FIELD OF THE INVENTION
The present invention relates to an improved method for speckle reduction/suppression in an
ultra sound imaging system. Particularly, the present invention relates to improved method
for speckle reduction where the speckle reduction and scan conversion are performed
simultaneously or the speckle reduction is done before scan conversion, preferably speckle
reduction and scan conversion being performed simultaneously. More particularly, the
present invention relates to an improved method for speckle reduction using an improved
filter in said ultra sound imaging system. The invention also relates to an improved ultra
sound imaging system having reduced speckles. Further the present invention also includes
improved filter for improved speckle reduction in ultra sound imaging system.
BACKGROUND OF THE INVENTION
Ultrasound imaging has got immense importance as diagnostic tool in medical applications
for its low cost and non-invasive imaging modality. But the resolutions and speckle noises
are the dominant issues, which reduces its utility in some medical diagnostics applications.
Many speckle reduction techniques has already been proposed so far. All these techniques are
applied either on the raw scan data in pre-processing stage (before scan conversion) or on the
scan-converted image as post-processing operation. It is found that the image quality is
relatively better if filtering is applied in the preprocessing stage (before scan conversion)
rather than post processing stage (after scan conversion). But in this case, the amount of data
to be handled is larger. This is true for all the popular types of speckle reduction filters.
Furthermore, after noise reduction from raw data, interpolation is performed as part of scan
conversion.
Ultrasound imaging modality is one of the most widely used imaging modality in diagnostic
medical applications because, it is noninvasive, non-ionizing, real-time, practically harmless
to human body, portable and cost effective. Unfortunately, the image quality of medical
ultrasound imaging system is limited by some physical phenomena underlying in the
acquisition system.
Speckle noise generated in the image is one such limitation. Ultrasound speckle is a quasirandom
phenomenon as discloses in Czerwinski, R.N., Jones, D.L., William D. O'Brien, Jr.,
"Ultrasound Speckle Reduction by Directional Median Filtering", Proceedings, International
Conference on Image Processing, Vol: 1, (1995) which occurs due to backscatters ultrasound
2
pulses from the rough surface of the internal soft tissues. Thus ultrasound speckle is similar in
origin to laser or radar speckle. It degrades the resolutions, contrast and obscures the
underlying anatomy and makes human interpretation and computer-assisted detection
techniques difficult and inconsistent as disclosed in Michailovich Oleg V. Tannenbaum
Allen, "Despeckling of Medical Ultrasound Images", IEEE Trans. on Ultrasonics
Ferroelectrics and Frequency Control, Vol. 53, No.1, pp. January (2006).
Hence, reduction of speckles is one of the most important challenges to the ultrasound system
designers'. Many attempts are made by the engineers and scientists to develop speckle
reduction methods during last three decades, and, many techniques have also been developed
as taught in Vera Behar, Dan Adam, Zvi Friedman, "A new method of spatial compounding
imaging", Ultrasonics 41, pp. 377-384, (2003), Pai-Chi Li and Mei-Ju Chen, " Strain
Compounding: A New Approach for speckle reduction", IEEE Trans on Ultrasonics
Ferroelectrics and Frequency Control, Vol. 49, No.1, January (2002), Jong-Sen Lee,
"Digital Image Enhancement and Noise Filtering by Use of Local Statistics", IEEE Trans. on
Pattern Analysis And Machine Intelligence, Vol. 2, No.2, March (1980), Gupta N, Swamy
M. N. S., Plotkin E., "Despeckling of Medical Ultrasound Images Using Data and Rate
Adaptive Lossy Compression", IEEE Trans. on Medical Imaging, Vol. 24, No.6, pp. 743-
754, June (2005). These methods are basically applied either on the raw scan data in the
preprocessing stage (Le. before scan conversion) or on the scan-converted image in the postprocessing
stage (i.e. after scan conversion).
