BEMD Based Ultrasound Image Speckle Reduction Technique Using Pixel-Wise Wiener Filtering

DIGITAL IMAGE PROCESSING AND COMPUTER GRAPHICS VOLUME: 19 |NUMBER: 2 |2021 |JUNE BEMD Based Ul asound Image Speckle Reduc ion Technique Using Pixel-Wise Wiene Fil e ing Bhawna GUPTA , Vinee KHANDELWAL Depa men o Elec onics and Communica ion Enginee ing, Jaypee Ins i u e o In o ma ion Technology, Sec o -62, 201309 Noida, U a P adesh, India bha[email p o ec ed], inee .khandelw[email p o ec ed] DOI: 10.15598/aeee.19i2.4100 A icle his o y: Recei ed Jan 30, 2021; Re ised Ma 17, 2021; Accep ed Ap 01, 2021; Published Jun 30, 2021. This is an open access a icle unde he BY-CC license. Abs ac . In his pape , an imp o ed Bidimensional Empi ical Mode Decomposi ion (BEMD) based speckle educ ion echnique o ul asound images has been p o- posed. The noisy image has been decomposed in o i s In insic Mode Func ions (IMFs) and a esidue. The noise componen o he low o de IMFs is emo ed wi h he pixel-wise Wiene il e ing. The image is econ- s uc ed wi h hese il e ed low o de IMFs, high o de IMFs and he esidue. The pe o mance o he p o- posed me hod has been es ed on syn he ic as well as eal ul asound images ha ing noise componen s o di - e en a iance. The expe imen al esul s show ha he p oposed algo i hm pe o ms be e han o he exis ing me hods o syn he ic images as well as eal ul asound images in e ms o a ious image quali y ma ices. Keywo ds BEMD, noise educ ion, speckle, ul asound, Wiene . 1. In oduc ion Speckle, which is a mul iplica i e noise, is an unwan ed phenomenon ha is p esen in ul asound images due o sca e ing a he ime o acqui ing he image [1]. Speckle educ ion in ul asound images is an essen- ial s ep and is a ge ing imp o emen in he quali y o he image in e ms o PSNR (Peak Signal o Noise Ra io), CC (Co ela ion Coe icien ), SNR (Signal o Noise Ra io), FoM (Figu e o Me i ) and SSIM (S uc u al SIMila i y) [2]. Due o he high- equency cha ac e is ic o he noise componen , he main chal- lenge o speckle educ ion echnique is ha while e- mo ing noise he in o ma ion in he edges should no be los as his is impo an o diagnosis. The me ic used o measu ing he same is he EKI (Edge Keeping Index). Image denoising speci ically speckle educ ion in ul- asound images has been s udied ex ensi ely, and i has been b oadly classi ied in o di e en domains, such as spa ial domain echniques, ans o m do- main echniques, and hyb id echniques as shown in Fig. 1. The spa ial domain echniques wo k di- ec ly on he image pixels while ans o m domain echnique applies an app op ia e ans o m o con- e he image o equency domain be o e p o- cessing. Fu he mo e, he e a e hyb id echniques ha a e combina ion o spa ial domain and ans o m domain me hods. The spa ial domain echniques use local s a is ics o in o ma ion edundancy be ween simila pa ches and eplace he pixel alue by p ocessing he nea by pixel alues. Mos success ul amongs his ca e- go y a e di usion-based il e s like Speckle Reduc- ing Aniso opic Di usion (SRAD), De ail P ese - ing Aniso opic Di usion (DPAD), Pe ona-Malik’s Aniso opic Di usion (PMAD) [3], [4], [5] and [6], Bi- la e al il e s [7] and [8], and pa ch-based me hods like Non Local Mean il e (NLM) [9], [10], [11] and [12] and Op imized Bayesian Nonlocal Mean il e (OBNLM) [13] and [14]. The simila pa ch-based me hods need o selec he candida e pa ch easonably so ha he e- mo al o noise does no lead o lose o edge in o ma