Assessment of noise reduction in ultrasound images of common carotid and brachial arteries

Mehravar Rafati; Masoud Arabfard; Mehrdad Rafati Rahim Zadeh; Mazaher Maghsoudloo

Assessment of noise reduction in ultrasound images of common carotid and brachial arteries

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Author(s): Mehravar Rafati ¹ ; Masoud Arabfard ² ; Mehrdad Rafati Rahim Zadeh ³ ; Mazaher Maghsoudloo ²
- Affiliations: 1: Department of Paramedical, Kashan University of Medical Sciences, Kashan, Islamic Republic of Iran ;
  2: Department of Bioinformatics, Tehran University, Kish International Campus, Kish, Islamic Republic of Iran ;
  3: Department of Nursing, Babol University of Medical Sciences, Babol, Islamic Republic of Iran
Source: Volume 10, Issue 1, February 2016, p. 1 – 8
DOI: 10.1049/iet-cvi.2014.0151 , Print ISSN 1751-9632, Online ISSN 1751-9640

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Received 12/03/2014, Accepted 19/06/2015, Revised 23/05/2015, Published 01/02/2016

The present study assessed the use of filters for noise reduction in ultrasound images of the common carotid artery (CCA) and brachial artery using intima–media thickness, which is a safe and non-invasive technique for determining subclinical atherosclerosis and cardiovascular risk. A new combined speckle reducing anisotropic diffusion (SRAD) filter for noise reduction is then proposed. Ultrasonic examination of both arteries was performed on 30 men (aged 40 ± 5 years). The programme was designed using MATLAB software to extract consecutive images in bit map format from the audio video interleaves. An additional programme was designed in MATLAB to apply the region of interest (ROI) to the thickness of the intima–media of the posterior walls of the arteries. Block-matching techniques were used to estimate arterial motion from ultrasound images of the B-mode CCA and brachial artery. Different noise reduction filters and Canny edge detection were carried out separately in the ROI. The programme measured mean square error (MSE) and peak signal-to-noise ratio (PSNR). The results demonstrated that the new combined SRAD filter with Canny edge detection identified the lowest value for MSE and the highest value for PSNR in 90 consecutive frames (∼3 cardiac cycles). The results indicate that MSE and PSNR were better detected by the proposed combined SRAD filter with Canny edge detection than did several commonly used filters with Canny detection for speckle suppression and preservation detail in carotid and brachial arteries ultrasound images.

