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CN109615578A - Medical image interpolation processing method and device applied to various medical scenes - Google Patents

Medical image interpolation processing method and device applied to various medical scenes Download PDF

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Publication number
CN109615578A
CN109615578A CN201811305785.8A CN201811305785A CN109615578A CN 109615578 A CN109615578 A CN 109615578A CN 201811305785 A CN201811305785 A CN 201811305785A CN 109615578 A CN109615578 A CN 109615578A
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image
psnr
restored
expected
medical
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徐文峰
廖晓玲
徐紫宸
覃浪
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Chongqing University of Science and Technology
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Chongqing University of Science and Technology
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    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00Geometric image transformations in the plane of the image
    • G06T3/40Scaling of whole images or parts thereof, e.g. expanding or contracting
    • G06T3/4007Scaling of whole images or parts thereof, e.g. expanding or contracting based on interpolation, e.g. bilinear interpolation
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00ICT specially adapted for the handling or processing of medical images
    • G16H30/20ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS

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  • Health & Medical Sciences (AREA)
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  • Radiology & Medical Imaging (AREA)
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  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Primary Health Care (AREA)
  • Public Health (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
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Abstract

The invention discloses a kind of Study of Medical Image Interpolation processing methods and its device applied to plurality of medical scene, it carries out color interpolation treated to go back the evaluation index parameter of original image by obtaining three kinds of common color interpolation algorithms in advance to sample image, so as to by the actual requirement parameter of itself and different medical scenes, as expected processing speed range goes back the prospective quality fraction range of original image and goes back the expection PSNR range of original image and is compared, to be matched to most suitable color interpolation algorithm, and then interpolation processing is carried out using the most suitable color interpolation algorithm.

Description

Study of Medical Image Interpolation processing method and its device applied to plurality of medical scene
Technical field
The present invention relates to technical field of image processing, and with being related to, a kind of medical image applied to a variety of same medical scenes is slotting It is worth processing method and its device.
Background technique
In Digital image technology, colorful optical filter array CFA technology is particularly significant, and sensor only passes through CFA ability Generate image, structural reference bionics principle, for cellular optical filter.On CFA, a pixel has to three kinds substantially One of color light.CFA colorful optical filter array generallys use RGB color model, and distribution pattern is green, blue or green, red. In RGB color model, CFA mainly has Bayer type, stripe and Mosaic style three types.Wherein, have in Bayer format Tri- Essential colour of RGB, compare other two kinds of colors, and human eye is more sensitive to green, thus in Bayer format G pixel quantity It is the summation of other two kinds of pixels.And when color filter array acquisition image information, each pixel can only obtain a kind of component, The component of other two kinds of missings then is estimated to obtain according to adjacent pixel value.This process is referred to as image interpolation.Pass through Controlling computer and completing the algorithm of process of image interpolation is color interpolation algorithm.Color interpolation algorithm is adopted in medical digital images There is very important application in the colored real-time display of collecting system.Classical interpolation algorithm has bilinear interpolation algorithm, color Than constant method and adaptive-interpolation algorithm etc..
Color interpolation algorithm is mainly reflected in the reduction to medical image in the utilization in terms of medicine, is widely used in In CT imaging, MRI imaging, ultrasonic imaging and fujinon electronic video endoscope imaging system, wherein again the most with fujinon electronic video endoscope imaging applications Extensively.Main application equipment has infrared scope, fluorescence scope, confocal laser scope and contact Room Mirror etc..According to people Body is by inspection position and by the difference of procuratorial organ's formula, and medical environment when instrument acquires medical image also differs widely, such as electronics small intestine The examined position of mirror is behaved inside internal small intestine, and instrument need to carry light source and be shot into small enteron aisle, in point blank, The phenomenon that inevitably generating image local exposure.This just needs to handle image using color interpolation algorithm, with It avoids overexposure imaged image clarity and diagnose doctor can not.In terms of medicine, color interpolation algorithm is accurate Medical diagnosis provide strong image information and support, also has for reduce that the secondary injury of patient provides in medical procedure The guarantee of power.
But under different medical scenes, medical environment is different, and the requirement to interpolation algorithm is also different, therefore, not With medical scene under the adaptable color interpolation algorithm of reasonable selection be very it is necessary to.In view of this, the present invention mentions For a kind of medical image color interpolation processing method that can be applied to plurality of medical scene and its device, three kinds of base common color Color interpolation process method, based on for bilinear interpolation algorithm, color ratio constant method and adaptive-interpolation algorithm, i.e., according to not Same medical scene, recommends most suitable algorithm to handle medical image from these three color interpolation algorithms.
