CN113112432A - Method for automatically identifying image strips - Google Patents
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- 102000004169 proteins and genes Human genes 0.000 description 11
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Abstract
The application discloses a method and a system for automatically identifying image strips, which comprises the following steps of carrying out noise reduction processing on a first image containing the strips to obtain a second image; extracting edge difference characteristics of one image dimension from the second image to obtain gray level jump information of the second image, wherein the strips extend along the image dimension direction at intervals; and performing stripe identification and extraction according to the gray level jump information of the second image to obtain stripe information. The method comprises the steps of carrying out noise reduction on an image, reducing the influence of noise on strip identification, accurately identifying the strip, obtaining gray level jump information by extracting edge difference characteristics on the line dimension or the column dimension of the image, screening the gray level jump information according to a set range to obtain the strip information, and having high operation speed and high efficiency, wherein the edge of the identified strip image is fine.
Description
Technical Field
The invention relates to the field of image processing, in particular to electrophoretic protein image processing.
Background
In the biochemical field, protein molecules are separated from a mixed solution by electrophoresis, and molecules with different sizes move to different depth positions to form a strip. The position information is recorded by photographing, and the position of the strip is identified by image processing to obtain the information of the protein molecule for further analysis in clinic and laboratory.
At present, most of the edge detection methods are used to identify image bands, and the commonly used edge detection methods include Sobel (Sobel), Prewitt (Prewitt), robert (Roberts), Canny, and so on. The edges of the strips obtained by the Sobel operator have the defects of noise pollution, too thick and too wide edge lines and the like; the Robert operator and the Privitt operator have high operation rate and have a certain inhibition effect on noise, but the detected strip edge quality is not high, such as the edge is thick, the positioning is inaccurate, and the number of discontinuities is large; the Canny operator is a relatively advanced edge detection operator, is not easily interfered by noise, and obtains a fine and accurate stripe edge, but has high operation cost and is difficult to operate in real-time image processing.
Disclosure of Invention
The invention aims to solve the technical problem of providing a method for automatically identifying image strips, and the image strips identified by the method are accurate in positioning, fine in edges, high in operation speed and high in efficiency.
The invention provides a method for automatically identifying image strips, which comprises the following steps:
carrying out noise reduction processing on a first image containing a strip to obtain a second image;
obtaining the gray level jump information of one image dimension of the second image, wherein the strips extend at intervals along the image dimension direction;
and performing stripe identification and extraction according to the gray level jump information of the second image to obtain stripe information.
In one embodiment, the noise reduction processing includes a first noise reduction for removing a background interference factor of the first image and a second noise reduction for removing a noise point included in the first image.
In one embodiment, after the denoising processing is performed on the first image containing the strip and the second image is obtained, the sharpening processing is further performed on the edge of the strip.
In one embodiment, the strips extend along the longitudinal interval of the first image, and the obtaining of the gray jump information of one image dimension of the second image includes: and respectively adding the gray level average value of the next row of each row of the second image to the gray level average value of the previous row of the second image and subtracting twice the gray level average value of the previous row to obtain the gray level jump value of the row, wherein the gray level jump information comprises the gray level jump value.
In one embodiment, the strips extend along the longitudinal interval of the first image, and the obtaining of the gray jump information of one image dimension of the second image includes: obtaining a gray level gradient value of each line of the second image; and obtaining the gray jump value of each line according to the gray gradual change values of every two adjacent lines, wherein the gray jump information comprises the gray jump values.
In one embodiment, the obtaining of the gray scale gradual change value of each line of the second image includes: respectively subtracting the gray average value of the next line of each line of the second image from the gray average value of the line to obtain the gray gradual change value of the line; the obtaining of the gray level jump value of each line according to the gray level gradual change values of every two adjacent lines comprises the following steps: and respectively subtracting the gray scale gradient value of one line on the line from the gray scale gradient value of each line on the second image to obtain the gray scale jump value of the line.
In one embodiment, the performing stripe recognition and extraction according to the gray level jump information to obtain stripe information includes: and judging the gray level jump values of the gray level jump information one by one, and sequentially using the second image rows corresponding to the gray level jump values which are larger than zero and smaller than a set value as the starting rows of the strips and the ending rows of the strips.
A system based on a method of automatically identifying image strips, comprising:
the noise reduction module is used for carrying out noise reduction processing on the first image containing the strips to obtain a second image;
the gray level jump information acquisition module is used for acquiring gray level jump information of one image dimension of the second image, and the strips extend at intervals along the image dimension direction;
and the strip extraction module is used for identifying and extracting strips according to the gray level jump information of the second image to obtain strip information.
A 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 automatically identifying image strips.
An electronic device, comprising: a memory for storing a computer program; a processor for implementing the steps of the method for automatically identifying image strips when executing said computer program.
The method for automatically identifying the image strips reduces the influence of noise on the strip identification by carrying out noise reduction on the image, ensures accurate strip identification, obtains the gray level jump information on the row dimension or the column dimension of the image, screens the gray level jump information according to the set range to obtain the strip information, and has high operation speed and high efficiency, and the identified strip image has fine edges.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles and effects of the invention.
