SPECKLE NOISE REDUCTION FROM MEDICAL ULTRASOUND IMAGES USING WAVELET THRESH

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SPECKLE NOISE REDUCTION FROM MEDICAL ULTRASOUND IMAGES USING WAVELET THRESH Powered By Docstoc
					International Journal of Electronics and Communication Engineering & Technology (IJECET),
         INTERNATIONAL JOURNAL OF ELECTRONICS AND
ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 4, July-August (2013), © IAEME
 COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET)

ISSN 0976 – 6464(Print)
ISSN 0976 – 6472(Online)
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Volume 4, Issue 4, July-August, 2013, pp. 283-290
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 SPECKLE NOISE REDUCTION FROM MEDICAL ULTRASOUND IMAGES
   USING WAVELET THRESHOLDING AND ANISOTROPIC DIFFUSION
                         METHOD

       Ratil Hasnat Ashique1, Md Imrul Kayes2, M T Hasan Amin3, Badrun Naher Lia4
       1
         Primeasia University, Department of EEE, 12, Kamal Atartuk Avenue, Banani, Dhaka
             2
               International Islamic University Chittagong, Department of CSE, Chittagong
                3
                  University of Surrey, Department of Electronics Engineering, Surrey, UK
        4
          Primeasia University, Department of EEE, 12, Kamal Atartuk Avenue, Banani, Dhaka


ABSTRACT

        Medical Images are very often corrupted by various types of noise including speckle noise,
salt and pepper noise etc. This corruption of noise is introduced to the original image during image
acquisition and transmission. The various image denoising techniques that are proposed from time to
time are offering denoising techniques preserving the original image features. The denoising is so
important because ultrasound imaging today has gained wide acceptance due to its safety, easy
imaging procedure, low cost and adaptability. However the main shortcomings of this process is poor
quality of images which is further degraded due to the presence of speckle noise and other types of
noise. Hence it has become vital to remove noise while preserving important datails and features of
the image. This paper will introduce a unique method to speckle noise filtering using median filters,
wavelet and SRAD filters.

Keyword: Ultrasound Image, Ultrasonography, Speckle Noise, Wavelet, Hard Threshold, Soft
threshold, SNR, PSNR, MSE, RMSE, Median filter, SRAD filter.

1. INTRODUCTION

        Compared to other medical imaging techniques ultrasound images suffers from lower image
contrast, low Signal to noise ratio, signal dropouts, shadowing of structures, variable intensity
problem etc. Moreover very often they contain high noise contents against poor contrast. This
ultimately results in blurred image, missing edge points, fake edge points etc. Hence due to complex
and changing shapes it becomes difficult to obtain a correct edge map which is vital for diagnosis
purpose. Speckle noise comes up as a result of interference between multiple scattering beams and
main reflected signal. Speckle noise is a granular noise that inherently exists in and degrades the
quality of the images. Generally it is found in ultrasound image and radar image. This noise is, in

                                                 283
International Journal of Electronics and Communication Engineering & Technology (IJECET),
ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 4, July-August (2013), © IAEME

fact, caused by errors in data transmission. The corrupted pixels are either set to the maximum value,
which is something like a snow in image or have single bits flipped over. This kind of noise affects
the ultrasound images. Speckle noise has the characteristic of multiplicative noise. Speckle noise
follows a gamma distribution and is given as




Where, α is variance and g is the gray level.

2. NECESSITY TO REMOVE SPECKLE NOISE

        As most prevalent artifact in ultrasound image which makes object detection and recognition
more difficult, reduction of speckle directly improves the value of the sonogram.
        Because Ultrasound Images have very little contrast, edge detection is essential to object
detection. Ultrasounds depend more heavily on edge detection than other medical imaging
modalities. Speckle noise can distort or hide edges making object detection less reliable. Objects
such as tumors or birth defects can go undetected and thus untreated

3. SPECKLE NOISE STATISTICS

        The Rayleigh density function, and its extension, the Rice density function, provide a good
starting point for the model for the statistics of the envelope signal. The Rayleigh density function
provides a good model for the backscattered echo signals when the scatterer density is very large
(>10 scatterers per resolution cell). This model has been used extensively for such fully formed
speckle situation. Similarly, the Rice model provides a good model for the presence of coherent
backscatter.