Summary of the invention
For the above technical problems, it is color to provide a kind of medical image that can be applied to plurality of medical scene by the present invention Color interpolation process method.
In order to solve the above-mentioned technical problem, the technical solution adopted by the present invention are as follows:
A kind of Study of Medical Image Interpolation method applied to plurality of medical scene comprising step:
Sample image is obtained respectively after bilinear interpolation algorithm, color ratio constant method and adaptive-interpolation algorithm process Three are gone back the evaluation index parameter of original image, and the evaluation index parameter includes processing speed, picture quality and image fidelity;
The demand parameter of image procossing needed for the current medical scene of user's input and the evaluation index parameter are carried out Matching so that matching is suitble to the optimal color interpolation Processing Algorithm of the current medical scene, and is inserted using the optimal colour Value Processing Algorithm carries out color interpolation processing to the image that user currently uploads;
Wherein, the demand parameter includes expected processing speed range, goes back the prospective quality fraction range of original image and go back The expection PSNR range of original image.
Further, sample image is obtained respectively to calculate by bilinear interpolation algorithm, color ratio constant method and adaptive-interpolation Method treated the step of going back the evaluation index parameter of original image, specifically include step:
Duration spent by individual sample image of three kinds of timing color interpolation algorithm process respectively, obtains processing speed;
The respective average mass fraction of original image is gone back described in calculating separately three, obtains picture quality;Wherein, the quality Score is respectively by least 20 observer's typings;
The respective Y-PSNR PSNR of original image is gone back described in calculating separately three, obtains image fidelity.
Further, every average mass fraction for going back original imageCalculation formula are as follows:
Wherein, N is the total number of grades of mass fraction, N=5;Ci is the quality point of i-stage mass fraction Number, i=1,2,3,4,5;Ki is the number that the observer of original image i-stage mass fraction is gone back described in imparting.
Further, the calculation formula of every Y-PSNR PSNR for going back original image are as follows:
Wherein, MSE is mean square error, and calculation isM is figure The height of picture, N are the width of image, fc(m, n) andIt is sample head portrait respectively and goes back original image and set at (m, n) in place The component value of Color Channel c.
Further, described by the demand parameter of image procossing needed for the current medical scene of user's input and institute's commentary Valence index parameter compares, to obtain the step of being suitble to the color interpolation algorithm of the current medical scene, specifically includes Step:
The mass fraction of original image is gone back described in judging three respectively whether in the prospective quality fraction range, Yi Jisuo The Y-PSNR PSNR for going back original image is stated whether within the scope of the expected PSNR;
If wherein at least having the mass fraction for going back original image in the prospective quality fraction range, and peak value noise Also again within the scope of the expected PSNR, then continue whether judgement described at least one gone back the corresponding processing speed of original image than PSNR In the range of the expected processing speed, if so, obtaining the optimal color interpolation Processing Algorithm for being suitble to current medical scene.
Based on the above-mentioned Study of Medical Image Interpolation processing method applied to plurality of medical scene, the present invention also provides one kind It can be applied to the Study of Medical Image Interpolation processing unit of plurality of medical scene comprising:
Evaluation parameter obtains module, is used to obtain respectively and passes through bilinear interpolation algorithm, color ratio constant method and adaptively insert Value-based algorithm treated three evaluation index parameters for going back original image, the evaluation index parameter includes processing speed, image matter Amount and image fidelity;
Interpolation processing module, the demand parameter of image procossing needed for the current medical scene for inputting user with it is described Evaluation index parameter is matched, so that matching is suitble to the optimal color interpolation Processing Algorithm of the current medical scene, and benefit Color interpolation processing is carried out to the image that user currently uploads with the optimal color interpolation Processing Algorithm;Wherein, the demand Parameter includes expected processing speed range, goes back the prospective quality fraction range of original image and go back the expection PSNR range of original image.
Wherein, the evaluation parameter obtains module and specifically includes:
Timing unit, for distinguishing duration spent by individual sample image of three kinds of timing color interpolation algorithm process, i.e., Obtain processing speed;
Picture quality acquiring unit is obtained for going back the respective average mass fraction of original image described in calculating separately three Picture quality;Wherein, the mass fraction is respectively by least 20 observer's typings;
Image fidelity acquiring unit, for going back the respective Y-PSNR PSNR of original image described in calculating separately three, Obtain image fidelity.
