Improving the ultrasound image contrast ratio (CR) and contrast to noise ratio (CNR) has many clinical advantages. Breast cancer detection is one example. Anechoic cysts which fill with clutter noise can be easily misinterpreted and classified as malignant lesions instead of benign. Beamforming techniques contribute to off-axis side lobes and clutter. These two side effects inherent in beamforming are undesirable since they will degrade the image quality by lowering the image CR and CNR. To overcome this issue a new post-processing technique known as contrast enhanced delay and sum (CEDAS) is proposed. Here the energy of every envelope signals are calculated, mapped, and clustered in order to identify the cyst and clutter location. CEDAS reduce clutter inside the cyst by attenuating it from envelope signals before the new B-Mode image is formed. With CEDAS, the image CR and CNR improved by average 12 dB and 1.1 dB respectively for cysts size 2 mm to 6 mm and imaging depth from 40 mm to 80 mm

Cowell, D.M.J.

Freear, S.

Harput, S.

Moubark, A.M.

Moubark, AM

Harput, S

Cowell, DMJ

Freear, S

White Rose Research Online

Clutter noise reduction in B-Mode image through mapping and clustering signal energy for better cyst classification

Asraf Mohamed Moubark

Sevan Harput

David M. J. Cowell

Steven Freear

Crossref

This is a repository copy of Clutter noise reduction in B-Mode image through mapping and clustering signal energy for better cyst classification.White Rose Research Online URL for this paper:http://eprints.whiterose.ac.uk/110289/Version: Accepted VersionProceedings Paper:Moubark, AM, Harput, S, Cowell, DMJ et al. (1 more author) (2016) Clutter noise reductionin B-Mode image through mapping and clustering signal energy for better cyst classification. In: IEEE International Ultrasonics Symposium, IUS. 2016 International Ultrasonics Symposium, 18-21 Sep 2016, Tours, France. IEEE . ISBN 9781467398978 https://doi.org/10.1109/ULTSYM.2016.7728860(c) 2016, IEEE. This is an author produced version of a paper published in IEEE International Ultrasonics Symposium, IUS . Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Uploaded in accordance with the publisher’s self-archiving policy.eprints@whiterose.ac.ukhttps://eprints.whiterose.ac.uk/Reuse Unless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version - refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher’s website. Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. Clutter Noise Reduction in B-Mode Image ThroughMapping and Clustering Signal Energy for BetterCyst ClassificationAsraf Mohamed Moubark, Sevan Harput, David M. J. Cowell and Steven FreearUltrasound Group, School of Electronic and Electrical Engineering, University of Leeds, UK.E-mail: elamm@leeds.ac.uk and S.Freear@leeds.ac.ukAbstract—Improving the ultrasound image contrast ratio (CR)and contrast to noise ratio (CNR) has many clinical advantages.Breast cancer detection is one example. Anechoic cysts whichfill with clutter noise can be easily misinterpreted and classifiedas malignant lesions instead of benign. Beamforming techniquescontribute to off-axis side lobes and clutter. These two side effectsinherent in beamforming are undesirable since they will degradethe image quality by lowering the image CR and CNR. Toovercome this issue a new post-processing technique known ascontrast enhanced delay and sum (CEDAS) is proposed. Herethe energy of every envelope signals are calculated, mapped, andclustered in order to identify the cyst and clutter location. CEDASreduce clutter inside the cyst by attenuating it from envelopesignals before the new B-Mode image is formed. With CEDAS,the image CR and CNR improved by average 12 dB and 1.1dB respectively for cysts size 2 mm to 6 mm and imaging depthfrom 40 mm to 80 mm.I. INTRODUCTIONClutter is a type of noise artefact in ultrasound imagesthat often obscures anechoic regions such as cysts and bloodvessels. This complicates and limits measurement of the cystsize and other anatomical measurements such as blood vesseland urine-filled bladders [1]. Clutter noise not only reduces theB-Mode image contrast but at the same time limits the depthat which diagnostic information can be obtained [2] [3]. Afew main sources for clutter noise are reverberation, scatteringfrom off axis and random acoustic noise [4]. Much workhas been carried out to increase the image contrast ratio (CR)by eliminating or reducing the clutter noise effect. Howevernot all manage to improve the B-Mode image CR togetherwith contrast to noise ration (CNR). Work carried out by [5]manage to improve the image CR but not the CNR. But as aresult of losing the image CNR values, the dynamic range hasbeen increased by up to 100 dB. Increasing