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Contrast enhancement and phase-sensitive boundary detection in ultrasonic speckle using Bessel spatial filters

Contrast enhancement and phase-sensitive boundary detection in ultrasonic speckle using Bessel spatial filters

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Speckle in ultrasonic image systems adversely impacts the contrast and resolution in the image. This poses serious problems in the interpretation of B mode images of internal organs such as breast, liver, kidney and so on. In the absence of sufficient contrast, classifying the regions of interest into benign and malignant masses becomes error prone. Since some of the masses are uniquely identified in terms of the boundaries, poor contrast and resolution will result in difficulties with their identification. A new class of spatial filters based on cylindrical Bessel functions of the first kind is proposed for speckle reduction. These filters with complex impulse responses were explored for enhancing the contrast of speckled images. Hypothesising that the phase of the filtered image carries boundary information, the phase characteristics of four speckled images are also studied for detecting boundaries. Results indicate that these filters do improve the contrast and enhance the boundaries. It is shown that the phase map clearly indicates the existence of boundaries. A simple thresholding applied to the phase highlights the boundaries. The results show the strength of the Bessel spatial filters in improving contrast and highlighting boundaries without resorting to any additional edge-detection algorithms.

References

    1. 1)
      • R.G. Dantas , E.T. Costa . Ultrasound speckle reduction using modified Gabor filters. IEEE Trans. Ultrason. Ferroelectr. Freq. Control , 530 - 538
    2. 2)
      • J.G. Daugman . Uncertainty relation for resolution in space, spatial frequency, andorientation optimized by two-dimensionalvisual cortical filters. J. Opt. Soc. Am. A , 1160 - 1169
    3. 3)
      • C.B. Burckhardt . Speckle in ultrasound B-mode scans. IEEE Trans. Sonics Ultrason. , 1 - 6
    4. 4)
      • O. Michailovich , A. Tannenbaum . Despeckling of medical ultrasound images. IEEE Trans. Ultrason. Ferroelectr. Freq. Control , 64 - 78
    5. 5)
      • T. Loupas , W.N. McDicken , P.L. Allan . Adaptive weighted median filter for speckle suppression in medical ultrasonic images. IEEE Trans. Circuits Syst. , 129 - 135
    6. 6)
      • P.M. Shankar . A general statistical model for ultrasonic backscattering from tissues. IEEE Trans. Ultrason. Ferroelectr. Freq. Control , 727 - 736
    7. 7)
      • E.B. MacDougall . Spatial filtering. Econ. Geography , 425 - 434
    8. 8)
      • Moreno, P., Bernardino, A., Santos-Victor, J.: `Gabor parameter selection for local feature detection', 2ndIberian Conf. Patt. Rec. and Image Analysis, 7–9 June 2005, Estoril, Portugal.
    9. 9)
      • P.M. Shankar . Speckle reduction in ultrasonic images through a maximum likelihood based adaptive filter. Phys. Med. Biol. , 5591 - 5602
    10. 10)
      • C.M. Chen , H.H.S. Lu , K.C. Han . A textural approach based on Gabor functions for texture edge detection in ultrasound images. Ultra. Med. Biol. , 515 - 534
    11. 11)
      • D.C. Howlett , N.D.P. Marchbank , S.M. Allan . Sonographic assessment of the symptomatic breast – a pictorial review. J. Diagn. Radiogr. Imag. , 3 - 12
    12. 12)
      • Rangayyan, R.M., Oloumi, F., Oloumi, F., Eshghzadeh-Zanjani, P., Ayres, F.J.: `Detection of blood vessels in the retina using gabor filters', Canadian Conf. Electrical and Computer Engineering 2007, 22–26 April 2007, p. 717–720.
    13. 13)
      • D. Sauter , L. Parson . Spatial filtering for speckle reduction, contrast enhancement, and texture analysis of GLORIA images. IEEE J. Ocean. Eng. , 563 - 576
    14. 14)
      • T.A. Stavros , D. Thickman , C.L. Rapp , M.A. Dennis , S.H. Parker , G.A. Sisney . Solid breast nodules: use of sonography to distinguish between benign and malignant lesions. Radiology , 123 - 34
    15. 15)
      • V.F. Canales , M.P. Cagigal . Pupil filter design by using a Bessel functions basis at the image plane. Opt. Exp. , 10393 - 10402
    16. 16)
      • A.C. Bovik , M. Clark , W.S. Geisler . Multichannel texture analysis using localized spatial filters. IEEE Trans. Pattern Anal. Mach. Intell. , 55 - 73
    17. 17)
      • C-S. Guo , Y-J. Han , J-B. Xu . Radial Hilbert transform with Laguerre–Gaussian spatial filters. Opt. Lett. , 1394 - 1396
    18. 18)
      • J.A. Buck . (1995) Fundamentals of optical fibers.
    19. 19)
      • A. Achim , A. Bezerianos , P. Tsakalides . Novel Bayesian multiscale method for speckle removal in medical ultrasound images. IEEE Trans. Med. Imaging , 772 - 783
    20. 20)
      • P.M. Shankar . The use of the compound pdf in ultrasonic tissue characterization. Phys. Med. Bio. , 1007 - 1015
    21. 21)
      • N. Abramowitz , I. Stegun . (1965) Handbook of mathematical functions.
    22. 22)
      • C.M. Sehgal , T.W. Cary , S.A. Kangas . Computer-based margin analysis of breast sonography for differentiating malignant and benign masses. J. Ultrasound Med. , 1201 - 1209
    23. 23)
      • P.F. Stetson , F.G. Sommer , A. Macovski . Lesion contrast enhancement in medical ultrasound imaging. IEEE Trans. Med. Imaging , 416 - 425
    24. 24)
      • A. Van der Lugt . (1991) Optical signal processing.
    25. 25)
      • J-K. Kamarainen , V. Kyrki , H. Kälviäinen . Invariance properties of Gabor filter-based features – overview and applications. IEEE Trans. Image Process. , 1088 - 1099
    26. 26)
      • R.F. Wagner , S.W. Smith , J.M. Sandrik , H. Lopez . Statistics of speckle in ultrasound B-scans. IEEE Tran. Sonics Ultrason. , 156 - 163
    27. 27)
      • A.N. Evans , M.S. Nixon . Mode filtering to reduce ultrasound speckle for feature extraction. IEE Proc., Vis Image Signal Process. , 87 - 94
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