CN109191456A - Lung CT image processing method and system based on two-dimentional S-transformation - Google Patents

Lung CT image processing method and system based on two-dimentional S-transformation Download PDF

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CN109191456A
CN109191456A CN201811098822.2A CN201811098822A CN109191456A CN 109191456 A CN109191456 A CN 109191456A CN 201811098822 A CN201811098822 A CN 201811098822A CN 109191456 A CN109191456 A CN 109191456A
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vertical direction
horizontal direction
lung
image
transformation
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彭真明
孙翎马
蒲红
彭凌冰
郭璐
蒲怡
王卓然
王月波
袁国慧
唐雨潇
陈江华
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University of Electronic Science and Technology of China
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University of Electronic Science and Technology of China
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • 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
    • 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]
    • 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
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30061—Lung

Abstract

The invention discloses a kind of lung CT image processing method and system based on two-dimentional S-transformation, the lung CT image processing method, the following steps are included: carrying out the two-dimentional S-transformation on horizontal direction and vertical direction to the original lung CT image of input, horizontal direction transform domain and vertical direction transform domain are obtained；The extraction that energy properties are carried out to the horizontal direction transform domain and vertical direction transform domain, obtains horizontal direction energy properties figure and vertical direction energy properties figure；The horizontal direction energy properties figure and vertical direction energy properties figure are subjected to Threshold segmentation, obtain the lung images with frequency domain character.The present invention is based on two-dimentional S-transformations, and the extraction of energy properties is carried out to obtained transform domain, Threshold segmentation is then carried out, finally obtains processing image.By calculating energy properties, a kind of feature of new type, i.e. frequency domain character are provided for the processing of lung CT image, to obtain the lung images with frequency domain character.

Description

Lung CT image processing method and system based on two-dimentional S-transformation
Technical field
The invention belongs to lung images process field, in particular to a kind of lung CT image processing based on two-dimentional S-transformation Method and system.
Background technique
The processing of lung CT image, the main feature high using extraction discrimination is come normal and different in area's lung images Often, the discrimination of extracted feature directly influences processing result.
Currently, common feature can be roughly divided into three classes: gray feature, morphological feature, textural characteristics.Gray feature is normal There are mean value, standard deviation and statistical moment；There are commonly eccentricities, circularity, the degree of packing, maximum radius, two-dimensional for shape feature Area, three-dimensional volume etc., there are also some shape descriptors based on Hessian matrix, description based on HOG feature；Line Reason Feature Descriptor has very much, usually using description based on GLCM, description of LBP, description such as comentropy.But these Spatial information and statistical information is only utilized in feature, and there is no utilize frequency information.
Summary of the invention
It is an object of the invention to: it is extracted for the processing of existing lung CT image in the method for feature and space is only utilized Information and statistical information provide a kind of lung CT image processing based on two-dimentional S-transformation there is no frequency information is utilized Method and system provides a kind of feature of new type, i.e. frequency using the energy feature in S-transformation domain for lung CT image processing Characteristic of field, to obtain the lung images with frequency domain character.
The technical solution adopted by the invention is as follows:
A kind of lung CT image processing method based on two-dimentional S-transformation, comprising the following steps:
Step 1: the two-dimentional S-transformation on horizontal direction and vertical direction being carried out to the original lung CT image of input, is obtained Horizontal direction transform domain and vertical direction transform domain；
Step 2: carrying out the extraction of energy properties to the horizontal direction transform domain and vertical direction transform domain, obtain level Oriented energy attributed graph and vertical direction energy properties figure；
Step 3: the horizontal direction energy properties figure and vertical direction energy properties figure being subjected to Threshold segmentation, had There are the lung images of frequency domain character.
Further, step 1 specifically comprises the following steps:
Step 1.1: two-dimensional Fourier transform being carried out to the original lung CT image of input, obtains its spectrogram；
Step 1.2: the value of initialization horizontal direction frequency variable and vertical direction frequency variable；
Step 1.3: the frequency according to the value of the horizontal direction frequency variable and vertical direction frequency variable, after calculating translation Spectrogram；
Step 1.4: according to the value of the horizontal direction frequency variable and vertical direction frequency variable, calculating dimensional Gaussian window Spectrum of function；
Step 1.5: the product of spectrogram and dimensional Gaussian window function frequency spectrum after calculating the translation；
Step 1.6: two-dimentional inversefouriertransform being carried out to the result of step 1.5, respectively obtains horizontal direction and vertical side To S-transformation result；
Step 1.7: after executing step 1.6, whether the value of determined level direction frequency variable and vertical direction frequency variable All values have been looped through, if not having, the horizontal direction frequency variable and vertical direction frequency variable of all values will not traversed From after increasing, step 1.3 is continued to execute to step 1.6, otherwise jumps out circulation.