the dynamic rangeeventually decreases the image CR by returning the removedor reduced clutter noise inside the cyst. Thus a new signalpost-processing technique known as CEDAS is proposed inthis paper. Here, the clutter noise inside the cyst is attenuated,CR increased and CNR maintained by mapping and clusteringthe energy level of the envelope signal.II. METHODSThe first step in identifying the location of a cyst andeliminating the clutter inside it starts with calculating theenergy of the envelope signal for each of the image linesusing the windowing technique [6]. Mapping the envelopesignal into energy through the windowing process helps toclassify and differentiate from the speckle destructive regionand the clutter inside the cyst. Classifying clutter insidethe cyst with radio frequency (RF) or envelope signals isnot feasible. This is because the speckle destructive regionproduces the same values as the clutter inside cyst.The energyof the envelope signal, Gi calculated from a small segmentseparately. According to rectangular window size, N, Gi isgiven by following equation:Gi =N−1+k∑j=0|Xj+k|2 (1)i = 1, 2, ..., n; n = (m−N)/s; k = 0, s, 2s, ..., ns.Where X is number of sample in envelope signal, i isnumber of windowing, k is the step increment from onewindow to another, s is an integer even number, m is thelength of the envelope signal and finally n is total numberof windowing. All small chunks of energy calculated for eachwindow, Gi are merged so that it becomes one single energyline, El as given byEl = 20log10{Gi=1(0 : s), Gi=2(2s : 3s), ......, Gi=n((n− 1)s : ns)} (2)Where l represents the number of imaging lines. Next,before grouping or clustering the energy level into differentgroups, the transition of the energy level or change in theenergy mean are determined. The main objective is to findthe most significant changes in the energy level to identifyhyperechoic, speckle and hypoechoic region. The highestlevels of energy indicate a hyperechoic region. Medium levelsof energy indicate speckle region while the lowest energiesindicate cyst or hyperechoic regions. Optimal detectionof change-points algorithm created by [7] have been usedto find the points where the energy signal mean changesmost significantly by specifying a minimum residual errorimprovement in the function. More detailed mathematicalworks on finding abrupt points can be found in [7] [8]. Allchanging points, qld obtained for every energy line, El arecontained in the following matrix,Ql =q11 · · · q1c.... . ....qx1 · · · qxc(3)qld ∈ El; d = 1, 2, ..., c.Where the first horizontal direction in the matrix, Ql=1represents all changing points, (q11 , q12 , ..., q1c) in the firstenergy line, El=1 while c represents the total number ofchanging points.Once the changing points on the energy signal level areidentified for all the lines, they are next grouped or clusteredinto four different groups by using k-means clustering tech-nique as given by [9]J(a) =p∑a=1x∑l=1‖ Ql − va ‖2 (4)p < xWhere p is the number of clusters and va are the centroidsfor cluster a. The second lowest cluster, J(a−1) was used asa threshold to determine the clutter present. The new envelopesignal, X̀ was formed for every image line according to thecondition stated belowX̀ ={X, Ql ≥ J(a− 1)X × 0.25, Ql < J(a− 1)(5)New envelope signal formed, X̀ is equal to former envelopesignal, X if the changing points, Ql is more than the datainside the second lowest cluster, J(a− 1) else X is attenuatedby factor of 0.25 if the changing point, Ql is lower than datainside the second lowest cluster, J(a-1). The new envelopesignals are converted into a log scale to form B-Mode image.III. SIMULATION AND PERFORMANCE EVALUATIONIn order to evaluate the performance of the proposedmethod, Field II [10] simulations have been carried out onmultiple cyst diameter sizes from 2 mm to 6 mm at differentdepths, between 40 to 80 mm. The simulation parameters areshown is Table 1. A B-mode image was formed with planewave imaging (PWI) steered at a 0◦ angle. The window size, Nused for the simulation was 64 and the increment size, k is 2.If the increment step is small, the overlapped region betweenthe windows will be more and this will eventually producesmoother transition in the energy values calculated betweenthe windows. The whole process of calculating the energyfrom the envelope signal is shown in Fig. 1(a). The variationof speckle formation that is produced from constructive anddestructive interference of the scattering signal as can beenseen in Fig. 1(b) are now becoming less as in Fig. 1(c).TABLE ISIMULATION PARAMETERSParameters ValuesSpeed of Sound, m/s 1540Sampling Frequencies, MHz 160Centre Frequencies, MHz 5Bandwidth, % 100No. of Elements 128Elements Spacing λExcitation Signal Hanning Windowed2 Cycles Sinusoidal0 200 400 600 800 1000 120000.20.40.60.8Normalized Amplitude(a)0 200 400 600 800 1000 1200-60-40-200Normalized Amplitude [dB](b)200 400 600 800 1000 1200Number of Samples, l-200-150-100-500Energy of Normalized Amplitude, El [dB] (c)Energy SignalLocal MeanChanging Pointsi=1 i=2 i=nj j+s N-1 N+s-1 j+ns N+ns-1......................   