ion. The e o e, ecen wo k as p oposed in [15], [16] and [17] uses modi ied NLM and BM3D algo i hms while y- ing o p ese e he edge in o ma ion. Wiene il e s [18] and [19] a e he op imum linea il e s, ha a e widely in use o image p ocessing. Pe o mance o he classical poin wise Wiene il e is enhanced o noise 168 ©2021 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING DIGITAL IMAGE PROCESSING AND COMPUTER GRAPHICS VOLUME: 19 |NUMBER: 2 |2021 |JUNE Fig. 1: Classi ica ion o Speckle educ ion echniques. co up ed images, wi h he use o non-local pa ame e es ima ion p oposed by And e e al. [20]. T ans o m domain me hods assume ha he im- age can be spa sely ep esen ed by i s low- equency componen s and he speckle is p esen in he high- equency componen s o he image [21]. The eby, he image high- equency componen s a e deal wi h by ap- plying he h esholding (ha d o so ) as in wa ele h esholding me hod o emo e he noise [22]. While hese me hods gi e sa is ac o y esul s, choosing he igh h eshold is icky and should a oid he Gibbs phenomenon and o e smoo hening e ec s. Thus, u - he ex ension in he ans o m domain echniques includes da a-d i en echniques and modeling ech- niques. Hyb id echniques a e he ones ha combine spa ial domain me hods wi h ans o m domain me hods o he imp o emen o denoising pe o mance. Some o he ecen hyb id echniques a e as in oduced in [23], [24] and [25]. Empi ical Mode Decomposi ion (EMD) was in o- duced by Huang e . al [26] in 1998. This is a e y use ul algo i hm ha decomposes he signal in i s In- insic Mode Func ions (IMFs) [27]. This decomposi- ion is done based on hei local equency o oscilla- ion in he spa ial domain. Unlike Fou ie ans o m and Wa ele ans o ms, he basis unc ions calcula ed a e signal-dependen and a e used o es ima e a se ies o IMFs ia an i e a i e p ocedu e called si ing [28]. EMD was in oduced in images in 2003 [29] and he Bidimensional Empi ical Mode Decomposi ion (BEMD) o images was in oduced in 2005 in [30]. BEMD is also a signal-dependen adap i e echnique decomposing he image in a se ies o IMFs and a esidue. The low-o de IMFs a e he high- equency componen s and he high o de IMFs a e low- equency componen s. BEMD is an adap i e mul i- esolu ion analysis echnique ha is d i en by he inpu signal. The e o e, i is used o a ious applica ions in image p ocessing [31]. As he noise in image is mainly concen a ed in he high- equency componen s, he low o de IMFs a e ha ing mo e noise componen s as compa ed o he high o de IMFs. Acco dingly, some BEMD based denoising algo i hms use his ac o supp ess he noise exis ing in low o de IMFs. Bu his may no always be ue and a signi ican noise componen may also be p esen in u he IMFs as well. Some denoising algo i hms deal wi h his issue and use mu ual in o ma ion [32] o o he il e ing echniques [33]. These echniques use mu ual in o ma ion o a simila i y measu e be ween he p obabili y densi y unc ions o he inpu signal and IMFs, o de e mine he noise dominan low o de IMFs [28], [34] and [35]. These noise-dominan IMFs a e dis- ca ded and he signal dominan IMFs a e e ained o ob ain he denoised signal. Howe e , mos o he high- equency componen s o he image also con ain de ail in o ma ion such as edges. These algo i hms he e o e lose impo an edge de ails al hough gi ing good de- noising pe o mance. These de ails a e o impo ance o diagnos ic pu poses when dealing wi h ul asound images in pa icula . This pape in oduces