AU2020103375A4 - Speckle Denoising System for Ultrasound Images with Framelet Transform and Gaussian Filter - Google Patents
Speckle Denoising System for Ultrasound Images with Framelet Transform and Gaussian Filter Download PDFInfo
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- 238000002604 ultrasonography Methods 0.000 title claims abstract description 56
- 238000000354 decomposition reaction Methods 0.000 claims abstract description 15
- 230000000694 effects Effects 0.000 claims description 4
- 238000000034 method Methods 0.000 abstract description 30
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- 230000001629 suppression Effects 0.000 abstract description 8
- 210000003484 anatomy Anatomy 0.000 abstract description 4
- 230000008030 elimination Effects 0.000 abstract description 4
- 238000003379 elimination reaction Methods 0.000 abstract description 4
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- 230000000996 additive effect Effects 0.000 description 5
- 230000035945 sensitivity Effects 0.000 description 5
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- 238000012800 visualization Methods 0.000 description 2
- 238000012952 Resampling Methods 0.000 description 1
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- G06T5/70—
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/10—Image enhancement or restoration by non-spatial domain filtering
-
- 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
- G06T2207/10132—Ultrasound image
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20048—Transform domain processing
- G06T2207/20064—Wavelet transform [DWT]
Abstract
SPECKLE DENOISING SYSTEM FOR ULTRASOUND IMAGES WITH
FRAMELET TRANSFORM AND GAUSSIAN FILTER
ABSTRACT
Two Dimensional transforms are used enormously for the reduction of Speckle Noise in
Ultrasound Medical Images. The Present Invention disclosed here in uses a Double
bank anatomy with Framelet transform combined with Gaussian filter. This
combination even consists of fuzzy kind Clustering Approach for Despeckling
Ultrasound Medical Images, productively rejects the noise based on the grey scale
relative thresholding where Double Filter Bank (DFB) preserves the Edge Information.
The Present Invention disclosed here in provided Fuzzy kind Clustering methods to be
better than the conventional threshold methods for noise dismissal, gives a reconcilable
development as compared to other modern speckle reduction procedures as it preserves
the geometric features even after the noise dismissal, would be judged by different
quality indicators such as Mean Square Error (MSE), Signal to Noise Ratio (SNR),
Signal to Root Mean Square Error (SRMSE), Peak Signal to Noise Ratio (PSNR),
Speckle Suppression Index (SSI), Mean Structural Similarity Index (MSSI), Speckle
suppression index (SSI), Speckle Signal to Noise Ratio (SSNR), Signal to MSE, Edge
Preservation Index (EPI), Quality Index, Average Difference, and Cross Correlation.
The Present Invention disclosed here in is Speckle Denoising System for Ultrasound
Images with Framelet Transform and Gaussian Filter comprising of Input Image (201),
Noise (202), Framelet Transform (203), SVD Transform (204), Double Filter Bank
(205), Clustering (206), Inverse Double Filter Bank (207), Gaussian Filter (208), and
Inverse Framelet Transform (209) provides the state of art performance in Speckle
Noise reduction in Ultrasound Medical Images.
2/3
Input Image
Framelet Transform
Decomposition
302
Based on SVD Bands are -W
selected
IfAVertical If Horizontal
Subband Subband
Application Of DFB Application Of DFB
and Elimination of and Elimination of
Horizontal Subbands Vertical Subbands
Possibilistic Fuzzy C-Means (PFCM)
Clustering
Apply
Gaussian
Filter 31
Applying Inverse DFB and
Reconstruct Subbands 4)309
Inverse Framelet to Reconstruction the Image31
Figure 3: Flow Chart for Speckle Denoising System for Ultrasound Images with
Framelet Transformn and Gaussian Filter.
Description
2/3
Input Image
Framelet Transform Decomposition
302 Based on SVD Bands are -W selected
IfAVertical If Horizontal Subband Subband
Application Of DFB Application Of DFB and Elimination of and Elimination of Horizontal Subbands Vertical Subbands
Possibilistic Fuzzy C-Means (PFCM) Clustering
Apply Gaussian Filter 31 Applying Inverse DFB and Reconstruct Subbands 4)309
Inverse Framelet to Reconstruction the Image31
Figure 3: Flow Chart for Speckle Denoising System for Ultrasound Images with Framelet Transformn and Gaussian Filter.