Further, in step 1.1, the formula of two-dimensional Fourier transform is as follows:
Wherein, F (α, β) indicates that obtained spectrogram, f (x, y) indicate that original lung CT image, α indicate that horizontal direction is empty Between frequency, β indicate vertical direction spatial frequency.
Further, step 1.2 specifically:
When calculating horizontal direction transform domain, fixed vertical direction frequency variable ky=0, initialize horizontal direction frequency variable kx=1；
When calculating vertical direction transform domain, fixed horizontal direction frequency variable kx=0, initialize vertical direction frequency variable ky=1.
Further, in step 1.4, the formula for calculating dimensional Gaussian window function frequency spectrum is as follows:
Wherein, W (kx, ky) indicate calculated dimensional Gaussian window function frequency spectrum, kxAnd kyIndicate the frequency domain number of S-transformation Value, α and β indicate the frequency domain frequency variable of dimensional Gaussian window function frequency spectrum.
Further, in step 1.6, the calculation formula for carrying out inversefouriertransform to the result of step 1.5 is as follows:
Wherein, S (x, y, kx, ky) it is S-transformation as a result, M (α, β) is the resulting result of product of step 1.5, α and β indicate anti- The frequency domain frequency variable of Fourier transformation, x indicate the spatial position of horizontal direction, and y indicates the spatial position of vertical direction.
Further, in step 2, the extraction of energy properties is carried out to horizontal direction transform domain and vertical direction transform domain, Obtain the formula of energy properties figure are as follows:
Wherein, S (x, y, kx, ky) indicate original lung CT image f (x, y) frequency variable k in the horizontal directionxWith vertical side To frequency variable kyRespectively certain value when S-transformation as a result, Re expression takes real part, Im expression takes imaginary part, and m and n respectively indicate defeated The height and width of the original lung CT image f (x, y) entered.
Further, step 3 specifically:
Step 3.1: the horizontal direction energy properties figure and vertical direction energy properties figure acquire to step 2 carries out threshold value point It cuts, respectively obtains horizontal direction binary image and vertical direction binary image；
Step 3.2: horizontal direction direction binary image and vertical direction binary image being carried out and operated, is obtained To the lung images with frequency domain character.
Further, in step 3.1, the calculation formula of the threshold value are as follows: T=m+ ε σ；
Wherein, T indicate threshold value, m indicate energy properties figure mean value, σ indicate energy properties figure variance, ε be control because Son, value range are 0.3~0.5.
A kind of lung CT image processing system based on two-dimentional S-transformation, comprising:
Image input units, for inputting original lung CT image；
Image processing unit obtains final processing image for handling the original lung CT image of input；
Image output unit, for exporting the final processing image；
The original lung CT image of described image processing unit processes input, the step of obtaining final processing image Include:
Step 1: the two-dimentional S-transformation on horizontal direction and vertical direction being carried out to the original lung CT image of input, is obtained Horizontal direction transform domain and vertical direction transform domain；
Step 2: carrying out the extraction of energy properties to the horizontal direction transform domain and vertical direction transform domain, obtain level Oriented energy attributed graph and vertical direction energy properties figure；
Step 3: the horizontal direction energy properties figure and vertical direction energy properties figure being subjected to Threshold segmentation, had There are the lung images of frequency domain character.
In conclusion by adopting the above-described technical solution, the beneficial effects of the present invention are:
The present invention is based on two-dimentional S-transformations, and the extraction of energy properties is carried out to obtained transform domain, then carry out threshold value point It cuts, finally obtains processing image；By calculating energy properties, a kind of spy of new type is provided for the processing of lung CT image Sign, i.e. frequency domain character, to obtain the lung images with frequency domain character.S-transformation compared to other Time-Frequency Analysis Methods, have compared with Good time-frequency focusing, the interference for item of also not reporting to the leadship after accomplishing a task, frequency-domain information obtained have better discrimination.Threshold process rank The adaptive thresholding of section has preferable robustness to clinical complicated lung images.
