1. An image processing method, comprising:

determining M frames of images corresponding to a target to be compressed in a medical image sequence to be compressed, wherein M is a positive integer greater than 1;

determining compressed data corresponding to an Nth frame of image based on the Nth frame of image in the M frames of images, wherein N is a positive integer smaller than M;

compressing the (N + 1) th frame image to the (M) th frame image in the M frame images based on the (N) th frame image to obtain compressed data corresponding to the (N + 1) th frame image to the (M) th frame image;

and determining image compressed data corresponding to the medical image sequence to be compressed based on the compressed data corresponding to the N frame image to the M frame image.

2. The image processing method according to claim 1, wherein the target to be compressed comprises a continuous target to be compressed with continuous regions, and the determining the compressed data corresponding to the nth frame image based on the nth frame image in the M frame images comprises:

determining P pixel rows corresponding to the continuous target to be compressed in the N frame of image, wherein P is a positive integer;

determining starting point information and end point information corresponding to the P pixel rows respectively;

and determining compressed data corresponding to the N frame of image based on the starting point information and the end point information corresponding to the P pixel lines and the P pixel lines respectively.

3. The image processing method according to claim 2, wherein the determining the compressed data corresponding to the nth frame image based on the start point information and the end point information corresponding to the P pixel rows and the P pixel rows respectively comprises:

for each pixel row in the P pixel rows, determining compression information corresponding to the pixel row based on row information of the pixel row, and start point information and end point information corresponding to the pixel row;

and determining compressed data corresponding to the N frame of image based on the compression information corresponding to the P pixel lines respectively.

4. The image processing method according to claim 2, wherein the compressing the N +1 th to M th frame images of the M frame images based on the nth frame image to obtain compressed data corresponding to the N +1 th to M th frame images respectively comprises:

determining Q pixel rows corresponding to the continuous target to be compressed in the (N + 1) th frame image, wherein Q is a positive integer;

determining compressed data corresponding to the N +1 frame image based on difference information between the P pixel lines and the Q pixel lines;

for each of the N +2 th frame image to the mth frame image,

and determining compressed data corresponding to each frame of image based on difference information between the pixel line of each frame of image corresponding to the continuous target to be compressed and the pixel line of the image of the previous frame of each frame of image corresponding to the continuous target to be compressed.

5. The image processing method according to any one of claims 1 to 4, wherein the target to be compressed includes a discrete target to be compressed with discrete regions, and the determining the compressed data corresponding to the nth frame image based on the nth frame image in the M frame images includes:

determining R pixel points corresponding to the discrete target to be compressed in the Nth frame of image, wherein R is a positive integer;

determining coordinate information corresponding to the R pixel points;

and determining compressed data corresponding to the N frame of image based on the coordinate information corresponding to the R pixel points.

6. The image processing method according to claim 5, wherein the compressing the (N + 1) th to M th frame images of the M frame images based on the Nth frame image to obtain compressed data corresponding to the (N + 1) th to M th frame images comprises:

determining S pixel points corresponding to the discrete target to be compressed in the (N + 1) th frame of image, wherein S is a positive integer;

determining compressed data corresponding to the (N + 1) th frame of image based on difference information between the R pixel points and the S pixel points;

for each of the N +2 th frame image to the mth frame image,

and determining compressed data corresponding to each frame of image based on the difference information between the pixel point corresponding to each frame of image and the discrete target to be compressed and the pixel point corresponding to the discrete target to be compressed.

7. The image processing method according to any one of claims 1 to 4, wherein the medical image sequence to be compressed is a CT image sequence including Z frame images, where Z is a positive integer greater than or equal to M, and before the determining the M frame images corresponding to the target to be compressed in the medical image sequence to be compressed, the method further includes:

determining original CT value information corresponding to the Z frame images respectively;

performing first CT value conversion operation on original CT value information corresponding to the Z frame images respectively based on a preset minimum CT threshold and a preset maximum CT threshold to obtain first CT value information corresponding to the Z frame images respectively;

and performing second CT value conversion operation on the first CT value information corresponding to the Z frame images based on a preset additional CT threshold value to obtain second CT value information corresponding to the Z frame images.

8. The image processing method according to any one of claims 1 to 4, wherein the object to be compressed includes at least one of a bone, a tissue, air, and water.

9. An image storage method, comprising:

determining image compression data corresponding to a medical image sequence to be compressed, wherein the image compression data is obtained based on the image processing method of any one of the claims 1 to 8;

storing the sequence of medical images to be compressed based on the image compression data.

10. An image processing apparatus characterized by comprising:

the device comprises a first determining module, a second determining module and a third determining module, wherein the first determining module is used for determining M frames of images corresponding to a target to be compressed in a medical image sequence to be compressed, and M is a positive integer larger than 1;

the second determining module is used for determining compressed data corresponding to an Nth frame image in the M frame images, wherein N is a positive integer smaller than M;

the compression module is used for compressing the (N + 1) th frame image to the (M) th frame image in the M frame images based on the (N) th frame image to obtain compressed data corresponding to the (N + 1) th frame image to the (M) th frame image;

and the third determining module is used for determining image compressed data corresponding to the medical image sequence to be compressed based on the compressed data corresponding to the N frame image to the M frame image.

11. An image storage apparatus, comprising:

an image compression data determination module, configured to determine image compression data corresponding to a medical image sequence to be compressed, where the image compression data is obtained based on the image processing method according to any one of claims 1 to 8;

a storage module for storing the sequence of medical images to be compressed based on the image compression data.

12. A computer-readable storage medium, characterized in that the storage medium stores a computer program for executing the image processing method of any one of the above claims 1 to 8 and/or the image storage method of the above claim 9.

13. An electronic device, characterized in that the electronic device comprises:

a processor;

a memory for storing the processor-executable instructions;

the processor is configured to execute the image processing method according to any one of claims 1 to 8 and/or the image storage method according to claim 9.

Background

In medical clinical trials or clinical practice, a large number of medical images, for example three-dimensional medical image sequences, are produced. It is well known that three-dimensional medical image sequences typically correspond to a large amount of image data.

However, while the large amount of image data brings detailed image information to the reader, higher requirements are undoubtedly put on the storage and transmission conditions of the images. The image characteristics of the medical image are not considered in the conventional image compression and storage method, so that the storage and transmission problems of the medical image are difficult to solve.