Fig. 1. (a) Energy calculated by applying the windowing technique on theenvelope signal. (b) Envelope signal in dB scale. (c) Shows the energy valuescalculated from (a) mapped into single lines.The energy changing points were sorted from minimumto maximum before they were clustered in order to havebetter visualisation on the cluster hierarchy. All four clustersare shown in Fig. 2 with their centroids points. Note thatthe clusters are not in order since k-means assigned centroidpoints randomly . Thus centroids points are sorted before eachone of the cluster identified in order. The lowest clusters areconsidered as clutter regions and the preceding cluster groupis used as the threshold level. In Fig. 2 data in cluster 1 ,red,was used as a threshold. In the case where only two changingpoints in the energy level are present, the clustering dividedwhole points into four groups where upper or the top twogroups represent the same energy region and the lowest groupsrepresent clutter.IV. PERFORMANCE EVALUATIONIn order to evaluate the final B-Mode images qualitiesformed with DAS and CEDAS techniques, two key perfor-mance indicator haven used. First the CR is used to express the0 100 200 300 400 500 600 700 800 900Number of Changing Points-140-120-100-80-60-40Changing Points     in Energy Lines [dB]Cluster 1Cluster 2Cluster 3Cluster 4CentroidsFig. 2. Cluster assignment for changing points in the energy level.detectability of the object contrast between region of interest(ROI) inside the cyst and its background. Second the CNRis used the measure the cyst contrast with speckle or noisevariation inside and outside of the cyst [11]. High CNR valuemeans cyst can be visualize easily and the acoustic noisestandard deviation is small or more uniform. Both CR andCNR equation are given byCR(dB) = 20log10(µcystµback) (6)CNR(dB) = 20log10(|µcyst − µBack|√(σcyst2 + σBack2)) (7)Where µcyst and µBack are means of image intensitiesinside and outside of the cyst respectively while σcyst2 andσBack2 are their variances. CR and CNR were calculated onthe cysts at different depths on the B-Mode images producedby creating two different regions with the same dimensions.The first region is inside the cyst while the other region islocated outside the cyst at the same depth. This is to ensurethat the attenuation caused by frequency doesn’t affect themeasurements.V. RESULTS AND DISCUSSIONSIn this section, performance of both conventional DAS andCEDAS were evaluated qualitatively as shown in Fig. 3 andquantitatively as presented in graphical form in Fig 4. B-Modeimage of conventional DAS as shown in Fig. 3(a) clearlyshows that all ten cysts are effected by clutter noise. The noiselevel inside the cysts increase with depth. One of the reasonfor high noise level is due to low signal-to-noise ratio fromPWI [12]. On the other hand, the proposed method, CEDAS asshown in Fig. 3(b) successfully detect and attenuated almostall clutter noise that is present inside those cysts.These results are supported by the axial distance graphshown in Fig. 4(a) and the lateral distance graph as shownin Fig. 4(b). Both graphs show that clutter noise variationsinside the cyst are attenuated.(a)-5 0 5Lateral distance [mm]35404550556065707580Axial Distance [mm](b)-5 0 5Lateral distance [mm]35404550556065707580-40-35-30-25-20-15-10-50Fig. 3. B-Mode images for (a) DAS, (b) CEDAS.35 40 45 50 55 60 65 70 75 80 85Axial Distance [mm]-60-40-200(a)-8 -6 -4 -2 0 2 4 6 8Lateral Distance [mm]-60-40-200Normalized Amplitude [dB](b)DASCEDASFig. 4. (a) Axial Distance plotted from -4 mm, (b) Lateral Distance plottedfrom 40 mm location referring to Fig. 3.As predicted, the CR for CEDAS out performed DAS. At adepth of 40 mm, the CR for DAS is -21 dB while for CEDASis -33 dB. The CR difference between both techniques retainedthroughout the depth regarding the cyst sizes. In average, CRfor CEDAS is 12 dB higher than DAS. Complete resultspresented in Fig. 5(a). At the same time, the CNR valuesfor CEDAS also increases compared to DAS even though nochanges are applied to the speckle formation outside the cyst.Initial hypothesis was made that the CNR values will be samein both techniques. However due to less noise variance insidethe cyst for CEDAS compared to DAS, the CNR value alsoincrease. At 40 mm depth the CNR values for CEDAS is 5 dBcompared to 4.3 dB for DAS. CEDAS