuration is not needed, and the method is convenient to take at any time. Medical imaging tools are also beginning to appear on the web page side.
The three-dimensional reconstruction technology of medical images based on a webpage end is currently in a development stage. One of the inevitable problems is that the amount of single-group medical image data is generally large, the value range of voxel data corresponding to each voxel point of the medical image is between plus or minus thousands, and the memory space occupied by each voxel data is 16 bits, so that the medical image occupies a large memory space, the transmission rate of the medical image is affected, the image reconstruction speed of a receiving end is slowed, and even the browser is crashed.
Disclosure of Invention
The purpose of the present disclosure is to provide a medical image data compression method, a medical image rendering device, and a medical image data compression medium, which can effectively reduce the memory occupied by medical image data, and improve the transmission rate of medical images while ensuring the definition of rendering images at a receiving end, so as to improve the image reconstruction speed at the receiving end and prevent the browser at the receiving end from being crashed.
In order to achieve the above object, a first aspect of the present disclosure provides a method for compressing medical image data, the method including:
acquiring a first voxel data set, wherein the first voxel data set comprises voxel data corresponding to 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 scale in a target gray scale total number, wherein the target gray scale total number is less than the gray scale total number of a gray scale image;
determining a gray value interval to which the first gray data belongs, and updating the first gray data according to a gray level order 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 scale data to enable a receiving end to render images according to the second gray scale data.
Optionally, the performing data processing on each voxel data according to a target voxel data range to obtain first gray-scale data corresponding to each voxel data includes:
updating 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 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 to obtain a second voxel data set;
and performing 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 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 conversion on the second voxel data set to obtain the first gray scale 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 different degrees of gray enhancement.
Optionally, the determining a first gray reference value and a second gray reference value corresponding to each voxel data in the second voxel data set includes:
determining a first gray reference value corresponding to the mth voxel data in the second voxel data set by the following formula:
wherein ,xm For the mth voxel data, f in the second voxel data set 1 (x m ) A first gray reference value corresponding to the mth voxel data, min is a lower limit of a target voxel data range, max is an upper limit of the target voxel data range, v is a first parameter, and the value range of v is 0 to 1;
determining a second gray reference value corresponding to the mth individual voxel data in the second voxel data set by the following formula:
wherein ,f2 (x m ) And the second gray reference value is a second gray reference value corresponding to the mth voxel data, gamma is a second parameter, and the value range of gamma is 0 to 1.
Optionally, an upper limit of the target value range is the smaller of a preset upper limit of voxel data and a maximum value in the first voxel data set, and a lower limit of the target value range is the larger of a preset lower limit of voxel data and a minimum value in the first voxel data set.
Optionally, the acquiring the first voxel 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 compressing 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 the target voxel point and the reference voxel point respectively;
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 method for rendering a medical image, the method comprising:
receiving gray data, wherein the gray data is generated and sent by a sending terminal according to the method provided by the first aspect of the disclosure;
determining a gray scale interval 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 image;
determining target gray data according to the product of the gray data and the gray scale interval;
and rendering the image based on the target gray scale data.
A third aspect of the present disclosure provides a medical image data compression apparatus, including:
an obtaining module, configured to obtain a first voxel data set, where the first voxel data set includes voxel data corresponding to a plurality of voxel points, and a 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 a 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 scale in 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 image;
the second determining module is 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 order 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 is used for sending the second gray data so that a receiving end can perform image rendering according to the second gray data.
A fourth aspect of the present disclosure provides 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 provided by the first aspect of the disclosure;
the third determining module is used for determining the gray scale interval 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 image;
the fourth determining module is used for determining target gray data according to the product of the gray data and the gray scale interval;
and the rendering module is used for rendering the image 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, performs the steps of the method provided by the first or second aspect of the present disclosure.
A sixth aspect of the present disclosure provides a medical image data compression apparatus, including:
a memory having a computer program stored thereon;
a controller, which when executed by the controller, implements the steps of the method provided by the first aspect of the disclosure.