References

1. 1)
  - 28. Devi, S.S., Devi, D.S.: ‘Find filtering techniques for eliminating speckle noise in ultrasound medical images via image cleaning algorithm’, Int. J. Comput. Sci. Manage. Res., 2012, 1, pp. 271–275.
2. 2)
  - 37. Shruthi, G., Usha, B.S., Syndya, S.: ‘A novel approach for speckle reduction and enhancement of ultrasound images’, Int. J. Comput. Appl., 2012, 45, pp. 14–20.
3. 3)
  - 46. Pratt, W.K.: ‘Digital Image Processing: PIKS Scientific Inside/William K.Pratt.-NY’ (2007).
4. 4)
  - 31. Lorenz, M.W., Polak, J.F., Kavousi, M., et al: ‘Carotid intima–media thickness progression to predict cardiovascular events in the general population (the PROG-IMT collaborative project): a meta-analysis of individual participant data’, Lancet, 2012, 379, (9831), pp. 2053–2062 (doi: 10.1016/S0140-6736(12)60441-3).
5. 5)
  - 49. Benes, R., Riha, K., Fu, D.: ‘Automatic measurement of intima media thickness with preselection of the most suitable places’. 2013 36th Int. Conf. on Telecommunications and Signal Processing (TSP), 2013.
6. 6)
  - 16. Garg, A., Goal, J., Malik, S., et al: ‘De-speckling of medical ultrasound images using Wiener filter and wavelet transform’, Int. J. Electron. Commun. Technol., 2011, 2, pp. 21–24.
7. 7)
  - 23. Krissian, K., Westin, C.F., Kikinis, R.: ‘Oriented speckle reducing anisotropic diffusion’, IEEE Trans. Image Process., 2007, 16, pp. 1412–1424 (doi: 10.1109/TIP.2007.891803).
8. 8)
  - 7. Narayanan, S.K., Wahidabanu, R.S.D.: ‘A view on despeckling in ultrasound imaging’, Int. J. Signal Process., Image Process. Pattern Recognit., 2009, 2, pp. 85–98.
9. 9)
  - 33. Nisha, , Kumar, S.: ‘Image quality assessment techniques’, Int. J. Adv. Res. Comput. Sci. Softw. Eng., 2013, 3, (7), pp. 636–640.
10. 10)
  - 3. Hafizah, W.M., Supriyanto, E.: ‘Comparative evaluation of ultrasound kidney image enhancement techniques’, Int. J. Comput. Technol. Appl., 2011, 21, pp. 15–19.
11. 11)
  - 38. Singh, R., Kuar, A.: ‘Comparative analysis of speckle noise reduction techniques and their affect on image edge localization’, Int. J. Comput. Sci. Technol., 2011, 2, pp. 78–82.
12. 12)
  - 44. Raj, V.N.P., Venkateswarlu, T.: ‘Denoising of medical images using total variation method’, Signal Image Process., Int. J. (SIPIJ), 2012, 3, pp. 131–142 (doi: 10.5121/sipij.2012.3209).
13. 13)
  - 13. Om, H., Biswas, M.: ‘A new image denoising scheme using soft-thresholding’, J. Signal Inf. Process., 2012, 3, pp. 360–363.
14. 14)
  - 20. Zhu, R., Wang, Y.: ‘Application of improved median filter on image processing’, J. Comput., 2012, 7, pp. 838–841.
15. 15)
  - 10. Gonzalez Rafael, C., Woods, R.E.: ‘Digital image processing’ (Prentice Hall, Upper Saddle River, NJ, 2009).
16. 16)
  - 27. Mahmood, N.H., Rusydi, M., Razif, M., et al: ‘Comparison between median, unsharp and wiener filter and its effect on ultrasound stomach tissue image segmentation for pyloric stenosis’, Int. J. Appl. Sci. Technol., 2011, 1, pp. 218–226.
17. 17)
  - 43. Geethalakshmi, S.N., Suguna, J.: ‘Hybrid models for denoising ultrasonic images’, J. Glob. Res. Comput. Sci., 2010, 1, pp. 38–45.
18. 18)
  - 17. Vishwa, A., Sharma, S.: ‘Speckle noise reduction in ultrasound images by wavelet thresholding’, Int. J. Adv. Res. Comput. Sci. Softw. Eng., 2012, 2, pp. 25–30.
19. 19)
  - 5. Kaur, J., Kaur, J., Kaur, M.: ‘Survey of despeckling techniques for medical ultrasound images’, Int. J. Comput. Technol. Appl., 2011, 2, pp. 1003–1007.
20. 20)
  - 42. Karthikeyan, K., Chandrasekar, C.: ‘Speckle noise reduction of medical ultrasound images using Bayes shrink wavelet threshold’, Int. J. Comput. Appl., 2011, 22, pp. 8–14.
21. 21)
  - 36. Abdullah, H.N., Aziz, J.S., Mohammed, A.N.: ‘Complex discrete wavelet transform-based image denoising’, Eng. Technol. J., 2011, 29, pp. 833–850.
22. 22)
  - 29. Acharjya, P.P., Das, R., Ghoshal, D.: ‘Study and comparison of different edge detectors for image segmentation’, Glob. J. Comput. Sci. Technol. Graph. Vis., 2012, 12, pp. 29–32.
23. 23)
  - 12. Sudha, S., Suresh, G.R., Sukanesh, R.: ‘Speckle noise reduction in ultrasound images using context-based adaptive wavelet thresholding’, IETE J. Res., 2009, 55, pp. 135–143 (doi: 10.4103/0377-2063.54905).
24. 24)
  - 21. Rajan, J., Kaimal, M.R.: ‘Image denoising using wavelet embedded anisotropic diffusion (WEAD)’. Proc. IET Int. Conf. on Visual Information Engineering (VIE), 2006, pp. 589–593.
25. 25)
  - 41. Chithra, M.S.: ‘Bayesian denoising of ultrasound images using heavy-tailed Levy distribution’, IET Image Process., 2014, 9, (4), pp. 338–345.
26. 26)
  - 39. Damodaran, N., Ramamurthy, S., Velusamy, S.: ‘Speckle noise reduction in ultrasound biomedical B-scan images using discrete topological derivative’, Ultrasound Med. Biol., 2012, 38, pp. 276–286 (doi: 10.1016/j.ultrasmedbio.2011.10.021).
27. 27)
  - 8. Fuguo, D., Hui, F., Da, Y.: ‘A novel image median filtering algorithm based on incomplete quick sort algorithm’, Int. J. Digit. Technol. Appl., 2010, 4, (6), pp. 79–84.