continuously producesbetter results compared to DAS by average 1.1 dB for all cystsize at all depth as shown in Fig. 5(b).The ability for an object to be easily detected depends onthe contrast. Since CEDAS enhanced the CR, the border of thecyst can be easily detected. Edge detection technique applied40 50 60 70 80-40-30-20-10CR [dB](a)DASCEDAS40 50 60 70 80Axial Distance [mm]2468CNR [dB](b)Fig. 5. (a) CR, (b) CNR for DAS and CEDAS.on 6 mm diameter cyst located at 40 mm depth using Sobelmethods with fudge factor of 0.3 is shown in Fig. 6. CEDASable to trace the edge inside the cyst while DAS could notsince the noise variation is high. (a)                               (b)Fig. 6. Border detection using Sobel method on (a) DAS, (b) CEDAS.A histogram is plotted for the number of amplitude oc-currences on both B-Modes images as shown in Fig. 3(a)and (b). The histrogram as shown in Fig. 7 shows significantchanges in the distribution of the amplitude. The speckleand strong reflective points which dominate higher amplitudeportion shows more number of occurrence with CEDAS whencompared to DAS. This is due to reduction or attenuationof clutter noise. Normalizing signal with less noise leveleventually give rise to high value signal. Thus non-cyst regionin Fig. 3(b) shows more prominent when compared with sameregion in Fig. 3(a). At the same time, it can be seen onthe histogram that second smaller peaks appear on CEDASamplitude distribution approximately at -57 dB. This portionrepresent clutter noise which have been brought down throughattenuation process. Displaying the B-Mode image with 40 dBdynamic range eventually dimmed its appearance and increasethe image CR and CNR. Area under the curves for bothamplitude distribution is same.-70 -60 -50 -40 -30 -20 -10Normalized Amplitude [dB]024681012Number of Occurrence×104  DAS  CEDASFig. 7. Histogram for DAS and CEDAS normalized amplitude distribution.VI. CONLUSIONThe proposed new technique, CEDAS has successfullydemonstrated its ability to eliminate clutter inside the cystswith diameter of 6 mm to 2 mm from 40 mm to 80 mm depths.High CR and CNR is achieved without changing or modifyingany formation of the B-Mode image. Edge detection on cystborder also improved with CEDAS. Since the idea is onlyimplemented on PWI with DAS, future works will be carriedout on compound PWI and other beam-forming techniquessuch as filtered delay and sum (FDMAS). Data from laboratoryexperiments on a multi-purpose phantom also will be used totest the workability of this new technique.REFERENCES[1] M. A. Lediju, M. J. Pihl, J. J. Dahl, and G. E. Trahey, “Quantitativeassessment of the magnitude, impact and spatial extent of ultrasonicclutter,” Ultrasonic imaging, vol. 30, no. 3, pp. 151–168, 2008.[2] S. Huber, M. Wagner, M. Medl, and H. Czembirek, “Real-time spatialcompound imaging in breast ultrasound,” Ultrasound in medicine &biology, vol. 28, no. 2, pp. 155–163, 2002.[3] R. Entrekin, P. Jackson, J. Jago, and B. Porter, “Real time spatialcompound imaging in breast ultrasound: technology and early clinicalexperience,” medicamundi, vol. 43, no. 3, pp. 35–43, 1999.[4] F. Tranquart, N. Grenier, V. Eder, and L. Pourcelot, “Clinical use ofultrasound tissue harmonic imaging,” Ultrasound in medicine & biology,vol. 25, no. 6, pp. 889–894, 1999.[5] S. A. Izadi, A. Mahloojifar, and B. M. Asl, “Weighted capon beamformercombined with coded excitation in ultrasound imaging,” Journal ofMedical Ultrasonics, vol. 42, no. 4, pp. 477–488, 2015.[6] S. Nisar, O. U. Khan, and M. Tariq, “An efficient adaptive window sizeselection method for improving spectrogram visualization,” Computa-tional Intelligence and Neuroscience, vol. 2016, 2016.[7] R. Killick, P. Fearnhead, and I. Eckley, “Optimal detection of change-points with a linear computational cost,” Journal of the AmericanStatistical Association, vol. 107, no. 500, pp. 1590–1598, 2012.[8] M. Lavielle, “Using penalized contrasts for the change-point problem,”Signal processing, vol. 85, no. 8, pp. 1501–1510, 2005.[9] A. Likas, N. Vlassis, and J. J. Verbeek, “The global k-means clusteringalgorithm,” Pattern recognition, vol. 36, no. 2, pp. 451–461, 2003.[10] J. A. Jensen, “Users guide for the field ii program,” Technical Universityof Denmark, vol. 2800, 2001.[11] J. S. Ullom, M. Oelze, and J. R. Sanchez, “Ultrasound speckle reductionusing coded excitation, frequency compounding, and postprocessingdespeckling filters,” in 2010 IEEE International Ultrasonics Symposium.IEEE, 2010, pp. 2291–2294.[12] A. M. Moubark, Z. Alomari, S. Harput, and S. Freear, “Comparisonof spatial and temporal averaging on ultrafast imaging in presenceof quantization errors,” in Ultrasonics Symposium (IUS), 2015 IEEEInternational. IEEE, 2015, pp. 1–4.