AUSTRALIA Patents Act 1990
The following statement is a full description of this invention, including the best method of performing it known to me:
[0001] The present invention relates to the technical field of Medical Image Processing of Biomedical Engineering.
[0002] Particularly, the present invention is related to Speckle Denoising System for Ultrasound Images with Framelet Transform and Gaussian Filter of the broader field of Image Denoising in Medical Image Processing of Biomedical Engineering.
[0003] More particularly, the present invention is relates to Speckle Denoising System for Ultrasound Images with Framelet Transform and Gaussian Filter in which the Speckle Noise Present in Ultrasound Medical Images is removed by the Double bank anatomy with Framelet transform combined with Gaussian filter.
[0004] Speckle Denoising System for Ultrasound Images with Framelet Transform and Gaussian Filter disclosed here is the most useful in Medical Image Analysis to identify the effected regions of the human organs. Ultrasound Imaging is a non invasive diagnostic test that uses high frequency sound waves to produce images of the internal organs, blood vessels and other structures within the body.
[0005] A High Frequency of 1-20MHz sound waves are used in the area of Medical Ultrasound Imaging, Ultrasound is applied in the Medicine in different scope from Sonography to a guided surgery. Ultrasonography is conveyable low cost and a simple imaging system. Although they get affected by noise during the transmission and the acquisition process. Noise affects medical images by curtaining the image particulars and contrast of the image is reduced. Speckle noise is multiplicative noise and one of the utmost noises in the ultrasound images.
[0006] A non-coherent detector whenever is used for testing a rough medium where multiple scattering of ultrasound images is undergone with a coherent source results in a phenomenon called Speckle.
[0007] Fine particulars of the image are blurred due to the texture type outcome which is a consequence of interference of the backscattered echoes destructively and constructively giving a track to the energy of information which ranges from maximum to minimum.
[0008] The diagnostic values of the Ultrasound Image Modality will be affected if the Speckle Noise is present in the Ultrasound Images as the Speckle Noise is having the inherent property of reducing resolution and contrast.
[0009] The statistical representation of the Speckle noise can be defined mathematically as given in Equation 1. Where I(x,y) is Output Image with Noise, h(x,y) is noise free Input Image, and n(x,y) is the Speckle Noise coefficient of Image.
I(x,y) = h(x,y)ij,(x,y) Equation 1
[0009a] By applying the logarithmic transform conversion of additive noise is done from multiplicative speckle noise and is shown in Equation 2.
log[I(x,y)] = log[h(x,y)] + log[ijn(x,y)] Equation 2
[0010] Speckle Noise could be reduced in both transform and spatial domain. Mostly Linear Filters are not recommended for the reduction of speckle noise as the edges are smoothened while the speckle is reduced.
[0011] The median filters label the existing nonlinear noises in ultrasound images and the pixels are replaced with median of neighboring values. In order to reduce the existing Speckle component in ultrasound image the special symmetry and the nearest neighbor is explored by the SNN filter. The SRAD filters face a blocky emergence on the images. For multiplicative noise, the nonlinear homomorphic filter is particularly used, where reflectance and illumination components are handled separately.
[0012] Although the method results in fading out the important features of the image.
A Gaussian filter which is an alternative for anisotropic diffusion filter and it implies combination of a pair of Gaussian kernels. Apart from filtering edge detectors should be used for preserving the edges. For improved Speckle reduction outcomes a combination of SRAD filter and Canny edge detector is presented.
[0013] Due to the multiresolutional and sparsity approach of wavelet-based transforms they are used for denoising of images. They even follow three policies like Bayesian estimation methods, thresholding methods and coefficient of correlation methods. The process of performing the point singularities productively, limiting to process the geometric characters such as ridges, edge, and lines is done by DWT.