Basic theory Ultrasound Speckle and speckle statistics: The ultrasound B-scan imaging
process is a result of a set of complicated physical phenomena such as absorption, reflection
and coherent scattering of ultrasound pulse-echo signal from scattering medium. The backscattered
echo is received and used to display as image. The images, formed by such a
process, involve granular structure called speckle. Basically, ultrasound speckle is generated
from phasors' summation of coherent scatterings within the resolution cell as it is scanned
through the phantom. This phenomenon can be treated geometrically as random walk of
component phasors as disclosed in Robert F. Wagner, Stephen W. Smith, John M. Sandrik,
H. Lopez, "Statistics of Speckle in Ultrasound B-Scans", IEEE Trans. on Sonics and
Ultrasonics, Vol. 30, No.3, pp.156-163, May (1983). If the number scatters within resolution
cell is large, and the phase of the scattered waves is uniformly distributed within 0 and 21f
3
independent of amplitude, the envelope of the complex phasor resulting from the summation
of the scattered waves exhibits Rayleigh distribution.
The accumulation of the random scatterings can be represented by phasor summation of the
scatterings as,
(1)
where each scatterer bears aj amount of signal and has a phase shift of fPj' If aj and fPj are
assumed to be independent and identically distributed, the joint pdf of the real and imaginary
component of the complex phasor can be given by central limit theory as,
(2)
where cr2 = E[A/] = E[A/] is the second moments of the real and complex components
ARand AI'
The envelope of the complex phasor can be calculated as,
(3)
Therefore the probability density function of the envelope is given by,
a' a --
p(a) =-2 e 200', a~O
cr
= 0, otherwise
(4)
The function in equation (4) is known as Rayleigh pdf. The speckle pattern formed in the
image under Rayleigh distribution is called "fully developed" pattern as disclosed in Dutt V.
"Statistical Analysis of ultrasound Echo Envelope", Ph.D. Thesis. Many other speckle
statistics such as k-distribution, Rician distribution, Generalized gamma distribution, Weibull
distribution, Nakagami distribution etc. are also considered in different literatures.
Most of the literatures of speckle reduction consider the multiplicative noise model for
speckle noise as disclosed in Jain A. K., "Fundamentals of digital image processing" Book,
Prentice-Hall, Inc and Kuan D.T., Sawchuk Alexander A. et aI., "Adaptive restoration of
images with speckle," IEEE Trans. Acoustics, Speech and Sig. Proc., Vol. 35, pp. 373-383,
4
March (1987). This multiplicative noise models for speckle is only a rough approximation,
and ignore the correlation ofthe speckle that should be considered in speckle reduction.
Some important and popularly used speckle reduction techniques and the state-of-art briefly
are mentioned below:
Speckle reduction techniques can be broadly categorized into three categories:
• Compounding
• Single scale spatial linear and nonlinear filtering
• Multiscale method.
Compounding techniques include
• Spatial compounding
• Frequency compounding
• Strain compounding
Underlying philosophy of compounding is the averaging of multiple images of the same
target taken either from different positions, or with different frequencies or under different
strains.
A number of works has been done on spatial compounding as taught in Fleming J. E. E., Hall
A. J., "Two dimensional compound scanning-effects of maladjustment and calibration", pp.
160-166, Ultrasonics, July (1968),Berson M., Roncio A., Pourcelot L., "Compound Scanning
with an Electrically Steered Beam", Ultrasonic Imaging 3, pp. 303-308, (1981) and Ping He,
Kefu Xuet, Yiwei Wangt, "Effects of Spatial Compounding Upon Image Resolution",
Proceedings, 19th International Conference, IEEEIEMBS Oct. 30-Nov. 2, (1997) Chicago,
IL. USA.
In spatial compounding, multiple ultrasound images of a target are acquired by different
spatial locations. Speckle in the common region of these images are partially correlated or not
correlated. It is known that averaging of multiple images containing partially correlated or
uncorrelated noises can reduce the effect of the noise. Hence, speckle can be reduced by
forming a composite image averaging the acquired multiple images.