Detailed description of the invention
In order to illustrate the technical solution of the embodiments of the present invention more clearly, below will be to needed in the embodiment attached Figure is briefly described, it should be understood that the following drawings illustrates only certain embodiments of the present invention, therefore is not construed as pair The restriction of range for those of ordinary skill in the art without creative efforts, can also be according to this A little attached drawings obtain other relevant attached drawings.
Fig. 1 is that the present invention is based on the flow charts of the lung CT image processing method of two-dimentional S-transformation；
Fig. 2 is that the present invention is based on the detail flowcharts of the lung CT image processing method of two-dimentional S-transformation；
Fig. 3 is the original lung CT image that the present invention inputs；
Fig. 4 is the horizontal direction energy properties figure that the present invention obtains；
Fig. 5 is the vertical direction energy properties figure that the present invention obtains；
Fig. 6 is the horizontal direction binary image that the present invention obtains；
Fig. 7 is the vertical direction binary image that the present invention obtains；
Fig. 8 is the lung images with frequency domain character that the present invention obtains；
Fig. 9 is that the present invention is based on the structural schematic diagrams of the lung CT image processing system of two-dimentional S-transformation.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right The present invention is further elaborated.It should be appreciated that described herein, specific examples are only used to explain the present invention, not For limiting the present invention, i.e., described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is logical The component for the embodiment of the present invention being often described and illustrated herein in the accompanying drawings can be arranged and be designed with a variety of different configurations.
Therefore, the detailed description of the embodiment of the present invention provided in the accompanying drawings is not intended to limit below claimed The scope of the present invention, but be merely representative of selected embodiment of the invention.Based on the embodiment of the present invention, those skilled in the art Member's every other embodiment obtained without making creative work, shall fall within the protection scope of the present invention.
It should be noted that the terms "include", "comprise" or its any other variant are intended to the packet of nonexcludability Contain, so that the process, method, article or equipment for including a series of elements not only includes those elements, but also including Other elements that are not explicitly listed, or further include for elements inherent to such a process, method, article, or device. In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that including the element Process, method, article or equipment in there is also other identical elements.
Feature and performance of the invention are described in further detail with reference to embodiments.
Embodiment 1
The present embodiment provides a kind of lung CT image processing methods based on two-dimentional S-transformation, as shown in Figure 1, including following Step:
Step 1: the two-dimentional S-transformation on horizontal direction and vertical direction being carried out to the original lung CT image of input, is obtained Horizontal direction transform domain and vertical direction transform domain；
Step 2: carrying out the extraction of energy properties to the horizontal direction transform domain and vertical direction transform domain, obtain level Oriented energy attributed graph and vertical direction energy properties figure；
Step 3: the horizontal direction energy properties figure and vertical direction energy properties figure being subjected to Threshold segmentation, had There are the lung images of frequency domain character.
Specifically, as shown in Fig. 2,
Step 1 includes the following steps:
Step 1.1: two-dimensional Fourier transform being carried out to the original lung CT image as shown in Figure 3 of input, obtains its frequency Spectrogram F (α, β)；The formula of two-dimensional Fourier transform is as follows:
Wherein, f (x, y) indicates that original lung CT image, α indicate horizontal direction spatial frequency, and β indicates vertical direction space Frequency:
Step 1.2: initialization horizontal direction frequency variable kxWith vertical direction frequency variable kyValue；Specifically:
When calculating horizontal direction transform domain, fixed vertical direction frequency variable ky=0, initialize horizontal direction frequency variable kx=1；
When calculating vertical direction transform domain, fixed horizontal direction frequency variable kx=0, initialize vertical direction frequency variable ky=1；
Step 1.3: according to the horizontal direction frequency variable kxWith vertical direction frequency variable kyValue, calculate translation after Spectrogram；Specifically:
In fixed vertical direction frequency variable kySpectrogram F (α+k when=0, after calculating horizontal direction translationx, β)；