Wherein, every average mass fraction for going back original imageCalculation formula are as follows:
Wherein, N is the total number of grades of mass fraction, N=5;Ci is the quality point of i-stage mass fraction Number, i=1,2,3,4,5;Ki is the number that the observer of original image i-stage mass fraction is gone back described in imparting.
Wherein, the calculation formula of every Y-PSNR PSNR for going back original image are as follows:
Wherein, MSE is mean square error, and calculation isM is figure The height of picture, N are the width of image, fc(m, n) andIt is sample head portrait respectively and goes back original image and set at (m, n) in place The component value of Color Channel c.
Wherein, the interpolation processing module specifically includes:
Recommendation unit, for whether going back the mass fraction of original image in the prospective quality score described in judging three respectively In range and whether the Y-PSNR PSNR for going back original image is within the scope of the expected PSNR;If wherein at least having One is gone back the mass fraction of original image in the prospective quality fraction range, and the Y-PSNR PSNR also expection again Within the scope of PSNR, then continues judgement described at least one and whether go back the corresponding processing speed of original image in the expected processing speed In the range of, if so, obtaining the optimal color interpolation Processing Algorithm for being suitble to current medical scene;
Image processing unit, for being carried out in conjunction with the optimal color interpolation Processing Algorithm to the image that user currently uploads Color interpolation processing.
The invention has the beneficial effects that:
The invention discloses a kind of Study of Medical Image Interpolation processing methods and its device applied to plurality of medical scene, lead to It crosses to obtain three kinds of common color interpolation algorithms in advance and sample image is carried out color interpolation treated going back the evaluation of original image and refer to Parameter is marked, so as to by the actual requirement parameter of itself and different medical scenes, processing speed range as expected goes back the pre- of original image Phase mass fraction range and the expection PSNR range for going back original image are compared, and are calculated to be matched to most suitable color interpolation Method, and then interpolation processing is carried out using the most suitable color interpolation algorithm.
Detailed description of the invention
Fig. 1 is an a kind of implementation of medical image color interpolation processing method applied to plurality of medical scene of the invention The flow chart of example;
Fig. 2 a is sample image;
Fig. 2 b, Fig. 2 c and Fig. 2 d are respectively that sample image passes through bilinear interpolation algorithm, color ratio constant method and adaptively inserts Value-based algorithm treated image;
Fig. 3 is an a kind of implementation of medical image color interpolation processing unit applied to plurality of medical scene of the invention The functional block diagram of example.
Specific embodiment
With reference to the accompanying drawing, the present invention is described in detail.
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 the specific embodiments described herein are merely illustrative of the present invention, and It is not used in the restriction present invention.
It is a kind of medical image color interpolation processing method applied to plurality of medical scene of the invention referring to Fig. 1 The flow chart of one embodiment, specifically, the Study of Medical Image Interpolation method in the present embodiment comprising steps of
S101 obtains sample image by bilinear interpolation algorithm, color ratio constant method and adaptive-interpolation algorithm respectively Three after reason are gone back the evaluation index parameter of original image.
In the present embodiment, when in advance by a sample image pass through respectively bilinear interpolation algorithm, color ratio constant method and from Interpolation algorithm processing is adapted to, when obtaining three and going back original image, it is also necessary to evaluate the quality for going back original image, the present embodiment In, then using subjective estimate method and objective evaluation and every kind of color interpolation algorithm process single image institute time-consuming come pair Also original image is evaluated, and obtains corresponding evaluation index parameter, comprising: processing speed, picture quality and image fidelity.
Wherein, processing speed then refers to every kind of color interpolation algorithm process single image institute time-consuming, therefore, can directly adopt Three kinds of color interpolation algorithms are recorded with modes such as timers to handle a sample image respectively respectively time-consuming for institute, and are made For one of evaluation index parameter.
Wherein, subjective estimate method refers to multiple observers (including professional observer and non-viewing person) according to international Pyatyi quality yardstick and scale (referring to table one) is interfered, goes back the quality of original image to going back original image and evaluating in the form of mass fraction Then quality calculates the average mark of mass fraction given by all observers, i.e., the average mark is as measurement also original image The index of the picture quality of picture.Therefore, in the present embodiment, being averaged for mass fraction is provided using multiple observers (at least 20) Score is evaluated original image is gone back, and by the average mark (or picture quality) as one of evaluation index parameter.