[0014] The thresholding methods used for reducing the noise is related to the effectiveness of the wavelet based methods. The semi-soft methods of thresholding are stronger than the other methods used for cracking down the noise. Preservation of the input image with the reference of a guidance image and removing the noise is done by guided filter.
[0015] Loss of edge information is never caused by the filter in the process of reducing the noise. Gaussian filter combined to Framelet scheduling algorithm results in greater execution measures. Different geometrical wavelet filters have a greater preservation of edges and higher sensitivity, the only thing is that they are having higher shift dependency.
[0016] The shuffling of the frequencies made DFBs limited in the applications of denoising. The high frequency noises and the low frequency cells are mixed up as a resultant of the wedge shaped decomposed cells.
[0017] Soft evaluating techniques are introduced for reducing the noise in the ultrasound medical images for avoiding the restrictions of traditional techniques. Noise reduction filters are sketched by combining a Bayesian denoising in correspondence with Markov random field modeling.
[0018] The study comprises of a noise reduction technique and preservation of edge information. Classification of noise and data is done using clustering techniques. The execution results are validated by a set of quantitative values like Mean Square Error
(MSE), Signal to Noise Ratio (SNR), Signal to Root Mean Square Error (SRMSE), Peak Signal to Noise Ratio (PSNR), Speckle Suppression Index (SSI), Mean Structural Similarity Index (MSSI), Speckle suppression index (SSI), Speckle Signal to Noise Ratio (SSNR), Signal to MSE, Edge Preservation Index (EPI), Quality Index, Average Difference, and Cross Correlation.
[0019] Referring to Figure 1, General Method of Medical Image Denoising comprising of Medical Image (101) may be an Ultrasound Image having Noise, and Noise degrades the quality of the Medical Image for further analysis, Noise Modeling (102) is for modeling the noise mathematically as it is statistical in nature and behaviour of noise can be understand if modeled mathematically, Filtering Method (103) can be any filtering method to remove the mathematically modeled noise to the maximum possible extent, Denoised Image (104) is an image after removing noise.
[0019a] The General Method of Medical Image Denoising is applicable for any type of noise reduction in the medical images or in the General natural Images.
[0020] Speckle Denoising System for Ultrasound Images with Framelet Transform and Gaussian Filter disclosed here is used to remove Speckle Noise present in Ultrasound Medical Images by the Double bank anatomy with Framelet transform combined with Gaussian filter.
[0021] Referring to Figure 2, Speckle Denoising System for Ultrasound Images with Framelet Transform and Gaussian Filter comprising of Input Image (201), Noise (202), Framelet Transform (203), SVD Transform (204), Double Filter Bank (205), Clustering (206), Inverse Double Filter Bank (207), Gaussian Filter (208), and Inverse Framelet Transform (209) provides the state of art performance in Speckle Noise Reduction in Ultrasound Medical Images.
[0022] The Present invention disclosed here Two Dimensional transforms are used enormously for the reduction of Speckle Noise in Ultrasound Medical Images.
[0023] The Present Invention disclosed here in uses a Double bank anatomy with
Framelet transform combined with Gaussian filter. This combination even consists of fuzzy kind Clustering Approach for Despeckling Ultrasound Medical Images, productively rejects the noise based on the grey scale relative thresholding where Double Filter Bank (DFB) preserves the Edge Information.
[0024] The disclosure uses Fuzzy kind Clustering methods to be better than the conventional threshold methods for noise dismissal, gives a reconcilable development as compared to other modern speckle reduction procedures as it preserves the geometric features even after the noise dismissal, would be judged by different quality indicators such as Mean Square Error (MSE), Signal to Noise Ratio (SNR), Signal to Root Mean Square Error (SRMSE), Peak Signal to Noise Ratio (PSNR), Speckle Suppression Index (SSI), Mean Structural Similarity Index (MSSI), Speckle suppression index (SSI), Speckle Signal to Noise Ratio (SSNR), Signal to MSE, Edge Preservation Index (EPI), Quality Index, Average Difference, and Cross Correlation.
