The evolution of magnetic resonance imaging (MRI) leads to the study of the internal anatomy of the breast. It maps the physical features along with functional characteristics of selected regions. However, its mapping accuracy is affected by the presence of Rician noise. This noise limits the qualitative and quantitative measures of breast image. This paper proposes recasted nonlinear complex diffusion filter for sharpening the details and removal of Rician noise. It follows maximum likelihood estimation along with optimal parameter selection of complex diffusion where the overall functionality is balanced by regularization parameters. To make recasted nonlinear complex diffusion, the edge threshold constraint “k” of diffusion coefficient is reformed. It is replaced by the standard deviation of the image. It offers a wide range of threshold as variability present in the image with respect to edge. It also provides an automatic selection of “k” instead of user-based value. A series of evaluation has been conducted with respect to different noise ratios further quality improvement of MRI. The qualitative and quantitative assessments of evaluations are tested for the Reference Image Database to Evaluate Therapy Response (RIDER) Breast database. The proposed method is also compared with other existing methods. The quantitative assessment includes the parameters of the full-reference image, human visual system, and no-reference image. It is observed that the proposed method is capable of preserving edges, sharpening the details, and removal of Rician noise.
GuptaAShridharKDhillonPK, et al. A review of breast cancer awareness among women in India: cancer literate or awareness deficit?Eur J Cancer2015; 51: 2058–2066.
2.
SreeSVNgEYKAcharyaRU, et al. Breast imaging: a survey. World J Clin Oncol2011; 2: 171–178.
3.
GoyalBDograAAgrawalS, et al. Noise issues prevailing in various types of medical images. Biomed Pharmacol J2018; 11: 1227–1237.
4.
SagheerSVMGeorgeSN. A review on medical image denoising algorithms. Biomed Signal Proces2020; 61: 102036.
5.
HudaWAbrahamsRB. Radiographic techniques, contrast, and noise in x-ray imaging. AJR Am J Roentgenol2015; 204: W126–W131.
6.
SrivastavaRGuptaJRPParthasarathyH, et al. An adaptive non-linear PDE-based speckle reduction technique for ultrasound images. Int J Biomed Eng Tech2011; 6: 304–322.
7.
KarellasAVedanthamS. Breast cancer imaging: a perspective for the next decade. Med Phys2008; 35: 4878–4897.
8.
PalCDasPChakrabartiA, et al. Rician noise removal in magnitude MRI images using efficient anisotropic diffusion filtering. Int J Imag Syst Tech2017; 27: 248–264.
9.
CoupéPManjónJVGedamuE, et al. Robust Rician noise estimation for MR images. Med Image Anal2010; 14: 483–493.
10.
SrivastavaSSrivastavaRSharmaN, et al. A non-linear complex diffusion based filter adapted to Rayleigh’s speckle noise for de-speckling ultrasound images. Int J Biomed Eng Tech2012; 10: 101–117.
11.
SrivastavaRSrivastavaS. Restoration of Poisson noise corrupted digital images with nonlinear PDE based filters along with the choice of regularization parameter estimation. Pattern Recogn Lett2013; 34: 1175–1185.
12.
YoussefKJarenwattananonNNBouchardLS. Feature-preserving noise removal. IEEE T Med Imaging2015; 34: 1822–1829.
13.
YadavRBSrivastavaSSrivastavaR. An efficient PDE-based nonlinear filter adapted to Rician noise for restoration and enhancement of magnetic resonance images. In: Proceedings of the 2016 1st India international conference on information processing (IICIP), Delhi, India, 12–14 August 2016, 4 pp. New York: IEEE.
14.
YadavRBSrivastavaSSrivastavaR. Modified complex diffusion based nonlinear filter for restoration and enhancement of magnetic resonance images. Int J Biomed Eng Tech2017; 23: 19–37.
15.
SrivastavaRSrivastavaSYadavRB. Identification and removal of different categories of noises from magnetic resonance image using hybrid partial differential equation-based filter. Int J Digit Signal Smart Syst2017; 1: 87.
16.
YadavRBSrivastavaSSrivastavaR. A partial differential equation-based general framework adapted to Rayleigh’s, Rician’s and Gaussian’s distributed noise for restoration and enhancement of magnetic resonance image. J Med Phys2016; 41: 254–265.
17.
ParmarPSharmaASahuAK. Improved denoising technique for medical MRI brain images. In: Proceedings of the 2018 international conference on computer communication and informatics (ICCCI), Coimbatore, India, 4–6 January 2018, pp. 4–7. New York: IEEE.
18.
SamBBFredAL. Denoising medical images using hybrid filter with firefly algorithm. In: Proceedings of the 2019 international conference on recent advances in energy-efficient computing and communication (ICRAECC), Nagercoil, India, 7–8 March 2019, pp. 1–5. New York: IEEE.
19.