A seventh aspect of the present disclosure provides a medical image rendering apparatus, including:
a memory having a computer program stored thereon;
a controller, which when executed by the controller, implements the steps of the method provided by the second aspect of the disclosure.
By 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 is converted into first gray data. Since the range of the gray scale data is 0 to 255, the memory space occupied by the gray scale data is smaller than that occupied by the voxel data with a larger numerical range. And then, determining a gray value interval corresponding to each gray value in the total number of the target gray values, and updating the first gray data according to the gray level order corresponding to the gray value interval to which the first gray data belongs to obtain second gray data. The total number of the target gray scales is smaller than that of the gray scales of the gray scale image, so that the occupation of the storage space can be further reduced. Compared with voxel data in the first voxel data set, the storage space occupied by the second gray data is smaller, so that the occupation of medical image data to a memory can be effectively reduced, the transmission rate of medical images is improved under the condition of ensuring the definition of a rendering image of a receiving end, the image reconstruction speed of the receiving end is increased, and the browser of the receiving end is prevented from collapsing.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure without limiting the disclosure. In the drawings:
fig. 1 is a flowchart of a medical image data compression method according to an exemplary embodiment of the present disclosure;
FIG. 2 is a schematic diagram illustrating the selection of voxel points at compressed scale intervals provided by the disclosed 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 apparatus according to an exemplary embodiment of the present disclosure;
fig. 5 is a block diagram of a medical image rendering apparatus according to an exemplary embodiment of the present disclosure;
fig. 6 is a block diagram of a medical image data compression apparatus 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
The following detailed description of specific embodiments of the present disclosure is provided in connection with the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present disclosure, are given by way of illustration and explanation only, not limitation.
It should be noted that all actions of acquiring signals, information or data in the present disclosure are performed under the premise of complying with the corresponding data protection regulation 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 image according to the present disclosure may include a CT (Computed Tomography) image, an MRI (Magnetic Resonance Imaging) image, a PET (Positron Emission Computed Tomography) image, 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 a CT image as an example should not be construed as limiting the scope of the present disclosure. The method can be applied to the transmitting end of the medical image. As shown in fig. 1, the method may include S101 to S105.
S101, a first voxel data set is obtained, the first voxel data set comprises voxel data corresponding to a plurality of voxel points, and a storage space occupied by each voxel data is a first storage size.
Illustratively, the medical image is a CT image, and the voxel data may be a CT value in Hu.
The size of the voxel data corresponding to each voxel point of the medical image is usually between plus or minus thousands. In order to ensure the uniformity of the size of the occupied storage space when the data is stored and the integrity of the stored data, the maximum value of the storage space occupied by each voxel data is often used as the storage space corresponding to each voxel data. For example, at the web page side, 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 16bit of voxel data. 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 browser may break down during rendering. Accordingly, the present disclosure performs a compression process on the first voxel data set by the following steps.
And 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.
Illustratively, the target voxel data range may be preset according to actual needs. The data processing performed on each voxel data may be normalization processing for gray scale conversion to obtain first gray scale data corresponding to each voxel 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. That is, each voxel data can be mapped into the range of 0 to 255 by data processing. Thus, data out of the range of 0 to 255 does not exist in all the first gradation data. Compared with voxel data with a larger numerical range, the storage space occupation can be reduced by converting the voxel data into the first gray scale data. For example, at the web page side, the storage space occupied by each first gray data is 8 bits, and compared with the storage space occupied by converting the previous voxel data, the storage space occupied by each first gray data is 16 bits, the occupation of the memory is greatly reduced.
S103, determining a gray value interval corresponding to each gray level in the total number of the target gray levels.
Wherein, the total number of the target gray scales is less than that of the gray scales of the gray scale image.
Illustratively, the target gray-scale total number may be set in advance, for example, may be set to 10. The value range (0 to 255) corresponding to 256 gray scales can be divided at equal intervals according to the total number of the target gray scales. The gray scale spacing between adjacent gray scales within the target total number of gray scales can be determined by a formula
It is determined that glW is the gray scale interval and N is the total number of target gray scales.