Improving the ultrasound image contrast ratio (CR) and contrast to noise ratio (CNR) has many clinical advantages. Breast cancer detection is one example. Anechoic cysts which fill with clutter noise can be easily misinterpreted and classified as malignant lesions instead of benign. Beamforming techniques contribute to off-axis side lobes and clutter. These two side effects inherent in beamforming are undesirable since they will degrade the image quality by lowering the image CR and CNR. To overcome this issue a new post-processing technique known as contrast enhanced delay and sum (CEDAS) is proposed. Here the energy of every envelope signals are calculated, mapped, and clustered in order to identify the cyst and clutter location. CEDAS reduce clutter inside the cyst by attenuating it from envelope signals before the new B-Mode image is formed. With CEDAS, the image CR and CNR improved by average 12 dB and 1.1 dB respectively for cysts size 2 mm to 6 mm and imaging depth from 40 mm to 80 mm.

(c) 2016, IEEE. This is an author produced version of a paper published in IEEE International Ultrasonics Symposium, IUS . Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Uploaded in accordance with the publisher’s self-archiving policy

LSBU Research Open

Clutter Noise Reduction in B-Mode Image ThroughMapping and Clustering Signal Energy for BetterCyst ClassificationAsraf Mohamed Moubark, Sevan Harput, David M. J. Cowell and Steven FreearUltrasound Group, School of Electronic and Electrical Engineering, University of Leeds, UK.E-mail: elamm@leeds.ac.uk and S.Freear@leeds.ac.ukAbstract—Improving the ultrasound image contrast ratio (CR)and contrast to noise ratio (CNR) has many clinical advantages.Breast cancer detection is one example. Anechoic cysts whichfill with clutter noise can be easily misinterpreted and classifiedas malignant lesions instead of benign. Beamforming techniquescontribute to off-axis side lobes and clutter. These two side effectsinherent in beamforming are undesirable since they will degradethe image quality by lowering the image CR and CNR. Toovercome this issue a new post-processing technique known ascontrast enhanced delay and sum (CEDAS) is proposed. Herethe energy of every envelope signals are calculated, mapped, andclustered in order to identify the cyst and clutter location. CEDASreduce clutter inside the cyst by attenuating it from envelopesignals before the new B-Mode image is formed. With CEDAS,the image CR and CNR improved by average 12 dB and 1.1dB respectively for cysts size 2 mm to 6 mm and imaging depthfrom 40 mm to 80 mm.I. INTRODUCTIONClutter is a type of noise artefact in ultrasound imagesthat often obscures anechoic regions such as cysts and bloodvessels. This complicates and limits measurement of the cystsize and other anatomical measurements such as blood vesseland urine-filled bladders [1]. Clutter noise not only reduces theB-Mode image contrast but at the same time limits the depthat which diagnostic information can be obtained [2] [3]. Afew main sources for clutter noise are reverberation, scatteringfrom off axis and random acoustic noise [4]. Much workhas been carried out to increase the image contrast ratio (CR)by eliminating or reducing the clutter noise effect. Howevernot all manage to improve the B-Mode image CR togetherwith contrast to noise ration (CNR). Work carried out by [5]manage to improve the image CR but not the CNR. But as aresult of losing the image CNR values, the dynamic range hasbeen increased by up to 100 dB. Increasing the dynamic rangeeventually decreases the image CR by returning the removedor reduced clutter noise inside the cyst. Thus a new signalpost-processing technique known as CEDAS is proposed inthis paper. Here, the clutter noise inside the cyst is attenuated,CR increased and CNR maintained by mapping and clusteringthe energy level of the envelope signal.II. METHODSThe first step in identifying the location of a cyst andeliminating the clutter inside it starts with calculating theenergy of the envelope signal for each of the image linesusing the windowing technique [6]. Mapping the envelopesignal into energy through the windowing process helps toclassify and differentiate from the speckle destructive regionand the clutter inside the cyst. Classifying clutter insidethe cyst with radio frequency (RF) or envelope signals isnot feasible. This is because the speckle destructive