Ultrasound images, having low contrast and noise, adversely impact in the detection of abnormalities. In view of this, an enhancement method is proposed in this work to reduce noise and improve contrast of ultrasound images. The proposed method is based on scaling with neutrosophic similarity score (NSS), where an image is represented in the neutrosophic domain through three membership subsets T, I, and F denoting the degree of truth, indeterminacy, and falseness, respectively. The NSS measures the belonging degree of pixel to the texture using multi-criteria that is based on intensity, local mean intensity and edge detection. Then, NSS is utilized to extract the enhanced coefficient and this enhanced coefficient is applied to scale the input image. This scaling reflects contrast improvement and denoising effect on ultrasound images. The performance of proposed enhancement method is evaluated on clinical ultrasound images, using both subjective and objective image quality measures. In subjective evaluation, with proposed method, overall best score of 4.3 was obtained and that was 44% higher than the score of original images. These results were also supported by objective measures. The results demonstrated that the proposed method outperformed the other methods in terms of mean brightness preservation, edge preservation, structural similarity, and human perception-based image quality assessment. Thus, the proposed method can be used in computer-aided diagnosis systems and to visually assist radiologists in their interactive-decision-making task.
ThijssenJM. Ultrasonic speckle formation, analysis and processing applied to tissue characterization. Pattern Recognit Lett. 2003;24(4-5):659-75.
2.
ShrimaliVAnandRKumarV. Comparing the performance of ultrasonic liver image enhancement techniques: a preference study. IETE J Res. 2010;56(1):4-10.
3.
YuYActonST. Speckle reducing anisotropic diffusion. IEEE Trans Image Process. 2002;11(11):1260-70.
4.
GuptaSKaurLChauhanRCSaxenaSC. A versatile technique for visual enhancement of medical ultrasound images. Digit Signal Process. 2007;17(3):542-60.
5.
PoddarSTewarySSharmaDKararVGhoshAPalSK. Non-parametric modified histogram equalisation for contrast enhancement. IET Image Process. 2013;7(7):641-52.
6.
BhartiPMittalDAnanthasivanR. Computer-aided characterization and diagnosis of diffuse liver diseases based on ultrasound imaging: a review. Ultrason Imaging. 2016;39(1):33-61.
7.
PeronaPMalikJ. Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell. 1990;12(7):629-39.
8.
YuJTanJWangY. Ultrasound speckle reduction by a SUSAN-controlled anisotropic diffusion method. Pattern Recognit. 2010;43(9):3083-92.
9.
DamodaranNRamamurthySVelusamySKanakaraj ManickamG. Speckle noise reduction in ultrasound biomedical B-scan images using discrete topological derivative. Ultrasound Med Biol. 2012;38(2):276-86.
MittalDKumarVSaxenaSCKhandelwalNKalraN. Enhancement of the ultrasound images by modified anisotropic diffusion method. Med Biol Eng Comput. 2010;48(12):1281-91.
12.
GuoYWangYHouT. Speckle filtering of ultrasonic images using a modified non local-based algorithm. Biomed Signal Process Control. 2011;6(2):129-38.
13.
CoupePHellierPKervrannCBarillotC. Nonlocal means-based speckle filtering for ultrasound images. IEEE Trans Image Process. 2009;18(10):2221-9.
14.
DeledalleCADenisLTupinF. Iterative weighted maximum likelihood denoising with probabilistic patch-based weights. IEEE Trans Image Process. 2009;18(12):2661-72.
15.
GleichDKsenemanMDatcuM. Despeckling of TerraSAR-X data using second-generation wavelets. IEEE Geosci Remote Sens Lett. 2010;7(1):68-72.
16.
HouBZhangXBuXFengH. SAR image despeckling based on nonsubsampled shearlet transform. IEEE J Sel Top Appl Earth Obs Remote Sens. 2012;5(3):809-23.
17.
BhutadaGGAnandRSSaxenaSC. Edge preserved image enhancement using adaptive fusion of images denoised by wavelet and curvelet transform. Digit Signal Process A Rev J. 2011;21(1):118-30.
18.