The value range of the gray scale N in the total number of the target gray scales is 0-N-1, and when N is equal to 0, the gray scale value interval corresponding to the gray scale 0 in the total number of the target gray scales is [0, 0 ]; when n is greater than 0, the gray value interval corresponding to the gray level n in the total number of the target gray levels is ((n-1) × glW, n × glW), for example, the total number of the target gray levels is 10, the calculated glW is 28, the gray value interval corresponding to the gray level 1 is (0, 28), the gray value interval corresponding to the gray level 2 is (28, 56), the gray value interval corresponding to the gray level 3 is (56, 84), … …, and so on, the gray value interval corresponding to the gray level 9 is (224, 252), since the gray value does not exceed 255 during the actual application process, the value range of the last gray value interval can be written as ((n-1) × glW, 255), and the above example, the gray value interval corresponding to the gray level 9 is (224, 255).
S104, determining a gray value interval to which the first gray data belongs, and updating the first gray data according to a gray level order corresponding to the gray value interval to obtain second gray data.
The 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.
For example, according to a specific numerical value of the first gray data, a gray value interval corresponding thereto may be determined. As described above, if the first gray scale data is 31, the gray scale interval to which the first gray scale data belongs is determined to be (28, 56) and the second gray scale data is 2, which is the updated data of the first gray scale data according to the gray scale order corresponding to the gray scale interval, and thus, assuming that the first gray scale data is 0, 71, 14, 191, 160, 95, respectively, the second gray scale data obtained in the above manner corresponds to 0, 3, 1, 7, 6, 4, and the data amount of the second gray scale data is smaller than that of the first gray scale data, so that the occupation of the memory space can be further reduced.
And S105, sending the second gray data to enable the receiving end to render the image according to the second gray data.
By 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 is converted into first gray data. Since the range of the gray scale data is 0 to 255, the memory space occupied by the gray scale data is smaller than that occupied by the voxel data with a larger numerical range. And then, determining a gray value interval corresponding to each gray value in the total number of the target gray values, and updating the first gray data according to the gray level order corresponding to the gray value interval to which the first gray data belongs to obtain second gray data. The total number of the target gray scales is smaller than that of the gray scales of the gray scale image, so that the occupation of the storage space can be further reduced. Compared with voxel data in the first voxel data set, the storage space occupied by the second gray data is smaller, so that the occupation of medical image data to a memory can be effectively reduced, the transmission rate of medical images is improved under the condition of ensuring the definition of a rendering image of a receiving end, the image reconstruction speed of the receiving end is increased, and the browser of the receiving end is prevented from collapsing.
Preferably, in S103, the total number of target gray levels may be a positive integer no greater than 16.
In the process of observing images, the number of gray scales which can be distinguished by human eyes is usually not more than 16. Even if the target total number of gradations is set to a value larger than 16, for example, 48, the image rendered by the receiving side can be rendered more accurately, but the human eye is not sensitive to an excessive number of gradations, and it is difficult to make a more accurate determination with an image having a large number of gradations (larger than 16 gradations). Therefore, the total number of the 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 of ensuring the definition of a rendering image of a receiving end. For example, if the total number of target gray levels is set to 16, the storage space occupied by a single datum at the web page end 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 grayscale data is 0, 3, 1, 7, 6, 4, and if each of them occupies 8 bits of storage space, the second grayscale data is represented in binary form as 00000000, 00000011, 00000001, 00000111, 00000110, and 00000100, and occupies 6 bytes. Each second gray data can be represented by 4 bits by assignment and shift operations, for example, left shift by 4 bits, so that the above can be compressed and stored in 3 bytes, specifically 00000011, 00010111, 01100100.
In order to ensure the definition of the image rendered by the receiving end, the total number of target gray levels may be set to 16 or an integer similar thereto.
Alternatively, the upper limit of the target numerical range may be the smaller of the preset upper limit of voxel data and the maximum value in the first voxel data set, and the lower limit of the target numerical range may be the larger of the preset lower limit of voxel data and the minimum value in the first voxel data set.