In fixed horizontal direction frequency variable kxSpectrogram F (α, β+k when=0, after calculating vertical direction translationy)；
Step 1.4: according to the horizontal direction frequency variable kxWith vertical direction frequency variable kyValue, it is high to calculate two dimension This window function frequency spectrum W (kx, ky)；Calculation formula is as follows:
Wherein, W (kx, ky) indicate calculated dimensional Gaussian window function frequency spectrum, kxAnd kyIndicate the frequency domain number of S-transformation Value, α and β indicate the frequency domain frequency variable of dimensional Gaussian window function frequency spectrum；
Specifically:
In fixed vertical direction frequency variable kyWhen=0, the Gauss function frequency spectrum of horizontal direction is calculated
In fixed horizontal direction frequency variable kxWhen=0, the Gauss function frequency spectrum of vertical direction is calculated
Step 1.5: the product M (α, β) of spectrogram and dimensional Gaussian window function frequency spectrum after calculating the translation；Specifically Ground:
In fixed vertical direction frequency variable kySpectrogram and horizontal direction when=0, after calculating horizontal direction translation The product Mh (α, β) of dimensional Gaussian window function frequency spectrum=F (α+kx, β) and Wh(kx, ky)；
In fixed horizontal direction frequency variable kxSpectrogram and vertical direction when=0, after calculating vertical direction translation The product M of dimensional Gaussian window function frequency spectrumv(α, β)=F (α, β+ky)·Wv(kx, ky)；
Step 1.6: two-dimentional inversefouriertransform being carried out to the result of step 1.5, respectively obtains horizontal direction and vertical side To S-transformation as a result, calculation formula is as follows:
Wherein, S (x, y, kx, ky) it is S-transformation as a result, M (α, β) is the resulting result of product of step 1.5, α and β indicate anti- The frequency domain frequency variable of Fourier transformation, x indicate the spatial position of horizontal direction, and y indicates the spatial position of vertical direction；
Specifically,
In fixed vertical direction frequency variable kyWhen=0, to two of spectrogram and horizontal direction after horizontal direction translation The product Mh (α, β) for tieing up Gauss function frequency spectrum carries out inversefouriertransform, obtains horizontal direction transform domain Sh(x, y, kx, ky)；
In fixed horizontal direction frequency variable kxWhen=0, to two of spectrogram and vertical direction after vertical direction translation Tie up the product M of Gauss function frequency spectrumv(α, β) carries out inversefouriertransform, obtains vertical direction transform domain Sv(x, y, kx, ky)；
Step 1.7: after executing step 1.6, judging the horizontal direction frequency variable kxWith vertical direction frequency variable ky's Whether value has looped through all values, if not having, will not traverse the horizontal direction frequency variable k of all valuesxAnd vertical direction Frequency variable kyFrom after increasing, step 1.3 is continued to execute to step 1.6, otherwise jumps out circulation；
Specifically:
In fixed vertical direction frequency variable ky=0, initialize horizontal direction frequency variable kxWhen=1, horizontal direction is obtained S-transformation as a result, then determined level direction frequency variable kxWhether all values, k have been looped throughxValue range 0~m it Between, indicate the height of original lung CT image.If not having, by horizontal direction frequency variable kxFrom increasing 1, i.e. execution kxAfter+1, Step 1.3 is continued to execute to step 1.6, otherwise jumps out circulation；
In fixed horizontal direction frequency variable kx=0, initialize vertical direction frequency variable kyWhen=1, vertical direction is obtained S-transformation as a result, then judging vertical direction frequency variable kyWhether all values, k have been looped throughyValue range 0~n it Between, indicate the width of original lung CT image.If no, by horizontal direction frequency variable kyFrom increasing 1, i.e. execution kyAfter+1, after The continuous step 1.3 that executes arrives step 1.6, otherwise jumps out circulation.
Step 2 is specifically, to horizontal direction transform domain Sh(x, y, kx, ky) and vertical direction transform domain Sv(x, y, kx, ky) The extraction for carrying out energy properties, obtains the formula of energy properties figure are as follows:
Wherein, S (x, y, kx, ky) indicate original lung CT image f (x, y) frequency variable k in the horizontal directionxWith vertical side To frequency variable kyRespectively certain value when S-transformation as a result, Re expression takes real part, Im expression takes imaginary part, and m and n respectively indicate defeated The height and width of the original lung CT image f (x, y) entered；
Specifically, to the horizontal direction transform domain Sh(x, y, kx, ky) carry out energy properties extraction, obtain such as Fig. 4 institute The horizontal direction energy properties figure E shownh(x, y)；
To the vertical direction transform domain Sv(x, y, kx, ky) extraction that carries out energy properties, it obtains as shown in Figure 5 perpendicular Histogram is to energy properties figure Ev(x, y).