regionproduces the same values as the clutter inside cyst.The energyof the envelope signal, Gi calculated from a small segmentseparately. According to rectangular window size, N, Gi isgiven by following equation:Gi =N−1+k∑j=0|Xj+k|2 (1)i = 1, 2, ..., n; n = (m−N)/s; k = 0, s, 2s, ..., ns.Where X is number of sample in envelope signal, i isnumber of windowing, k is the step increment from onewindow to another, s is an integer even number, m is thelength of the envelope signal and finally n is total numberof windowing. All small chunks of energy calculated for eachwindow, Gi are merged so that it becomes one single energyline, El as given byEl = 20log10{Gi=1(0 : s), Gi=2(2s : 3s), ......, Gi=n((n− 1)s : ns)} (2)Where l represents the number of imaging lines. Next,before grouping or clustering the energy level into differentgroups, the transition of the energy level or change in theenergy mean are determined. The main objective is to findthe most significant changes in the energy level to identifyhyperechoic, speckle and hypoechoic region. The highestlevels of energy indicate a hyperechoic region. Medium levelsof energy indicate speckle region while the lowest energiesindicate cyst or hyperechoic regions. Optimal detectionof change-points algorithm created by [7] have been usedto find the points where the energy signal mean changesmost significantly by specifying a minimum residual errorimprovement in the function. More detailed mathematicalworks on finding abrupt points can be found in [7] [8]. Allchanging points, qld obtained for every energy line, El arecontained in the following matrix,Ql =q11 · · · q1c.... . ....qx1 · · · qxc (3)qld ∈ El; d = 1, 2, ..., c.Where the first horizontal direction in the matrix, Ql=1represents all changing points, (q11 , q12 , ..., q1c) in the firstenergy line, El=1 while c represents the total number ofchanging points.Once the changing points on the energy signal level areidentified for all the lines, they are next grouped or clusteredinto four different groups by using k-means clustering tech-nique as given by [9]J(a) =p∑a=1x∑l=1‖ Ql − va ‖2 (4)p < xWhere p is the number of clusters and va are the centroidsfor cluster a. The second lowest cluster, J(a−1) was used asa threshold to determine the clutter present. The new envelopesignal, X` was formed for every image line according to thecondition stated belowX` ={X, Ql ≥ J(a− 1)X × 0.25, Ql < J(a− 1)(5)New envelope signal formed, X` is equal to former envelopesignal, X if the changing points, Ql is more than the datainside the second lowest cluster, J(a− 1) else X is attenuatedby factor of 0.25 if the changing point, Ql is lower than datainside the second lowest cluster, J(a-1). The new envelopesignals are converted into a log scale to form B-Mode image.III. SIMULATION AND PERFORMANCE EVALUATIONIn order to evaluate the performance of the proposedmethod, Field II [10] simulations have been carried out onmultiple cyst diameter sizes from 2 mm to 6 mm at differentdepths, between 40 to 80 mm. The simulation parameters areshown is Table 1. A B-mode image was formed with planewave imaging (PWI) steered at a 0◦ angle. The window size, Nused for the simulation was 64 and the increment size, k is 2.If the increment step is small, the overlapped region betweenthe windows will be more and this will eventually producesmoother transition in the energy values calculated betweenthe windows. The whole process of calculating the energyfrom the envelope signal is shown in Fig. 1(a). The variationof speckle formation that is produced from constructive anddestructive interference of the scattering signal as can beenseen in Fig. 1(b) are now becoming less as in Fig. 1(c).TABLE ISIMULATION PARAMETERSParameters ValuesSpeed of Sound, m/s 1540Sampling Frequencies, MHz 160Centre Frequencies, MHz 5Bandwidth, % 100No. of Elements 128Elements Spacing λExcitation Signal Hanning Windowed2 Cycles Sinusoidal      	      	     	 !!"