CN104270638A - Compression and quality evaluation method for region of interest (ROI) of CT (Computed Tomography) image - Google Patents
Compression and quality evaluation method for region of interest (ROI) of CT (Computed Tomography) image Download PDFInfo
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Abstract
The invention provides a compression and quality evaluation method for the region of interest (ROI) of a CT (Computed Tomography) image. The method comprises ROI extraction, ROI compression and quality evaluation. The method comprises the following steps of defining the ROI according to CT image characteristics and human vision characteristics; extracting the ROI based on an image segmentation principle; marking the ROI and a non-ROI separately by adopting a MAXSHIFT algorithm; performing layered compressed encoding on the ROI based on DWT (Discrete Wavelet Transform) and EBCOT (Embedded Block Coding with Optimized Truncation); computing a structural similarity index matrix (SSIM) based on a human vision characteristic contrast sensitivity function (CSF) and contourlet transform (CT); and verifying ROI compressed image quality through CT-SSIM. The image segmentation principle is applied to the extraction of the ROI, so that the ROI comprising internal and external outline information can be extracted automatically and accurately, ROI compression is performed on a CT medical image, and a file is compressed while medical diagnosis information is kept. Secondly, through an objective quality evaluation method based on the human vision characteristics, a human vision evaluation result is approached to the maximum extent, and subjective evaluation can be replaced.
Description
Technical field
The invention belongs to image compression and quality evaluation field, relate to a kind of area-of-interest compressed in layers based on Iamge Segmentation and the objective quality assessment method based on CT-SSIM.
Background technology
The many data volumes of medical image kind are large, and what wherein CT collection storage was transmitted is not single image file but a series, at substantial memory space and wireless transmission channel.In order to reduce storage and transmitted data amount, need to carry out compression process to medical image.At present, medical circle carries out Lossless Compression process to image, in order to avoid lose medical evidence, but compression ratio is limited.According to Lossy Compression Algorithm, compression ratio is large, but possible loss medical diagnosis data, cause medical science dispute.
For overcoming above-mentioned defect, researcher proposes area-of-interest layered compression method, and to medical image ROI region Lossless Compression, to non-ROI region lossy compression method, retaining medical diagnosis evidence simultaneously again can compressed file size.
From ROI extracting mode interested area division layered compression method, be roughly divided into three kinds: artificially define area-of-interest, select the rectangular area comprising area-of-interest as ROI; Manual selection initial point, expands outwardly to outline based on initial point, extracts ROI based on algorithm of region growing; A manually selected rectangular area, adopts Canny border detection algorithm determination area-of-interest profile, using profile inner region as ROI.
Above-mentioned ROI compression method all needs artificial participation, and automaticity is poor, is unfavorable for compression CT image sequence in enormous quantities or is embedded in Mobile medical system.Secondly, above-mentioned compression method all selects full wafer image as ROI, and scope is too large, and ROI compression is simplified not, and can not contain human eye sensitivity region.
Through the image of compression process interested, whether remain medical diagnostic information, the requirement of diagnosis focus can be met, need to assess the quality of image of compression.Subjective quality assessment passes judgment on picture quality by observer's subjective consciousness, operation inconvenience, at substantial time and efforts.Conventional quality evaluation method is all that only computed image gray scale difference value exists limitation, can not react human eye vision assessment result according to the pixel domain difference of original image and compressed image to calculate fractional value.
Summary of the invention
Technical problem to be solved by this invention there is provided the compression of a kind of CT image area-of-interest and method for evaluating quality, to overcome above-mentioned defect.
In order to solve above-mentioned technology Problems existing, the invention provides the compression of a kind of CT image area-of-interest and method for evaluating quality, comprising the steps:
(1) the inside and outside contour region defining human eye vision more violent than more sensitive greyscale transformation is ROI, extracts roughly the rough profile of described ROI based on differential operator,
(2) carry out gap based on morphological operator to rough contour area to fill up, expand and smoothly, accurately extract and comprise image inside and outside contour ROI;
(3) based on MAXSHIFT algorithm, the ROI extracted in step (3) is marked, make ROI region transformation of scale magnitude be greater than background area, distinguish region of interest ROI and background area BG completely;
(4) based on the ROI quantization encoding of KAKADU to marked ROI and BG region, carry out absolute coding quantification respectively, to the area-of-interest near lossless compression of mark, to background area lossy compression method.