Disclosure of Invention

The present application is proposed to solve the above-mentioned technical problems. The embodiment of the application provides an image processing method and device and an image storage method and device.

In a first aspect, an embodiment of the present application provides an image processing method, including: determining M frames of images corresponding to a target to be compressed in a medical image sequence to be compressed, wherein M is a positive integer greater than 1; determining compressed data corresponding to an Nth frame of image based on the Nth frame of image in the M frames of images, wherein N is a positive integer smaller than M; compressing the (N + 1) th frame image to the (M) th frame image in the M frame image based on the (N) th frame image to obtain compressed data corresponding to the (N + 1) th frame image to the (M) th frame image; and determining image compressed data corresponding to the medical image sequence to be compressed based on the compressed data corresponding to the N frame image to the M frame image.

With reference to the first aspect, in some implementations of the first aspect, the determining compressed data corresponding to an nth frame of image based on the nth frame of image in the M frames of images includes: determining P pixel rows corresponding to continuous targets to be compressed in the Nth frame of image, wherein P is a positive integer; determining starting point information and end point information corresponding to the P pixel rows respectively; and determining compressed data corresponding to the N frame of image based on the starting point information and the end point information corresponding to the P pixel lines and the P pixel lines respectively.

With reference to the first aspect, in certain implementations of the first aspect, determining compressed data corresponding to the nth frame of image based on start point information and end point information corresponding to the P pixel lines and the P pixel lines, respectively, includes: for each pixel row in the P pixel rows, determining compression information corresponding to the pixel row based on the row information of the pixel row, the start point information and the end point information corresponding to the pixel row; and determining compressed data corresponding to the N frame of image based on the compression information corresponding to the P pixel lines respectively.

With reference to the first aspect, in some implementations of the first aspect, compressing, based on the nth +1 frame image to the mth frame image of the M frame images, compressed data corresponding to the nth +1 frame image to the mth frame image respectively includes: determining Q pixel rows corresponding to continuous targets to be compressed in the (N + 1) th frame of image, wherein Q is a positive integer; determining compressed data corresponding to the (N + 1) th frame of image based on difference information between the P pixel rows and the Q pixel rows; and determining compressed data corresponding to each frame of image based on the pixel line of each frame of image corresponding to the continuous target to be compressed and the difference information between the previous frame of image of each frame of image and the pixel line of each frame of image corresponding to the continuous target to be compressed aiming at each frame of image from the (N + 2) th frame of image to the M (M) th frame of image.

With reference to the first aspect, in some implementations of the first aspect, the determining compressed data corresponding to an nth frame of image based on the nth frame of image in the M frames of images includes: determining R pixel points corresponding to discrete targets to be compressed in the Nth frame of image, wherein R is a positive integer; determining coordinate information corresponding to the R pixel points; and determining compressed data corresponding to the N frame of image based on the coordinate information corresponding to the R pixel points.

With reference to the first aspect, in some implementations of the first aspect, compressing, based on the nth +1 frame image to the mth frame image of the M frame images, compressed data corresponding to the nth +1 frame image to the mth frame image respectively includes: determining S pixel points corresponding to a discrete target to be compressed in the (N + 1) th frame of image, wherein S is a positive integer; determining compressed data corresponding to the (N + 1) th frame of image based on difference information between the R pixel points and the S pixel points; and determining compressed data corresponding to each frame of image based on the difference information between the pixel point corresponding to each frame of image and the discrete target to be compressed and the pixel point corresponding to the discrete target to be compressed in each frame of image from the (N + 2) th frame of image to the M (M) th frame of image.

With reference to the first aspect, in certain implementations of the first aspect, the medical image sequence to be compressed is a CT image sequence including Z frame images, where Z is a positive integer greater than or equal to M, and before determining M frame images corresponding to a target to be compressed in the medical image sequence to be compressed, the image processing method further includes: determining original CT value information corresponding to the Z frame images; performing first CT value conversion operation on original CT value information corresponding to the Z frame images respectively based on a preset minimum CT threshold and a preset maximum CT threshold to obtain first CT value information corresponding to the Z frame images respectively; and performing second CT value conversion operation on the first CT value information corresponding to the Z frame images respectively based on the preset additional CT threshold value to obtain second CT value information corresponding to the Z frame images respectively.

With reference to the first aspect, in certain implementations of the first aspect, the target to be compressed includes at least one of bone, tissue, air, and water.

In a second aspect, an embodiment of the present application provides an image storage method, including: determining image compression data corresponding to a medical image sequence to be compressed, wherein the image compression data is obtained based on the image processing method mentioned in any embodiment; the sequence of medical images to be compressed is stored on the basis of the image compression data.

In a third aspect, an embodiment of the present application provides an image processing apparatus including: the device comprises a first determining module, a second determining module and a third determining module, wherein the first determining module is used for determining M frames of images corresponding to a target to be compressed in a medical image sequence to be compressed, and M is a positive integer larger than 1; the second determining module is used for determining compressed data corresponding to an Nth frame image based on the Nth frame image in the M frame images, wherein N is a positive integer smaller than M; the compression module is used for compressing the (N + 1) th frame image to the (M) th frame image in the M frame image based on the (N) th frame image to obtain compressed data corresponding to the (N + 1) th frame image to the (M) th frame image; and the third determining module is used for determining image compressed data corresponding to the medical image sequence to be compressed based on the compressed data corresponding to the N frame image to the M frame image.

In a fourth aspect, an embodiment of the present application provides an image storage apparatus, including: the image compression data determining module is used for determining image compression data corresponding to a medical image sequence to be compressed, wherein the image compression data is obtained based on the image processing method mentioned in any embodiment; and the storage module is used for storing the medical image sequence to be compressed based on the image compression data.

In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, where the storage medium stores a computer program, and the computer program is configured to execute the image processing method according to any of the above embodiments and/or the image storage method according to any of the above embodiments.

In a sixth aspect, an embodiment of the present application provides an electronic device, including: a processor; a memory for storing processor-executable instructions; a processor for executing the image processing method mentioned in any of the above embodiments and/or the image storage method mentioned in any of the above embodiments.