In the frequency compounding, the bandwidth of a radio-frequency (RF) signal is divided
into a number of frequency sub-bands. Ultrasound signals from those bands are transmitted to
5
form different images called sub-band images of the same target. A compounding image is,
then produced by averaging the sub-band images. Speckles of the sub-band images are less
correlated if the bandwidths of the sub-bands are narrower, since it is mainly determined by
the difference of center frequencies, which is normalized by a 16 dB pulse envelope length of
the sub-band signals as disclosed in Jin Ho Chang, Hyung Ham Kim, Jungwoo Lee, K. Kirk
Shung, "Frequency compounded imaging with a high-frequency dual element transducer",
Ultrasonics 50, pp. 453-457, (2010).
Strain compounding as disclosed in Pai-Chi Li and Mei-Ju Chen, " Strain Compounding: A
New Approach for speckle reduction", IEEE Trans on Ultrasonics Ferroelectrics and
Frequency Control, Vol. 49, No.1, January (2002) exploits the decorrelation between signals
under different strain states. Different strain states 'can be created using externally applied
forces as the one used in sonoelastography. Such force produces three dimensional tissue
motion. By correcting only in-plane motion, the images acquired under different strain states
have similar characteristics except for speckle appearance caused by the uncorrelated out-ofplane
motion. These images are combined for speckle reduction with less degradation in the
in-plane spatial resolution.
However, all these compounding techniques suffers from different limitations:
• They suffer from loss of temporal and/or spatial resolution.
• Clinical application of strain compounding is limited
• Contrast resolution for small objects may be degraded.
• Complexity of the system increases.
Single scale spatial linear and nonlinear filtering: Speckle reduction spatial filters perform
smoothing according to local variance and local mean as discussed in different literatures i.e.
Jong-Sen Lee, "Digital Image Enhancement and Noise Filtering by Use of Local Statistics",
IEEE Trans. on Pattern Analysis And Machine Intelligence, Vol. 2, No.2, March (1980),
Kuan D.T., Sawchuk Alexander A. et aI., "Adaptive restoration of images with speckle,"
IEEE Trans. Acoustics, Speech and Sig. Proc., Vol. 35, pp. 373-383, March (1987), Jong-Sen
Lee, "Refined Filtering of Image Noise Using Local Statistics", Computer Graphics And
Image Processing 15, pp.380-389, (1981), Jong-Sen Lee, "Speckle Analysis and Smoothing
of Synthetic Aperture Radar Images", Computer Gr~phics And Image Processing 17, pp. 24-
6
32, (1981),Frost V.S., Stiles J.A., Shanmugan K.S., Holtzman J.c., "A model for radar
images and its application to adaptive digital filtering for multiplicative noise," IEEE Trans.
on Pattern Analysis And Machine Intelligence, Vol-4, pp. 157-166, March (1982) and
Bamber J.e., Daft C., "Adaptive filtering for reduction of speckle in ultrasonic pulse-echo
images", Ultrasonic, January (1986).
In the above mentioned filtering, techniques smoothing is increased in homogeneous region
of the image and reduced or avoided elsewhere to preserve edges. These filters are basically
adaptive filters. Adaptive filtering for reduction of speckle from ultrasonic pulse-echo images
was proposed by Bamber J.e., Daft C., "Adaptive filtering for reduction of speckle in
ultrasonic pulse-echo images", Ultrasonic, January (1986). It proposed an adaptive twodimensional
filter which uses local features of image texture to recognize and maximally
low-pass filter those parts of the image which correspond to fully developed speckle, while
substantially preserving information associated with resolved-object structure. The filter is
un-sharp masking filter and its output is mathematically given as,
x = x +k(x-x) (5)
where x is the new (processed) value of a pixel to be computed from the old (unprocessed)
value (x), and the local mean (x) of the old value's surrounding and including that pixel.
The parameter k is controlled by the ratio of the lo.cal variance to the local mean. Dutt V.,
Greenleaf J.F, "Adaptive speckle reduction filter for log compressed B-scan images", IEEE
Trans. on Medical Imaging, VoLl5, No.6, pp. 802-813, December (1996) discloses the same
technique and used the same equation in their literature. But they considered statistics of
speckles for log compressed ultrasound image the parameter k was chosen as,
k = 1-j(a) (6)
where j is the statistics and given by,
(7)
7
Here b is an estimate of log compression parameter from the dynamic range and V is the
local sample variance.
