Ultrasound is a well-known imaging modality for the interpretation of breast cancer. It is playing very important role for breast cancer detection that are missed by mammograms. The image acquisition is usually affected by the presence of noise, artifacts, and distortion. To overcome such type of issues, there is a need of image restoration and enhancement to improve the quality of image. This paper proposes a single framework for denoising and enhancement of ultrasound images, where a smoothing filter is replaced with an extended complex diffusion-based filter in an unsharp masking technique. The performance evaluation of the proposed method is tested on real ultrasound breast cancer images database and synthetic ultrasound image. The performance evaluation comprises qualitative and quantitative evaluation along with comparative analysis of pre-existing and proposed method. The quantitative evaluation metrics are mean squared error, peak-signal-to-noise ratio, correlation parameter, normalized absolute error, universal quality index, similarity structure index, edge preservation index, a measure of enhancement, a measure of enhancement by entropy, and second derivative like measurement. The result specifies that the proposed method is better suited approach for the removal of speckle noise which follows Rayleigh distribution, restoration of information, enhancement of abnormalities, and proper edge preservation.
SinghVPSrivastavaSSrivastavaR. Effective mammogram classification based on center symmetric-LBP features in wavelet domain using random forests. Technol Health Care2017; 25(4): 709–727.
2.
ShaikhKKrishnanSThankiR. Types, diagnosis, and treatment of breast cancer. In: Artificial intelligence in breast cancer early detection and diagnosis. Cham: Springer, 2021, pp.21–35.
3.
MathurPSathishkumarKChaturvediM, et al. Cancer statistics, 2020: report from national cancer registry programme, India. J Glob Oncol2020; 6: 1063–1075.
4.
SrivastavaSKumarGMishraRK, et al. A Complex Diffusion Based Modified Fuzzy C-Means Approach for Segmentation of Ultrasound Image in Presence of Speckle Noise for Breast Cancer Detection., Rev. d'Intelligence Artif2020; 34(4): 419–427.
5.
LoizouCPPattichisCSChristodoulouCI, et al. Comparative evaluation of despeckle filtering in ultrasound imaging of the carotid artery. IEEE Trans Ultrason Ferroelectr Freq Control2005; 52(10): 1653–1669.
6.
BurckhardtCB. Speckle in ultrasound B-mode scans. IEEE Trans Sonics Ultrason1978; 25(1): 1–6.
7.
GoodmanJW. Some fundamental properties of speckle. J Opt Soc Am1976; 66(11): 1145–1150.
8.
ThijssenJM. Ultrasonic speckle formation, analysis and processing applied to tissue characterization. Pattern Recognit Lett2003; 24(4-5): 659–675.
9.
BushbergJTBooneJM. The essential physics of medical imaging. Baltimore, MD: Lippincott Williams & Wilkins, 2011.
10.
WagnerRFSmithSWSandrikJM, et al. Statistics of speckle in ultrasound B-scans. IEEE Trans Sonics Ultrason1983; 30(3): 156–163.
11.
TuthillTASperryRHParkerKJ. Deviations from Rayleigh statistics in ultrasonic speckle. Ultrason Imaging1988; 10(2): 81–89.
GonzalezRCWoodsRE. Digital image processing. Saddle River, NJ: Prentice hall Upper, 2002.
14.
LuoW. Efficient removal of impulse noise from digital images. IEEE Trans Consum Electron2006; 52(no. 2): 523–527.
15.
LeeJ-S. Digital image enhancement and noise filtering by use of local statistics. IEEE Trans Pattern Anal Mach Intell1980; 2(2): 165–168.
16.
FrostVSStilesJAShanmuganKS, et al. A model for radar images and its application to adaptive digital filtering of multiplicative noise. IEEE Trans Pattern Anal Mach Intell1982; 4(2): 157–166.
17.
KuanDTSawchukAAStrandTC, et al. Adaptive restoration of images with speckle. Appl Digital Image Process IV1983; 359: 28–38.
PeronaPMalikJ. Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell1990; 12(7): 629–639.
20.
ChenP-YLienC-Y. An efficient edge-preserving algorithm for removal of salt-and-pepper noise. IEEE Signal Process Lett2008; 15: 833–836.
21.
TohKKIsaNA. Noise adaptive fuzzy switching median filter for salt-and-pepper noise reduction. IEEE Signal Process Lett2010; 17(3): 281–284.
22.
YuanCLiY. Switching median and morphological filter for impulse noise removal from digital images. Optik2015; 126(18): 1598–1601.
23.
YangJFanJAiD, et al. Local statistics and non-local mean filter for speckle noise reduction in medical ultrasound image. Neurocomputing2016; 195: 88–95.
24.
CoupePHellierPKervrannC, et al. Nonlocal means-based speckle filtering for ultrasound images. IEEE Trans Image Process2009; 18(10): 2221–2229.
25.
GuoYWangYHouT. Speckle filtering of ultrasonic images using a modified non local-based algorithm. Biomed Signal Process Control2011; 6(2): 129–138.