According to the image processing method provided by the embodiment of the application, a part of image data is reduced primarily by determining the M frames of images corresponding to the target to be compressed in the medical image sequence to be compressed and determining the mode of compressed data corresponding to the N frame of images based on the N frame of images in the M frames of images, and then compressing the (N + 1) th frame of images to the M frame of images based on the N frame of images to obtain the compressed data corresponding to the (N + 1) th frame of images to the M frame of images, and determining the mode of image compressed data corresponding to the medical image sequence to be compressed based on the compressed data corresponding to the N frame of images to the M frame of images, so that the image data is further reduced, and the image transmission efficiency is improved. In addition, according to the image storage method provided by the embodiment of the application, the image compression data corresponding to the medical image sequence to be compressed is determined, wherein the image compression data is obtained based on the image processing method mentioned in any one of the embodiments, and then the medical image sequence to be compressed is stored based on the image compression data, so that most of the storage space is saved, and the purpose of optimizing the storage space of the medical image is achieved.

Drawings

Fig. 1 is a schematic view of a scenario in which an exemplary embodiment of the present application is applied.

Fig. 2 is a schematic diagram of another scenario in which an exemplary embodiment of the present application is applied.

Fig. 3 is a schematic flowchart illustrating an image processing method according to an exemplary embodiment of the present application.

Fig. 4 is a schematic flowchart illustrating a process of determining compressed data corresponding to an nth frame image based on the nth frame image in M frame images according to an exemplary embodiment of the present application.

Fig. 5 is a schematic diagram illustrating an nth frame image, an N +1 th frame image and difference information according to an exemplary embodiment of the present application.

Fig. 6 is a schematic flowchart illustrating a process of determining compressed data corresponding to an nth frame image based on start point information and end point information corresponding to P pixel lines and P pixel lines, respectively, according to an exemplary embodiment of the present application.

Fig. 7 is a schematic flow chart illustrating a process of compressing an N +1 th frame image to an M th frame image in M frame images based on an nth frame image to obtain compressed data corresponding to the N +1 th frame image to the M th frame image according to an exemplary embodiment of the present application.

Fig. 8 is a schematic flowchart illustrating a process of determining compressed data corresponding to an nth frame image based on the nth frame image in M frame images according to another exemplary embodiment of the present application.

Fig. 9 is a schematic diagram illustrating an nth frame image, an N +1 th frame image and difference information according to another exemplary embodiment of the present application.

Fig. 10 is a schematic flow chart illustrating a process of compressing an N +1 th frame image to an M th frame image in M frame images based on an nth frame image to obtain compressed data corresponding to the N +1 th frame image to the M th frame image according to another exemplary embodiment of the present application.

Fig. 11 is a flowchart illustrating an image processing method according to another exemplary embodiment of the present application.

Fig. 12 is a schematic diagram illustrating a 12-bit binary system according to an exemplary embodiment of the present application.

Fig. 13 is a flowchart illustrating an image storage method according to an exemplary embodiment of the present application.

Fig. 14 is a schematic structural diagram of an image processing apparatus according to an exemplary embodiment of the present application.

Fig. 15 is a schematic structural diagram of an image storage device according to an exemplary embodiment of the present application.

Fig. 16 is a schematic structural diagram of an electronic device according to an exemplary embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

Fig. 1 is a schematic view of a scenario in which an exemplary embodiment of the present application is applied. As shown in fig. 1, a scenario to which the embodiment of the present application is applied includes a server 1 and an image capturing device 2, where there is a communication connection relationship between the server 1 and the image capturing device 2.

Specifically, the image acquisition device 2 is configured to acquire a medical image sequence to be compressed including a target to be compressed, the server 1 is configured to determine, based on the medical image sequence to be compressed acquired by the image acquisition device 2, M frames of images corresponding to the target to be compressed in the medical image sequence to be compressed, where M is a positive integer greater than 1, determine compressed data corresponding to an nth frame of image in the M frames of images, where N is a positive integer smaller than M, compress, based on the nth frame of image, the (N + 1) th frame of image to the mth frame of image in the M frames of images to obtain compressed data corresponding to the (N + 1) th frame of image to the mth frame of image, and determine, based on the compressed data corresponding to the nth frame of image to the mth frame of image, image compressed data corresponding to the medical image sequence to be compressed. That is, the scene implements an image processing method. Since the scene shown in fig. 1 implements the image processing method by using the server 1, the scene not only can improve the adaptability of the scene, but also can effectively reduce the calculation amount of the image acquisition device 2.

It should be noted that the present application is also applicable to another scenario. Fig. 2 is a schematic diagram of another scenario in which an exemplary embodiment of the present application is applied. Specifically, the medical imaging system 3 is included in the scene, wherein the medical imaging system 3 includes a data acquisition module 301 and a data processing module 302, and a communication connection relationship exists between the data acquisition module 301 and the data processing module 302.

Specifically, the data acquiring module 301 is configured to acquire a medical image sequence to be compressed including a target to be compressed, where the medical image sequence to be compressed may be in a DICOM format, and further, related medical image information is automatically imported directly from data of the medical image, where the medical image information includes information such as a shooting type, a shooting date, and a shooting location of the medical image. The data processing module 302 is configured to determine, based on the medical image sequence to be compressed acquired by the data acquisition module 301, M frames of images corresponding to a target to be compressed in the medical image sequence to be compressed, where M is a positive integer greater than 1, determine, based on an nth frame of image in the M frames of images, compressed data corresponding to an nth frame of image, where N is a positive integer less than M, compress, based on the nth frame of image, an (N + 1) th frame of image to the mth frame of image in the M frames of images to obtain compressed data corresponding to the (N + 1) th frame of image to the mth frame of image, and determine, based on the compressed data corresponding to the nth frame of image to the mth frame of image, image compressed data corresponding to the medical image sequence to be compressed. Namely, the scene realizes an image processing method, and the aim of optimizing the storage space of the medical image is fulfilled, so that the image storage efficiency is improved.

Fig. 3 is a schematic flowchart illustrating an image processing method according to an exemplary embodiment of the present application. As shown in fig. 3, an image processing method provided in an embodiment of the present application includes the following steps.

And step 40, determining M frames of images corresponding to the target to be compressed in the medical image sequence to be compressed.

Illustratively, the object to be compressed in the sequence of medical images to be compressed may comprise at least one object of bone, tissue, air and water.