an imp o ed echnique, in which he low-o de high- equency IMFs, which a e noise dominan , a e no comple ely disca ded. Ra he pixel-based selec i e il e ing in o m o Wiene il e is applied o he low o de IMFs, o educe he noise componen , while p ese ing he edge de ails. The emainde o he pape is o ganized as ollows. Sec ion 2. , p o ides a b ie o e iew o BEMD al- go i hm along wi h pa ame e me ics used o com- pa ison. In Sec. 3. , he p oposed denoising me hod is desc ibed. The pe o mance e alua ion o he p o- posed me hod is illus a ed in Sec. 4. , and Sec. 5. p esen s he conclusions. ©2021 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING 169 DIGITAL IMAGE PROCESSING AND COMPUTER GRAPHICS VOLUME: 19 |NUMBER: 2 |2021 |JUNE 2. Backg ound 2.1. Bidimensional Empi ical Mode Decomposi ion EMD in signals was in oduced by Huang e . al [26], which is an adap i e echnique, which does no ha e p ede ined basis unc ions and decomposes he signal in o a ious IMFs. Simila o EMD in one dimension, BEMD is an adap i e echnique being applied o he images [31]. This decomposes he signal in o IMFs and a esidue. Fo he sake o illus a ion, BEMD algo i hm is p esen ed he e b ie ly. Le (m, n)be he gi en image. Le l(m, n) ep- esen he esidue o l- h IMF. To ind he nex IMF, he esidue o he p e ious IMF is aken as he inpu . Le il,k(m, n)be he inpu image o he gene a ion o an IMF, whe e he i s index is l- h numbe IMF, l= 1, . . . , L, he second index is k- h i e a ion o he si ing p ocess, k= 1, . . . , K and (m, n)being wo spa- ial dimensions. S ep 1: S a wi h he gi en image as he inpu sig- nal. i1,0(m, n) = (m, n). S ep 2: Ex ac all local maxima and minima o il,k(m, n). S ep 3: In e pola e all local maxima o ge he up- pe en elope eu(m, n)and in e pola e all local minima o ge he lowe en elope el(m, n). The applica ion- speci ic spline can be used o in e pola ion. S ep 4: Calcula e he en elope mean el,k(m, n)o he uppe and he lowe en elopes ob ained in s ep 3: el,k(m, n) = eu(m, n) + el(m, n) 2.(1) S ep 5: The inpu signal is upda ed by sub ac ing he en elope mean el,k(m, n) o he nex i e a ion: il,k(m, n) = il,k−1(m, n)−el,k(m, n), k →k+1.(2) S ep 6: This s ep is o check i he esul ob ained in s ep 5 is an IMF o no . Fo his s anda d de ia ion, is calcula ed as: = M−1 X m=0 N−1 X n=0 |il,k(m, n)−il,k−1(m, n)|2 i2 l,k−1(m, n).(3) S ep 7: Check he s anda d de ia ion  o be less han a p ede ined alue (gene ally 0.2–0.3). I he alue is g ea e han he c i e ion, epea s eps 2–6. When he alue o is below he p ede ined alue, he esul o s ep 5 is he equi ed l- h IMF, l(m, n): l(m, n) = il,k(m, n).(4) S ep 8: The esidue o he l- h IMF is de ined as: l(m, n) = il,0(m, n)− l(m, n).(5) S ep 9: The nex IMF is calcula ed by aking he esidue calcula ed as he inpu signal and s a ing o e om s ep 2: il+1,0(m, n) = l(m, n).(6) All subsequen IMFs a e calcula ed by epea ing he s eps om 2–9. The p ocess is s opped when he esidue calcula ed has no mo e ex ema. So, wi h he o al Lnumbe o IMFs calcula ed and he las esidue wi hou ex ema L, he o iginal signal can be ep e- sen ed as: (m, n) = L X l=1 l(m, n) + L(m, n).(7) I is impo an o men ion he e ha he low o de IMFs, a e co esponding o he high equency while he high o de IMFs a e co esponding o he low e- quency. 