Jong-Sen Lee, "Speckle Analysis and Smoothing of Synthetic Aperture Radar Images",
Computer Graphics And Image Processing 17, pp. 24-32, (1981) proposed a smoothing
algorithm based on local statistics on a fixed window size and was successfully applied to
remove speckles form SAR images. They considered multiplicative noise model for speckle
where the noise is independent to the signal having mean 1 and variance uv
2
• The basis of
this filter is: in homogeneous region the filtered output is linear average of pixels in the
neighborhood, whereas in the region of extremely large intensity variation the output
becomes the value of the input pixel itself. The output of the Lee filter is given as,
x = x + k(z -v.x). (8)
Here z is the observed pixel, v = 1, and the value of k is calculated as follows:
k = Var(x)
x2uv
1 +Var(x)
(9)
x=z
and
V () Var(z)+z2 -2 ar x = -z
U 2 +v1
v
The quantities z and Var(z) are approximated by local mean and local variance of speckle
corrupted image.
The main limitation of the Bamber, Dutt, and Lee filters is that the use of too large window
introduces a loss of fine details in the image. On the other hand, the use of small window
implies insufficient speckle suppression homogeneous region. To avoid this problem adaptive
8
windowing and modified adaptive filtering with variable window size are also proposed in
Park 1 M., Song W. J., Pearlman W. A., " Speckle Reduction for SAR Images based on
adaptive windowing", lEE Proceedings Vol. 146, No.4, August (1999).
Kuan D.T., Sawchuk Alexander A. et aI., "Adaptive restoration of images with speckle,"
IEEE Trans. Acoustics, Speech and Sig. Proc., Vol. 35, pp. 373-383, March (1987) used
same formulation with different assumption of signal model. They assumed that the speckle
samples are independent of each other. They derived a local linear minimum mean square
(LLMMSE) filter using non-stationary mean and non-stationary variance (NMNV) image
model. The correlation properties are also taken into account in their derivation. The
parameter k for Kuan filter is determined as,
k = Var(x)
Var(x) +x +Var(x)
(10)
The MMSE filter proposed by Frost V.S., Stiles I.A., Shanmugan K.S., Holtzman lC., "A
model for radar images and its application to adaptive digital filtering for multiplicative
noise," IEEE Trans. on Pattern Analysis And Machine Intelligence, Vol-4, pp. 157-166,
March (1982) is a balance between averaging and all pass filter. The one dimensional
impulse response of the MMSE Frost filter is derived as,
h(t) = Aa e-al'I (11)
where A is the normalizing constant and a is the ratio of square root of local variance to
local mean of the observed image in a window.
Directional median filter as disclosed in Czerwinski, R.N., Jones, D.L., William D. O'Brien,
Jr., "Ultrasound Speckle Reduction by Directional Median Filtering", Proceedings,
International Conference on Image Processing, Vol: 1, (1995) and adaptive weighted median
filter as disclosed in Loupas T., McDicken W. N., Allan P. 1.," An Adaptive Weighted
Median Filter for Speckle Suppression in Medical Ultrasonic Images", IEEE Trans. on
Circuits and Systems, Vol. 36, No.1, pp. 129-135, January (1989) are also in use for
9
reducing of speckle due to their robustness and edge preserving capability. These filters are
nonlinear filters and produce relatively less blurred image. However, their computational
complexity is large.
In many cases Maximum-a-posteriori (MAP) filters are used for speckle reduction. MAP
filters require assumption about the distribution of the true process and the degradation
model. Different MAP estimators are proposed with different assumptions and different
complexities as disclosed in Kalaivani S., Narayanan, Wahidabanu R.S.D., " A View on
Despeckling in Ultrasound Imaging", International Journal of Signal Processing, Image
Processing and Pattern Recognition Vol. 2, No.3, pp. 85-97, September (2009).