For example, the preset upper limit of voxel data and the preset lower limit of voxel data may be set according to actual requirements. Medical images of the same position often include 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 different, so that the human tissues to be observed in a focused manner can be highlighted through a preset voxel data upper limit and a preset voxel data lower limit. For example, if the human tissue displayed by the medical image is bone tissue, the preset upper limit of the voxel data may be set to 1000Hu, and the preset lower limit of the voxel data may be set to 400 Hu. If the maximum value and the minimum value in the actually acquired voxel data in the first voxel data set are 800Hu and 500Hu respectively, the target value range can be set to be 500Hu to 800 Hu; if the maximum value and the minimum value of the voxel data in the first voxel data set that is actually acquired are 1100Hu and 500Hu, the target value range may be set to 500Hu to 1000 Hu. Therefore, the target numerical 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-scale data corresponding to each voxel data may include:
s1021, updating the voxel data, which is larger than the upper limit of the target voxel data range, in the first voxel data set to be the upper limit of the target voxel data range, and updating the voxel data, which is smaller than the lower limit of the target voxel data range, in the first voxel data set to be the lower limit of the target voxel data range to obtain a second voxel data set;
and S1022, performing 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 position often contains a plurality of human tissues, and therefore, although the numerical range of the voxel data is between plus or minus several thousand, the voxel data range corresponding to the human tissue to be highlighted actually does not contain the voxel data corresponding to all the voxel points on the medical image. The voxel data in the first voxel data set which is larger than the upper limit of the target voxel data range can be updated to be the upper limit of the target voxel data range, and the voxel data in the first voxel data set which is smaller than the lower limit of the target voxel data range can be updated to be the lower limit of the target voxel data range, so that the second voxel data set is obtained, and the human tissue to be observed is highlighted. For example, the target voxel data range is 200Hu to 500Hu, and the voxel data corresponding to each voxel point includes data outside the target voxel range, such as 100Hu and 600Hu, and 100Hu may be updated to 200Hu and 600Hu may be updated to 500 Hu.
In S1022, the second voxel data set is subjected to grayscale conversion, i.e., each voxel data in the second voxel data set is mapped into a range of 0 to 255. As such, the first gray-scale data may occupy less memory space than the voxel data in the first voxel data set.
For example, in S1022, the voxel data in the second voxel data set may be subjected to gradation conversion by the Min-Max normalization method to obtain first gradation data.
The formula for determining the first gray scale data corresponding to the mth individual voxel data in the second voxel data set by the Min-Max standardization method is as follows:
wherein ,xm For the mth voxel data in the second voxel data set, f 0 (x m ) And corresponding first gray scale data determined by a Min-Max standardization method for the mth voxel data, wherein Min is the lower limit of the target voxel data range, and Max 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-scale data present a linear relationship (i.e., linear normalization), that is, the voxel data in the second voxel data set can be averagely mapped in a range of 0 to 255.
For another example, in S1022, performing gray scale conversion on the second voxel data set to obtain the 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 different degrees of gray enhancement.
For example, the first gray reference value corresponding to the mth individual voxel data in the second voxel data set may be determined by the following formula:
wherein ,xm For the mth voxel data in the second voxel data set, f 1 (x m ) The first gray reference value corresponding to the mth voxel data, min is the lower limit of the target voxel data range, max is the upper limit of the target voxel data range, v is a first parameter, and the value range of v is 0 to 1.
The second gray reference value corresponding to the mth individual voxel data in the second voxel data set may be determined by the following formula:
wherein ,f2 (x m ) And the second gray reference value is a second gray reference value corresponding to the mth voxel data, gamma is a second parameter, and the value range of gamma is 0 to 1.
The first and second gray reference values each exhibit a non-linear relationship (i.e., non-linear normalization) with the voxel data.