#$%	 "  "#$%$#&'(#)*#$#$+#   	  	 	 	 Fig. 1. (a) Energy calculated by applying the windowing technique on theenvelope signal. (b) Envelope signal in dB scale. (c) Shows the energy valuescalculated from (a) mapped into single lines.The energy changing points were sorted from minimumto maximum before they were clustered in order to havebetter visualisation on the cluster hierarchy. All four clustersare shown in Fig. 2 with their centroids points. Note thatthe clusters are not in order since k-means assigned centroidpoints randomly . Thus centroids points are sorted before eachone of the cluster identified in order. The lowest clusters areconsidered as clutter regions and the preceding cluster groupis used as the threshold level. In Fig. 2 data in cluster 1 ,red,was used as a threshold. In the case where only two changingpoints in the energy level are present, the clustering dividedwhole points into four groups where upper or the top twogroups represent the same energy region and the lowest groupsrepresent clutter.IV. PERFORMANCE EVALUATIONIn order to evaluate the final B-Mode images qualitiesformed with DAS and CEDAS techniques, two key perfor-mance indicator haven used. First the CR is used to express the0 100 200 300 400 500 600 700 800 900Number of Changing Points-140-120-100-80-60-40Changing Points     in Energy Lines [dB]Cluster 1Cluster 2Cluster 3Cluster 4CentroidsFig. 2. Cluster assignment for changing points in the energy level.detectability of the object contrast between region of interest(ROI) inside the cyst and its background. Second the CNRis used the measure the cyst contrast with speckle or noisevariation inside and outside of the cyst [11]. High CNR valuemeans cyst can be visualize easily and the acoustic noisestandard deviation is small or more uniform. Both CR andCNR equation are given byCR(dB) = 20log10(µcystµback) (6)CNR(dB) = 20log10(|µcyst − µBack|√(σcyst2 + σBack2)) (7)Where µcyst and µBack are means of image intensitiesinside and outside of the cyst respectively while σcyst2 andσBack2 are their variances. CR and CNR were calculated onthe cysts at different depths on the B-Mode images producedby creating two different regions with the same dimensions.The first region is inside the cyst while the other region islocated outside the cyst at the same depth. This is to ensurethat the attenuation caused by frequency doesn’t affect themeasurements.V. RESULTS AND DISCUSSIONSIn this section, performance of both conventional DAS andCEDAS were evaluated qualitatively as shown in Fig. 3 andquantitatively as presented in graphical form in Fig 4. B-Modeimage of conventional DAS as shown in Fig. 3(a) clearlyshows that all ten cysts are effected by clutter noise. The noiselevel inside the cysts increase with depth. One of the reasonfor high noise level is due to low signal-to-noise ratio fromPWI [12]. On the other hand, the proposed method, CEDAS asshown in Fig. 3(b) successfully detect and attenuated almostall clutter noise that is present inside those cysts.These results are supported by the axial distance graphshown in Fig. 4(a) and the lateral distance graph as shownin Fig. 4(b). Both graphs show that clutter noise variationsinside the cyst are attenuated.(a)-5 0 5Lateral distance [mm]35404550556065707580Axial Distance [mm](b)-5 0 5Lateral distance [mm]35404550556065707580-40-35-30-25-20-15-10-50Fig. 3. B-Mode images for (a) DAS, (b) CEDAS.35 40 45 50 55 60 65 70 75 80 85Axial Distance [mm]-60-40-200(a)-8 -6 -4 -2 0 2 4 6 8Lateral Distance [mm]-60-40-200Normalized Amplitude [dB](b)DASCEDASFig. 4. (a) Axial Distance plotted from -4 mm, (b) Lateral Distance plottedfrom 40 mm location referring to Fig. 3.As predicted, the CR for CEDAS out performed DAS. At adepth of 40 mm, the CR for DAS is -21 dB while for CEDASis -33 dB. The CR difference between both techniques retainedthroughout the depth regarding the cyst sizes. In average, CRfor CEDAS is 12 dB higher than DAS. Complete resultspresented in Fig. 5(a). At the same time, the CNR valuesfor CEDAS also increases compared to DAS even though nochanges are applied to the speckle formation outside the cyst.Initial hypothesis was made that the CNR values will be samein both techniques. However due to less noise variance insidethe cyst for CEDAS compared to DAS, the CNR value alsoincrease. At 40 mm depth the CNR values for CEDAS is 5 dBcompared to 4.3 dB for DAS. CEDAS continuously producesbetter results compared to DAS by average 1.1 dB for all cystsize at all depth as shown in Fig. 5(b).The ability for an object to be easily detected depends onthe contrast. Since CEDAS enhanced the CR, the border of thecyst can be easily detected. Edge detection technique applied40 50 60 70 80-40-30-20-10CR [dB](a)DASCEDAS40 50 60 70 80Axial Distance [mm]2468CNR [dB](b)Fig. 5. (a) CR, (b) CNR for DAS and CEDAS.on 6 mm diameter cyst located at 40 mm depth using Sobelmethods with fudge