Step 3 includes the following steps:
Step 3.1: the horizontal direction energy properties figure E that step 2 is acquiredh(x, y) and vertical direction energy properties figure Ev (x, y) carries out Threshold segmentation, obtains horizontal direction binary image B as shown in FIG. 6h(x, y) and vertical side as shown in Figure 7 To binary image Bv(x, y)；
Further, the calculation formula of threshold value T are as follows: T=m+ ε σ；
Wherein, m indicates the mean value of energy properties figure E (x, y), and σ indicates the variance of energy properties figure E (x, y), and ε is control The factor, value range are 0.3~0.5；
Step 3.2: to the horizontal direction direction binary image Bh(x, y) and vertical direction binary image Bv(x, y) It carries out and operates, obtain the lung images O (x, y) with frequency domain character as shown in Figure 8, calculation formula are as follows: O (x, y)=Bh (x, y) ∩ Bv(x, y).
Embodiment 2
The present embodiment provides a kind of lung CT image processing systems based on two-dimentional S-transformation, as shown in Figure 9, comprising:
Image input units, for inputting original lung CT image；
Image processing unit obtains final processing image for handling the original lung CT image of input；
Image output unit, for exporting the final processing image；
The original lung CT image of described image processing unit processes input, the step of obtaining final processing image Include:
Step 1: the two-dimentional S-transformation on horizontal direction and vertical direction being carried out to the original lung CT image of input, is obtained Horizontal direction transform domain and vertical direction transform domain；
Step 2: carrying out the extraction of energy properties to the horizontal direction transform domain and vertical direction transform domain, obtain level Oriented energy attributed graph and vertical direction energy properties figure；
Step 3: the horizontal direction energy properties figure and vertical direction energy properties figure being subjected to Threshold segmentation, had There are the lung images of frequency domain character.
It is apparent to those skilled in the art that the convenience and letter for description are bought, foregoing description based on The specific work process of the lung CT image processing system of two-dimentional S-transformation and its each functional unit can refer to aforementioned implementation The corresponding process in intelligent control method in example 1, details are not described herein.
Above-mentioned bright each functional unit can integrate in one processing unit, is also possible to the independent physics of each unit and deposits It can also be integrated in one unit with two or more units.Above-mentioned integrated unit can both use the shape of hardware Formula is realized, can also be realized in the form of software functional units.
If integrated each functional unit is realized in the form of SFU software functional unit and sells as independent product Or it in use, can store in a computer readable storage medium.Based on this understanding, technical solution of the present invention Substantially all or part of the part that contributes to existing technology or the technical solution can be with software product in other words Form embody, which is stored in a storage medium, including some instructions use so that one Computer equipment (can be smart phone, tablet computer, personal computer, server or the network equipment etc.) executes this hair The all or part of the steps of the bright lung CT image processing method based on two-dimentional S-transformation.And storage medium above-mentioned includes: USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), the various media that can store program code such as magnetic or disk.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all in essence of the invention Made any modifications, equivalent replacements, and improvements etc., should all be included in the protection scope of the present invention within mind and principle.

Claims (10)

1. a kind of lung CT image processing method based on two-dimentional S-transformation, which comprises the following steps:
Step 1: the two-dimentional S-transformation on horizontal direction and vertical direction being carried out to the original lung CT image of input, obtains level Direction transformation domain and vertical direction transform domain；
Step 2: carrying out the extraction of energy properties to the horizontal direction transform domain and vertical direction transform domain, obtain horizontal direction Energy properties figure and vertical direction energy properties figure；
Step 3: the horizontal direction energy properties figure and vertical direction energy properties figure being subjected to Threshold segmentation, obtain that there is frequency The lung images of characteristic of field.
2. as described in claim 1 based on the lung CT image processing method of two-dimentional S-transformation, which is characterized in that step 1 is specific Include the following steps:
Step 1.1: two-dimensional Fourier transform being carried out to the original lung CT image of input, obtains its spectrogram；
Step 1.2: the value of initialization horizontal direction frequency variable and vertical direction frequency variable；
Step 1.3: the frequency spectrum according to the value of the horizontal direction frequency variable and vertical direction frequency variable, after calculating translation Figure；
Step 1.4: according to the value of the horizontal direction frequency variable and vertical direction frequency variable, calculating dimensional Gaussian window function Frequency spectrum；
Step 1.5: the product of spectrogram and dimensional Gaussian window function frequency spectrum after calculating the translation；
Step 1.6: two-dimentional inversefouriertransform being carried out to the result of step 1.5, respectively obtains the S of horizontal direction and vertical direction Transformation results；
Step 1.7: after executing step 1.6, whether the value of determined level direction frequency variable and vertical direction frequency variable is recycled All values have been traversed, if not having, the horizontal direction frequency variable for not traversing all values and vertical direction frequency variable have been increased certainly Afterwards, step 1.3 is continued to execute to step 1.6, otherwise jumps out circulation.
