comparative study of denoising techniques used for ultra … · 2018-09-29 · comparative study of...

COMPARATIVE STUDY OFDENOISING TECHNIQUES USEDFOR ULTRA SOUND IMAGES &

VIDEOS

Berksha.B1, Godwin Premi.M.S2

1PG Scholar,2Professor,Department of ETCE,

Sathyabama University,Chennai, India.

[email protected],[email protected]

June 21, 2018

Abstract

Ultrasound videos are extensively used for medical diag-nostics.There is a degradation in quality of diagnosis due tothe existence of speckle noise. Various filters are proposed tominimize the speckle in order to enhance the quality of diag-nostics and clear interpretation of ultrasound videos. In thispaper, a relative study is done to analyze various despeck-ling filters used in ultrasound videos and images. The simu-lated results show that the major noisy component, speckleis very well removed with the help of Anistropic DiffusionFilter with Non-Sub Sampled Shearlet Wavelet Transformrather than Non-Sub Sampled Cintourlet Transform. Theperformance metric, Peak Signal to Noise Ratio obtained forAnistropic Diffusion Filter with Non-Sub Sampled ShearletWavelet Transform is 28.55 where as for Non-Sub SampledCintourlet Transform is 27.48. This shows the effectiveness

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International Journal of Pure and Applied MathematicsVolume 120 No. 6 2018, 6351-6363ISSN: 1314-3395 (on-line version)url: http://www.acadpubl.eu/hub/Special Issue http://www.acadpubl.eu/hub/

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of anistropic filter with transform for denoising in ultra-sound images or videos.

Keywords: Denoising, Speckle Noise, Ultrasound Videos.

1 INTRODUCTION

Ultrasound (US) is an oscillating sound pressure signal with a fre-quency greater than the 20KHz which is the upper limit of theaudio frequency or human hearing range. Ultrasound systems func-tion with frequencies from 20khz to several Giga hertz. Ultrasoundis extensively used in medical diagnosis due to its low cost, non-invasive nature, and the possibility to work in real time. Severalmodes of ultrasound are used in medical diagnosis. A-mode (am-plitude mode) is the simplest type of ultrasound with length anddepth measurements. B-mode (brightness mode) is a 2-dimensionalreconstruction of the image. M-mode (motion mode) is a moving1-dimensional image.

Ultrasound videos or images will have noises due to liquid bub-bles during medical diagnostics. Speckle noise2 occurs in almostall coherent systems. One of the systems is ultrasound. The basisof this noise is random interference between the coherent takings.Speckle noise is a unwanted high-frequency component of the im-age. Speckle noise become visible in wavelet coefficients. It is atype of multiplicative noise, so it is difficult to remove as comparedto additive noise. The traditional techniques are not excellent inspeckle noise reduction. Wavelet transform is used to recover sig-nals from noisy images. High frequency of the ultrasound image canbe easily isolated by using thresholding technique using wavelets.Parametric thresholding is used to reduce the effects of speckle noisein ultrasound videos or images.

The main intention of image denoising techniques is to lessennoises while keeping maximum possible image features. Denoisingis performed on each image samples. Denoising method improvesultrasound image quality. Various methods are used to reducespeckle noise4.5, which depends on different mathematical modelsof speckle level present in the ultrasound. Adaptive filters adapttheir weights based on the presence of speckle in the image. Non-adaptive filters adapt their weights evenly independent of specklepresence in the entire image. Adaptive speckle filtering6 is bet-

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ter at preserving edges in high texture areas. Due to the evenweights, Non-adaptive speckle filtering requires less computationalpower during implementation. There are two types of non-adaptivespeckle filtering, first method is based on mean and the secondmethod is based on median. Adaptive speckle filter includes theLee filter and the Frost filter.

The ultrasound image and noise are independent of each other.Speckle noise is directly proportional to the changes in the local graylevel. Mean and variance of the area that is centred on the pixelis the mean and variance of that single processing pixel. Specklenoise has a limiting factor for the quality of an ultrasound B-modeimage7. Encoding is one of the effective method for despeckle fil-tering. Advantage of despeckle filtering is, dropping noisy com-ponents from higher frequency components. In order to preserveedges, contours, textures etc., the amount of speckle noise presentin ultrasound images can be reduced using adaptive binary oper-ations. Sometimes, the speckles present in the image may repre-sent some information, mostly when it is correlated to the dynamicspeckle. Dynamic speckle has the changes in the speckle patternwith respect to time and it can be a measured based on the sur-face activity. The performance of noise removal8,9 is evaluatedby calculating Peak Signal-to-Noise Ratio (PSNR). In addition tothat Structural Similarity Index Measure (SSIM) supports for theevaluation of denoised images.

2 DENOISING TECHNIQUES

In this paper few techniques which are used for reducing specklenoise in ultrasound videos and ultrasound images are compared.The selected techniques are anisotropic diffusion filter, despecklingusing non-sub sampled shearlet transform and non-sub sampledcontourlet transform.

