Improved Despeckle Filtering Technique for Liver Cirrhosis US Images

  • Authors

    • G Rajesh
    • A Selwin Mich Priyadharson
    2018-04-18
    https://doi.org/10.14419/ijet.v7i2.20.14775
  • Liver US image, MHON, SRAD, UQI, EPI, and LMSE..
  • The liver ultrasonic (US) images suffer from inherent speckle noise, degrading the image quality; thereby, affecting human interpretation and also reducing the accuracy in computer-assisted diagnostic techniques. In this paper, we propose an improved despeckle filtering technique by combining, maximum homogeneity over a pixel neighborhood filtering (MHON) and speckle-reducing anisotropic diffusion filtering (SRAD) using a binary classifier map (BCM). The textural and fine details of residual image are denoised by SRAD filtering.  The SRAD filter denoised image and MHON filter denoised image combines to attain BCM. A BCM is generated by examining the local coefficient of dispersion (CoD) assessed using a kernel with the one obtained from a specific region. The proposed method is evaluated on clinical US image set of liver, by assessing the quality factors PSNR, SNR, ISC, LMSE and EPI. Results are compared with specified algorithms separately while accomplishing for proposed method.

     

  • References

    1. [1] Burckhardt CB, “Speckle in ultrasound B-mode scansâ€, IEEE Transactions on Sonics and Ultrasonics, Vol.25, No.1, (1978), pp. 1-6.

      [2] Olfa M & Nawres K, “Ultrasound image denoising using a combination of bilateral filtering and stationary wavelet transformâ€, International Image Processing, Applications and Systems Conference, (2014), pp.1-5.

      [3] Lee JS, “Digital image enhancement and noise filtering by use of local statisticsâ€, IEEE Trans. Pattern Anal. Mach. Intell., Vol.PAMI-2, No.2, (1980), pp.165-168.

      [4] Frost V, Stiles J, Shanmugan K & Holzman J, “A model for radar images and its application to adaptive digital filtering of multiplicative noiseâ€, IEEE Trans. Pattern Anal. Mach. Intell., (1982), pp.157–166.

      [5] Kuan DT, Sawchuk AA, Strand TC & Chavel P, “Adaptive noise smoothing filter for images with signal-dependent noiseâ€, IEEE Trans. Pattern Anal. Mach. Intell., Vol.PAMI-7, No.2, (1985), pp. 165–177.

      [6] Perona P & Malik J, “Scale-space and edge detection using anisotropic diffusionâ€, IEEE Trans. Pattern Anal. Mach. Intell., Vol.12, No.7, (1990), pp.629–639.

      [7] Yu Y & Acton S, “Speckle reducing anisotropic diffusionâ€, IEEE Trans. Image Process., Vol.11, No.11, (2002), pp.1260–1270.

      [8] Loizou CP & Pattichis CS, Despeckle filtering for ultrasound imaging and video, Selected applications, Synthesis lectures on algorithms and software in engineering, Vol.7, No.2, (2015), pp.1-180.

      [9] Garg A & Khandelwal V, “Speckle noise reduction in medical ultrasound images using coefficient of dispersionâ€, International Conference on Signal Processing and Communication (ICSC), (2016), pp.208-212.

      [10] Rajesh G & SelwinMichPriyadharson A, “Liver cancer detection and classification based on optimum hierarchical feature fusion with PeSOA and PNN classifierâ€, Biomedical Research, Vol.29, No.1, (2018), pp.22-32.

      [11] Rajesh G & SelwinMichPriyadharson A, “Survey on Identification Tools for Hepatocellular Carcinoma- A Reviewâ€, Annual Research & Review in Biology, Vol.23, No.2, (2018), pp.1-25.

      [12] Zeinali MH, Saryazdi S & Rafsanjani HK, “Image Denoising via Combination Anisotropic Diffusion and Bilateral Filteringâ€, International Conference on Computational Intelligence and Communication Networks, (2011), pp.421-425.

      [13] http://www.vision.ee.ethz.ch/en/datasets/.

  • Downloads

  • How to Cite

    Rajesh, G., & Selwin Mich Priyadharson, A. (2018). Improved Despeckle Filtering Technique for Liver Cirrhosis US Images. International Journal of Engineering & Technology, 7(2.20), 267-271. https://doi.org/10.14419/ijet.v7i2.20.14775