Illustratively, the medical image sequence to be compressed mentioned in step 40 may be a Computed Tomography (CT) image sequence, and may also be other types of medical image sequences to be compressed, which is not further limited in this application. In addition, M is a positive integer greater than 1.

It is to be understood that the embodiment of the present application is not limited to a specific form of the medical image sequence to be compressed, and may be an original medical image sequence, a preprocessed medical image sequence, or a partial image sequence in the original medical image sequence, that is, a part of the original medical image sequence. In addition, the acquisition object corresponding to the medical image sequence to be compressed can be a human body or an animal body.

And step 50, determining compressed data corresponding to the N frame image based on the N frame image in the M frame images.

Exemplarily, an nth frame image in the M frames of images is used as a reference image corresponding to a target to be compressed in the medical image sequence to be compressed, and then the nth frame image is compressed to obtain compressed data corresponding to the nth frame image, where N is a positive integer smaller than M.

And step 60, compressing the (N + 1) th frame image to the (M) th frame image in the M frame image based on the (N) th frame image to obtain compressed data corresponding to the (N + 1) th frame image to the (M) th frame image.

And step 70, determining image compressed data corresponding to the medical image sequence to be compressed based on the compressed data corresponding to the N frame image to the M frame image.

According to the image processing method provided by the embodiment of the application, a part of image data is reduced primarily by determining the M frames of images corresponding to the target to be compressed in the medical image sequence to be compressed and determining the compressed data corresponding to the Nth frame of image based on the Nth frame of image in the M frames of images, and then the (N + 1) th frame of image to the Mth frame of image in the M frames of images are compressed based on the Nth frame of image, so that the compressed data corresponding to the (N + 1) th frame of image to the Mth frame of image are obtained, the image data are further reduced, and the image transmission efficiency is improved.

Fig. 4 is a schematic flowchart illustrating a process of determining compressed data corresponding to an nth frame image based on the nth frame image in M frame images according to an exemplary embodiment of the present application. The embodiment shown in fig. 4 is extended based on the embodiment shown in fig. 3, and the differences between the embodiment shown in fig. 4 and the embodiment shown in fig. 3 will be emphasized below, and the descriptions of the same parts will not be repeated. Fig. 5 is a schematic diagram illustrating an nth frame image, an N +1 th frame image and difference information according to an exemplary embodiment of the present application. A specific method for determining compressed data corresponding to an nth frame image based on the nth frame image in the M frame images is illustrated in conjunction with fig. 4 and 5.

As shown in fig. 4, in the image processing method provided in the embodiment of the present application, the objects to be compressed include continuous objects to be compressed having continuous areas. Specifically, the step of determining the compressed data corresponding to the nth frame image based on the nth frame image in the M frame images includes the following steps.

And step 51, determining P pixel rows corresponding to continuous targets to be compressed in the Nth frame of image.

Illustratively, the continuous objects to be compressed with continuous areas can be human tissues, and in the nth frame of image, the continuous objects to be compressed correspond to P pixel lines, wherein P is a positive integer. With reference to the nth frame image shown in fig. 5, the nth frame image includes 64 pixel points, if the continuous object to be compressed is a shadow region in the image, P is equal to 6, and the continuous object to be compressed in the nth frame image corresponds to 6 pixel lines.

In step 52, start point information and end point information corresponding to each of the P pixel rows are determined.

For example, referring to the nth frame image shown in fig. 5 again, the left column number of the nth frame image may be the row number corresponding to each of P pixel rows, and the top row number of the nth frame image may be the column number corresponding to each of 64 pixel points. Therefore, the start point information mentioned in step 52 may be a column number corresponding to a start pixel point corresponding to each of the P pixel rows, and the end point information may be a column number corresponding to an end pixel point corresponding to each of the P pixel rows. Take the starting point information and the end point information (b, c) corresponding to a certain pixel row in the P pixel rows as an example, where b may be the column number corresponding to the starting pixel point of the pixel row, and c may be the column number corresponding to the ending pixel point of the pixel row.

Referring again to the N-th frame image shown in fig. 5, the start point information and the end point information corresponding to the pixel line with line number 1 are (2, 4), the start point information and the end point information corresponding to the pixel line with line number 2 are (1, 4), the start point information and the end point information corresponding to the pixel line with line number 3 are (2, 5), the start point information and the end point information corresponding to the pixel line with line number 4 are (3, 5), the start point information and the end point information corresponding to the pixel line with line number 5 are (3, 6), and the start point information and the end point information corresponding to the pixel line with line number 6 are (4, 5).

And step 53, determining compressed data corresponding to the N frame image based on the starting point information and the end point information corresponding to the P pixel lines and the P pixel lines respectively.

According to the image processing method provided by the embodiment of the application, the image data between the starting point information and the end point information corresponding to the P pixel lines are reduced by firstly determining the P pixel lines corresponding to the continuous target to be compressed in the N frame image and then determining the starting point information and the end point information corresponding to the P pixel lines, and then the compressed data corresponding to the N frame image is determined based on the starting point information and the end point information corresponding to the P pixel lines, so that the purpose of determining the compressed data corresponding to the N frame image based on the N frame image in the M frame images is achieved. Compared with the mode of not compressing the image data in the prior art, the embodiment of the application reduces the image data between the starting point information and the end point information corresponding to the P pixel lines aiming at the continuous target to be compressed, thereby reducing the image data corresponding to the N frame of image and providing a precondition for the high-efficiency transmission of the image data.

Fig. 6 is a schematic flowchart illustrating a process of determining compressed data corresponding to an nth frame image based on start point information and end point information corresponding to P pixel lines and P pixel lines, respectively, according to an exemplary embodiment of the present application. The embodiment shown in fig. 6 is extended based on the embodiment shown in fig. 4, and the differences between the embodiment shown in fig. 6 and the embodiment shown in fig. 4 will be emphasized below, and the descriptions of the same parts will not be repeated. A specific method for determining compressed data corresponding to the nth frame image based on the start point information and the end point information corresponding to the P pixel lines and the P pixel lines, respectively, is illustrated in conjunction with fig. 5 and 6.

As shown in fig. 6, in the image processing method provided in the embodiment of the present application, the step of determining the compressed data corresponding to the nth frame image based on the start point information and the end point information corresponding to the P pixel lines and the P pixel lines respectively includes the following steps.