4. REMOVING SPECKLE NOISE

       To develop a despeckling algorithm to filter out speckle noise we have know the
mathematical model of the speckle noise .The simplified model can be described as follows:
As we know speckle noise is a multiplicative noise, the logarithm of the noisy image is taken to
convert the noise function to an additive one .Hence for a possible imaging let

F1(m,n)=F2(m,n)*N(m,n) --------------------------(1)

where F1,F2 and N are the noisy image , original image without noise and noise function
respectively.By taking log on both sides eqn (1) becomes

Log{F1(m,n)}=Log{F2(m,n)}+Log{N(m,n)}---(2)

In eqn (2) as we observe the noise becomes an additive noise which is processed by various noise
removing filters. Denoised Image is obtained by taking the exponential of the image matrix.

5. MEDIAN FILTER

        Median filter is a nonlinear spatial filter which is good at removing pulse and spike noise.
The filtering process is described here-

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International Journal of Electronics and Communication Engineering & Technology (IJECET),
                                                                          August
ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 4, July-August (2013), © IAEME

         Here a NxN window is centered around each pixel. Generally N is a small odd number (~5 to
                                                                              pixel
50). Intensity values of each pixel in the window are sorted in an array. The pixel in the center of the
window is replaced in the final image with the median value of the pixels in the window. It is simple
filter to implement and good at removing “salt and pepper” type noise. Median filtering is shown
graphically below




Here following thing are done
a. Window centers on target pixel.
b. Intensity values are ordered for each pixel in the window.
                      e                               w
c. Mean Value is selected for new image.
     ean                            image

6.   ANISOTROPIC DIFFUSION FILTERS

       Anisotropic diffusion is an efficient nonlinear technique for simultaneously performing
contrast enhancement and noise reduction. It smoothes homogeneous image regions and retains
image edges. The main concept of anisotropic diffusion is the introduction of a function that inhabits
 moothing
smoothing at the image edges. This function is called diffusion coefficient. The diffusion coefficient
is chosen to vary spatially in such a way to encourage intra region smoothing in preference to inter
region smoothing. To smooth image on a continuous domdomain:




                                                  285
International Journal of Electronics and Communication Engineering & Technology (IJECET),
ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 4, July-August (2013), © IAEME

Where ∇ is the gradient operator, div is the divergence operator, || is the magnitude, c(x) is the
diffusion coefficient, and I0 is the initial image. For c(x), they have two coefficients options:




Where k is the edge magnitude parameter. c(x) is the conduct coefficient along four directions. In
practical design, the diffusion coefficient c(∇I ) is anisotropic, and thus it’s called anisotropic
diffusion. The option 1 of the diffusion coefficient favors high contrast edges over low contrast ones.
The option 2 of the diffusion coefficient favors wide regions over smaller ones. The edge magnitude
parameter k controls conduction as a function of gradient. If k is low, then small intensity gradients
are able to block conduction and hence diffusion across step edges. A large value of k can overcome
the small intensity gradient barrels and reduces the influence of intensity gradients on conduction.
Usually k ~ [20,100]. This method can be iteratively applied to the output image, and the iteration
equation is:




where I (n) is the output image after n iterations. λ is the diffusion conducting speed, usually we set
λ<=0.25.

7. WAVELET BASED DENOISING

        Wavelet transform(WT) is another transformation method like Fourier transform(FT) except
that time and frequency information is obtained simultaneously in the later. FT has the drawback that
it cannot provide time information of the frequency bands which is specially important in case of non
stationary signals. Though STFT although provides time frequency information of the signal ,it
suffers from resolution problem. WT removes this resolution problem also. As most of the practical
signals are non stationary type , it is crystal clear that WT has higher preference in signal analysis.
The continuous wavelet transform is given by

   Xw(a,b)=(1/√a)∫[h*{(t-b)/a}x(t)]

Where h(t) is the wavelet basis function and x(t) is the original signal.

        Wavelet techniques are widely used for image denoising and image compression. The
wavelet denoising method decomposes the signal image into wavelet basis and shrink the wavelet
coefficients to remove speckle. At every level the coefficients are run through soft thresholding
process. After thresholding inverse wavelet transform is performed to reconstruct the image.
In short,
    • Decomposition: A wavelet chosen with level N. The wavelet decomposition of the signal is
        computed at level N.
    • Threshold detail coefficients: For each level from 1 to N, a threshold selected and soft
        thresholding applied to the detail coefficients.
    • Reconstruction: Wavelet reconstruction using the original approximation coefficients of level
        N and the modified detail coefficients of levels from 1 to N is performed.
                                                   286
International Journal of Electronics and Communication Engineering & Technology (IJECET),
ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 4, July-August (2013), © IAEME

8. PROPOSED METHOD

        In our method we first produce artificial speckle noise which is then combined with synthetic
ultrasound image to produce noisy test image. At first, denoising is performed using median filter to
move long tailed noise such as negative exponential , salt and pepper noise ,spike or pulse noises etc.
This of course causes minimum blurring to the image. The process is specially useful if the image
contains strong unusual spikes.
        Secondly, the noisy image is then processed by Wavelet denoising by passing the noisy
signal through a wavelet filter and soft thresholding the detail coefficients for speckle removal.
        At the third step ,SRAD2 filter is used to enhance the contrast of the image, smoothing
homogeneous regions and to retain image edges.

