CN108320292A - Image processing method and device - Google Patents
Image processing method and device Download PDFInfo
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- CN108320292A CN108320292A CN201711382825.4A CN201711382825A CN108320292A CN 108320292 A CN108320292 A CN 108320292A CN 201711382825 A CN201711382825 A CN 201711382825A CN 108320292 A CN108320292 A CN 108320292A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/136—Segmentation; Edge detection involving thresholding
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
- G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10081—Computed x-ray tomography [CT]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30242—Counting objects in image
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Abstract
A kind of image processing method of this specification embodiment offer and device.The method includes：Obtain the Areal porosity of scan image；Based on Areal porosity, the segmentation threshold of the scan image is determined；Using the segmentation threshold, processing is split to the scan image.
Description
Technical field
This specification embodiment is related to technical field of image processing, more particularly to a kind of image processing method and device.
Background technology
With the rapid development of CT imaging techniques, the information such as microstructure of rock core can be reflected by CT scan image
Come, to which CT scan image has become the capsule information of analysis rock core microstructure.Normally, accuracy of instrument and noise are limited to
Presence, rock matrix and hole in CT scan image often can not be differentiated clearly, thus need to use image segmentation algorithm
Image segmentation is carried out to CT scan image, to determine the reasonable boundary position between rock matrix and hole.
Common image segmentation algorithm may include iterative method, simple statistic method, OTSU algorithms etc..But above-mentioned figure
As partitioning algorithm often considers merely gray value of image so that segmentation effect is poor, is unfavorable for side between rock matrix and hole
The determination of boundary position.
Invention content
The purpose of this specification embodiment is to provide a kind of image processing method and device, to improve image segmentation.
To achieve the above object, this specification embodiment provides a kind of image processing method, including：Obtain scan image
Areal porosity；Based on Areal porosity, the segmentation threshold of the scan image is determined；Using the segmentation threshold, to the scan image
It is split processing.
To achieve the above object, this specification embodiment provides a kind of image processing apparatus, including：Acquiring unit is used for
Obtain the Areal porosity of scan image；Determination unit determines the segmentation threshold of the scan image for being based on Areal porosity；Segmentation
Unit is split processing for using the segmentation threshold to the scan image.
The technical solution provided by above this specification embodiment is as it can be seen that this specification embodiment can obtain scan image
Areal porosity；It can be based on Areal porosity, determine the segmentation threshold of the scan image；The segmentation threshold can be used, to institute
It states scan image and is split processing.In this way, the present embodiment is during image segmentation, it is contemplated that gray value of image and rock
Physical message (Areal porosity) is conducive to the determination of boundary position between rock matrix and hole so as to improve segmentation effect.
Description of the drawings
In order to illustrate more clearly of this specification embodiment or technical solution in the prior art, below will to embodiment or
Attached drawing needed to be used in the description of the prior art is briefly described, it should be apparent that, the accompanying drawings in the following description is only
Some embodiments described in this specification, for those of ordinary skill in the art, in not making the creative labor property
Under the premise of, other drawings may also be obtained based on these drawings.
Fig. 1 is a kind of flow chart of image processing method of this specification embodiment；
Fig. 2 is a kind of CT scan image of tight sand rock core of this specification embodiment；
Fig. 3 is that this specification embodiment is a kind of being split the binary map obtained after processing to CT scan image shown in Fig. 2
Picture；
Fig. 4 is a kind of illustrative view of functional configuration of image processing apparatus of this specification embodiment.
Specific implementation mode
Below in conjunction with the attached drawing in this specification embodiment, the technical solution in this specification embodiment is carried out clear
Chu is fully described by, it is clear that described embodiment is only this specification a part of the embodiment, rather than whole implementation
Example.The embodiment of base in this manual, those of ordinary skill in the art are obtained without creative efforts
Every other embodiment, should all belong to this specification protection range.
It please refers to Fig.1.The embodiment of the present application provides a kind of image processing method, described image processing method may include with
Lower step.
Step S10：Obtain the Areal porosity of scan image.