26.
TeuberTLangA. A new similarity measure for nonlocal filtering in the presence of multiplicative noise. Comput Stat Data Anal2012; 56(12): 3821–3842.
27.
DeledalleC-ADenisLTupinF. Iterative weighted maximum likelihood denoising with probabilistic patch-based weights. IEEE Trans Image Process2009; 18(12): 2661–2672.
28.
BuadesACollBMorelJ-M. A non-local algorithm for image denoising. In: 2005 IEEE Computer society conference on computer vision and pattern recognition (CVPR’05), San Diego, CA, 20–25 June 2005, vol. 2, pp. 60–65. New York: IEEE.
29.
SudeepPVPalanisamyPRajanJ, et al. Speckle reduction in medical ultrasound images using an unbiased non-local means method. Biomed Signal Process Control2016; 28: 1–8.
30.
Gómez FloresWPereiraWCDAInfantosiAFC. Breast ultrasound despeckling using anisotropic diffusion guided by texture descriptors. Ultrasound Med Biol2014; 40(11): 2609–2621.
31.
LiuXLiuJXuX, et al. A robust detail preserving anisotropic diffusion for speckle reduction in ultrasound images. BMC Genomics2011; 12 (Suppl 5): S14.
32.
LiHWuJMiaoA, et al. Rayleigh-maximum-likelihood bilateral filter for ultrasound image enhancement. Biomed Eng Online2017; 16(1): 46–22.
33.
BaseliceFFerraioliGAmbrosanioM, et al. Enhanced Wiener filter for ultrasound image restoration. Comput Methods Programs Biomed2018; 153: 71–81.
34.
WangSHuangT-ZZhaoX-L, et al. Speckle noise removal in ultrasound images by first- and second-order total variation. Numer Algorithms2018; 78(2): 513–533.
35.
ChoiHJeongJ (eds). Speckle noise reduction in ultrasound images using SRAD and guided filter. In: 2018 International Workshop on Advanced Image Technology (IWAIT), 2018, pp. 1–4, Chiang Mai, Thailand.
36.
GaoMKangBFengX., et al. A Anisotropic diffusion based multiplicative speckle noise removal. Sensors2019; 19(14): 3164.
37.
SinghPShreeR. A new homomorphic and method noise thresholding based despeckling of SAR image using anisotropic diffusion. J King Saud Univ Comput Inf Sci2020; 32(1): 137–148.
38.
SalehiHVahidiJAbdeljawadT, et al. A SAR image despeckling method based on an extended adaptive Wiener filter and extended guided filter. Remote Sens2020; 12(15): 2371.
39.
DabassJDabassM. Denoising, edge correction, and enhancement of breast cancer ultrasound images. In: Gurdeep Singh Hura, Ashutosh Kumar Singh, and Lau Siong Hoe (eds) Advances in communication and computational technology. Singapore: Springer, 2021, pp.1153–1172.
40.
KoundalDGuptaSSinghS. Nakagami-based total variation method for speckle reduction in thyroid ultrasound images. Proc IMechE, Part H: J Engineering in Medicine2016; 230(2): 97–110.
41.
GuptaSKaurLChauhanRC, et al. A versatile technique for visual enhancement of medical ultrasound images. Digit Signal Process2007; 17(3): 542–560.
42.
YuCZhangCXieL. A multiplicative Nakagami speckle reduction algorithm for ultrasound images. Multidimens Syst Signal Process2012; 23(4): 499–513.
43.
ThanhDNHThanhLTHienNN, et al. Adaptive total variation L1 regularization for salt and pepper image denoising. Optik2020; 208: 163677.
44.
ShiJOsherS. A nonlinear inverse scale space method for a convex multiplicative noise model. SIAM J Imaging Sci2008; 1(3): 294–321.
45.
KoundalDGuptaSSinghS. Neutrosophic based Nakagami total variation method for speckle suppression in thyroid ultrasound images. IRBM2018; 39(1): 43–53.
46.
DuttVGreenleafJF. Ultrasound echo envelope analysis using a homodyned K distribution signal model. Ultrason Imaging1994; 16(4): 265–287.
47.
JainAK. Fundamentals of digital image processing. Upper Saddle River, NJ: Prentice-Hall, Inc., 1989.
48.
DuttVGreenleafJF. Adaptive speckle reduction filter for log-compressed B-scan images. IEEE Trans Med Imaging1996; 15(6): 802–813.
49.
AysalTCBarnerKE. Rayleigh-maximum-likelihood filtering for speckle reduction of ultrasound images. IEEE Trans Med Imaging2007; 26(5): 712–727.
50.
SrivastavaRGuptaJRParthasarathyH, et al. An adaptive non-linear PDE-based speckle reduction technique for ultrasound images. Int J Biomed Eng Technol2011; 6(3): 304–322.
51.
BernardesRMaduroCSerranhoP., et al. I Improved adaptive complex diffusion despeckling filterOpt Express2010; 18(23): 24048.