28. 28)
  - 6. Karthikeyan, K., Chandrasekar, C.: ‘Speckle noise reduction of medical ultrasound images using Bayes shrink wavelet threshold’, Int. J. Comput. Technol. Appl., 2011, 22, pp. 8–14.
29. 29)
  - 1. Rafati, M., Arabfard, M., Rafati Rahimzadeh, M.: ‘Comparison of different edge detections and noise reduction on ultrasound images of carotid and brachial arteries using a speckle reducing anisotropic diffusion filter’, Iran. Red Crescent Med. J., 2014, 16, (9), pp. 1–10 (doi: 10.5812/ircmj.14658).
30. 30)
  - 40. Hajiaboli, M.N.: ‘An anisotropic fourth-order diffusion filter for image noise removal’, Int. J. Comput. Vis., 2011, 92, pp. 177–191 (doi: 10.1007/s11263-010-0330-1).
31. 31)
  - 30. Preetha, J., Selvarajan, S., Suresh, P.: ‘Comparative analysis of various image edge detection techniques for two dimensional CT scan neck disc image’, Int. J. Comput. Sci. Commun., 2012, 3, pp. 57–61.
32. 32)
  - 47. Maini, J.R., Aggarwal, H.: ‘Study and comparison of various image edge detection techniques’, Int. J. Image Process., 2009, 3, pp. 1–12 (doi: 10.1049/iet-ipr:20080080).
33. 33)
  - 26. Vishwa, A., Sharma, S.: ‘Modified method for denoising the ultrasound images by wavelet thresholding’, Int. J. Intell. Syst. Appl., 2012, 6, pp. 25–30.
34. 34)
  - 11. Gupta, V., Chaurasia, V., Shandilya, M.: ‘A review on image denoising techniques’, Int. J. Emerging Technol. Comput. Appl. Sci. (IJETCAS), 2013, 5, pp. 204–208.
35. 35)
  - 45. Loizou, C.P., Pattichis, C.S., Christodoulou, C.I., et al: ‘Comparative evaluation of despeckle filtering in ultrasound imaging of the carotid artery’, IEEE Trans. Ultrasonics Ferroelectr. Freq. Control, 2005, 52, pp. 1653–1669 (doi: 10.1109/TUFFC.2005.1561621).
36. 36)
  - 14. Sudha, S., Suresh, G.R., Sukanesh, R.: ‘Speckle noise reduction in ultrasound images by wavelet thresholding based on weighted variance’, Int. J. Comput. Theory Eng., 2009, 1, pp. 7–12 (doi: 10.7763/IJCTE.2009.V1.2).
37. 37)
  - 19. Geethalkshmi, S.N., Suguna, J.: ‘Hybrid models for denoising ultrasonic images’, J. Glob. Res. Comput. Sci., 2010, 1, pp. 38–45.
38. 38)
  - 4. Benes, R., Riha, K.: ‘Medical image denoising by improved Kuan filter’, Adv. Electr. Electron. Eng., 2012, 10, pp. 43–49.
39. 39)
  - 48. Leftheriotis, G., Abraham, P., Le Corre, Y., et al: ‘Relation between ankle brachial index and arterial remodeling in pseudoxanthoma elastic’, J. Vasc. Surg., 2011, 54, pp. 1390–1394 (doi: 10.1016/j.jvs.2011.04.041).
40. 40)
  - 18. Yu, J., Wang, Y., Shen, Y.: ‘Noise reduction and edge detection via kernel anisotropic diffusion’, Pattern Recognit. Lett., 2008, 29, pp. 1496–1503 (doi: 10.1016/j.patrec.2008.03.002).
41. 41)
  - 25. Rajan, J., Kannan, K., Kaimal, M.R.: ‘An improved hybrid model for molecular image denoising’, J. Math. Imaging Vis., 2008, 31, pp. 73–79 (doi: 10.1007/s10851-008-0067-4).
42. 42)
  - 9. Kaur, J., Kaur, J., Kaur, M.: ‘Survey of despeckling techniques for medical ultrasound images’, Int. J. Comput. Technol. Appl., 2011, 2, pp. 1003–1007.
43. 43)
  - 2. Thijssen, D.H.J., Cable, N.T., Green, D.J.: ‘Impact of exercise training on arterial wall thickness in humans’, Clin. Sci., 2012, 122, pp. 311–322 (doi: 10.1042/CS20110469).
44. 44)
  - 50. Polak, J.F., Johnson, C., Harrigton, A., et al: ‘Changes in carotid intima-media thickness during the cardiac cycle: the multi-ethnic study of atherosclerosis’, J. Am. Heart Assoc., 2012, 1, pp. 1–8 (doi: 10.1161/JAHA.112.001420).
45. 45)
  - 22. Aja-Fernadez, S., Alberola-Lopez, C.: ‘On the estimation of the coefficient of variation for anisotropic diffusion speckle filtering’, IEEE Trans. Image Process., 2006, 15, pp. 2694–2701 (doi: 10.1109/TIP.2006.877360).
46. 46)
  - 32. Gastounioti, A., Golemati, S., Stoitsis, J.S., Nikita, K.S.: ‘Carotid artery wall motion analysis from B-mode ultrasound using adaptive block matching: in silico evaluation and in vivo application’, Phys. Med. Biol., 2013, 58, (24), p. 8647 (doi: 10.1088/0031-9155/58/24/8647).
47. 47)
  - 15. Nicolae, M.C., Moraru, L., Onose, L.: ‘Comparative approach for speckle reduction in medical ultrasound images’, Rom. J. Biophys., 2009, 1, pp. 13–21.
48. 48)
  - 35. Singh, R., Kaur, A.: ‘Comparative analysis of speckle noise reduction techniques and their affect on image edge localization’, Int. J. Comput. Sci. Technol., 2011, 2, pp. 78–82.
49. 49)
  - 24. Shruthi, G., Usha, B.S., Sandya, S.: ‘A novel approach for speckle reduction and enhancement of ultrasound images’, Int. J. Comput. Appl., 2012, 45, pp. 14–20.
50. 50)
  - 34. ECE, CSE, and Mullana MMU: ‘Image quality assessment techniques pn spatial domain’, Int. J. Comput. Sci. Technol., 2011, 2, (3), pp. 177–184.

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Assessment of noise reduction in ultrasound images of common carotid and brachial arteries

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