Step 531, for each of the P pixel rows, determining compression information corresponding to the pixel row based on the row information of the pixel row, the start point information and the end point information corresponding to the pixel row.

Illustratively, the compression information (a, b, c) corresponding to a certain pixel row in the P pixel rows is taken as an example, where a may be a row number (i.e. row information) corresponding to the pixel row.

Referring again to the nth frame image shown in fig. 5, the compression information corresponding to each of the 6 pixel lines is (1, 2, 4), (2, 1, 4), (3, 2, 5), (4, 3, 5), (5, 3, 6), (6, 4, 5) in this order.

Step 532, determining the compressed data corresponding to the nth frame image based on the compressed information corresponding to each of the P pixel lines.

Illustratively, the compressed information corresponding to each of the 6 pixel lines mentioned in step 531 is compressed data corresponding to the nth frame image.

The image processing method provided by the embodiment of the application specifically explains a method for determining compressed data corresponding to an nth frame image by determining compression information corresponding to each pixel row in P pixel rows and based on the compression information corresponding to each pixel row. In the embodiment of the application, the compressed data corresponding to the nth frame image comprises the line information of the pixel line, and the starting point information and the end point information corresponding to the pixel line, so that the accuracy of the image data is improved.

Fig. 7 is a schematic flow chart illustrating a process of compressing an N +1 th frame image to an M th frame image in M frame images based on an nth frame image to obtain compressed data corresponding to the N +1 th frame image to the M th frame image according to an exemplary embodiment of the present application. The embodiment shown in fig. 7 is extended based on the embodiment shown in fig. 4, and the differences between the embodiment shown in fig. 7 and the embodiment shown in fig. 4 will be emphasized below, and the descriptions of the same parts will not be repeated. With reference to fig. 5 and 7, an example will be described in which the (N + 1) th frame image is compressed based on the nth frame image to obtain compressed data corresponding to the (N + 1) th frame image.

As shown in fig. 7, in the image processing method provided in the embodiment of the present application, the step of obtaining compressed data corresponding to each of the N +1 th frame image to the M th frame image by compressing the N +1 th frame image to the M th frame image based on the N th frame image includes the following steps.

And step 61, determining Q pixel rows corresponding to the continuous target to be compressed in the (N + 1) th frame image.

Illustratively, in the N +1 th frame image, the objects to be compressed consecutively correspond to Q pixel rows, where Q is a positive integer. With reference to the (N + 1) th frame image shown in fig. 5, the (N + 1) th frame image includes 64 pixel points, Q is equal to 6 if the continuous object to be compressed is a shadow region in the image, and the continuous object to be compressed corresponds to 6 pixel lines in the (N + 1) th frame image.

And step 62, determining compressed data corresponding to the (N + 1) th frame of image based on the difference information between the P pixel rows and the Q pixel rows.

Illustratively, in conjunction with the difference information shown in fig. 5, the difference information between the P pixel rows and the Q pixel rows is a shaded area in the figure, and the difference information includes increasing information and decreasing information. Wherein the horizontally hatched area is information that Q pixel rows increase with respect to P pixel rows, and the vertically hatched area is information that Q pixel rows decrease with respect to P pixel rows. As the difference information shown in fig. 5, Q pixel rows have increased information of (4, 2, 2) and (5, 1, 2) and decreased information of (3, 5, 5) with respect to P pixel rows, and the compressed data corresponding to the N +1 th frame image has increased information of (4, 2, 2) and (5, 1, 2) and decreased information of (3, 5, 5).

And step 63, determining compressed data corresponding to each frame of image based on the pixel line of each frame of image corresponding to the continuous target to be compressed and the difference information between the previous frame of image of each frame of image and the pixel line of the continuous target to be compressed aiming at each frame of image from the (N + 2) th frame of image to the M (M) th frame of image.

It can be understood that, for each frame of the N +2 th frame image to the M th frame image, the method for determining the compressed data corresponding to each frame of the image is the same as the method for compressing the N +1 th frame image based on the N th frame image and determining the compressed data corresponding to the N +1 th frame image mentioned in step 61 and step 62, which is not described herein again.

According to the image processing method provided by the embodiment of the application, the image data corresponding to the (N + 1) th frame image is reduced by firstly determining Q pixel lines corresponding to the continuous target to be compressed in the (N + 1) th frame image and then determining the mode of the compressed data corresponding to the (N + 1) th frame image based on the difference information between the P pixel lines and the Q pixel lines, and then for each frame image from the (N + 2) th frame image to the M frame image, the mode of the compressed data corresponding to each frame image is determined based on the difference information between the pixel line corresponding to the frame image and the continuous target to be compressed and the pixel line corresponding to the previous frame image and the continuous target to be compressed in each frame image, and the image data corresponding to each frame image from the (N + 2) th frame image to the M frame image is reduced. Compared with the mode of not compressing image data in the prior art, the method and the device for compressing the medical image sequence obtain the compressed data corresponding to the (N + 1) th frame image to the M (M) th frame image based on the difference information between each frame image and the previous frame image, and greatly reduce the image data corresponding to the medical image sequence to be compressed.

Fig. 8 is a schematic flowchart illustrating a process of determining compressed data corresponding to an nth frame image based on the nth frame image in M frame images according to another exemplary embodiment of the present application. The embodiment shown in fig. 8 is extended based on the embodiment shown in fig. 3, and the differences between the embodiment shown in fig. 8 and the embodiment shown in fig. 3 will be emphasized below, and the descriptions of the same parts will not be repeated. Fig. 9 is a schematic diagram illustrating an nth frame image, an N +1 th frame image and difference information according to another exemplary embodiment of the present application. A specific method for determining compressed data corresponding to an nth frame image based on the nth frame image in the M frame images is illustrated in conjunction with fig. 8 and 9.

As shown in fig. 8, in the image processing method provided in the embodiment of the present application, the object to be compressed includes a discrete object to be compressed whose area is discrete. Specifically, the step of determining the compressed data corresponding to the nth frame image based on the nth frame image in the M frame images includes the following steps.

And step 54, determining R pixel points corresponding to the discrete target to be compressed in the Nth frame of image.