BediAKSunkariaRKMittalD. Enhancement of ultrasound images using modified anisotropic diffusion model in non-subsampled shearlet domain. In: Proceeding - IEEE International Conference on Computing, Communication and Automation, ICCCA 2017, pp. 1119-24 (Institute of Electrical and Electronics Engineers Inc., 2017).
19.
ChandraSKBajpaiMK. Fractional mesh-free linear diffusion method for image enhancement and segmentation for automatic tumor classification. Biomed Signal Process Control. 2020;58:101841.
20.
RudinLLionsP-LOsherS. Multiplicative denoising and deblurring: theory and algorithms. In: Geometric Level Set Methods in Imaging, Vision, and Graphics. New York: Springer-Verlag; 2003, pp. 103-19.
21.
AubertGAujolJ-F. A variational approach to removing multiplicative noise. SIAM J Appl Math. 2008;68(4):925-46.
22.
KoundalDGuptaSSinghS. Nakagami-based total variation method for speckle reduction in thyroid ultrasound images. Proc Inst Mech Eng H. 2016;230(2):97-110.
23.
SinghPMukundanRDe RykeR. Feature enhancement in medical ultrasound videos using contrast-limited adaptive histogram equalization. J Digit Imaging. 2020;33(1):273-85.
24.
MustafaZAAbrahimBAOmaraAMohammedAAHassanIAMustafaEA. Reduction of speckle noise and image enhancement in ultrasound image using filtering technique and edge detection. J Clin Eng. 2020;45(1):51-65.
25.
KeerthivasanABabuJJSudhaGF. Speckle noise reduction in ultrasound images using fuzzy logic based on histogram and directional differences. In: 2013 International Conference on Communication and Signal Processing, pp. 499-503 (IEEE, 2013).
26.
BayaniALangarizadehMRadmardARNejadAF. Quality improvement of liver ultrasound images using fuzzy techniques. Acta Inform Med. 2016;24(6):380-4.
27.
GuoYChengHDHuangJTianJZhaoWSunL, et al. Breast ultrasound image enhancement using fuzzy logic. Ultrasound Med Biol. 2006;32(2):237-47.
28.
ZhuangZLeiNJoseph RajANQiuS. Application of fractal theory and fuzzy enhancement in ultrasound image segmentation. Med Biol Eng Comput. 2019;57(3):623-632.
29.
KoundalDGuptaSSinghS. Neutrosophic based nakagami total variation method for speckle suppression in thyroid ultrasound images. IRBM. 2017;39(1):43-3.
30.
SinghSKoundalDGuptaS. Speckle reduction method for thyroid ultrasound images in neutrosophic domain. IET Image Process. 2016;10(2):167-75.
31.
GuoYChengHDZhangY. A new neutrosophic approach to image denoising. New Math Nat Comput. 2009;5(3):653-62.
32.
FarajiMRQiX. An effective neutrosophic set-based preprocessing method for face recognition. In: 2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), pp. 1–4 (IEEE, 2013).
33.
QiXLiuBXuJ. A neutrosophic filter for high-density Salt and Pepper noise based on pixel-wise adaptive smoothing parameter. J Vis Commun Image Represent. 2016;36:1-10.
34.
GuoYŞengürA. A novel image edge detection algorithm based on neutrosophic set. Comput Electr Eng. 2014;40(8):3-25.
35.
ShahinAIAminKMSharawiAAGuoY. A novel enhancement technique for pathological microscopic image using neutrosophic similarity score scaling. Optik (Stuttg). 2018;161:84-97.
36.
GuoYŞengürAYeJ. A novel image thresholding algorithm based on neutrosophic similarity score. Measurement. 2014;58:175-86.
37.
SmarandacheF. Neutrosophic logic - a generalization of the intuitionistic fuzzy logic. SSRN Electron J. 2016. doi:10.2139/ssrn.2721587.
38.
YeJ. Multicriteria decision-making method using the correlation coefficient under single-valued neutrosophic environment. Int J Gen Syst. 2013;42(4):386-94.
39.
SinghPShreeR. A new homomorphic and method noise thresholding based despeckling of SAR image using anisotropic diffusion. J King Saud Univ - Comput Inf Sci. 2020;32(1):137-48. doi:10.1016/j.jksuci.2017.06.006.