And the number of the first and second electrodes,
the first and second gray reference values determined by the equations (1) and (2) are not less than the first gray data determined by the linear normalization method. In this way, since the first gray reference value and the second gray reference value are larger, the image can be enhanced to a certain extent, that is, the brightness of the image can be improved as a whole, and particularly, the brightness of a dark part (that is, a voxel point with smaller voxel data) on the image can be obviously improved, that is, the dark part on the medical image can be improvedThe score is clearer. And the larger one of the first gray reference value and the second gray reference value is determined as the first gray data corresponding to the voxel data, so that the detail of the dark part on the medical image can be further ensured to be clearer.
Optionally, in S101, acquiring a first voxel 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, under the condition of ensuring the definition of the compressed image, the data volume of the original voxel data can be effectively compressed, 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 crashing.
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 actual data lengths of the medical image in the x, y, and z dimensions are xL, yL, and zL, respectively, and the data volume of the original voxel data is xL × yL × zL. 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 lengths in each dimension may be preset. Taking the medical image as a three-dimensional image as an example, the maximum allowable data lengths of the medical image in the three dimensions x, y and z are xl, yl and zl, respectively, and the target data amount is xl _ yl _ zl.
If the data volume of the original voxel data is larger than the target data volume, it can be determined that the data volume of the original voxel data is too large, and the medical image needs to be compressed. When compressing, the dimension to be compressed may be determined first according to the actual data length of the medical image in each dimension and the maximum allowable data length, where when the actual data length in a certain dimension is greater than the maximum allowable data length in the dimension, the dimension is determinedIs determined as the dimension to be compressed. And then, determining the compression ratio on the dimension to be compressed according to the ratio of the actual data length on the dimension to be compressed to the maximum allowable data length. Taking a medical image as a three-dimensional image as an example, if the actual data lengths of the medical image in the three dimensions x, y and z are all greater than the maximum allowable data length in each dimension, determining the dimension to be compressed as the three dimensions x, y and z, and the compression ratio in each dimension is respectively:
wherein xS is a compression ratio in the x direction, yS is a compression ratio in the y direction, and zS is a compression ratio in the z 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.
And after the compression ratio is determined, compressing the original voxel data according to the compression ratio to obtain a first voxel data set. Illustratively, voxel points are selected at intervals according to the compression ratio 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 an exemplary embodiment providing for selecting voxel points at compressed scale intervals. As shown in fig. 2, the size of the image 200 is 4 × 4, 0 to 15 indicate the index of each voxel point, and if the compression ratios in the x and y dimensions are both 2, voxel points are selected at the compression ratio interval, and voxel points 0, 2, 8, and 10 are selected as target voxel points.
In one embodiment, after the target voxel point is determined, the first voxel data set may be formed directly from the original voxel data corresponding to the target voxel point. Following the example above, the first voxel data set includes raw voxel data corresponding to each of voxel points 0, 2, 8, 10.
In another embodiment, after the target voxel point is determined, 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 the target voxel point and the reference voxel point. Then, a first voxel data set is generated based on the 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 the reference voxel point corresponding to the target voxel point. Following the example shown in fig. 2, for the target voxel point 0, the area of the target voxel point determined based on the compression ratio is indicated by a dashed-line box 201, and therefore voxel points 1, 4, and 5 are reference voxel points corresponding to the target voxel point 0. The area of the target voxel point determined based on the compression ratio is indicated by a dashed-line frame 202 for the target voxel point 10, and therefore the voxel points 5, 6, 7, 9, 11, 13, 14, and 15 are reference voxel points corresponding to the target voxel point 10.
And after the reference voxel point corresponding to the target voxel point is determined, determining 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 respectively. For example, an average value of the original voxel data corresponding to each of the target voxel point and the reference voxel point may be determined as the target voxel data corresponding to the target voxel point. For another example, the original voxel data corresponding to the target voxel point and the reference voxel point are weighted and averaged, and the obtained data is used as the target voxel data corresponding to the target voxel point, and so on. Therefore, the determined target voxel data can reflect the target voxel points and the original voxel data corresponding to the reference voxel points, and the compressed image definition can be ensured while the data volume of the original voxel data is effectively compressed.