Illustratively, the discrete target to be compressed with discrete regions may be a human skeleton, and in the nth frame image, the discrete target to be compressed corresponds to R pixel points, where R is a positive integer. With reference to the nth frame image shown in fig. 9, the nth frame image includes 64 pixel points, if the discrete object to be compressed is a shadow region in the image, R is equal to 11, and in the nth frame image, the discrete object to be compressed corresponds to 11 pixel points.

And step 55, determining the coordinate information corresponding to the R pixel points.

For example, referring to the nth frame image shown in fig. 9 again, the left row number of the nth frame image may be the row number corresponding to each of 64 pixels, and the top row number of the nth frame image may be the column number corresponding to each of 64 pixels. Therefore, the coordinate information mentioned in step 55 may be the row number and the column number corresponding to each of the R pixels. Take coordinate information (m, n) corresponding to a certain pixel point in the R pixel points as an example, where m may be a row number corresponding to the pixel point, and n may be a column number corresponding to the pixel point.

Referring again to the nth frame image shown in fig. 9, the coordinate information of the 11 pixel points corresponding to the discrete object to be compressed is (1, 2), (1, 4), (2, 1), (2, 4), (3, 2), (3, 5), (4, 3), (5, 3), (5, 6), (6, 4) and (6, 5).

And step 56, determining compressed data corresponding to the N frame of image based on the coordinate information corresponding to the R pixel points.

Exemplarily, the coordinate information of 11 pixel points corresponding to the discrete object to be compressed mentioned in step 55 is compressed data corresponding to the nth frame image.

According to the image processing method provided by the embodiment of the application, the R pixel points corresponding to the discrete target to be compressed in the N frame image are determined firstly, and then the coordinate information corresponding to the R pixel points is determined, so that the image data corresponding to the discrete target to be compressed is reduced, then the compressed data corresponding to the N frame image is obtained based on the coordinate information corresponding to the R pixel points, and the purpose of determining the compressed data corresponding to the N frame image based on the N frame image in the M frame image is achieved. Compared with the mode of not compressing the image data in the prior art, the image data corresponding to the Nth frame of image is reduced aiming at the discrete target to be compressed, and the precondition is provided for the high-efficiency transmission of the image data.

Fig. 10 is a schematic flow chart illustrating a process of compressing an N +1 th frame image to an M th frame image in M frame images based on an nth frame image to obtain compressed data corresponding to the N +1 th frame image to the M th frame image according to another exemplary embodiment of the present application. The embodiment shown in fig. 10 is extended based on the embodiment shown in fig. 8, and the differences between the embodiment shown in fig. 10 and the embodiment shown in fig. 8 will be emphasized below, and the descriptions of the same parts will not be repeated. With reference to fig. 9 and 10, an example will be described in which an N +1 th frame image is compressed based on an nth frame image to obtain compressed data corresponding to the N +1 th frame image.

As shown in fig. 10, in the image processing method provided in the embodiment of the present application, the step of obtaining compressed data corresponding to each of the N +1 th frame image to the M th frame image by compressing the N +1 th frame image to the M th frame image based on the N th frame image includes the following steps.

And step 64, determining S pixel points corresponding to the discrete target to be compressed in the (N + 1) th frame of image.

Exemplarily, in the N +1 th frame image, the discrete object to be compressed corresponds to S pixel rows, where S is a positive integer. With reference to the (N + 1) th frame image shown in fig. 9, the (N + 1) th frame image includes 64 pixel points, if the discrete object to be compressed is a shadow region in the image, S is equal to 12, and in the (N + 1) th frame image, the discrete object to be compressed corresponds to 12 pixel points.

And step 65, determining compressed data corresponding to the (N + 1) th frame of image based on the difference information between the R pixel points and the S pixel points.

Illustratively, in conjunction with the difference information shown in fig. 9, the difference information between the R pixel points and the S pixel points is a shaded area in the graph, and the difference information includes increased information and decreased information. The horizontal shadow area can be information of S pixel points increased relative to R pixel points, and the vertical shadow area can be information of S pixel points decreased relative to R pixel points. As the difference information shown in fig. 9, the S pixels are added with information (3, 4), (4, 5) and (5, 5) and the reduced information is (3, 5) and (5, 6) relative to the R pixels, and the compressed data corresponding to the N +1 th frame image is added with information (3, 4), (4, 5) and (5, 5) and reduced information (3, 5) and (5, 6).

And step 66, determining compressed data corresponding to each frame of image based on the pixel point corresponding to the discrete target to be compressed of each frame of image and the difference information between the pixel point corresponding to the discrete target to be compressed and the previous frame of image of each frame of image, aiming at each frame of image from the (N + 2) th frame of image to the M (M) th frame of image.

It can be understood that, for each frame of the N +2 th frame image to the M th frame image, the method for determining the compressed data corresponding to each frame of the image is the same as the method for compressing the N +1 th frame image based on the N th frame image and determining the compressed data corresponding to the N +1 th frame image mentioned in step 64 and step 65, which is not described herein again.

In some embodiments, the target to be compressed in the medical image sequence to be compressed may include a continuous target to be compressed and a discrete target to be compressed, and the image compressed data corresponding to the medical image sequence to be compressed may include both the image compressed data corresponding to the continuous target to be compressed and the image compressed data corresponding to the discrete target to be compressed.

The image processing method provided by the embodiment of the application reduces the image data corresponding to the (N + 1) th frame image by firstly determining S pixel points corresponding to the discrete target to be compressed in the (N + 1) th frame image and then determining the mode of compressed data corresponding to the (N + 1) th frame image based on the difference information between the R pixel points and the S pixel points, and then determines the mode of compressed data corresponding to each frame image according to the difference information between the pixel points corresponding to the frame image and the discrete target to be compressed and between the previous frame image of each frame image and the pixel points corresponding to the discrete target to be compressed aiming at each frame image from the (N + 2) th frame image to the M frame image, thereby reducing the image data corresponding to each frame image from the (N + 2) th frame image to the M frame image. Compared with the mode of not compressing image data in the prior art, the method and the device for compressing the medical image sequence obtain the compressed data corresponding to the (N + 1) th frame image to the M (M) th frame image based on the difference information between each frame image and the previous frame image, and greatly reduce the image data corresponding to the medical image sequence to be compressed.

Fig. 11 is a flowchart illustrating an image processing method according to another exemplary embodiment of the present application. The embodiment shown in fig. 11 is extended based on the embodiment shown in fig. 3, and the differences between the embodiment shown in fig. 11 and the embodiment shown in fig. 3 will be emphasized below, and the descriptions of the same parts will not be repeated.