Wherein, objective evaluation, which refers to, obtains the method that a certain numerical value again evaluates image by mathematical computations, and Gap size also between original image and sample image (i.e. original image), that is, the image fidelity for going back original image is in objective evaluation It is most common, and the measurement standard of image fidelity is then Y-PSNR PSNR, therefore, is believed in the present embodiment using peak value It makes an uproar than PSNR, i.e., image ratio is directed to and objectively evaluates to going back original image, and by Y-PSNR PSNR (or image fidelity Degree) as one of evaluation index parameter.
The international Pyatyi quality yardstick of table one and obstruction scale table
Mass fraction Interfere scale Quality yardstick
5 Picture quality quality is not seen at all Very well
4 It can be seen that picture quality variation but do not interfere to watch It is good
3 Picture quality is apparent to degenerate Generally
2 Picture quality has obstruction to viewing Difference
1 Picture quality has serious obstruction to viewing It is very poor
In one embodiment, above-mentioned steps S101 specifically includes step:
I, respectively duration spent by individual sample image of three kinds of timing color interpolation algorithm process.
II calculates separately three according to formula (1) and goes back the respective average mass fraction of original image (i.e. average mark), obtains Picture quality.
In the present embodiment, the average value of every mass fraction for going back original imageCalculation formula are as follows:
Wherein, N is the total number of grades of mass fraction, N=5;Mass fraction of the ci for i-stage mass fraction, i=1,2,3, 4,5;Ki is the number that the observer of original image i-stage mass fraction is gone back described in imparting.In one embodiment, mass fraction Provided respectively by least 22 observers, and the quantity of professional observer and amateur observer answer it is roughly equal.
III calculates separately three according to formula (2) and goes back the respective Y-PSNR PSNR of original image, obtains image fidelity Degree.
In the present embodiment, the calculation formula of every Y-PSNR PSNR for going back original image are as follows:
Wherein, MSE is mean square error, and calculation isM is figure The height of picture, N are the width of image, fc(m, n) andIt is sample head portrait respectively and goes back original image and set face at (m, n) in place The component value of chrominance channel c.
S103 carries out the demand parameter of image procossing needed for the current medical scene of user's input and evaluation index parameter Matching, to be matched to the optimal color interpolation Processing Algorithm of suitable current medical scene.
In the present embodiment, due to different medical scenes, it is different to the requirement for going back original image, carries out to medical image color The demand of the algorithm of color interpolation processing is also different, and therefore, it is necessary to corresponding professionals according to different medical scene typing phases The demand parameter answered, such as: it is expected that processing speed range, going back the prospective quality fraction range of original image and going back the expection of original image PSNR range.
Wherein, which, which refers to, wishes to handle individual sample image institute time-consuming under Medical scene It is long, the usual time-consuming a length of numerical intervals of institute.Such as real time imagery is needed under usual ultrasonic imaging scene, and therefore, the medicine Expection processing speed under scene is 0.1-0.09s;And under CT scene, real time imagery is not needed, therefore, its settable expection Processing speed is 0.5-5s.
Wherein, it is contemplated that mass fraction range, which refers to, wishes that individual sample image obtains after treatment under Medical scene The specific mass fraction range going back original image and capable of reaching, namely go back the fine or not degree of original image.For example, ultrasonic imaging field The prospective quality score for needing to go back original image under scape reaches 3-3.5, and needs to go back the prospective quality score of original image under CT scene Reach 3.5-3.9.
Wherein, it is contemplated that PSNR range refer to wish that individual sample image under Medical scene obtains after treatment also The specific expected PSNR that original image can reach, namely go back the fidelity of original image.For example, being needed under ultrasonic imaging scene also The expection PSNR range of original image reaches 11-11.2G, and the prospective quality score for needing to go back original image under CT scene reaches 11.2-11.4G。
In one embodiment, step S103 specifically includes step:
The mass fraction of original image is gone back described in judging three respectively whether in prospective quality fraction range, and also original image Whether the Y-PSNR PSNR of picture is within the scope of expected PSNR;For example, if
If wherein at least having the mass fraction for going back original image in the prospective quality fraction range, and peak value noise Also again within the scope of the expected PSNR, then continue whether judgement described at least one gone back the corresponding processing speed of original image than PSNR In the range of the expected processing speed, if so, obtaining the optimal color interpolation Processing Algorithm for being suitble to current medical scene.
S105 carries out the image that user currently uploads using optimal color interpolation Processing Algorithm obtained in step S103 Color interpolation processing.