The whole process can be shown using block diagram as follows:


                                                                     Process flow
                                                                       direction

                             Synthetic Ultrasound image +Speckle noise

                                  Noisy Image

                             Median Filtering

                                  WT Denoising

                               SRAD1 filtering

                                  Original Image without noise


                                      Figure 1: Block Diagram

        Finally MSE, RMSE, SNR, PSNR are calculated the proposed method and compared with
other types of filters. The process is done for three test images. Window size remains fixed 3×3.

9. SIMULATION

       Here we have used MATLAB as a simulation software .The filters codes are written and
image processing toolbox is also used to enhance the simulation.

10. COMPARISON PARAMETERS

      To determine the image enhancement we have measured The Root Mean Square Error
(RMSE), signal-to-Noise Ratio (SNR), and Peak Signal to Noise Ratio (PSNR) of the noisy image
The RMSE, SNR, and PSNR are provided below.




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International Journal of Electronics and Communication Engineering & Technology (IJECET),
ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 4, July-August (2013), © IAEME



                           Statistical                     Formula
                          Measurement
                             MSE                    ∑(f(i,j)-F(i,j)^2)/MN
                             RMSE                 √(∑(f(i,j)-F(i,j))^2)/MN)
                              SNR                  10*log((σ^2)/(σ(e)^2)
                             PSNR                  20*log10(255/RMSE)

                                 Figure 2: Measurement Parameters

Where,
f= original image function
F=enhanced image function
σ=variance of the original image
σe=variance of the enhanced image
i,j=position of pixel value of image matrix

11. COMPARISON TABLE

TEST IMAGE# 1

    Parameter            Lee                  Median              SRAD2        Proposed
       MSE             51.4582                58.9049             94.2244       60.5280
      RMSE              7.1734                7.6750              9.7069        6.9737
       SNR             12.8863                17.2534             15.7889       15.6327
      PSNR             31.0163                30.4293             28.3892       29.0713

TEST IMAGE# 2

      Parameter                Lee               Median              SRAD2      Proposed
         MSE               59.7539               36.5858             85.7866     51.9390
        RMSE                7.7301                6.0486             9.2621      6.2069
         SNR                9.9015               16.1473             13.0639     12.1305
        PSNR               30.3671               32.4977             28.7966     29.9759

TEST IMAGE# 3

      Parameter                Lee               Median              SRAD2      Proposed
        MSE                64.7943               52.6241             96.9385     62.9299
        RMSE                8.0495                7.2542             9.8457      7.8399
         SNR               11.8560               16.7346             14.3236     11.8973
        PSNR               30.0154               30.9190             28.2658     22.5017



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International Journal of Electronics and Communication Engineering & Technology (IJECET),
ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 4, July-August (2013), © IAEME

                                             salt peepered+speckled image       step3:enhanced image with proposed filter




       TEST IMAGE 1                       NOISY IMAGE                          DENOISED WITH
                                                                              PROPOSED FILTER
             speckled image                  salt peepered+speckled image       step3:enhanced image with proposed filter




       TEST IMAGE 2                      NOISY IMAGE                          DENOISED WITH
                                                                              PROPOSED FILTER
             speckled image                    salt peepered+speckled image      step3:enhanced image with proposed filter




       TEST IMAGE 3                        NOISY IMAGE                        DENOISED WITH
                                                                              PROPOSED FILTER

12. CONCLUSION

        The tested result shows that the proposed multilevel filtering technique provides better
resolution, edge preservation with improved SNR compared with other linear and nonlinear filtering
techniques.
        Moreover, signal to noise ratio(SNR),mean square error(MSE) are significantly improved by
using the proposed filtering method.

13. REFERENCES

 [1]   Anil K.Jain, “Fundamentals of Digital Image Processing” first edition, 1989, Prentice – Hall,
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 [4]   “IEEE Computational Science and Engineering, summer” 1995, vol. 2, num.2, Published by
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ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 4, July-August (2013), © IAEME

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14. BIBLIOGRAPHY

 [22] Rafael C Gonzalez , Richard E. Woods (2002),DIGITAL IMAGE PROCESSING, 2nd ed.,
      Prentice Hall ,Upper saddle River, NJ




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