In the present embodiment, the scan image can be the CT scan image of rock core body or core sample, the rock core
Body can be used for drilling through core sample.The scan image for example can be gray level image.
In the present embodiment, the Areal porosity of scan image can be directly acquired.Alternatively, can also to the scan image into
Journey pre-processes, and can obtain the Areal porosity of pretreated scan image.The pretreatment may include using filtering algorithm
Scan image is handled to eliminate the noise in scan image.The filtering algorithm may include median filtering algorithm etc..
In the present embodiment, formula can be usedCalculate the Areal porosity of scan image；Wherein, φ is indicated
Areal porosity；DfIndicate Pore fractal dimension；λmaxIndicate maximum pore radius；λminIndicate minimum pore radius.
In the present embodiment, scan image can be handled using edge detection algorithm, is obtained containing marginal information
Bianry image.The edge detection algorithm for example may include Sobel (Sobel) edge detection algorithm, the inspection of the edges Robert
Method of determining and calculating, Prewitt edge detection algorithms etc..It is various in view of the complex shape of hole in bianry image, it can be by each hole
Gap is considered as the border circular areas with its homalographic, can characterize the hole using the border circular areas, can be by the border circular areas
Equivalent redius of the radius as the hole.In this way, can be based on bianry image obtains equivalent redius set and described equivalent half
The cumulative porosity quantity of each equivalent redius in diameter set；Based in the equivalent redius set and the equivalent redius set
The cumulative porosity quantity of each equivalent redius, carries out fitting a straight line, obtains the D of the scan imagef、λmaxAnd λmin。
Specifically, the equivalent redius that each hole in bianry image can be obtained, as equivalent in equivalent redius set
Radius.For each equivalent redius in the equivalent redius set, equivalent redius in bianry image can be counted and be less than or wait
In the hole quantity of the equivalent redius, the cumulative porosity quantity as the equivalent redius.For example, in the equivalent redius set
Equivalent redius may include 1,2,3,4,5,6.1 corresponding hole quantity of equivalent redius is 1；2 corresponding number of pores of equivalent redius
Amount is 2；3 corresponding hole quantity of equivalent redius is 3；4 corresponding hole quantity of equivalent redius is 2；5 corresponding hole of equivalent redius
Gap quantity is 2；6 corresponding hole quantity of equivalent redius is 3.So, the cumulative porosity quantity of equivalent redius 1 can be 1；It is equivalent
The cumulative porosity quantity of radius 2 can be 3；The cumulative porosity quantity of equivalent redius 3 can be 6；The cumulative porosity of equivalent redius 4
Quantity can be 7；The cumulative porosity quantity of equivalent redius 5 can be 9；The cumulative porosity quantity of equivalent redius 6 can be 12.
In dimensionless interzone, the cumulative porosity quantity N of equivalent redius λ and equivalent redius λcFollow formula
ln[Nc]=- Dflnλ+Dflnλmax.As it can be seen that the cumulative porosity quantity N of equivalent redius λ and equivalent redius λcIn line
The slope of sexual intercourse, straight line is Pore fractal dimension Df.In this way, least square method may be used in equivalent redius set
The accumulation hole quantity of equivalent redius and equivalent redius carries out linear fit, and the slope of fitting a straight line is Pore fractal dimension
Df；Two endpoints of fitting a straight line are respectively λmaxAnd λmin。
Step S12：Based on Areal porosity, the segmentation threshold of the scan image is determined.
In the present embodiment, following formula can be used to determine the segmentation threshold of the scan image.
In above formula (1),
φ is the Areal porosity of scan image；
k*For the segmentation threshold of scan image；
ImaxFor the maximum gradation value of scan image；
IminFor the minimum gradation value of scan image；
P (i) is the pixel quantity that gray value is i in scan image.
For example, Areal porosity φ=12.3% for the scan image that step S10 is obtained；By the Areal porosity φ of scanning figure image=
12.3% substitutes into above formula (1), can obtain segmentation threshold k*=64.
Step S14：Using the segmentation threshold, processing is split to the scan image.