It is illustrated below with reference to Study of Medical Image Interpolation processing method of the specific example to the present embodiment:
Sample image is passed through bilinear interpolation algorithm, color ratio constant method and adaptive-interpolation algorithm by a referring to fig. 2 respectively Processing, obtains going back original image, referring to fig. 2 b, Fig. 2 c and Fig. 2 d;
Getting the processing speed that three are gone back original image is respectively 0.093s, 0.536s and 2.422s, 22 observers couple It goes back the mass fraction that original image provides for three and every reduction is calculated as shown in following table two, and according to the mass fraction in table two Corresponding average (quality) score of image, which is respectively as follows:, goes back the flat of original image (Fig. 2 b) by what bilinear interpolation algorithm was handled Equal score is 3.14;It is 3.31 by the average mark for going back original image (Fig. 2 c) that color ratio constant method is handled;It is adaptive to insert It is 3.73 that value-based algorithm was handled, which goes back the average mark of original image (Fig. 2 d),;It gets three and goes back R, G of original image, B peak Signal-to-noise ratio calculates every reduction fidelity for going back original image as shown in following table three, and according to table three, i.e. Y-PSNR PSNR divides Not are as follows: the Y-PSNR PSNR for going back original image (Fig. 2 b) handled by bilinear interpolation algorithm is 11.049G;By What color ratio constant method was handled goes back the Y-PSNR PSNR of original image (Fig. 2 c) as 9.737G;Adaptive-interpolation algorithm process The obtained Y-PSNR PSNR for going back original image (Fig. 2 d) is 11.274, finally obtains evaluation result as shown in following table four.
Two or three, table are gone back the image quality score table of original image
Three or three, table are gone back R, G of original image, B peak signal-to-noise ratio (db)
Four or three, table are gone back the evaluation result of original image
In one embodiment, it is 0.09- that the demand parameter of ultrasonic imaging scene, which is respectively as follows: expected processing speed, 0.1s, it is contemplated that mass fraction reaches 3-3.5, it is contemplated that PSNR range reaches 11-11.2G.By parameters and above-mentioned evaluation knot Parameters in fruit table compare it is recognized that while, the PSNR of adaptive-interpolation algorithm is greater than in expected PSNR range most Big value, average mass fraction are greater than the maximum value in prospective quality fraction range, and processing speed is less than expected processing speed model Maximum value in enclosing, in other words, although what adaptive-interpolation algorithm process obtained goes back the picture quality of original image beyond expection (i.e. very true to nature or distortion is seldom), and image fidelity also exceeds expection, but its processing speed is but unsatisfactory for demand, because This, does not recommend;Similarly, the picture quality for going back original image that color ratio constant method is handled reaches expected, but its image fidelity Degree does not reach expected but;And the picture quality, image fidelity and place for going back original image that bilinear interpolation algorithm is handled Reason speed reaches expected, and therefore, under ultrasonic scene, recommendation bilinear interpolation algorithm is optimal/most suitable color interpolation Processing method carries out interpolation processing to image.
In one embodiment, it is 0.5-5s that the demand parameter of CT or MRI field scape, which is respectively as follows: expected processing speed, in advance Phase mass fraction reaches 3.5-3.9, it is contemplated that PSNR range reaches 11.2-11.4G.By parameters and above-mentioned evaluation result table In parameters compare it is found that the picture quality for going back original image and image fidelity that color ratio constant method is handled are equal Do not reach expected, therefore, does not recommend;What bilinear interpolation algorithm was handled goes back the picture quality and image fidelity of original image Degree is not up to expected, and therefore, is not recommended;And the picture quality for going back original image that adaptive-interpolation algorithm process obtains reaches pre- Phase, and image fidelity also reaches expected, processing speed also meet demand, therefore, it is recommended that adaptive-interpolation method be it is optimal/ Most suitable color interpolation processing method;I.e. under ultrasonic scene, it is optimal/most suitable colored slotting for recommending adaptive-interpolation method It is worth processing method and interpolation processing is carried out to image.
Embodiment two
Based on the above-mentioned Study of Medical Image Interpolation processing method applied to plurality of medical scene, the present invention also provides one kind Applied to the Study of Medical Image Interpolation processing unit of plurality of medical scene, it is carried out in the following with reference to the drawings and specific embodiments in detail Explanation.