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 the 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 the transmitting end according to the medical image data compression method described in any of the foregoing embodiments.
S302, determining the gray scale interval according to the total number of the target gray scales.
Wherein, the total number of the target gray scales is less than that of the gray scales of the gray scale image.
For example, the target gray scale total number may be preset in the receiving end, and is consistent with the target gray scale total number used by the transmitting end. Alternatively, the target gray scale total number may be received from the transmitting side. The determination of the gray scale interval may be performed according to the method described above in S103, and will not be described herein.
S303, determining target gray data according to the product of the gray data and the gray scale interval.
Illustratively, the received gray data may be split by a shift, assign operation opposite to storing two adjacent second gray data to 1 byte. For example, if the received 3 bytes of storage are 00000011, 00010111, 01100100, they can be re-split into 00000000, 00000011, 00000001, 00000111, 00000110, and 00000100 by right shifting by 4 bits and assigning, i.e. it is determined that the second grayscale data are 0, 3, 1, 7, 6, and 4, respectively. If the gray scale interval is 28, the target gray scale data is determined to be 0, 84, 28, 196, 168, 112, respectively.
And S304, rendering the image based on the target gray data.
Therefore, the occupation of the medical image data on the memory can be effectively reduced, the image reconstruction speed of the receiving end can be improved under the condition of ensuring the definition of the rendering image of the receiving end, and the browser of the receiving end is prevented from collapsing.
Based on the same inventive concept, the disclosure also provides a medical image data compression device. Fig. 4 is a block diagram of a medical image data compression apparatus 400 according to 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 a plurality of voxel points, and a storage space occupied by each 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 to obtain first gray-scale data corresponding to each voxel data;
a first determining module 403, configured to determine a gray value interval corresponding to each gray scale in a total number of target gray scales, where the total number of target gray scales is smaller than a total number of gray scales of a gray 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 order 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;
a sending module 405, configured to send the second grayscale data, so that a receiving end performs image rendering according to the second grayscale data.
By 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 is converted into first gray data. Since the range of the gray scale data is 0 to 255, the memory space occupied by the gray scale data is smaller than that occupied by the voxel data with a larger numerical range. And then, determining a gray value interval corresponding to each gray level in the total number of the target gray levels, and updating the first gray data according to the gray level order corresponding to the gray value interval to which the first gray data belongs to obtain second gray data. The total number of the target gray scales is smaller than that of the gray scales of the gray scale image, so that 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 data is smaller, so that the occupation of the medical image data on the memory can be effectively reduced, the transmission rate of the medical image is improved under the condition of ensuring the definition of the rendering image of the receiving end, the image reconstruction speed of the receiving end is increased, and the browser of the receiving end is prevented from collapsing.
Optionally, the processing module 402 includes:
the updating sub-module is used for updating the voxel data, which is larger than the upper limit of the target voxel data range, in the first voxel data set to be the upper limit of the target voxel data range, and updating the voxel data, which is smaller than the lower limit of the target voxel data range, in the first voxel data set to be the 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 scale conversion on the second voxel data set to obtain the first gray scale data, wherein the storage space occupied by the first gray scale 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.
Optionally, the conversion sub-module is configured to perform gray scale conversion on the second voxel data set to obtain the first gray scale data by:
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 different degrees of gray enhancement.
Wherein the conversion sub-module is configured to determine a first gray reference value corresponding to the mth voxel data in the second voxel data set by the following formula:
wherein ,xm For the mth voxel data, f in the second voxel data set 1 (x m ) A first gray reference value corresponding to the mth voxel data, min is a lower limit of the target voxel data range, max is an upper limit of the target voxel data range, v is a first parameter, and v ranges from 0 to 1;
the conversion sub-module is configured to determine a second gray reference value corresponding to the mth individual voxel data in the second voxel data set by the following formula:
wherein ,f2 (x m ) And the second gray reference value is a second gray reference value corresponding to the mth voxel data, gamma is a second parameter, and the value range of gamma is 0 to 1.