As shown in fig. 11, in the image processing method provided in the embodiment of the present application, the medical image sequence to be compressed is a CT image sequence including Z frame images, where Z is a positive integer greater than or equal to M. Before the step of determining M frames of images corresponding to a target to be compressed in a medical image sequence to be compressed, the image processing method provided by the embodiment of the application further includes the following steps.

And step 10, determining original CT value information corresponding to the Z frame images respectively.

Illustratively, each frame of the Z-frame image includes X number of pixel points. And aiming at each pixel point in the X pixel points, determining original CT value information corresponding to each frame of image based on the original CT value information corresponding to each pixel point, and thus determining the original CT value information corresponding to each Z frame of image based on the original CT value information corresponding to each frame of image.

For example, taking the target to be compressed as a bone as an example, the original CT value information corresponding to the bone may be 1000HU, taking the target to be compressed as a tissue as an example, the original CT value information corresponding to the tissue may be-24 HU, the original CT value information corresponding to air is-1000 HU, and the original CT value information corresponding to water is 0 HU.

And 20, performing first CT value conversion operation on original CT value information corresponding to the Z frame images respectively based on a preset minimum CT threshold and a preset maximum CT threshold to obtain first CT value information corresponding to the Z frame images respectively.

Illustratively, since the portion of the medical image where the original CT value information is less than-1024 HU and/or greater than 3071HU may be regarded as a non-human component, the preset minimum CT threshold may be-1024 HU and the preset maximum CT threshold may be 3071 HU.

For example, the first CT value information corresponding to the Z frame image is obtained by performing a first CT value conversion operation on the original CT value information corresponding to each Z frame image, which may be that the original CT value information smaller than-1024 HU is regarded as-1024 HU, that is, the first CT value information corresponding to the original CT value information smaller than-1024 HU is-1024 HU. For original CT value information larger than 3071HU, it may be regarded as 3071HU, that is, the first CT value information corresponding to the original CT value information larger than 3071HU is 3071 HU.

And step 30, performing second CT value conversion operation on the first CT value information corresponding to the Z-frame images respectively based on the preset additional CT threshold value to obtain second CT value information corresponding to the Z-frame images respectively.

Illustratively, the data corresponding to the medical image is stored in a binary form, and the image processing method provided by the embodiment of the application limits the compressed data to a 12-bit binary form (i.e. the preset additional CT threshold is 0HU to 4095 HU).

For example, in order to enable the first CT value information corresponding to each Z-frame image to satisfy the preset additional CT threshold, the second CT value conversion operation is performed on the first CT value information to obtain the second CT value information corresponding to each Z-frame image, which may be to sum the first CT value information with 1024HU to obtain the second CT value information corresponding to the first CT value information. Using the target to be compressed as a bone as an example, the second CT value information corresponding to the bone may be 2024HU, using the target to be compressed as a tissue as an example, the second CT value information corresponding to the tissue may be 1000HU, the second CT value information corresponding to air is 24HU, and the second CT value information corresponding to water is 1024 HU.

Fig. 12 is a schematic diagram illustrating a 12-bit binary system according to an exemplary embodiment of the present application. As shown in FIG. 12, in some embodiments, 12-bit binaries may also be numbered sequentially from right to left (bit 1 to bit 12). When the second CT value information corresponding to the target to be compressed appears at bit 11 and/or bit 12 as 1, the target to be compressed can be regarded as a discrete target to be compressed. For example, the target to be compressed is bone, and the second CT value information corresponding to the bone may be 2024HU (1 appears at 11 th bit), and at this time, the bone may be regarded as a discrete target to be compressed.

In some embodiments, when the second CT value information corresponding to the object to be compressed appears 1 in at least one of the 8 th bit to the 10 th bit, the object to be compressed may be regarded as a continuous object to be compressed. For example, the target to be compressed is a tissue, and the second CT value information corresponding to the tissue may be 1000HU (1 appears at the 10 th bit), and the tissue may be regarded as a continuous target to be compressed.

It should be noted that, the existing medical image data storage form mainly uses a 16-bit binary system, and the embodiment of the present application compresses the 16-bit binary system to a 12-bit binary system, thereby primarily saving 25% of image data, and in addition, the medical image sequence to be compressed adopts the image processing method mentioned in any of the above embodiments, thereby further saving 25% of image data, saving 50% of image data in total, and greatly improving the efficiency of image transmission.

Fig. 13 is a flowchart illustrating an image storage method according to an exemplary embodiment of the present application. As shown in fig. 13, the image storage method provided in the embodiment of the present application includes the following steps.

And step 80, determining image compression data corresponding to the medical image sequence to be compressed.

Specifically, the image compressed data is obtained based on the image processing method mentioned in any of the above embodiments.

Step 90, storing the sequence of medical images to be compressed based on the image compression data.

According to the image storage method provided by the embodiment of the application, the storage space corresponding to the medical image sequence to be compressed is greatly saved by determining the image compression data corresponding to the medical image sequence to be compressed and then storing the medical image sequence to be compressed based on the image compression data.

Method embodiments of the present application are described in detail above in conjunction with fig. 3-13, and apparatus embodiments of the present application are described in detail below in conjunction with fig. 14 and 15. It is to be understood that the description of the method embodiments corresponds to the description of the apparatus embodiments, and therefore reference may be made to the preceding method embodiments for parts not described in detail.

Fig. 14 is a schematic structural diagram of an image processing apparatus according to an exemplary embodiment of the present application. As shown in fig. 14, an image processing apparatus according to an embodiment of the present application includes:

the first determining module 400 is configured to determine M frames of images corresponding to a target to be compressed in a medical image sequence to be compressed.

The second determining module 500 is configured to determine, based on an nth frame image of the M frame images, compressed data corresponding to the nth frame image.

The compression module 600 is configured to compress the (N + 1) th frame image to the (M) th frame image in the M frame image based on the nth frame image, so as to obtain compressed data corresponding to the (N + 1) th frame image to the M frame image.

A third determining module 700, configured to determine, based on compressed data corresponding to each of the nth frame image to the mth frame image, image compressed data corresponding to the medical image sequence to be compressed.