factor of 0.3 is shown in Fig. 6. CEDASable to trace the edge inside the cyst while DAS could notsince the noise variation is high.Fig. 6. Border detection using Sobel method on (a) DAS, (b) CEDAS.A histogram is plotted for the number of amplitude oc-currences on both B-Modes images as shown in Fig. 3(a)and (b). The histrogram as shown in Fig. 7 shows significantchanges in the distribution of the amplitude. The speckleand strong reflective points which dominate higher amplitudeportion shows more number of occurrence with CEDAS whencompared to DAS. This is due to reduction or attenuationof clutter noise. Normalizing signal with less noise leveleventually give rise to high value signal. Thus non-cyst regionin Fig. 3(b) shows more prominent when compared with sameregion in Fig. 3(a). At the same time, it can be seen onthe histogram that second smaller peaks appear on CEDASamplitude distribution approximately at -57 dB. This portionrepresent clutter noise which have been brought down throughattenuation process. Displaying the B-Mode image with 40 dBdynamic range eventually dimmed its appearance and increasethe image CR and CNR. Area under the curves for bothamplitude distribution is same.-70 -60 -50 -40 -30 -20 -10Normalized Amplitude [dB]024681012Number of Occurrence×104  DAS  CEDASFig. 7. Histogram for DAS and CEDAS normalized amplitude distribution.VI. CONLUSIONThe proposed new technique, CEDAS has successfullydemonstrated its ability to eliminate clutter inside the cystswith diameter of 6 mm to 2 mm from 40 mm to 80 mm depths.High CR and CNR is achieved without changing or modifyingany formation of the B-Mode image. Edge detection on cystborder also improved with CEDAS. Since the idea is onlyimplemented on PWI with DAS, future works will be carriedout on compound PWI and other beam-forming techniquessuch as filtered delay and sum (FDMAS). Data from laboratoryexperiments on a multi-purpose phantom also will be used totest the workability of this new technique.REFERENCES[1] M. A. Lediju, M. J. Pihl, J. J. Dahl, and G. E. Trahey, “Quantitativeassessment of the magnitude, impact and spatial extent of ultrasonicclutter,” Ultrasonic imaging, vol. 30, no. 3, pp. 151–168, 2008.[2] S. Huber, M. Wagner, M. Medl, and H. Czembirek, “Real-time spatialcompound imaging in breast ultrasound,” Ultrasound in medicine &biology, vol. 28, no. 2, pp. 155–163, 2002.[3] R. Entrekin, P. Jackson, J. Jago, and B. Porter, “Real time spatialcompound imaging in breast ultrasound: technology and early clinicalexperience,” medicamundi, vol. 43, no. 3, pp. 35–43, 1999.[4] F. Tranquart, N. Grenier, V. Eder, and L. Pourcelot, “Clinical use ofultrasound tissue harmonic imaging,” Ultrasound in medicine & biology,vol. 25, no. 6, pp. 889–894, 1999.[5] S. A. Izadi, A. Mahloojifar, and B. M. Asl, “Weighted capon beamformercombined with coded excitation in ultrasound imaging,” Journal ofMedical Ultrasonics, vol. 42, no. 4, pp. 477–488, 2015.[6] S. Nisar, O. U. Khan, and M. Tariq, “An efficient adaptive window sizeselection method for improving spectrogram visualization,” Computa-tional Intelligence and Neuroscience, vol. 2016, 2016.[7] R. Killick, P. Fearnhead, and I. Eckley, “Optimal detection of change-points with a linear computational cost,” Journal of the AmericanStatistical Association, vol. 107, no. 500, pp. 1590–1598, 2012.[8] M. Lavielle, “Using penalized contrasts for the change-point problem,”Signal processing, vol. 85, no. 8, pp. 1501–1510, 2005.[9] A. Likas, N. Vlassis, and J. J. Verbeek, “The global k-means clusteringalgorithm,” Pattern recognition, vol. 36, no. 2, pp. 451–461, 2003.[10] J. A. Jensen, “Users guide for the field ii program,” Technical Universityof Denmark, vol. 2800, 2001.[11] J. S. Ullom, M. Oelze, and J. R. Sanchez, “Ultrasound speckle reductionusing coded excitation, frequency compounding, and postprocessingdespeckling filters,” in 2010 IEEE International Ultrasonics Symposium.IEEE, 2010, pp. 2291–2294.[12] A. M. Moubark, Z. Alomari, S. Harput, and S. Freear, “Comparisonof spatial and temporal averaging on ultrafast imaging in presenceof quantization errors,” in Ultrasonics Symposium (IUS), 2015 IEEEInternational. IEEE, 2015, pp. 1–4.

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Clutter noise reduction in B-Mode image through mapping and clustering signal energy for better cyst classification

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