Step (5): compressibility carries out module coding to data stream, ROI encoded data stream and BG encoded data stream finally merge, and the view data of ROI compression can be used for storing or transmission.
Step (6) carries out objective quality assessment in conjunction with human-eye visual characteristic to ROI compressing image.
Preferably, describedly in conjunction with human-eye visual characteristic, objective quality is carried out to ROI compressing image and comprises the following steps:
Based on profile transformation, multi-level and multi-direction frequency decomposition is carried out, for medical image content analysis to image;
Select index similarity matrix S SIM as evaluation factor;
According to Contrast sensitivity function curve calculation human eye vision to spatial frequency sensitivity value, and as the weights assessed based on profile transformation SSIM.
More excellent; the described ROI quantization encoding based on KAKADU is to marked ROI and BG region; carry out respectively in absolute coding quantification; setting rate-distortion slope threshold value and Weights state modulator ROI and BG compression bit rate; and then specify ROI region and background area credit rating; to the area-of-interest near lossless compression of mark, to background area lossy compression method, in the medical diagnostic information compressed image size simultaneously that protection is important.
This system extracts area-of-interest automatically, accurately according to human-eye visual characteristic and CT characteristics of image, adopts MAXSHIFT method to distinguish ROI and BG.Carry out ROI Lossless Compression based on DWT and EBCOT, lossy compression method is carried out to background area, under protection human eye sensitivity information state, realize CT image area-of-interest compressed in layers.Adopt based on profile variations index similarity matrix (CT-SSIM), from human eye vision angle, quality evaluation is carried out to compression medical image interested, make objective evaluation result to greatest extent close to human eye vision subjective evaluation, final certification herein compression method for interest region is better than full compression.
Combining image characteristic sum visual characteristics of human eyes of the present invention is analyzed, and proposes the index similarity matrix objective evaluation method based on profile transformation.Wherein profile transformation carries out multi-level and multi-direction frequency decomposition to image, is more suitable for for medical image content analysis.According to profile transformation principle, profile transformation can catch the directivity information of different frequency, can retain the profile of original image at each directional subband, so do not lose the structural information between pixel through the image of profile transformation.The present invention is based on profile transformation calculate index similarity Matrix C T ?SSIM, and using the weights of Contrast sensitivity function (CSF) as evaluation factor.In conjunction with human visual system CT ?SSIM objective evaluation method and opinion score correlation higher, the method is used for ROI compressed video quality assessment.
Tool of the present invention has the following advantages:
1) in conjunction with the susceptibility of human eye vision to variation of image grayscale, propose ROI boundary extraction algorithm, accurately, automatically can extract the inside and outside contour of human eye sensitivity, it can be used as ROI.
2) ROI based on MAXSHIFT algorithm marks, and carries out level conversion to ROI and BG region, and the present invention is supported, and arbitrary shape ROI compresses, and can be separated area-of-interest and background area completely.
3) CT ?SSIM objective evaluation method, adopts CSF function model as assessment weights, make objective evaluation result to greatest extent close to opinion score, but subjective evaluation method is more easily implemented relatively.
Accompanying drawing explanation
Below in conjunction with the drawings and specific embodiments, technical scheme of the present invention is further described in detail.
Fig. 1 is ROI compressibility flow chart of the present invention.
Fig. 2 is area-of-interest compressed encoding flow chart of the present invention.
Fig. 3 is that area-of-interest inside and outside contour of the present invention extracts result figure.Wherein Fig. 3 is a) original CT image, 3b) be the rough profile border of Canny operator extraction, 3c) be the profile border after expansive working, be d) through smoothing processing rear profile border.
Fig. 4 is that ROI of the present invention marks SHIFT method and MAXSIFT method comparison diagram.
Fig. 5 is ROI compression result comparison diagram of the present invention.Wherein, Fig. 5 is a) original image, 5b) be the ROI method compressed image proposed, 5c) be rectangle ROI compressed image, 5d) be full compression image.
Fig. 6 is that ROI of the present invention compresses regional area comparison diagram.Wherein, Fig. 6 is a) original image, 6b) the ROI method compressed image that proposes, 6c) be rectangle ROI compressed image, 6d) full compression image.