2.2. Image Quali y Pe o mance Me ics The pe o mance o he p oposed BEMD based noise educ ion echnique using a Wiene il e is analyzed and compa ed bo h quan i a i ely and quali a i ely wi h he exis ing echniques. Fo syn he ic US im- ages gene a ed using Field II so wa e [36] de eloped by J. A. Jensen, a quan i a i e analysis is ca ied ou using pe o mance me ics iz., Peak Signal o Noise Ra io (PSNR) [37], Edge Keeping Index (EKI) [38], S uc u e SIMila i y Index Measu es (SSIM) [39], Co - ela ion Coe icien (CC) [40], Signal o Noise Ra io (SNR) [37] and Figu e o Me i (FoM) [41]. Howe e , o eal ul asound images, as no noise- ee image is a ailable, he Mean o Va iance Ra io (MVR) [42] and an Equi alen Numbe o Looks (ENL) [43] a e used o quan i a i e e alua ion. Fo a noisy image (m, n)and he econs uc ed denoised image (m, n), he me ics used o quan i a i e e alua ion a e de ined as ollows: •Peak Signal o Noise Ra io (PSNR) is a equen ly used measu e o access he e icacy o he denoising algo i hm. I is he a io o peak signal powe o he noise powe gi en by Eq. (8), whe e l= 255 o an 8-bi g ayscale image o size M×N. •Edge Keeping Index (EKI) is a pa ame e o mea- su e he edge keeping capabili y o a denoising al- go i hm, see Eq. (9), whe e ∆ and ∆ a e high pass il e ed e sion o and , espec i ely, ob- ained using 3×3Laplacian ope a o and ∆ and 170 ©2021 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING DIGITAL IMAGE PROCESSING AND COMPUTER GRAPHICS VOLUME: 19 |NUMBER: 2 |2021 |JUNE PSNR = 10 log10 l2 1 MN M−1 P m=0 N−1 P n=0 ( (m, n)− (m, n))2 ,(8) EKI = M−1 P m=0 N−1 P n=0 ∆ (m, n)−∆ ∆ (m, n)−∆  sN−1 P n=0 ∆ (m, n)−∆ 2∆ (m, n)−∆ 2 ,(9) CC = M−1 P m=0 N−1 P n=0  (m, n)−  (m, n)−  sM−1 P m=0 N−1 P n=0  (m, n)− 2M−1 P m=0 N−1 P n=0  (m, n)− 2 .(12) ∆ a e he mean alues o ∆ and ∆ , espec- i ely. •S uc u al SIMila i y (SSIM) is o measu ing he s uc u e sa ing capabili y o he denoising algo- i hm. I and a e he mean and σiand σi a e he s anda d de ia ion o o iginal and econ- s uc ed images espec i ely, hen: SSIM = 2 (m, n) (m, n) + c1(2σ , +c2)  2+ 2+c1σ2 i+σ2 , +c2, (10) whe e c1= (0.01l)2,c2= (0.03l)2and co a iance image ma ix is gi en by: σ2 , =1 N−1 N−1 X k=0  k−  k − .(11) •Co ela ion Coe icien (CC) de ines he in e de- pendence o he noisy image and he econs uc ed image. I is de ined as shown in Eq. (12). •Signal o Noise Ra io (SNR) is de ined as he a io o signal powe o noise powe : SNR = 10 log10 M−1 P m=0 N−1 P n=0 ( (m, n))2 sM−1 P m=0 N−1 P n=0 ( (m, n)− (m, n))2 . (13) •Figu e o Me i (FoM) de ined by Eq. (14) is a measu e o p ese ing he edge in o ma ion while denoising he image: FoM = 1 max (ND, NI)X n=1 ND1 1 + γd2 n,(14) whe e NDand NIa e he numbe s o edge pixels ha a e de ec ed and ideally p esen espec i ely; dnis he Euclidean dis ance be ween he n- h de- ec ed edge pixel and he closes ideal pixel ha is p esen ; γis a scala usually equal o 1 9 o image calcula ions. •Mean o Va iance Ra io (MVR) is calcula ed o eal images o a selec ed local egion as: MVR = µl σ2 l ,(15) whe e µland σ2 la e he local mean and a iance. •Equi alen Numbe o Looks (ENL) is ano he pa- ame e used o eal images. I is he a io o he squa e o mean o he a iance o a selec ed local egion gi en by: ENL = µ2 l σ2 l .