2.1 Anisotropic Diffusion Filter

Anisotropic diffusion filter (ADF) is a technique1 aiming at reduc-ing speckle noise without eliminating noteworthy elements of theimage stuffing, usually lines, edges, boundaries or other details thatare significant for the clear analysis of the image during diagnostics.

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Anisotropic diffusion looks like the method that forms a scale space,where an image produces a bounded family of additional blurredconsecutive images using diffusion technique. It is widely used forreducing speckle noise in ultrasound images.

Algorithm:

• Initialization of the parameters, relaxation time τ(x) and tensoroperator {•}

• Probabilistic characterization of tissues based on the speckle statis-tics are given by the local effects of τ in the memory mecha-nism isWhen, τ → 0, no memory is locally appliedWhen, τ → ∞, all the information is preserved

• pc(x, t) is an instantaneous probability

• For relaxation time τ , a 1D function pc (x, t) is given by, τ = τ(pc (x, t))

• Function τ :[0,1]→ (0,∞) should meet some requirements to turnON/OFF the memory mechanism

• For relevant tissues, pc (x, t)→0

• The memory mechanism should tend to ON, (i.e) τ (pc(x, t))→∞

• For meaningless region, pc (x, t)→1

• The memory mechanism should tend to OFF (i.e) τ (pc (x, t))→ 0

• The family of rational function of one parameter n≥1, g n (y) =(1-y)/ y n , with y>0n→ parameter measure the conservative behavior of memorymechanismy → constant

• Effect of n for, τ = gn (pc (x, t))

• For meaningless regions, turn the memory OFF (i.e) when pc (x,t) → 1

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2.2 Using Non-Subsampled Shearlet Transform

Non-subsampled Shearlet transform (NSST) was introduced withthe expressed intent to provide a highly efficient representation ofimages with distinct edges and boundaries. In fact, the elements ofthe shearlet representation form a collection of waveforms confinedto a small area, ranging at various positions, scales and orientationswith extremely anisotropic outline.

Algorithm:

• Initialization of the parameters s(x, y), s´(x, y), s NSST(x, y), sNSST (x, y), s´(x, y) and s (x,y) where,s(x, y) → noisy images´(x, y) → circular shifts NSST(x, y) → noisy NSST coefficientsNSST(x,y) →unknown noiseless coefficients´(x, y) → invert multiscale decompositions (x, y) → inverse shiftNSST → non-subsampled shearlet transform.

• Apply the circular shifts on the noisy images s(x, y),

• s´(x, y) = circular shift (s(x, y), [x shift, y shift])

• To obtain the noisy NSST Coefficients,s NSST(x, y) = NSST (s´(x, y))

• Apply the thresholding on every noisy NSST coefficients,sNSST (x, y) = θthr (s NSST (x, y))

• Invert the multiscale decomposition to reconstruct the denoisedimage,s´(x, y) = NSST1 (s NSST (x, y))

• After taking the NSST1, perform the inverse shift and resultingdenoised images(x, y) = circular shift (s´(x, y), [-x shift, -y shift])

2.3 Using Non-Subsampled Contourlet Trans-form

Non-Subsampled Contourlet transform3 (NSCT) is a dual filterbank structure; it is used to get the smooth contours of images.

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The Contourlet transform presents a flexible directional decompo-sition and multi-resolution for images; it permits dissimilar numbersof directions at each level. The need of Contourlet transform is toprovide better directionality and anisotropy. It is more effectivein removing the noise. In dual filter bank, the point discontinu-ities are captured by using the Laplacian Pyramid (LP) and linearstructures are formed from point discontinuities by using Direc-tional filter bank.

Algorithm:

• Preliminary setting of the parameters R( x, y) and R´ (x, y)where,R´(x, y) – log transformed imageR(x, y) ultrasound image

• Calculate logarithmic transformation on R( x, y) of size MN toobtain R´(x, y)R(x, y) = I (x, y).n(x, y)R´(x, y) = log [I(x, y)] + log[ n(x, y) ] where,(x, y) – pixel of observed imageI(x, y) – noise free imagen(x, y) – multiplicative noiseR´ (x, y) – log transformed image

• Apply NSCT on R (x, y) for L scales and directions per scale toobtain X ( L, θ )

• Set initial threshold T=0

• Convert gray image to binary image XT(L, θ) using threshold T

• For different shape of structural elements Wθi of size MM where,θi = 2 L , L=2,3,4.

• Apply morphological operation on XT (L, θ)

• Do closing & opening operation.

• Opening of an image,I ◦ W = (I W) ⊕ W where,I noisy imageW– denoised output

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• Equivalent to union of all translation of W that contained in I.

• Closing of an image,I•W = (I ⊕ W) W where,

• Equivalent to complement of union of all translation of W thatare contained in I complement.