Optionally, an upper limit of the target value range is the smaller of a preset upper limit of voxel data and a maximum value in the first voxel data set, and a lower limit of the target value range is the larger of a preset lower limit of voxel data and a minimum value in the first voxel data set.
Optionally, the obtaining module 401 includes:
the acquisition submodule is used for acquiring original voxel data corresponding to each voxel point included in the medical image;
the first determining submodule is used for determining a compression ratio according to the data volume of the original voxel data and the target data volume when the data volume of the original voxel data is larger than a preset value;
and the compression submodule is used for compressing the original voxel data according to the compression ratio to obtain the first voxel data set.
Optionally, the compression submodule includes:
a second determining submodule, configured to determine a plurality of target voxel points from voxel points included in the medical image according to the compression ratio;
a third determining submodule, 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 the target voxel point and the reference voxel point;
a generation sub-module 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 apparatus. Fig. 5 is a block diagram of a medical image rendering apparatus 500 according to 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 of the foregoing embodiments;
a third determining module 502, configured to determine a gray scale interval according to a total number of target gray scales, where the total number of target gray scales is smaller than a total number of gray scales of a gray scale map;
a fourth determining module 503, configured to determine target gray-scale data according to a product of the gray-scale data and the gray-scale interval;
a rendering module 504, configured to perform image rendering based on the target grayscale data.
Therefore, the occupation of the medical image data on the memory can be effectively reduced, the image reconstruction speed of the receiving end is increased under the condition of ensuring the definition of the rendering image of the receiving end, and the browser of the receiving end is prevented from collapsing.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
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, so as to complete 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 the operation of the medical image data compression apparatus 600, and the data may include, for example, instructions for any application or method operating on the medical image data compression apparatus 600, and application-related data such as contact data, transmitted and received messages, pictures, audio, video, and the like. The Memory 602 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk or optical disk. The multimedia components 603 may include a screen and audio components. Wherein the screen may be, for example, a touch screen and the audio component is used for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may further be stored in the memory 602 or transmitted through the communication component 605. The audio assembly also includes at least one speaker for outputting audio signals. The I/O interface 604 provides an interface between the processor 601 and other interface modules, such as a keyboard, mouse, buttons, and the like. 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 (NFC for short), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or a combination of one or more of them, which is not limited herein. The corresponding communication component 605 may therefore include: 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 (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components, and is used to perform the medical image data compression method.
In another exemplary embodiment, a computer readable storage medium including program instructions which, when executed by a processor, implement the steps of the medical image data compression method described above is also provided. For example, the computer readable storage medium may be the memory 602 including the program instructions, which are executable by the processor 601 of the medical image data compression apparatus 600 to perform the medical image data compression method.
In another exemplary embodiment, a computer program product is also provided, which contains a computer program executable by a programmable apparatus, the computer program having code portions for performing the above-mentioned 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 and a memory 702. The medical image rendering apparatus 700 may further 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, so as to complete all or part of the steps in the medical image rendering method. The memory 702 is used to store various types of data to support operations at the medical image rendering apparatus 700, which may include, for example, instructions for any application or method operating on the medical image rendering apparatus 700, as well as application-related data, such as contact data, transceived messages, pictures, audio, video, and so forth. The Memory 702 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk. The multimedia components 703 may include screen and audio components. Wherein the screen may be, for example, a touch screen and the audio component is used for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may further be stored in the memory 702 or transmitted through the communication component 705. The audio assembly also includes at least one speaker for outputting audio signals. The I/O interface 704 provides an interface between the processor 701 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 705 is used for wired or wireless communication between the medical image rendering apparatus 700 and other devices. Wireless Communication, such as Wi-Fi, bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or a combination of one or more of them, which is not limited herein. The corresponding communication component 705 may thus include: 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 (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components, and is used to perform the medical image rendering method.
In another exemplary embodiment, a computer readable storage medium including 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, 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, which comprises a computer program executable by a programmable apparatus, the computer program having code portions for performing the medical image rendering method described above when executed by the programmable apparatus.