Fig. 7 is of the present invention based on profile transformation index similarity estimation flow figure.
Embodiment
This embodiment, for the process of medicine CT thoracic cavity image, improves ROI compression algorithm based on JPEG2000 benchmark, on the original compression frame basis of JPEG2000, proposes ROI compressibility.Pretreatment operation is improved in JPEG2000 compressibility, add and extract pixel data, ROI detection and mark handling process, improve compressed encoding module, make ROI and BG region separately coding, the CT image ROI compressibility handling process improved as shown in Figure 1, comprises preliminary treatment, ROI detection, class indication, compression, fused images 5 step.Handling process is illustrated in fig. 1 shown below,
Preliminary treatment: input picture is DICOM form CT thoracic cavity image in ROI compressibility, and this format-pattern equipment compatibility is poor, needs the pixel data extracting image, pixel data is stored as separately PGM lossless format image.Extract the pixel data of image, pixel data is stored as separately pgm lossless format image.Pretreatment operation has abandoned the descriptor of dicom standard patient, only processes for view data.
ROI detects: then to raw pixel data, based on Image Segmentation Theory, and defining the region comprising image inside and outside contour border is ROI.From physiology and Psychological Angle, human eye vision is more responsive to the boundary information that image gray-scale transformation is violent, and human eye thinks the relative picture rich in detail better quality of image inside and outside contour information, and vision is better.Medical image useful information is distributed in the discontinuous place of zones of different, mainly concentrates on gray scale and sharply changes place, and namely shade of gray changes sharply region and comprises more diagnostic messages, transmits more medical diagnostic information to medical personnel.The present invention is based on Image Segmentation Theory, to define the region comprising image inside and outside contour border be ROI, in conjunction with Boundary Extraction and morphological operator, carries out ROI detection to raw pixel data, extracts area-of-interest.The rough profile of described ROI is extracted roughly based on differential operator,
Carry out gap based on morphological operator to rough contour area to fill up, expand and smoothly, accurately extract and comprise image inside and outside contour ROI;
Class indication: adopt MAXSHIFT algorithm to distinguish ROI and BG, respectively to its mark, and gives ROI more high-resolution mark.Make ROI region transformation of scale magnitude be greater than background area, distinguish region of interest ROI and background area BG completely;
Compression: give harmless or the acceptable lossy compression method of medical science to ROI region, degree of depth lossy compression method is carried out to background area.
Fused images: carry out module coding based on the EBCOT of DWT to data stream in JPEG2000, ROI encoded data stream and BG encoded data stream finally merge, and the view data of ROI compression can be used for storing or transmission.
Fig. 3 gives the local ROI result figure that the present invention is based on Iamge Segmentation principle and extract, as shown in the figure.First the present invention adopts CANNY Boundary Extraction operator, extracts image boundary roughly, as Fig. 3 b) shown in.There is fracture in the image outline adopting differential operator to extract, sufficiently complete, needs in conjunction with gray level image morphological dilations method, and the background dot that border is externally contacted is expanded, and effectively fills up profile fracture.Morphological operator expansive working principle is described above, adopts a structural elements usually to substitute the pixel of this pixel and surrounding, can adopt rectangle, rhombus, circle, linear structure unit usually substitutes.Be the irregular envelope closed or do not close through the profile border that Canny operator detects, adopt linear structure element to be more suitable for.Consider that interior details profile is more, adopt first prime number to be 3,90 degree of broken lines
and horizontal linear
structural element, carries out expansive working to image outline, and play expansion and the effect of level and smooth profile border, profile expands result as Fig. 3 c) shown in.
After Boundary Extraction, filling chink and expansive working, the burr point of parectasis can be there is in the profile border of extracting, it is not the area-of-interest comprising boundary information, adopt diamond structure element to the smoothing process of profile, obtain the image outline comprising all boundary information, i.e. area-of-interest, as Fig. 3 d) shown in.Accurate extraction image inside and outside contour border simultaneously, proper expand bounds, minimal compression cell compression process in facilitating ROI to compress.