• Do morphological alternating filter,D = (CO•W) ⊕ W where,CO = (I•W) ◦ WCO – closing & opening operation

• Apply inverse NSCT on Do to get R (L ,θ) and take inverselogarithmic transform on R(L, θ) to get denoised image R(x,y)

• Find SSIM and EPI for R (L, θ) and obtain the relative percent-age difference

• Relative difference of SSIM= SSIM (n+1) SSIM (n) / SSIM (n)

• Relative difference of Edge Preservation Index (EPI),δ= EPI (n+1) EPI (n) / EPI (N) where,n → iterative index

SSIM and EPI are the two indices indicate the degree of simi-larity and also about the edge preservation details respectively.

3 RESULT

The results obtained for NSST and NSCT for ultrasound imagesand videos are shown below with the obtained Peak Signal to NoiseRatio and Structural Similarity Index Measure (SSIM).

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Coefficients Level NSCT

1

2

3

4

Figure 1: NSCT coefficient levels

Results are compared with other variations of Anisotropic Diffu-sion filter and using Non-Subsampled Contourlet Transform (NSCT)and Non-Subsampled Shearlet Transform (NSST). NSCT for dif-ferent coefficient levels and also original image with reconstructed

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Figure 2: Original (Left) and reconstructed Images (Right)

image are shown in Figure 1 and 2. This sample image is obtainedfrom ultrasound video. Filter performance can be seen subjectivelyfrom the Figure 3 and 4 for the sample image 1 and image 2. Para-metric evaluation for understanding the performance of the filtersis given in Table 1 and Table 2. Metrics are calculated usingPeak Signal to Noise Ratio, PSNR = 20log (255/MSE) andStructural Similarity Index Measure, SSIM(x, y) = (2µxµy+c1)(2σxy+c2)

Figure 3: Noisy and Denoised Images for Image 1

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Figure 4: Noisy and Denoised Images for Image 2

Table 1: Evaluation Parameters for Image1

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Table 2: Evaluation Parameters for Image2

4 CONCLUSION

The simulation results obtained for anisotropic filter, NSST andNSCT for different ultrasound images are compared. It is vividthat Anisotropic diffusion model in NSST performs well in removingspeckle noise from ultrasound images and videos.

References

[1] Gabriel Ramos-Llorden, et.al, Anisotropic diffusion filter withmemory based on speckle statistics for ultrasound images,IEEE Trans. Image Process. , vol. 24, no. 1, pp. 345 - 358,Jan. 2015.

[2] Radhey Shyam Anand, et.al, Speckle filtering of ultrasoundimages using a modified non-linear diffusion model in non-subsampled shearlet domain, IET Image Process, 2015, vol. 9, Iss.2, pp. 107 117

[3] Jayachandiran Jai Jaganath Babu, et.al, Non-sub sampled con-tourlet transform based image denoising in ultrasound thyroidimages using adaptive binary morphological operations, IETComput. vis. 2014, vol. 8, Iss. 6, pp. 718728.

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[4] Andreas S. Panayides, et.al, An Effective Ultrasound VideoCommunication System Using Despeckle Filtering and HEVC,IEEE Journal of Biomedical and Health Informatics, vol. 19,no. 2, pp. 668- 676, March. 2015.

[5] Christoph Hennersperger, Multi-Scale Tubular Structure De-tection in Ultrasound Imaging, IEEE Trans on Medical Imag-ing,2014, pp. 1-14.

[6] Gregorio Andria, et.al, A Suitable Threshold for Speckle Re-duction in Ultrasound Images, IEEE Trans On Instrumenta-tion and Measurement, vol. 62, no. 8, pp. 2270 2279, August.2013.

[7] G.J. Sullivan, et.al, Overview of the High Efficiency VideoCoding (HEVC) Standard, IEEE Trans. Circuits and Systemsfor Video Tech., vol 22, no. 12, pp. 1649-1668, Dec. 2012.

[8] G. Niranjana, M. Ponnavaikko, An Improved Hybrid Algo-rithm for Accurate Determination of Parameters of Lung Nod-ules with Dirichlet Boundaries in CT Images, Indian Journalof Science and Technology, 2016 Mar, 9(9).

[9] Neha Baraiya, Hardik Modi, Comparative Study of DifferentMethods for Brain Tumor Extraction from MRI Images usingImage Processing, Indian Journal of Science and Technology,2016 Jan, 9(4).

[10] M.Preethi Pauline Mary and Sivachidambaranathan.V, (2017)Fuzzy Logic Controller Based Multi-Port LED Driving, IEEEInternaltional Conference on Computation of Power EnergyInformation and Communication (ICCPEIC), ISSN : 978-1-5090-4324-8/17/31.00 2017 IEEE, 22-23 March 632-638

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comparative study of denoising techniques used for ultra … · 2018-09-29 · comparative study of...

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