The preferred embodiments of the present disclosure are described in detail above with reference to the accompanying drawings, however, the present disclosure is not limited to the specific details in the above embodiments, and various simple modifications may be made to the technical solution of the present disclosure within the technical idea of the present disclosure, and these simple modifications all belong to the protection scope of the present disclosure.
It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. To avoid unnecessary repetition, the disclosure does not separately describe various possible combinations.
In addition, any combination of various embodiments of the present disclosure may be made, and the same should be considered as the disclosure of the present disclosure, as long as it does not depart from the spirit of the present disclosure.
Claims (14)
1. A method for compressing medical image data, the method comprising:
acquiring a first voxel data set, wherein the first voxel data set comprises voxel data corresponding to 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 scale in a target gray scale total number, wherein the target gray scale total number is less than the gray scale total number of a gray scale image;
determining a gray value interval to which the first gray data belongs, and updating the first gray data according to a gray level order 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 scale data to enable a receiving end to render images according to the second gray scale data.
2. The method according to claim 1, wherein the performing data processing on each voxel data according to a target voxel data range to obtain first gray-scale data corresponding to each voxel data includes:
updating 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 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 to obtain a second voxel data set;
and performing 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 a third storage size, and the third storage size is smaller than the first storage size and larger than the second storage size.
3. The method of claim 2, wherein said grayscale converting the second voxel data set to obtain the first grayscale data comprises:
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 different degrees of gray enhancement.
4. The method of claim 3, wherein the determining a first and a second gray reference value for each voxel data in the second voxel data set comprises:
determining a first gray reference value corresponding to the mth individual voxel data in the second voxel data set by the following formula:
wherein ,xm For the mth voxel data, f in the second voxel data set 1 (x m ) A first gray reference value corresponding to the mth voxel data, min is a lower limit of the target voxel data range, max is an upper limit of the target voxel data range, v is a first parameter, and v ranges from 0 to 1;
determining a second gray reference value corresponding to the mth individual voxel data in the second voxel data set by the following formula:
wherein ,f2 (x m ) And the second gray reference value is a second gray reference value corresponding to the mth voxel data, gamma is a second parameter, and the value range of gamma is 0 to 1.
5. The method according to claim 1, wherein an upper limit of the target value range is the smaller of a preset upper limit of voxel data and a maximum value in the first voxel data set, and a lower limit of the target value range is the larger of a preset lower limit of voxel data and a minimum value in the first voxel data set.
6. The method of claim 1, wherein the obtaining a first voxel 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 compressing the original voxel data according to the compression ratio to obtain the first voxel data set.
7. The method according to claim 6, wherein the compressing the original voxel data according to the compression ratio to obtain the first voxel data set 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 the target voxel point and the reference voxel point respectively;
generating the first voxel data set based on the target voxel data corresponding to the target voxel point.
8. The method according to any one of claims 1-7, wherein the medical image is a CT image.
9. A method for rendering medical images, the method comprising:
receiving gray data, the gray data being generated and transmitted by a transmitting end according to the method of any one of claims 1-8;
determining a gray scale interval 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 image;
determining target gray data according to the product of the gray data and the gray scale interval;
and rendering the image based on the target gray scale data.
10. An apparatus for compressing medical image data, the apparatus comprising:
an obtaining module, configured to obtain a first voxel data set, where the first voxel data set includes voxel data corresponding to a plurality of voxel points, and a 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 a 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 scale in 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 image;
the second determining module is 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 order 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 is used for sending the second gray data so that a receiving end can perform image rendering according to the second gray data.
11. An apparatus for rendering medical images, 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 to 8;
the third determining module is used for determining the gray scale interval 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 image;
the fourth determining module is used for determining target gray data according to the product of the gray data and the gray scale interval;
and the rendering module is used for rendering the image based on the target gray data.
12. An apparatus for compressing medical image data, the apparatus comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to carry out the steps of the method of any one of claims 1 to 8.
13. An apparatus for rendering medical images, the apparatus comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to perform the steps of the method of claim 9.
14. A non-transitory computer readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 8 or carries out the steps of the method of claim 9.
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