It is a kind of Study of Medical Image Interpolation processing unit applied to plurality of medical scene of the invention, specifically referring to Fig. 3 The Study of Medical Image Interpolation processing unit on ground, the present embodiment includes:
Evaluation parameter obtains module 31, for being obtained respectively by bilinear interpolation algorithm, color ratio constant method and adaptive Interpolation algorithm treated three evaluation index parameters for going back original image;In the present embodiment, which includes processing Speed, picture quality and image fidelity;
Interpolation processing module 32, the demand parameter of image procossing needed for the current medical scene for inputting user with The evaluation index parameter stated is matched, so that it is matched to the optimal color interpolation Processing Algorithm of suitable current medical scene, and Color interpolation processing is carried out to the image that user currently uploads using the optimal color interpolation Processing Algorithm;It, should in the present embodiment Demand parameter includes expected processing speed range, goes back the prospective quality fraction range of original image and go back the expection PSNR model of original image It encloses.
In one embodiment, which obtains module and specifically includes:
Timing unit 311, for distinguishing duration spent by individual sample image of three kinds of timing color interpolation algorithm process, Processing speed is arrived to obtain the final product;
Picture quality acquiring unit 312 is obtained for going back the respective average mass fraction of original image described in calculating separately three To picture quality;Specifically, every mass fraction for going back original image be respectively by least 20 observer's typings, and every also The average mass fraction of original imageIt is the average value of the mass fraction provided by least 20 observers, calculation formula Are as follows:Wherein, N is the total number of grades of mass fraction, N=5;Ci is the mass fraction of i-stage mass fraction, i =1,2,3,4,5;Ki is the number that the observer of original image i-stage mass fraction is gone back described in imparting;
Image fidelity acquiring unit 313 is gone back the respective Y-PSNR PSNR of original image for calculating separately three, is obtained To image fidelity;Specifically, the calculation formula of every Y-PSNR PSNR for going back original image are as follows:
Wherein, MSE is mean square error, and calculation isM is figure The height of picture, N are the width of image, fc(m, n) andIt is sample head portrait respectively and goes back original image and set face at (m, n) in place The component value of chrominance channel c.
In one embodiment, which specifically includes:
Recommendation unit 321, for whether going back the mass fraction of original image in above-mentioned prospective quality described in judging three respectively In fraction range, and the Y-PSNR PSNR of original image is gone back whether within the scope of above-mentioned expected PSNR;If wherein at least having One is gone back the mass fraction of original image in above-mentioned prospective quality fraction range, and the Y-PSNR PSNR also expection again Within the scope of PSNR, then continue to judge that whether this at least one gone back the corresponding processing speed of original image in the range for being expected processing speed It is interior, if so, obtaining the optimal color interpolation Processing Algorithm for being suitble to current medical scene;
Image processing unit 322, the optimal color interpolation Processing Algorithm for being matched in conjunction with recommendation unit is to user The image currently uploaded carries out color interpolation processing.
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)