Fig. 4 gives the local ROI result figure that the present invention is based on Iamge Segmentation principle and extract, as shown in the figure.Fig. 4 is a) ROI interested for original image coefficient darker regions, and light areas is background area BG, 4b) for based on SHIFT algorithm pattern as transformation of coefficient, 4c) be as transformation of coefficient figure based on MAXSHIFT algorithm pattern.Based on merge module process, relative BG gives the higher bit of most important ROI.As shown in Figure 4, based on Scaling value, part ROI coefficient will be encoded together with non-ROI region.When decoding, ROI is preferentially decoded relative to background area, is reconstructed higher resolution image.Before entire image is encoded entirely, video bits stream is optimised to be blocked, and ROI region has higher quality relative to other regions of image.Some ROI coefficients of SHIFT algorithm are encoded together with background coefficient, therefore need to calculate ROI mask and mark, adopt SHIFT algorithm tag, also need to provide mask information during decoding.When avoiding decompressing, obscure ROI and BG coefficient.
Fig. 5 be CT local original image and R=0.5bit/pixel time compression figure comparing result, Fig. 5 is a) original harmless CT image, CT image 5b) for adopting this research ROI compression method to compress herein, 5c) be rectangle ROI region compression CT image, 5d) be full compression CT image.Due to display space restriction, see that complete image compression effect is not clearly, respectively the four width figure of Fig. 5 are intercepted respectively a part and amplify, as described in Figure 6.Fig. 6 is a) without compression original image portion regional enlarged drawing, 6b) be this research ROI compressed image regional enlarged drawing, 6c) be rectangle ROI compressed image regional enlarged drawing, 6d) be full compression image section regional enlarged drawing.As apparent from Fig. 6 can, the ROI compression method that the present invention adopts is closer to original CT image, and adopt in rectangle ROI compression method, obvious fault-layer-phenomenon appears in foreground area and background area, full compression CT image file profile obscurity boundary.
Compression process is carried out to CT thoracic cavity image, ROI packed pixel bit rate is that the packed pixel bit rate R of 4.0bit/pixel background area or full compression is respectively 2bit/pixel, 1.6bit/pixel, 1bit/pixel, 0.7bit/pixel, 0.5bit/pixel, 0.4bit/pixel, 0.33bit/pixel.After three kinds of compression method compression process, image file size is as shown in table 1, the 1st behavior original Lossless Compression CT image file size of content row in table 1.
Table 1
From table 1, CT image file size obvious reduction compared with original Lossless Compression CT image of ROI compression process, reaches compression CT image file object.Under packed pixel bit rate same case, originally the ROI compression method researched and proposed is close with rectangle ROI compression method compression ratio, full compression compression ratio is comparatively large by contrast, due to ROI compression method near lossless compression area-of-interest, so it is smaller to compare the compression of full compression method.As table 1 is shown, shown in the 6th row of content row, as R=0.5bit/pixel, to calculate compression ratio according to formula 1, above-mentioned three kinds of method compression ratios are respectively 17.06,17.06,51.20.In compression process, file size and image fault degree are conflicting, comprehensive compressed image quality and file size, and it is better that the present invention proposes ROI compression method performance.
In above-mentioned formula, size
orignalfor raw video file size, size
compressedfor compressing image file size, rate is compression ratio.
Fig. 7 give CT of the present invention ?SSIM objective quality assessment flow chart.
The present invention is based on profile transformation index similarity estimation flow as shown in Figure 2, calculate each directional subband SSIM of each level, calculate the weights of each directional subband, same level different directions subband SSIM carries out weights addition, calculate various level weights, different level subband SSIM weights are added.The weights of different directions different sub-band are calculated from the Contrast sensitivity function model of human visual system.
The objective evaluation method calculating index similarity matrix based on profile transformation is as follows:
Calculate original image f
rwith distorted image f
dprofile transformation coefficient, calculates profile transformation image different directions sub-bands of frequencies Fr
ij, Fd
ij.
The index similarity matrix S SIM of different directions subband is calculated according to formula 2 ~ formula 6
ij, to different directions subband SSIM
ijcarry out weights addition to average, calculate the SSIM of ith according to formula 7
i(x, y).