(16) 3. BEMD Based Pixel-Wise Wiene Fil e ing In his sec ion, he p oposed BEMD based speckle e- duc ion in Ul asound images using Wiene il e ing has been in oduced. Due o he high- equency cha - ac e is ics o he speckle noise, i is mainly cons ained in he low o de IMFs o he ul asound image. The e- o e, conside ing he low o de IMFs o noise educ ion is he choice ha has been conside ed. A he same ime, he edge in o ma ion is also a piece o c ucial in o ma ion om he poin o iew o he diagnos ic impo ance in ul asound images. As he edges a e signi ied by he ab up changes in he ampli- ude in he spa ial domain, he low o de IMFs canno be disca ded all oge he . The p oposed scheme u i- lizes he ac ha low o de IMFs has maximum noise ©2021 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING 171 DIGITAL IMAGE PROCESSING AND COMPUTER GRAPHICS VOLUME: 19 |NUMBER: 2 |2021 |JUNE componen and hus low pass il e s hese IMFs using an adap i e Wiene il e o p ese ing he edge in o - ma ion p esen in hese low o de IMFs. Fig. 2: Block Diag am o he p oposed scheme. The s eps in he p oposed algo i hm a e as ollows: S ep 1: Calcula e he BEMD o he ul asound image co up ed wi h speckle noise. S ep 2: Selec he low o de IMFs which ha e he high- equency componen s and a e he e o e ha ing bo h he noise and he edge in o ma ion. S ep 3: Calcula e he pixel-wise Wiene il e ing o he selec ed IMFs [44] assuming addi i e noise (n1, n2)wi h ze o mean and a iance σ2 . Wiene il- e es ima es he local mean and a iance a ound each pixel o he chosen IMF: µe=1 NM X m,n∈w l(m, n),(17) σ2 e=1 NM X m,n∈w 2 l(m, n)−µ2 e,(18) whe e wis he window co esponding o he N×M neighbo hood o each pixel in he IMF. This il e hen c ea es a pixel-wise es ima e gi en as: e(m, n) = µe+σ2 e−σ2 σ2 e ( l(m, n)−µe).(19) S ep 4: The low-o de il e ed IMFs combined wi h he high-o de IMFs and he esidue a e used o econ- s uc he denoised image. 4. Expe imen al Resul s The kidney and e us Field II images ha e been simu- la ed and used o he expe imen s pe o med on MAT- LAB. The speckle noise has been added o he image wi h σ2= 0.1,σ2= 0.2, and σ2= 0.3. Figu e 3 shows he BEMD IMFs o syn he ic kidney image and syn he ic e us image, ha a e ob ained o noise a iance σ2= 0.1. The spline in e pola ion ha is used in ou case is a cubic spline. As can be seen in Fig. 3, he low o de IMFs a e ha ing only he high- equency componen s co esponding o speckle and edges. The Wiene il e is bes de ined o addi i e noise. The e o e, he mul iplica i e speckle noise has been log- ans o med and con e ed o addi i e noise be o e applying BEMD. (a) (b) Fig. 3: BEMD IMFs o (a) Syn e ic Kidney Field II image and (b) Syn he ic Fe us Field II image. Pixel-wise Wiene il e ing has been applied on low o de IMFs. I has been obse ed ha he esul s we e bes o he Wiene il e applied o IMF1 and IMF2. While applying Wiene il e ing, 8-neighbo hood has been u ilized o calcula ion o local egion. The Wiene il e ed IMFs along wi h highe -o de IMFs and he esidue is econs uc ed o ge he de- 172 ©2021 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING DIGITAL IMAGE PROCESSING AND COMPUTER GRAPHICS VOLUME: 19 |NUMBER: 2 |2021 |JUNE Tab. 1: Co ela ion Coe icien (CC) o a ious echniques. Technique CC Noise a iance σ2 Syn he ic e us image Syn he ic kidney image 0.1 0.2 0.3 0.1 0.2 0.3 P oposed EMD 0.96234 0.94607 0.92693 0.94284 0.90968 0.87969 Con en ional EMD 0.95102 0.92555 0.87480 0.92645 0.87332 0.81060 Bila e al 0.91368 0.84291 0.78464 0.84947 0.74776 0.67000 SRAD 0.94191 0.87916 0.81739 0.91540 0.8292 0.75551 NLM 0.93126 0.87093 0.82044 0.87618 0.78484 0.71328 OBNLM 0.95155 0.90697 0.86630 0.90923 0.83734 0.77571 PMAD 0.94638 0.88481 0.82668 0.90595 0.80801 0.72333 Tab. 2: Signal o Noise Ra io (SNR) o a ious echniques. Technique SNR Noise a iance σ2 Syn he ic e us image Syn