52.
GilboaGSochenNZeeviYY. Image enhancement and denoising by complex diffusion processes. IEEE Trans Pattern Anal Mach Intell2004; 26(8): 1020–1036.
53.
SrivastavaSSrivastavaRSharmaN, et al. A fourth-order PDE-based non-linear filter for speckle reduction from Optical Coherence Tomography images. Int J Biomed Eng Technol2012; 10(1): 55–69.
SrivastavaRSrivastavaSYadavRB. Modified complex diffusion based nonlinear filter for restoration and enhancement of magnetic resonance images. Int J Biomed Eng Technol2017; 23(1): 19.
56.
AparnaRJosemartinMJ. Application of image intensity local variance measure for analysis of distorted images. In: 2017 International Conference on Networks & Advances in Computational Technologies (NetACT), Thiruvananthapuram, India, 2017, pp.382–386. New York: IEEE.
57.
SinghVPSrivastavaSSrivastavaR. Automated and effective content-based image retrieval for digital mammography. J Xray Sci Technol2018; 26(1): 29–49.
58.
PuYF. Fractional-order Euler-Lagrange equation for fractional-order variational method: a necessary condition for fractional-order fixed boundary optimization problems in signal processing and image processing. IEEE Access2016; 4: 10110–10135.
59.
SrivastavaRSrivastavaS. Restoration of Poisson noise corrupted digital images with nonlinear PDE based filters along with the choice of regularization parameter estimation. Pattern Recognit Lett2013; 34(10): 1175–1185.
60.
NjehIBen SassiOChtourouK, et al. Speckle noise reduction in breast ultrasound images: SMU (SRAD median unsharp) approch. In: Proceedings of the Eighth international multi-conference on systems, signals & devices, Sousse, Tunisia, 2011, pp.1–6.
61.
SrivastavaSSharmaNSinghSK, et al. A combined approach for the enhancement and segmentation of mammograms using modified fuzzy C-means method in wavelet domain. J Med Phys2014; 39(3): 169–183.
62.
Al-DhabyaniWGomaaMKhaledH, et al. Dataset of breast ultrasound images. Data Brief2020; 28: 104863.
63.
JensenJ.A.MunkP. (1997) Computer Phantoms for Simulating Ultrasound B-Mode and CFM Images. In: LeesS.FerrariL.A. (eds) Acoustical Imaging, vol 23. Boston: Springer. 1997, pp. 80–75. https://doi.org/10.1007/978-1-4419-8588-0_12.
64.
SrivastavaSSharmaNSrivastavaR, et al. Restoration of digital mammographic images corrupted with quantum noise using an adaptive total variation (TV) based nonlinear filter. In: 2012 International Conference on Communications, Devices and Intelligent Systems (CODIS), Kolkata, India, 2012, pp.125–128.
65.
Damera-VenkataNKiteTDGeislerWS, et al. Image quality assessment based on a degradation model. IEEE Trans Image Process2000; 9(4): 636–650.
66.
GirodB. “What’s wrong with mean-squared error?Digit Images Hum Vis1993; 207–220.
67.
KumarRRKumarASrivastavaS. Anisotropic diffusion based unsharp masking and crispening for denoising and enhancement of MRI images. Int Conf Emerg Front Electr Electron Technol2020; 0–5. DOI: 10.1109/icefeet49149.2020.9186966
68.
YenKYenEKJohnstonRG. The ineffectiveness of the correlation coefficient for image comparisons. Retrieved 1996; 7(17): 2011.
69.
MemonFUnarMAMemonS. Image quality assessment for performance evaluation of focus measure operators. Mehran Univ Res J Eng Technol2015; 34(4): 379–386.
70.
ThungKHRaveendranP. A survey of image quality measures. In: Proceedings of the International Conference for Technical Postgraduates (TECHPOS), 2009, pp.17–20. New York: IEEE.s
71.
BovikAC. A universal image quality index. IEEE Signal Process Lett2002; 9(3): 81–84.
72.
WangZBovikACSheikhHR, et al. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process2004; 13(4): 600–612.
73.
SattarFFlorebyLSalomonssonG, et al. Image enhancement based on a nonlinear multiscale method. IEEE Trans Image Process1997; 6(6): 888–895.
74.
AgaianSSLentzKPGrigoryanAM. A new measure of image enhancement. IASTED Int Conf Signal Process Commun2000; 19–22.
75.
PrajapatiPNarmawalaZDarjiNP, et al. Evaluation of perceptual contrast and sharpness measures for meteorological satellite images. Procedia Comput Sci2015; 57: 17–24.
76.
JayaVLGopikakumariR. IEM: a new image enhancement metric for contrast and sharpness measurements. Int J Comput Appl2013; 79(9): 1–9.
77.
ZhouYPanettaKAgaianS. Human visual system based mammogram enhancement and analysis. In: 2010 2nd international conference on image processing theory, tools and applications, Paris, France, 2010, pp.229–234.