3. as claimed in claim 2 based on the lung CT image processing method of two-dimentional S-transformation, which is characterized in that step 1.1 In, the formula of two-dimensional Fourier transform is as follows:
Wherein, F (α, β) indicates that obtained spectrogram, f (x, y) indicate that original lung CT image, α indicate horizontal direction space frequency Rate, β indicate vertical direction spatial frequency.
4. as claimed in claim 2 based on the lung CT image processing method of two-dimentional S-transformation, which is characterized in that step 1.2 tool Body are as follows:
When calculating horizontal direction transform domain, fixed vertical direction frequency variable ky=0, initialize horizontal direction frequency variable kx= 1；
When calculating vertical direction transform domain, fixed horizontal direction frequency variable kx=0, initialize vertical direction frequency variable ky= 1。
5. as claimed in claim 2 based on the lung CT image processing method of two-dimentional S-transformation, which is characterized in that step 1.4 In, the formula for calculating dimensional Gaussian window function frequency spectrum is as follows:
Wherein, W (kx,ky) indicate calculated dimensional Gaussian window function frequency spectrum, kxAnd kyIndicate S-transformation frequency domain numerical value, α and The frequency domain frequency variable of β expression dimensional Gaussian window function frequency spectrum.
6. as claimed in claim 2 based on the lung CT image processing method of two-dimentional S-transformation, which is characterized in that step 1.6 In, the calculation formula for carrying out inversefouriertransform to the result of step 1.5 is as follows:
Wherein, S (x, y, kx,ky) it is S-transformation as a result, M (α, β) is the resulting result of product of step 1.5, α and β are indicated in anti-Fu The frequency domain frequency variable of leaf transformation, x indicate the spatial position of horizontal direction, and y indicates the spatial position of vertical direction.
7. as described in claim 1 based on the lung CT image processing method of two-dimentional S-transformation, which is characterized in that in step 2, The extraction that energy properties are carried out to horizontal direction transform domain and vertical direction transform domain, obtains the formula of energy properties figure are as follows:
Wherein, S (x, y, kx,ky) indicate original lung CT image f (x, y) frequency variable k in the horizontal directionxWith vertical direction frequency Rate variable kyRespectively certain value when S-transformation as a result, Re expression takes real part, Im expression takes imaginary part, and m and n respectively indicate input The height and width of original lung CT image f (x, y).
8. as described in claim 1 based on the lung CT image processing method of two-dimentional S-transformation, which is characterized in that step 3 is specific Are as follows:
Step 3.1: the horizontal direction energy properties figure and vertical direction energy properties figure acquire to step 2 carries out Threshold segmentation, Respectively obtain horizontal direction binary image and vertical direction binary image；
Step 3.2: horizontal direction direction binary image and vertical direction binary image being carried out and operated, is had There are the lung images of frequency domain character.
9. as claimed in claim 8 based on the lung CT image processing method of two-dimentional S-transformation, which is characterized in that step 3.1 In, the calculation formula of the threshold value are as follows: T=m+ ε σ；
Wherein, T indicates threshold value, and m indicates the mean value of energy properties figure, and σ indicates the variance of energy properties figure, and ε is controlling elements, is taken Being worth range is 0.3~0.5.
10. a kind of lung CT image processing system based on two-dimentional S-transformation characterized by comprising
Image input units, for inputting original lung CT image；
Image processing unit obtains final processing image for handling the original lung CT image of input；
Image output unit, for exporting the final processing image；
The original lung CT image of described image processing unit processes input, the step of obtaining final processing image packet It includes:
Step 1: the two-dimentional S-transformation on horizontal direction and vertical direction being carried out to the original lung CT image of input, obtains level Direction transformation domain and vertical direction transform domain；
Step 2: carrying out the extraction of energy properties to the horizontal direction transform domain and vertical direction transform domain, obtain horizontal direction Energy properties figure and vertical direction energy properties figure；
Step 3: the horizontal direction energy properties figure and vertical direction energy properties figure being subjected to Threshold segmentation, obtain that there is frequency The lung images of characteristic of field.
CN201811098822.2A 2018-09-19 2018-09-19 Lung CT image processing method and system based on two-dimentional S-transformation Pending CN109191456A (en)

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