[0025] The Medical Image Dataset used in the disclosure is prepared in consultation with the Ultrasound Experts; the images are captured by the instrument GE LOGIQ 3 Expert system with 5 MHz transducer frequency in JPEG format. The data set consists of 50 ultrasound images of size 1024x1024, of liver.
[0026] Speckle Noise is added mathematically in Matlab 2019a Environment to the Ultrasound Images present in the dataset prepared. After that the log transform is applied to convert the multiplicative noise into additive noise.
[0027] The Three Level decomposing is performed on the input image taken from the dataset along with noise, generates 64 subbands by Framelet Transform, Singular Valued Decomposition (SVD) selects the primary bands.
[0028] Directional decomposition is performed for picked up subbands and individual subbands are decomposed into 8D cells which increase the sensitivity excluding the subband of lowest frequency.
[0029] The vertical and horizontal allied to horizontal and vertical subbands are zeroed respectively and they hold specific edge information.
[0030] The noise is separated from information in cells by applying clustering algorithm. The component values approaching zero are grouped to be noise and the
component values far away from zero are considered to be information. The clustering method is PFCM. The noisy clusters are zeroed and the signal contained clusters are retained.
[0031] The despeckled cells undergo the inverse DFB to retrieve back the subbands, to separate the primary information from the speckle noise the frequency subband is passed through a bilateral filter; Inverse Framelet Transform is applied to combination of low frequency subband for reconstructing the input ultrasound image.
[0032] The Accompanying Drawings are included to provide further understanding of the invention disclosed here, and are incorporated in and constitute a part this specification. The drawing illustrates exemplary embodiments of the present disclosure and, together with the description, serves to explain the principles of the present disclosure. The Drawings are for illustration only, which thus not a limitation of the present disclosure.
[0033] Referring to Figure 1, illustrates General Method of Medical Image Denoising, in accordance with an exemplary embodiment of the present disclosure.
[0034] Referring to Figure 2, illustrates Speckle Denoising System for Ultrasound Images with Framelet Transform and Gaussian Filter comprising of Input Image (201), Noise (202), Framelet Transform (203), SVD Transform (204), Double Filter Bank (205), Clustering (206), Inverse Double Filter Bank (207), Gaussian Filter (208), and Inverse Framelet Transform (209), in accordance with another exemplary embodiment of the present disclosure.
[0035] Referring to Figure 3, Flow Chart for Speckle Denoising System for Ultrasound Images with Framelet Transform and Gaussian Filter, in accordance with another exemplary embodiment of the present disclosure.
[0036] Referring to Figure 4, illustrates Despeckled Images using Different Filters, in accordance with another exemplary embodiment of the present disclosure.
[0037] Referring to Figure 2, Speckle Denoising System for Ultrasound Images with Framelet Transform and Gaussian Filter comprising of Input Image (201), Noise (202), Framelet Transform (203), SVD Transform (204), Double Filter Bank (205), Clustering (206), Inverse Double Filter Bank (207), Gaussian Filter (208), and Inverse Framelet Transform (209) provides the state of art performance in Speckle Noise reduction in Ultrasound Medical Images.
[0038] Input Image (201) is from Medical Image Dataset used in the disclosure and is prepared in consultation with the Ultrasound Experts; the images are captured by the instrument GE LOGIQ 3 Expert system with 5 MHz transducer frequency in JPEG format. The data set consists of 50 ultrasound images of size 1024x1024, of liver.
[0039] Noise (202) is the disturbance in the Ultrasound input image used in the disclosure occurs due to the problems in the sensor equipment of instrument GE LOGIQ 3 Expert system. For validating the present invention, speckle noise can be added to the ultrasound input image taken from the instrument mentioned here in.
[0040] Framelet Transform (203) is a transform whose resolution of frequencies could be altered with mathematical process o(N log N). The iteration occurred at all the wavelet branches in all the levels for the attainment of greater resolution decomposition at greater frequencies. That is the reason why wavelet frames possess equally spaced frequency resolution. As compared to wavelet based decomposition it provides superior edge preservation for diminishing speckle in images as both low and high frequency components are affected.