In above-mentioned formula, x, y direction average is μ
x, μ
y; Standard deviation is σ
x, σ
y; σ
xycovariance, constant C
1, C
2, C
3avoid unsteadiness.SSIM computing formula is defined as follows:
SSIM (x, y)=[l (x, y)]
α[c (x, y)]
β[s (x, y)]
γformula 5
Under usual calculated case, computing formula gets α=β=γ=1, C
1=C
2=C
3=0, computing formula can simplify as follows:
Wherein X, Y represent original image and ROI compressed image respectively, M
ibe the number of sub-bands that the i-th tomographic image contour direction decomposes, w
ijbe the weights of i-th layer of profile transformation jth directional subband, obtain without visual sensitivity on direction calculates according in human eye vision Contrast sensitivity function model.
By to all subband SSIM
i(X, Y) weights are added and average, and calculate entire image similarity CT-SSIM (X, Y) according to formula.
Wherein N is that profile decomposes exponent number, w
ibe i-th layer of profile transformation weights, calculate w according to Contrast sensitivity function CSF
i.
Adopt herein, based on profile transformation similarity, objective quality assessment is carried out to the image that ROI compresses, " pkva " wavelet basis is wherein adopted to carry out Laplacian pyramid decomposition to view data, application " Daubechies " (9,7) wavelet basis travel direction decomposes.Carry out three rank Laplace tower conversion (i.e. N=4) to the image of original image and ROI compression, each hierarchical decomposition is 8 directions, wherein M
1=1, M
2=8, M
3=8, M
4=8.Weight w
icalculate according to CSF (Contrast Sensitivity Function) functional value in HVS (Human Vision system human visual system) and obtain.W
ijbe respectively 0.7279,0.8014,0.8086,0.6899,0.590767,0.8288,0.8457,0.696.W is calculated according to HVS contrast function CSF
1=0.6921, w
2=0.8925, w
3=0.4233, w
4=0.0519.
It should be noted last that, above embodiment is only in order to illustrate technical scheme of the present invention and unrestricted, although with reference to preferred embodiment to invention has been detailed description, those of ordinary skill in the art is to be understood that, can modify to technical scheme of the present invention or equivalent replacement, and not departing from the spirit and scope of technical solution of the present invention, it all should be encompassed in the middle of right of the present invention.
Claims (5)
1. the compression of CT image area-of-interest and a method for evaluating quality, is characterized in that, comprise following steps:
(1) the inside and outside contour region defining human eye vision more violent than more sensitive greyscale transformation is ROI, extracts roughly the rough profile of described ROI based on differential operator,
(2) carry out gap based on morphological operator to rough contour area to fill up, expand and smoothly, accurately extract and comprise image inside and outside contour ROI;
(3) based on MAXSHIFT algorithm, the ROI extracted in step (3) is marked, make ROI region transformation of scale magnitude be greater than background area, distinguish region of interest ROI and background area BG completely;
(4) based on the ROI quantization encoding of KAKADU to marked ROI and BG region, carry out absolute coding quantification respectively, to the area-of-interest near lossless compression of mark, to background area lossy compression method.
2. CT image area-of-interest compression according to claim 1 and method for evaluating quality, it is characterized in that, also comprise step (5): compressibility carries out module coding to data stream, ROI encoded data stream and BG encoded data stream finally merge, and the view data of ROI compression can be used for storing or transmission.
3. CT image area-of-interest compression according to claim 2 and method for evaluating quality, is characterized in that, also comprise step (6): carry out objective quality assessment in conjunction with human-eye visual characteristic to ROI compressing image.
4. CT image area-of-interest compression according to claim 3 and method for evaluating quality, it is characterized in that, described step (6) specifically comprises the following steps:
Based on profile transformation, multi-level and multi-direction frequency decomposition is carried out to image;
Select index similarity matrix S SIM as evaluation factor;
According to Contrast sensitivity function curve calculation human eye vision to spatial frequency sensitivity value, and as the weights assessed based on profile transformation SSIM.
5. CT image area-of-interest compression according to claim 1 and method for evaluating quality, it is characterized in that, the described ROI quantization encoding based on KAKADU is to marked ROI and BG region, carry out respectively in absolute coding quantification, setting rate-distortion slope threshold value and Weights state modulator ROI and BG compression bit rate, and then specify ROI region and background area credit rating.
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