he ic kidney image 0.1 0.2 0.3 0.1 0.2 0.3 P oposed EMD 14.0620 12.6740 11.4590 15.6020 13.6940 12.4430 Con en ional EMD 13.9140 11.0110 10.2900 14.2440 12.0820 10.2030 Bila e al 10.7200 7.9790 6.4897 10.7320 8.0182 6.5425 SRAD 12.6000 9.2820 7.3429 13.7580 10.2860 8.4398 NLM 11.7240 8.8585 7.3089 11.7650 8.9261 7.3852 OBNLM 13.3050 10.3730 8.6871 13.3320 10.4920 8.8753 PMAD 12.9410 9.4890 7.5779 13.2020 9.6131 7.6633 Tab. 3: Figu e o Me i (FoM) o a ious echniques. Technique FoM Noise a iance σ2 Syn he ic e us image Syn he ic kidney image 0.1 0.2 0.3 0.1 0.2 0.3 P oposed EMD 0.82919 0.86146 0.84317 0.86334 0.86188 0.82560 Con en ional EMD 0.82761 0.84320 0.82133 0.86112 0.83281 0.81143 Bila e al 0.81285 0.74764 0.70383 0.77549 0.72620 0.69278 SRAD 0.86349 0.78487 0.73090 0.83815 0.75237 0.73401 NLM 0.87531 0.79219 0.75194 0.83151 0.78397 0.74317 OBNLM 0.89364 0.84390 0.80875 0.88295 0.78922 0.76806 PMAD 0.86438 0.79341 0.73724 0.85802 0.76742 0.71880 noised image. The quali a i e assessmen is shown in Fig. 4 o syn he ic kidney and in Fig. 5 o syn he ic e us o he noise o a iance σ2= 0.1. As can be seen, he denoised image is pe cep ually o he same quali y as ha o he o iginal image. (a) (b) (c) Fig. 4: Syn he ic kidney o σ2= 0.1: (a) o iginal image, (b) noisy image and (c) denoised image. Table 1, Tab. 2, Tab. 3, Tab. 4, Tab. 5 and Tab. 6 shows he compa ison o a ious pa ame e s ob ained o di e en alues o σ o he wo Field II syn he ic images, e us and kidney. As can be seen, he p oposed (a) (b) (c) Fig. 5: Syn he ic e us o σ2= 0.1: (a) o iginal image, (b) noisy image and (c) denoised image. algo i hm pe o ms be e han he exis ing echniques in e ms o PSNR, SNR, and CC, o noise a iance σ2= 0.1. The esul s ob ained o hese pa ame- e s gi e simila esul s e en a highe noise a iances, σ2= 0.2and σ2= 0.3. Mo eo e , he esul in he ables show ha he p o- posed echnique gi es compa able esul s wi h he ex- is ing echniques in e ms o EKI, SSIM, and FoM o ©2021 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING 173 DIGITAL IMAGE PROCESSING AND COMPUTER GRAPHICS VOLUME: 19 |NUMBER: 2 |2021 |JUNE Tab. 4: Peak Signal o Noise Ra io (PSNR) o a ious echniques. Technique PSNR Noise a iance σ2 Syn he ic e us image Syn he ic kidney image 0.1 0.2 0.3 0.1 0.2 0.3 P oposed EMD 24.946 23.577 22.383 24.322 22.388 21.158 Con en ional EMD 22.398 20.671 20.330 22.317 20.672 19.821 Bila e al 21.056 18.009 16.260 19.025 15.992 14.254 SRAD 23.058 19.463 17.260 22.192 18.459 16.398 NLM 22.336 19.310 17.594 20.288 17.239 15.518 OBNLM 24.002 21.030 19.306 21.971 19.054 17.350 PMAD 23.539 19.837 17.690 21.730 17.878 15.686 Tab. 5: Edge Keeping Index (EKI) o a ious echniques. Technique EKI Noise a iance σ2 Syn he ic e us image Syn he ic kidney image 0.1 0.2 0.3 0.1 0.2 0.3 P oposed EMD 0.64603 0.59358 0.53587 0.54885 0.50024 0.49630 Con en ional EMD 0.61720 0.52081 0.50012 0.51320 0.48230 0.45321 Bila e al 0.59360 0.54006 0.52306 0.53703 0.49294 0.47574 SRAD 0.55134 0.49815 0.49498 0.49201 0.45619 0.43757 NLM 0.59768 0.54228 0.51775 0.53614 0.49024 0.47184 OBNLM 0.62562 0.54757 0.52198 0.55135 0.49023 0.46734 PMAD 0.55606 0.50568 0.49567 0.49833 0.46407 0.44813 Tab. 6: S uc u al SIMila i y (SSIM) o a ious echniques. Technique SSIM Noise a iance σ2 Syn he ic e us image Syn he ic kidney image 0.1 0.2 0.3 0.1 0.2 0.3 P oposed EMD 0.70575 0.66684 0.62823 0.61088 0.53961 0.49747 Con en ional EMD 0.72345 0.65482 