In the present embodiment, gray value can characterize hole less than or equal to the pixel of segmentation threshold in scan image；
The pixel that gray value is more than segmentation threshold can characterize rock matrix.In this way, the segmentation threshold can be used, and by such as
Lower formula is split processing to the scan image, obtains the two-value that can indicate boundary position between rock matrix and hole
Image.
In above formula (2),
I (x, y) is the gray value of pixel (x, y) in bianry image；
I is the gray value of pixel (x, y) in scan image；
I (x, y)=0 is for indicating hole；I (x, y)=255 is for indicating rock matrix.
For example, Fig. 2 is the CT scan image of certain oily area tight sand rock core, Fig. 3 is obtained after being split processing
Bianry image.
In the present embodiment, the Areal porosity of scan image can be obtained；It can be based on Areal porosity, determine the scan image
Segmentation threshold；The segmentation threshold can be used, processing is split to the scan image.In this way, the present embodiment is being schemed
During as segmentation, it is contemplated that gray value of image and rock physics information (Areal porosity) have so as to improve segmentation effect
Conducive to the determination of boundary position between rock matrix and hole.In particular, the present embodiment reservoir strong to anisotropism and densification
The rock core of reservoir has a clear superiority.
Please refer to Fig. 4.The embodiment of the present application also provides a kind of image processing apparatus, including：
Acquiring unit 20, the Areal porosity for obtaining scan image；
Determination unit 22 determines the segmentation threshold of the scan image for being based on Areal porosity；
Cutting unit 24 is split processing for using the segmentation threshold to the scan image.
In the 1990s, the improvement of a technology can be distinguished clearly be on hardware improvement (for example,
Improvement to circuit structures such as diode, transistor, switches) or software on improvement (improvement for method flow).So
And with the development of technology, the improvement of current many method flows can be considered as directly improving for hardware circuit.
Designer nearly all obtains corresponding hardware circuit by the way that improved method flow to be programmed into hardware circuit.Cause
This, it cannot be said that the improvement of a method flow cannot be realized with hardware entities module.For example, programmable logic device
(Programmable Logic Device, PLD) (such as field programmable gate array (Field Programmable Gate
Array, FPGA)) it is exactly such a integrated circuit, logic function determines device programming by user.By designer
Voluntarily programming comes a digital display circuit " integrated " on a piece of PLD, designs and makes without asking chip maker
Dedicated IC chip 2.Moreover, nowadays, substitution manually makes IC chip, and this programming is also used instead mostly
" logic compiler (logic compiler) " software realizes that software compiler used is similar when it writes with program development
Seemingly, and the source code before compiling also handy specific programming language is write, this is referred to as hardware description language
(Hardware Description Language, HDL), and HDL is also not only a kind of, but there are many kind, such as ABEL
(Advanced Boolean Expression Language)、AHDL(Altera Hardware Description
Language)、Confluence、CUPL(Cornell University Programming Language)、HDCal、JHDL
(Java Hardware Description Language)、Lava、Lola、MyHDL、PALASM、RHDL(Ruby
Hardware Description Language) etc., VHDL (Very-High-Speed are most generally used at present
Integrated Circuit Hardware Description Language) and Verilog2.Those skilled in the art
It will be apparent to the skilled artisan that only needing method flow slightly programming in logic and being programmed into integrated circuit with above-mentioned several hardware description languages
In, so that it may to be readily available the hardware circuit for realizing the logical method flow.
System, device, module or the unit that above-described embodiment illustrates can specifically realize by computer chip or entity,
Or it is realized by the product with certain function.
System, device, module or the unit that above-described embodiment illustrates can specifically realize by computer chip or entity,
Or it is realized by the product with certain function.It is a kind of typically to realize that equipment is computer.Specifically, computer for example may be used
Think personal computer, laptop computer, cellular phone, camera phone, smart phone, personal digital assistant, media play
It is any in device, navigation equipment, electronic mail equipment, game console, tablet computer, wearable device or these equipment
The combination of equipment.