Unless otherwise specified or defined, the same reference numerals in different figures refer to the same or similar features, and different reference numerals may be used for the same or similar features.
FIG. 1 is a schematic diagram of a first embodiment of a method for automatically identifying image strips according to the present invention;
FIG. 2 is a schematic representation of an electrophoretic protein image obtained in an embodiment of the present invention;
FIG. 3 is a schematic representation of a band image after electrophoretic protein image recognition of FIG. 2.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
For example, in the case of separating proteins by electrophoresis using a protein electrophoresis transfer device, the voltage is vertical, the proteins move along the vertical direction, and different molecular sizes stay at different places, so that the proteins are aggregated to form horizontal bands, and the image including the bands as shown in fig. 2 can be obtained by photographing.
The first embodiment of the method for automatically identifying image strips of the invention is shown in fig. 1 and comprises the following steps:
s10: and carrying out noise reduction processing on the image containing the strips to obtain a noise-reduced image.
If the image containing the strip is colored, the image can be changed into a black-and-white image by adopting a binary method, and then the noise reduction processing can be carried out on the image. Specifically, the noise reduction processing of the present embodiment includes:
s110: first denoising treatment: and obtaining a background gray value by adopting a local background method or a global background method, calculating a gray average value of each line of the image, and taking a value obtained by subtracting the background gray value from the gray average value of each line as the gray average value of the line used for subsequent calculation.
The gray value of a plurality of pixels in each line of the image is calculated by the following method: and summing the gray values of all pixels of each row, and calculating the average value to obtain the gray average value of the corresponding row.
By first denoising, interference information generated by background gray scale of the image can be removed.
Of course, the method for obtaining the background gray value is not limited to the local background method and the global background method, and other calculation methods such as a gray histogram method may be used. Other methods may also be employed to remove the effects of background gray scale values.
S120: and (3) second denoising treatment: and carrying out mean value filtering on the image subjected to the first denoising treatment to eliminate high-frequency noise points in the image.
Specifically, the mean filtering realizes filtering through neighborhood operation, and adopts a linear method to average pixel values in a window range of the whole field as the gray value of a central point, so that noise points and smooth images can be effectively eliminated, the speed is high, and the algorithm complexity is low. The selected neighborhood size can directly influence the result, the selected neighborhood is too large, a lot of detail information such as outlines and the like can be lost, and if the neighborhood is too small, noise points cannot be well eliminated, so that a user can customize an appropriate neighborhood size according to actual conditions. Of course, median filtering may be employed instead of mean filtering.
In one embodiment, the edge of the strip of the image after the first denoising and the second denoising may be sharpened, for example, erosion filtering may be adopted to highlight the boundary information in the strip of the image.
S20: and extracting edge difference characteristics of the denoised image.
Specifically, in the present embodiment, since the stripes are present at intervals along the longitudinal direction of the noise-reduced image, the gray scale gradual change values of each line of the noise-reduced image are calculated accordingly, and the set of these gray scale gradual change values constitutes the edge difference feature of the noise-reduced image.
The method for obtaining the gray level gradient value of each line comprises the following steps: and respectively subtracting the gray average value of the next line of each line from the gray average value of the next line of each line to obtain the gray gradient value of the line.
S30: and obtaining gray level jump information according to the edge difference characteristics.
And obtaining the gray jump value of each row according to the gray gradual change value in the edge difference characteristic, wherein all the gray jump values form gray jump information.
The method for obtaining the gray level jump value of each row comprises the following steps: and respectively subtracting the gray scale gradual change value of one row on the row from the gray scale gradual change value of each row to obtain the gray scale jump value of the row.
S40: and performing stripe identification and extraction according to the obtained gray level jump information to obtain stripe information.
After the gray level jump information is obtained, the size of the gray level change can be accurately reflected. When the gray level jump value of the row is larger than zero and smaller than the set value, the row is the edge of the strip, therefore, the row corresponding to the gray level jump value larger than zero and smaller than the set value is taken as the starting row of the strip and the ending row of the strip in sequence, and the rows between the starting row of the strip and the ending row of the strip form the body row of the strip, so that the strips can be identified and positioned one by one. Namely: the band information is the gray level jump value in the range of 0< X < Z, where X is the gray level jump value and Z is the upper limit of the set gray level jump value, and the upper limit is different according to the material forming the band. In this embodiment, the band image is a protein electrophoresis band image, and the upper limit Z of the grayscale jump value is 2000.
Optionally, the gray level gradual change value and the gray level jump value of the first row and the last row of the denoised image can be directly set to 0; it is also possible to skip directly the first and last lines when performing the band screening.
The second embodiment of the present invention differs from the first embodiment in that: step-by-step calculation is not performed on the gray level jump information of the noise-reduced image, namely: the gray level gradual change value formed by the gray level average value between every two lines of the image after noise reduction is not calculated, but the gray level jump information is quickly obtained by directly adopting the following method: and respectively adding the gray level average value of the next line of each line of the denoised image to the gray level average value of the previous line of the image and subtracting twice the gray level average value of the line to obtain the gray level jump value of the line, wherein the gray level jump values of all the lines form gray level jump information after the gray level jump value of each line is obtained. Other steps are the same as those of the first embodiment and are not repeated here.