In an embodiment of the present application, the second determining module 500 is further configured to determine P pixel rows corresponding to consecutive targets to be compressed in the nth frame of image, where P is a positive integer; determining starting point information and end point information corresponding to the P pixel rows respectively; and determining compressed data corresponding to the N frame of image based on the starting point information and the end point information corresponding to the P pixel lines and the P pixel lines respectively.

In an embodiment of the present application, the second determining module 500 is further configured to determine, for each pixel row of the P pixel rows, compression information corresponding to the pixel row based on the row information of the pixel row, the start point information and the end point information corresponding to the pixel row; and determining compressed data corresponding to the N frame of image based on the compression information corresponding to the P pixel lines respectively.

In an embodiment of the present application, the second determining module 500 is further configured to determine R pixel points corresponding to a discrete target to be compressed in an nth frame of image, where R is a positive integer; determining coordinate information corresponding to the R pixel points; and determining compressed data corresponding to the N frame of image based on the coordinate information corresponding to the R pixel points.

In an embodiment of the present application, the compression module 600 is further configured to determine Q pixel rows corresponding to consecutive targets to be compressed in an N +1 th frame of image, where Q is a positive integer; determining compressed data corresponding to the (N + 1) th frame of image based on difference information between the P pixel rows and the Q pixel rows; and determining compressed data corresponding to each frame of image based on the pixel line of each frame of image corresponding to the continuous target to be compressed and the difference information between the previous frame of image of each frame of image and the pixel line of each frame of image corresponding to the continuous target to be compressed aiming at each frame of image from the (N + 2) th frame of image to the M (M) th frame of image.

In an embodiment of the present application, the compression module 600 is further configured to determine S pixel points corresponding to a discrete target to be compressed in an N +1 th frame of image, where S is a positive integer; determining compressed data corresponding to the (N + 1) th frame of image based on difference information between the R pixel points and the S pixel points; and determining compressed data corresponding to each frame of image based on the difference information between the pixel point corresponding to each frame of image and the discrete target to be compressed and the pixel point corresponding to the discrete target to be compressed in each frame of image from the (N + 2) th frame of image to the M (M) th frame of image.

Fig. 15 is a schematic structural diagram of an image storage device according to an exemplary embodiment of the present application. As shown in fig. 15, an image storage apparatus provided in an embodiment of the present application includes:

an image compression data determining module 800, configured to determine image compression data corresponding to a medical image sequence to be compressed.

Specifically, the image compressed data is obtained based on the image processing method mentioned in any of the above embodiments.

A storage module 900 for storing a sequence of medical images to be compressed based on image compression data.

Next, an electronic apparatus according to an embodiment of the present application is described with reference to fig. 16. Fig. 16 is a schematic structural diagram of an electronic device according to an exemplary embodiment of the present application.

As shown in fig. 16, the electronic device 50 includes one or more processors 501 and memory 502.

Processor 501 may be a Central Processing Unit (CPU) or other form of Processing Unit having data Processing capabilities and/or instruction execution capabilities, and may control other components in electronic device 50 to perform desired functions.

Memory 502 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. Volatile Memory may include, for example, Random Access Memory (RAM), cache Memory (cache), and/or the like. The nonvolatile Memory may include, for example, a Read-Only Memory (ROM), a hard disk, a flash Memory, and the like. One or more computer program instructions may be stored on a computer readable storage medium and executed by the processor 501 to implement the image processing methods and/or the image storage methods of the various embodiments of the present application mentioned above and/or other desired functions. Various content such as a sequence of medical images to be compressed may also be stored in the computer readable storage medium.

In one example, the electronic device 50 may further include: an input device 503 and an output device 504, which are interconnected by a bus system and/or other form of connection mechanism (not shown).

The input device 503 may include, for example, a keyboard, a mouse, and the like.

The output device 504 may output various information including difference information and the like to the outside. The output devices 504 may include, for example, a display, speakers, a printer, and a communication network and its connected remote output devices, among others.

Of course, for the sake of simplicity, only some of the components related to the present application in the electronic device 50 are shown in fig. 16, and components such as a bus, an input/output interface, and the like are omitted. In addition, electronic device 50 may include any other suitable components, depending on the particular application.

In addition to the above-described methods and apparatuses, embodiments of the present application may also be a computer program product comprising computer program instructions that, when executed by a processor, cause the processor to perform the steps in the image processing method and/or the image storage method according to various embodiments of the present application described above in this specification.

The computer program product may include program code for carrying out operations for embodiments of the present application in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.

Furthermore, embodiments of the present application may also be a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, cause the processor to perform the steps in the image processing method and/or the image storage method according to various embodiments of the present application described above in this specification.

A computer-readable storage medium may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a Read-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROM or flash Memory), an optical fiber, a portable Compact Disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The foregoing describes the general principles of the present application in conjunction with specific embodiments, however, it is noted that the advantages, effects, etc. mentioned in the present application are merely examples and are not limiting, and they should not be considered essential to the various embodiments of the present application. Furthermore, the foregoing disclosure of specific details is for the purpose of illustration and description and is not intended to be limiting, since the foregoing disclosure is not intended to be exhaustive or to limit the disclosure to the precise details disclosed.

The block diagrams of devices, apparatuses, systems referred to in this application are only given as illustrative examples and are not intended to require or imply that the connections, arrangements, configurations, etc. must be made in the manner shown in the block diagrams. These devices, apparatuses, devices, systems may be connected, arranged, configured in any manner, as will be appreciated by those skilled in the art. Words such as "including," "comprising," "having," and the like are open-ended words that mean "including, but not limited to," and are used interchangeably therewith. The words "or" and "as used herein mean, and are used interchangeably with, the word" and/or, "unless the context clearly dictates otherwise. The word "such as" is used herein to mean, and is used interchangeably with, the phrase "such as but not limited to".

It should also be noted that in the devices, apparatuses, and methods of the present application, the components or steps may be decomposed and/or recombined. These decompositions and/or recombinations are to be considered as equivalents of the present application.

The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the application. Thus, the present application is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

The foregoing description has been presented for purposes of illustration and description. Furthermore, the description is not intended to limit embodiments of the application to the form disclosed herein. While a number of example aspects and embodiments have been discussed above, those of skill in the art will recognize certain variations, modifications, alterations, additions and sub-combinations thereof.