[0040a] The Three Level decomposing is performed on the input image taken from the dataset along with noise, generates 64 subbands by Framelet Transform, Singular Valued Decomposition (SVD) selects the primary bands. SVD Decomposition is performed by the SVD Transform (204).
[0041] For Double Filter Bank (205), Independent Framelets catch a restricted directional data due to low directional sensitivity. The DFBs provide additional directionality for transformed domain components. A Quincunx filter bank attached to fan filters hold a filter bank that cannot be separated advances for breaking 2D
Spectrum into vertical and horizontal cells. In order to obtain wedge shaped cells, resampling operation is applied on resulting cells. The wedge shaped cells are the results of decimation of subbands with the help of DFB. The vertical and horizontal allied to horizontal and vertical subbands are zeroed respectively by the DFB and they hold specific edge information.
[0042] Clustering (206) used in the present disclosure is Possibilistic Fuzzy C-Means (PFCM) Clustering. The noise is separated from information in cells by applying clustering algorithm. The component values approaching zero are grouped to be noise and the component values far away from zero are considered to be information. The noisy clusters are zeroed and the signal contained clusters are retained.
[0043] Inverse Double Filter Bank (207) is used to reconstruct the subbands from the despeckled cells.
[0044] A weighted sum of pixels is taken by Gaussian filter in local neighborhood. The weight is based on both intensity distance and spatial distance. Ascribed to which the effect of noise is lowered and the edge information is protected as well. Average of image values and weights are calculated which defines range filtering of Gaussian Filter (208). The weights of range filters are based on image intensity and hence results are nonlinear. They are much simple as compared to standard non separable filters. The most important factor is it conserves the edge information. In order to minimize the effect of noise in low frequency subband a Gaussian Filter (208) filter is applied.
[0045] Inverse Framelet Transform (209) is applied to combination of low frequency subband for reconstructing the input ultrasound image.
[0046] The Present invention disclosed here in would be judged by different quality indicators such as Mean Square Error (MSE), Signal to Noise Ratio (SNR), Signal to Root Mean Square Error (SRMSE), Peak Signal to Noise Ratio (PSNR), Speckle Suppression Index (SSI), Mean Structural Similarity Index (MSSI), Speckle suppression index (SSI), Speckle Signal to Noise Ratio (SSNR), Signal to MSE, Edge Preservation Index (EPI), Quality Index, Average Difference, and Cross Correlation.
[0046a] The following, Table 1, and Table 2 is an aggregate from the tables, showing the Quality indicators measured for the invention disclosed here.
TABLE 1
Quality Metrics of the Invention Disclosed
Noise Variance PSNR MSSI SSI SSNR S/MSE EPI
0.01 38.3 0.96 0.99 1.45 11.78 0.89 0.02 38.73 0.95 0.98 1.47 10.12 0.86 0.03 36.74 0.92 0.97 1.47 9.18 0.73 0.04 36.5 0.92 0.98 1.48 8.62 0.79 0.05 35.98 0.91 0.97 1.48 8.19 0.76 0.06 35.71 0.92 0.96 1.49 7.83 0.74 0.07 35.33 0.9 0.97 1.39 7.54 0.71 0.08 34.99 0.89 0.96 1.39 7.27 0.69 0.09 34.67 0.88 0.97 1.49 7.10 0.67 0.1 34.38 0.89 0.95 1.40 6.98 0.64
TABLE2
Quality Metrics of the Invention Disclosed compared with other inventions and Filters
MSE SNR SRMSE PSNR Quality Average Cross Filters Index Difference Correlation LEE 33.941 23.481 5.8258 35.5922 0.90 0.122 0.98301 FROST 6.411 30.511 2.5412 42.7905 0.99 1.8327 0.95902 SRAD 3.0959 34.024 1.7595 45.9915 0.99 -1.2607 1.0283 Anisotropic 140.6511 17.321 11.8596 29.4179 0.79692 -0.31879 0.98128 Additive 49.1009 21.8388 7.0072 33.9884 0.84563 0.024087 0.97997 Existing 33.79+1.10 0.96+0.04 0.88+0.02 0.90+0.07 0.987 0.3481 0.99586 Inventions Insentn 30.24+1.61 0.95+0.03 0.87+0.10 0.89+0.09 0.996 0.4864 0.99621
[0047] Referring to Figure 3, Flow Chart for Speckle Denoising System for Ultrasound Images with Framelet Transform and Gaussian Filter comprising of Input Image (301) is a ultrasound medical image, to which noise is added converted into additive noise, and Framelet Transform Decomposition (302) by which Image is decomposed into different subbands with the help of Framelet Transform. It results in 64 subbands for a level-3 decomposition, and Based on SVD Bands are selected (303) selects primary bands, if