Although the bands of the image extend along the longitudinal direction of the image at intervals in the present embodiment, it is easy to think that the bands may also extend along the lateral direction of the image at intervals, and the image edge difference feature and the gray level jump information are also calculated according to the columns of the image.
After the electrophoretic protein image is subjected to band recognition by adopting the automatic image band recognition method, an image containing recognized band information as shown in FIG. 3 can be obtained. Each column from left to right in the image is a lane, and the thin line connecting the left side and the right side of the lane in each lane is the central line of the identified band.
The application also provides a system based on the method for automatically identifying the image strips, which comprises the following steps:
the noise reduction module is used for carrying out noise reduction processing on the first image containing the strips to obtain a second image;
the gray level jump information acquisition module is used for extracting an edge difference characteristic of one image dimension of the second image to acquire gray level jump information of the second image, and the strips extend at intervals along the image dimension direction;
and the strip extraction module is used for identifying and extracting strips according to the gray level jump information of the second image to obtain strip information.
Of course, the method in the present application may also be other apparatuses for implementing the corresponding functions, such as a computer device or a computer readable medium.
Although the present application has been described with reference to the preferred embodiments, it is not intended to limit the present application, and those skilled in the art can make variations and modifications without departing from the spirit and scope of the present application, therefore, the scope of the present application should be determined by the claims that follow.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory. The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data.
Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include non-transitory computer readable media (transmyedia), such as modulated data signals and carrier waves.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Claims (10)
1. Method for automatically identifying image strips, characterized in that it comprises the following steps:
carrying out noise reduction processing on a first image containing a strip to obtain a second image;
obtaining the gray level jump information of one image dimension of the second image, wherein the strips extend at intervals along the image dimension direction;
and performing stripe identification and extraction according to the gray level jump information of the second image to obtain stripe information.
2. The method of automatically identifying image strips as claimed in claim 1, wherein said noise reduction process comprises a first noise reduction for eliminating a background interference factor of said first image and a second noise reduction for eliminating noise points contained in said first image.
3. The method of claim 1, wherein denoising the first image containing a strip and obtaining the second image further comprises sharpening edges of the strip.
4. The method of automatically identifying image strips as claimed in claim 1, wherein said strips extend along the longitudinal direction of said first image at intervals, and said obtaining information of gray transitions of one image dimension of said second image comprises: and respectively adding the gray level average value of the next row of each row of the second image to the gray level average value of the previous row of the second image and subtracting twice the gray level average value of the previous row to obtain the gray level jump value of the row, wherein the gray level jump information comprises the gray level jump value.
5. The method of automatically identifying image strips as claimed in claim 1, wherein said strips extend along the longitudinal direction of said first image at intervals, and said obtaining information of gray transitions of one image dimension of said second image comprises: obtaining a gray level gradient value of each line of the second image; and obtaining the gray jump value of each line according to the gray gradual change values of every two adjacent lines, wherein the gray jump information comprises the gray jump values.
6. The method of automatically identifying image strips as claimed in claim 5, wherein said obtaining gray scale gradient values for each line of said second image comprises: respectively subtracting the gray average value of the next line of each line of the second image from the gray average value of the line to obtain the gray gradual change value of the line; the obtaining of the gray level jump value of each line according to the gray level gradual change values of every two adjacent lines comprises the following steps: and respectively subtracting the gray scale gradient value of one line on the line from the gray scale gradient value of each line on the second image to obtain the gray scale jump value of the line.
7. The method for automatically identifying image strips as claimed in claim 4 or 5, wherein the strip identification and extraction is performed according to the gray jump information to obtain strip information, comprising: and judging the gray level jump values of the gray level jump information one by one, and sequentially using the second image rows corresponding to the gray level jump values which are larger than zero and smaller than a set value as the starting rows of the strips and the ending rows of the strips.
8. A system for automatically identifying image strips, comprising:
the noise reduction module is used for carrying out noise reduction processing on the first image containing the strips to obtain a second image;
the gray level jump information acquisition module is used for acquiring gray level jump information of one image dimension of the second image, and the strips extend at intervals along the image dimension direction;
and the strip extraction module is used for identifying and extracting strips according to the gray level jump information of the second image to obtain strip information.
9. Computer-readable storage medium, characterized in that it has stored thereon a computer program which, when being executed by a processor, carries out the steps of the method of automatically identifying image strips according to any one of claims 1 to 6.
10. Computer apparatus, comprising: a memory for storing a computer program; processor for implementing the steps of the method of automatically recognizing image strips according to any one of claims 1 to 6 when executing said computer program.
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| CN115937052A (en) * | 2023-03-14 | 2023-04-07 | 四川福莱宝生物科技有限公司 | Gel electrophoresis image processing method, device, equipment and medium |
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