As seen through the above description of the embodiments, those skilled in the art can be understood that this specification
The mode of required general hardware platform can be added to realize by software.Based on this understanding, the technical solution of this specification
Substantially the part that contributes to existing technology can be expressed in the form of software products in other words, the computer software
Product can be stored in a storage medium, such as ROM/RAM, magnetic disc, CD, including some instructions are used so that a computer
Equipment (can be personal computer, server either network equipment etc.) executes each embodiment of this specification or embodiment
Certain parts described in method.
Each embodiment in this specification is described in a progressive manner, identical similar portion between each embodiment
Point just to refer each other, and each embodiment focuses on the differences from other embodiments.Especially for system reality
For applying example, since it is substantially similar to the method embodiment, so description is fairly simple, related place is referring to embodiment of the method
Part explanation.
This specification can be used in numerous general or special purpose computing system environments or configuration.Such as：Personal computer,
Server computer, handheld device or portable device, laptop device, multicomputer system, microprocessor-based system,
Set top box, programmable consumer-elcetronics devices, network PC, minicomputer, mainframe computer including any of the above system are set
Standby distributed computing environment etc..
This specification can describe in the general context of computer-executable instructions executed by a computer, such as journey
Sequence module.Usually, program module include routines performing specific tasks or implementing specific abstract data types, program, object,
Component, data structure etc..This specification can also be put into practice in a distributed computing environment, in these distributed computing environment
In, by executing task by the connected remote processing devices of communication network.In a distributed computing environment, program module
It can be located in the local and remote computer storage media including storage device.
Although depicting this specification by embodiment, it will be appreciated by the skilled addressee that there are many become for this specification
Shape and the spirit changed without departing from this specification, it is desirable to which the attached claims include these deformations and change without departing from this
The spirit of specification.
Claims (9)
1. a kind of image processing method, which is characterized in that including：
Obtain the Areal porosity of scan image；
Based on Areal porosity, the segmentation threshold of the scan image is determined；
Using the segmentation threshold, processing is split to the scan image.
2. the method as described in claim 1, which is characterized in that the method further includes：
The scan image is pre-processed；
Correspondingly, the Areal porosity for obtaining scan image, including：
Obtain the Areal porosity of scan image after pre-processing.
3. method as claimed in claim 2, which is characterized in that the pretreatment includes being filtered.
4. the method as described in claim 1, which is characterized in that the Areal porosity for obtaining scan image, including：
Use formulaCalculate the Areal porosity of each scan image；Wherein, φ is Areal porosity；DfFor pore fractal
Dimension；λmaxFor maximum pore radius；λminFor minimum pore radius.
5. method as claimed in claim 4, which is characterized in that the method further includes：
The scan image is handled using edge detection algorithm, obtains bianry image；
The accumulative of each equivalent redius in equivalent redius set and the equivalent redius set is obtained based on the bianry image
Hole quantity；
Based on the cumulative porosity quantity of each equivalent redius in the equivalent redius set and the equivalent redius set, carry out
Fitting a straight line obtains the Pore fractal dimension, maximum pore radius and minimum pore radius of the scan image.
6. method as claimed in claim 5, which is characterized in that the Pore fractal dimension of the scan image is fitting a straight line
Slope.
7. the method as described in claim 1, which is characterized in that the segmentation threshold of the determination scan image, including：
The segmentation threshold of the scan image is determined using following formula；
Wherein,
φ is the Areal porosity of scan image；
k*For the segmentation threshold of scan image；
ImaxFor the maximum gradation value of scan image；
IminFor the minimum gradation value of scan image；
P (i) is the pixel quantity that gray value is i in scan image.
8. the method as described in claim 1, which is characterized in that it is described that processing is split to the scan image, including：Make
Processing is split to the scan image with following formula, obtains bianry image；
Wherein,
I (x, y) is the gray value of pixel (x, y) in bianry image；
I is the gray value of pixel (x, y) in scan image.
9. a kind of image processing apparatus, which is characterized in that including：
Acquiring unit, the Areal porosity for obtaining scan image；
Determination unit determines the segmentation threshold of the scan image for being based on Areal porosity；
Cutting unit is split processing for using the segmentation threshold to the scan image.
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Application publication date: 20180724 |