1U
vertical subband (304) then Directional decomposition is performed by the application of DFB (306) for picked up subbands and individual subbands are decomposed into 8D cells which increase the sensitivity excluding the subband of lowest frequency, and if Horizontal subband (305) then Directional decomposition is performed by the application of DFB (307) for picked up subbands and individual subbands are decomposed into 8D cells which increase the sensitivity excluding the subband of lowest frequency, Horizontal subbands are eliminated in case of vertical subband and vertical subbands are eliminated in case of the Horizontal subband, The vertical and horizontal allied to horizontal and vertical subbands are zeroed respectively and they hold specific edge information, and by Possibilistic Fuzzy C-Means (PFCM) (308) Clustering. The noise is separated from information in cells by applying clustering algorithm. The component values approaching zero are grouped to be noise and the component values far away from zero are considered to be information. The noisy clusters are zeroed and the signal contained clusters are retained, and the despeckled cells undergo the inverse DFB (309) to retrieve back the subbands, and Apply Gaussian Filter (310) to minimize the effect of noise in low frequency subbands, and Inverse Framelet Transform to reconstruct the Image (311) is applied to combination of low frequency subband for reconstructing the input ultrasound image
[0048] Referring to Figure 4, Despeckled Images using Different Filters comprising of (a) Original image (401), (b) Lee filter (402), (c) Frost filter (403), (d) SRAD filter (404), (e) Anisotropic filter (405), (f) Gaussian filter (406),(g) Additive filter (407), and (h) Proposed method (408). Figure 4 illustrates, clearly shows the quality of the present invention disclosed here in is better and having improved visualization as shown in the (h) Proposed method (408) compared to the other method. This qualitative visualization clearly shows the present invention disclosed here in removes the speckle noise from the Ultrasound Medical images by the Framelet Transform and Gaussian Filter compared to the state of art inventions available.
Claims (5)
1. Speckle Denoising System for Ultrasound Images with Framelet Transform and Gaussian Filter comprising of Input Image (201), Noise (202), Framelet Transform (203), SVD Transform (204), Double Filter Bank (205), Clustering (206), Inverse Double Filter Bank (207), Gaussian Filter (208), and Inverse Framelet Transform (209) provides the state of art performance in Speckle Noise reduction in Ultrasound Medical Images.
2. Speckle Denoising System for Ultrasound Images with Framelet Transform and Gaussian Filter as claimed in claim 1, wherein it uses Framelet Transform Decomposition (302) by which Image is decomposed into different subbands with the help of Framelet Transform. It results in 64 subbands for a level-3 decomposition, and based on SVD Bands are selected (303) selects primary bands.
3. Speckle Denoising System for Ultrasound Images with Framelet Transform and Gaussian Filter as claimed in claim 1, wherein it uses Double Filter Bank (205) performs directional decomposition, Horizontal subbands are forced to zero in case of vertical subbands and vertical subbands are forced to zero in case of Horizontal subband to hold specific edge information.
4. Speckle Denoising System for Ultrasound Images with Framelet Transform and Gaussian Filter as claimed in claim 1, wherein it uses Possibilistic Fuzzy C-Means (PFCM) (308) Clustering to separate noise from information cells.
5. Speckle Denoising System for Ultrasound Images with Framelet Transform and Gaussian Filter as claimed in claim 1, wherein it uses Gaussian Filter (310) to minimize the effect of noise in low frequency subbands, and Inverse Framelet Transform to reconstruct the Image (311) is applied to combination of low frequency subband for reconstructing the input ultrasound image.
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