0.61291 0.61098 0.49308 0.45320 Bila e al 0.71808 0.61478 0.5552 0.53328 0.39544 0.32945 SRAD 0.76831 0.67000 0.59414 0.65411 0.50453 0.41941 NLM 0.74678 0.64120 0.58082 0.56396 0.42391 0.35323 OBNLM 0.78688 0.68623 0.62356 0.62079 0.47589 0.39955 PMAD 0.78014 0.68009 0.60847 0.63845 0.47608 0.38675 σ2= 0.1. A he same ime, based on he analysis o he alues ob ained by he expe imen s, he scheme is gi ing be e esul s in e ms o hese pa ame e s o highe alues o σ2= 0.2and σ2= 0.3. Thus, we can summa ize ha he echnique pe o ms accep ably well e en a highe noise le els. Fo comple eness o he e icacy e alua ion o he p oposed algo i hm, an expe imen has also been pe - o med on he eal ul asound image da abase aken om [45]. The eal images ha e h ee se s o da a namely kidney, li e , and gall bladde images each ha - ing a ound 85 images. Th ee egions we e selec ed an- domly o all h ee se s and he MVR and ENL ha e been calcula ed. Figu e 6(a) shows he eal li e ul asound image and he h ee egions ha a e aken o calcula ions o MVR and ENL. Figu e 6(b) and Fig. 6(c) a e he e- sul s ha a e ob ained o he selec ed egions. Table 7 shows he MVR and ENL o he eal li e ul asound da abase. Simila expe imen s we e also pe o med on eal ul- asound image da a se s o kidney and gall bladde , howe e , due o pauci y o space we ha e shown MVR and ENL plo s o li e da abase only. The esul s ob- ained o he o he se s a e also in conjunc ion wi h he esul s shown he e. Tab. 7: MVR and ENL o Real Li e Ul asound Da abase. Technique MVR ENL P oposed EMD 18.51 ±3.76 5.25 ±2.79 Con en ional BEMD 17.91 ±5.32 5.01 ±2.54 Bila e al 15.42 ±5.16 3.96 ±2.32 SRAD 17.66 ±4.52 4.87 ±2.35 NLM 17.01 ±4.14 4.91 ±2.15 OBNLM 17.81 ±4.71 4.95 ±2.61 PMAD 16.39 ±6.21 4.33 ±2.79 174 ©2021 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING DIGITAL IMAGE PROCESSING AND COMPUTER GRAPHICS VOLUME: 19 |NUMBER: 2 |2021 |JUNE (a) (b) (c) Fig. 6: (a) Real li e image wi h selec ed egions, (b) MVR plo , (c) ENL plo . 5. Conclusion In his pape , a BEMD based image denoising algo- i hm using pixel-wise Wiene il e ing has been p e- sen ed. The p oposed me hod sol es he p oblem o losing he edge in o ma ion in he con en ional BEMD based denoising echnique. The con en ional BEMD based algo i hm is able o emo e low o de IMF, bu loses impo an edge in o ma ion which has been p e- se ed wi h he help o he me hod p oposed he ein. The pe o mance o he me hod has been e i ied quan- i a i ely in e ms o PSNR, EKI, SSIM, FoM, SNR, and CC o syn he ic ul asound images. The quan i a- i e analysis is also done on he eal ul asound images in e ms o MVR and ENL. I has been ound ha he p oposed algo i hm pe o ms be e han many o he exis ing s a e-o - he-a echniques and can p ese e he edge in o ma ion. Au ho Con ibu ions V.K. concei ed he p esen ed idea. B.G. de eloped he heo y and pe o med he expe imen s. V.K. encou - aged B.G. o in es iga e and supe ised he indings o his wo k. Bo h au ho s discussed he esul s and con ibu ed o he inal manusc ip . Re e ences [1] BURCKHARDT, C. B. Speckle in ul asound B-mode scans. IEEE T ansac ions on Sonics and Ul asonics. 1978, ol. 25, iss. 1, pp. 1–6. ISSN 2162-1403. DOI: 10.1109/T-SU.1978.30978. [2] SZABO, T. L. Diagnos ic Ul asound Imaging: Inside Ou . 2nd ed. Ams e dam: Academic P ess, 2014. ISBN 978-012-396487-8. [3] YU, Y. and S. T. ACTON. Speckle educ- ing aniso opic di usion. 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