1.一种应用于多种医学场景的医学图像插值方法,其特征在于,包括步骤:1. a medical image interpolation method applied to multiple medical scenes, is characterized in that, comprises the steps: 分别获取样本图像经过双线性插值算法、色比恒定法和自适应插值算法处理后的三张还原图像的评价指标参数,所述评价指标参数包括处理速度、图像质量和图像逼真度;Obtaining the evaluation index parameters of the three restored images processed by the bilinear interpolation algorithm, the constant color ratio method and the adaptive interpolation algorithm respectively, the evaluation index parameters include processing speed, image quality and image fidelity; 将用户输入的当前医疗场景所需图像处理的需求参数与所述评价指标参数进行匹配,从而匹配到适合所述当前医疗场景的最优彩色插值处理算法,并利用所述最优彩色插值处理算法对用户当前上传的图像进行彩色插值处理;Match the demand parameters of the image processing required for the current medical scene input by the user with the evaluation index parameters, so as to match the optimal color interpolation processing algorithm suitable for the current medical scene, and use the optimal color interpolation processing algorithm Perform color interpolation on the image currently uploaded by the user; 其中,所述需求参数包括预期处理速度范围、还原图像的预期质量分数范围和还原图像的预期PSNR范围。The requirement parameters include an expected processing speed range, an expected quality score range of the restored image, and an expected PSNR range of the restored image. 2.如权利要求1所述的推荐方法,其特征在于,分别获取样本图像经过双线性插值算法、色比恒定法和自适应插值算法处理后的还原图像的评价指标参数的步骤,具体包括步骤:2. The recommending method according to claim 1, characterized in that, the step of obtaining the evaluation index parameters of the restored image after the sample image is processed by the bilinear interpolation algorithm, the constant color ratio method and the adaptive interpolation algorithm respectively, specifically comprising: step: 分别计时三种彩色插值算法处理单张样本图像所耗费的时长,得到处理速度;Time the three color interpolation algorithms to process a single sample image separately to obtain the processing speed; 分别计算三张所述还原图像各自的平均质量分数,得到图像质量;其中,所述平均质量分数是指由至少20位观察员分别为每张还原图像给出的质量分数的平均值;Calculate the respective average quality scores of the three restored images to obtain the image quality; wherein, the average quality score refers to the average of the quality scores given by at least 20 observers for each restored image; 分别计算三张所述还原图像各自的峰值信噪比PSNR,得到图像逼真度。The respective peak signal-to-noise ratios (PSNRs) of the three restored images are calculated to obtain the image fidelity. 3.如权利要求2所述的医学图像插值方法,其特征在于,每张还原图像的平均质量分数的计算公式为:3. The medical image interpolation method according to claim 2, wherein the average quality score of each restored image is The calculation formula is: 其中,N为质量分数的等级总数,N=5;ci为第i级质量分数的质量分数,i=1,2,3,4,5;ki为赋予所述还原图像第i级质量分数的观察员的人数。 Among them, N is the total number of quality scores, N=5; ci is the quality score of the ith level quality score, i=1, 2, 3, 4, 5; ki is the quality score assigned to the i-th level of the restored image. the number of observers. 4.如权利要求2所述的推荐方法,其特征在于,每张还原图像的峰值信噪比PSNR的计算公式为:4. recommending method as claimed in claim 2 is characterized in that, the calculation formula of the peak signal-to-noise ratio (PSNR) of each restored image is: 其中,MSE为均方误差,其计算方式为M是图像的高度,N是图像的宽度,fc(m,n)和分别是样本头像和还原图像在位置(m,n)处颜色通道c的分量值。Among them, MSE is the mean square error, which is calculated as M is the height of the image, N is the width of the image, f c (m,n) and are the component values of the color channel c at positions (m, n) of the sample avatar and the restored image, respectively. 5.如权利要求3或4中所述的医学图像插值方法,其特征在于,所述将用户输入的当前医疗场景所需图像处理的需求参数与所述评价指标参数进行对比,从而得到适合所述当前医疗场景的彩色插值算法的步骤,具体包括步骤:5. The medical image interpolation method according to claim 3 or 4, characterized in that, the demand parameters of the image processing required for the current medical scene input by the user are compared with the evaluation index parameters, so as to obtain a suitable parameter for the medical image. Describe the steps of the color interpolation algorithm for the current medical scene, including the steps: 分别判断三张所述还原图像的质量分数是否在所述预期质量分数范围内,以及所述还原图像的峰值信噪比PSNR是否在所述预期PSNR范围内;Judging respectively whether the quality scores of the three restored images are within the expected quality score range, and whether the peak signal-to-noise ratio (PSNR) of the restored images is within the expected PSNR range; 若其中至少有一张还原图像的质量分数在所述预期质量分数范围内,且峰值信噪比PSNR也再所述预期PSNR范围内,则继续判断所述至少一张还原图像对应的处理速度是否在所述预期处理速度的范围内,若是,则得到适合当前医疗场景的最优彩色插值处理算法。If the quality score of at least one restored image is within the expected quality score range, and the peak signal-to-noise ratio (PSNR) is also within the expected PSNR range, continue to judge whether the processing speed corresponding to the at least one restored image is within Within the range of the expected processing speed, if yes, an optimal color interpolation processing algorithm suitable for the current medical scene is obtained. 6.一种应用于多种医学场景的医学图像插值处理装置,其特征在于,包括:6. A medical image interpolation processing device applied to a variety of medical scenarios, characterized in that, comprising: 评价参数获取模块,用于分别获取经过双线性插值算法、色比恒定法和自适应插值算法处理后的三张还原图像的评价指标参数,所述评价指标参数包括处理速度、图像质量和图像逼真度;The evaluation parameter acquisition module is used to obtain the evaluation index parameters of the three restored images processed by the bilinear interpolation algorithm, the color ratio constant method and the adaptive interpolation algorithm respectively, and the evaluation index parameters include processing speed, image quality and image quality. fidelity; 插值处理模块,用于将用户输入的当前医疗场景所需图像处理的需求参数与所述评价指标参数进行匹配,从而匹配适合所述当前医疗场景的最优彩色插值处理算法,并利用所述最优彩色插值处理算法对用户当前上传的图像进行彩色插值处理;其中,所述需求参数包括预期处理速度范围、还原图像的预期质量分数范围和还原图像的预期PSNR范围。