AhmadRBoumanCABuzzardGT, et al. Plug-and-play methods for magnetic resonance imaging: using denoisers for image recovery. IEEE Signal Proc Mag2020; 37: 105–116.
20.
KumarRRKumarASrivastavaS. Anisotropic diffusion based unsharp masking and crispening for denoising and enhancement of MRI images. In: Proceedings of the 2020 international conference on emerging frontiers in electrical and electronic technologies (ICEFEET), Patna, India, 10–11 July 2020, 5 pp. New York: IEEE.
Aja-FernándezSVegas-Sánchez-FerreroG. Statistical analysis of noise in MRI. Cham: Springer International Publishing, 2016.
23.
SijbersJDen DekkerAJScheundersP, et al. Maximum-likelihood estimation of Rician distribution parameters. IEEE T Med Imaging1998; 17: 357–361.
24.
KumarRSrivastavaSSrivastavaR. A fourth order PDE based fuzzy c- means approach for segmentation of microscopic biopsy images in presence of Poisson noise for cancer detection. Comput Meth Prog Bio2017; 146: 59–68.
25.
AbramowitzMStegunIA. Handbook of mathematical functions with formulas, graphs, and mathematical tables, vol. 55. Washington, DC: United States Government Printing Office, 1964.
26.
GilboaGSochenNZeeviYY. Image enhancement and denoising by complex diffusion processes. IEEE T Pattern Anal2004; 26: 1020–1036.
KhieovongphachanhVKanthavongSHamamotoK, et al. Image quality criterion of ultrasonic echo image using Standard Deviation. In: Proceedings of the 4th joint international conference on information and communication technology, electronic and electrical engineering (JICTEE), Chiang Rai, 5–8 March 2014, pp. 1–3. New York: IEEE.
29.
JayaVLGopikakumariR. IEM: a new image enhancement metric for contrast and sharpness measurements. Int J Comput Appl2013; 79: 1–9.
30.
SijbersJScheundersPBonnetN, et al. Quantification and improvement of the signal-to-noise ratio in a magnetic resonance image acquisition procedure. Magn Reson Imaging1996; 14: 1157–1163.
31.
ErdogmusDLarssonEGYanR, et al. Measuring the signal-to-noise ratio in magnetic resonance imaging: a caveat. Signal Process2004; 84: 1035–1040.
32.
GirodB. What’s wrong with mean-squared error? In: WatsonAB (ed.) Digital images and human vision. Cambridge, MA: MIT Press, 1993, pp. 207–220.
33.
Damera-VenkataNKiteTDGeislerWS, et al. Image quality assessment based on a degradation model. IEEE T Image Process2000; 9: 636–650.
34.
WangJ. Pearson correlation coefficient. In: DubitzkyWWolkenhauerOChoKH, et al. (eds) Encyclopedia of systems biology, vol. 2. New York: Springer, 2013, p. 1671.
35.
ChenSDRamliAR. Minimum mean brightness error bi-histogram equalization in contrast enhancement. IEEE T Consum Electr2003; 49: 1310–1319.
36.
BecharaBMcMahanCAMooreWS, et al. Contrast-to-noise ratio difference in small field of view cone beam computed tomography machines. J Oral Sci2012; 54: 227–232.
37.
ThungKHRaveendranP. A survey of image quality measures. In: Proceedings of the 2009 international conference for technical postgraduates (TECHPOS), Kuala Lumpur, Malaysia, 14–15 December 2009, pp. 17–20. New York: IEEE.
38.
WangZBovikAC. A universal image quality index. IEEE Signal Proc Let2002; 9: 81–84.
39.
WangZBovikACSheikhHR, et al. Image quality assessment: from error visibility to structural similarity. IEEE T Image Process2004; 13: 600–612.
40.
AgaianSSLentzKPGrigoryanAM. A new measure of image enhancement. In: Proceedings of the IASTED international conference on signal processing and communications, Málaga, 19–22 September 2000, pp. 19–22. ACTA Press.
41.
VenkatanathNPraneethDChandrasekhar BhM, et al. Blind image quality evaluation using perception based features. In: Proceedings of the 2015 21st national conference on communications (NCC), Mumbai, India, 27 February–1 March 2015, pp. 1–6. New York: IEEE.
42.
MittalAMoorthyAKBovikAC. No-reference image quality assessment in the spatial domain. IEEE T Image Process2012; 21: 4695–4708.
43.
KumarASrivastavaS. Restoration and enhancement of breast ultrasound images using extended complex diffusion based unsharp masking. Proc IMechE, Part H: J Engineering in Medicine. Epub ahead of print 18August2021. DOI: 10.1177544119211039317.
44.
ChenK.LinX.HuX., et al. An enhanced adaptive non-local means algorithm for Rician noise reduction in magnetic resonance brain images. BMC Medical Imaging2020; 20: 2–9.