The interpolation processing module is used to match the demand parameters of the image processing required for the current medical scene input by the user with the evaluation index parameters, so as to match the optimal color interpolation processing algorithm suitable for the current medical scene, and use the most The optimal color interpolation processing algorithm performs color interpolation processing on the image currently uploaded by the user; wherein, the demand parameters include the expected processing speed range, the expected quality score range of the restored image, and the expected PSNR range of the restored image. 7.如权利要求6所述的医学图像插值处理装置,其特征在于,所述评价参数获取模块具体包括:7. The medical image interpolation processing device according to claim 6, wherein the evaluation parameter acquisition module specifically comprises: 计时单元,用于分别计时三种彩色插值算法处理单张样本图像所耗费的时长,即得到处理速度;The timing unit is used to separately time the time taken by the three color interpolation algorithms to process a single sample image, that is, to obtain the processing speed; 图像质量获取单元,用于分别计算三张所述还原图像各自的平均质量分数,得到图像质量;其中,所述平均质量分数是指由至少20位观察员分别对每张还原图像给出的质量分数的平均值;An image quality acquisition unit, configured to calculate the respective average quality scores of the three restored images to obtain the image quality; wherein, the average quality scores refer to the quality scores given by at least 20 observers to each restored image respectively average of; 图像逼真度获取单元,用于分别计算三张所述还原图像各自的峰值信噪比PSNR,得到图像逼真度。The image fidelity obtaining unit is configured to calculate the respective peak signal-to-noise ratios PSNR of the three restored images to obtain the image fidelity. 8.如权利要求7所述的医学图像插值处理装置,其特征在于,每张还原图像的平均质量分数的计算公式为:8. The medical image interpolation processing device according to claim 7, wherein the average quality score of each restored image is The calculation formula is: 其中,N为质量分数的等级总数,N=5;ci为第i级质量分数的质量分数,i=1,2,3,4,5;ki为赋予所述还原图像第i级质量分数的观察员的人数。 Among them, N is the total number of quality scores, N=5; ci is the quality score of the ith level quality score, i=1, 2, 3, 4, 5; ki is the quality score assigned to the i-th level of the restored image. the number of observers. 9.如权利要求7所述的医学图像插值处理装置,其特征在于,每张还原图像的峰值信噪比PSNR的计算公式为:9. The medical image interpolation processing device as claimed in claim 7, wherein the calculation formula of the peak signal-to-noise ratio (PSNR) of each restored image is: 其中,MSE为均方误差,其计算方式为M是图像的高度,N是图像的宽度,fc(m,n)和分别是样本头像和还原图像在位置(m,n)处颜色通道c的分量值。Among them, MSE is the mean square error, which is calculated as M is the height of the image, N is the width of the image, f c (m,n) and are the component values of the color channel c at positions (m, n) of the sample avatar and the restored image, respectively. 10.如权利要求8或9所述的医学图像插值处理装置,其特征在于,所述插值处理模块具体包括:10. The medical image interpolation processing device according to claim 8 or 9, wherein the interpolation processing module specifically comprises: 推荐单元,用于分别判断三张所述还原图像的质量分数是否在所述预期质量分数范围内,以及所述还原图像的峰值信噪比PSNR是否在所述预期PSNR范围内;若其中至少有一张还原图像的质量分数在所述预期质量分数范围内,且峰值信噪比PSNR也再所述预期PSNR范围内,则继续判断所述至少一张还原图像对应的处理速度是否在所述预期处理速度的范围内,若是,则得到适合当前医疗场景的最优彩色插值处理算法;A recommendation unit, configured to respectively judge whether the quality scores of the three restored images are within the expected quality score range, and whether the peak signal-to-noise ratio (PSNR) of the restored images is within the expected PSNR range; if at least one of them The quality score of the restored image is within the expected quality score range, and the peak signal-to-noise ratio (PSNR) is also within the expected PSNR range, then continue to judge whether the processing speed corresponding to the at least one restored image is within the expected processing speed. Within the range of the speed, if so, the optimal color interpolation processing algorithm suitable for the current medical scene is obtained; 图像处理单元,用于结合所述最优彩色插值处理算法对用户当前上传的图像进行彩色插值处理。The image processing unit is configured to perform color interpolation processing on the image currently uploaded by the user in combination with the optimal color interpolation processing algorithm.
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