In Diffusion filtering the nonlinear partial differential equation based smoothing technique
utilizing the concept diffusion is proposed by Perona P and Malik J, "Scale-Space and Edge
Detection Using Anisotropic Diffusion", IEEE Trans. on Pattern Analysis And Machine
Intelligence, Vol. 4, No.-7, pp.629-639, July (1990). The diffusion is described by,
aI = div[c(IVII) VI] at
1(/=0)=10
(12)
where div is the divergence operator and I I is the magnitude, c is the diffusion constant and
10 is the initial image. Two diffusion constants are considered as,
1
c(x) = 2 1+(~)
and c(x)=ex{-(i)']
In the anisotropic diffusion method, the gradient magnitude is used to detect an image edge or
boundary as a step discontinuity in intensity.
If IVIi » k then clV11-7 0, and we have all pass filter,
10
If IVII«k then ciVIl ~ 1, and we achieve anisotropic diffusion (Gaussian filtering).
An edge sensitive diffusion method called speckle reducing anisotropic diffusion (SRAD) has
been proposed to suppress speckle while preserving edge information disclosed in Yongjian
Yu and Scott T. Acton, "Speckle Reducing Anisotropic Diffusion", IEEE Trans. on Image
Processing, Vol. -11, No.-11, pp. 1260-1270, November (2002). These methods have one
common limitation in retaining subtle features such as small cysts and lesions in ultrasound
images. A modified SRAD filter, which rely on the Kuan filter rather the Lee filter was
developed in Aja-femandaz S., Alberola-Lopez C., "On the estimation of coefficient of
variation for anisotropic diffusion speckle filtering", IEEE Trans. on Image processing,
VoLl5, No.9, pp. 2694-2701, September (2005) and this approach is called Detail preserving
Anisotropic Diffusion (DPAD). This method is combined with matrix anisotropic diffusion
method designed to preserve and enhance small vessel structures referred as oriented speckle
reducing anisotropic diffusion disclosed in Krissian K. Fedrij C, "Oriented Speckle reducing
anosotropicn diffusion", IEEE Trans. on Image Processing, VoLl5, No.5, pp. 1412-1424,
May (2007).
Multiscale methods include wavelet and pyramid based denoising and discussed in several
literatures Le. David L. Donoho, "De-Noising by Soft-Thresholding", IEEE Trans. on
Information Theory, Vol. 41, No.3, pp. 613-627, May (1995),S. Grace Chang, Bin Yu,
Martin Vetterli, "Adaptive Wavelet Thresholding for Image Denoising and Compression"
IEEE Trans. on Image Processing, Vol.-9, No.-9, pp. 1532-1546, September (2000),K. P.
Soman and K. I. Ramachandran, " Insight into wavelets: From Theory to Practice" PHI
(EEE) 2nd Edition, (2005) and DuVsanGleich, Mihai Datcu, "Wavelet-Based SAR Image
Despeckling and Information Extraction, Using Particle Filter", IEEE Trans. on Image
Processing, Vol. 18, No. 10, pp. 2167-2184, October (2009).
Wavelet denoising attempts to remove whatever noise present and retain whatever signal is
present regardless of the frequency content of the signal as mentioned in K. P. Soman and K.
I. Ramachandran, " Insight into wavelets: From Theory to Practice" PHI (EEE) 2nd Edition,
(2005). It is nothing but shrinkage of wavelet coefficients in wavelet transform domain.
Three basic steps are required for wavelet denoising. The steps are as follows:
I. A linear forward wavelet transform,
II
2. A non-linear shrinking denoising,
3. A linear inverse wavelet transform.
Stepl: Calculate the wavelet coefficients of the observed data by applying wavelet
transform.
Step2: Modify the detail coefficients to obtain the estimate of the original signal.
Step3: Take the inverse transform of the modified detail coefficient to obtain the denoised
signal.
The main challenge of wavelet denoising is the proper choice of shrinkage function and the
threshold.
Two categories of thresholding are in use:
Global thresholds: Single threshold (A,) (is chosen to apply globally to all wavelet coefficient
Level dependent threshold: Possibly different thresholds are chosen for each resolution level.
One should estimate the noise level (0') to determine the threshold. The above two categories
ofthresholding include hard thresholding and soft thresholding techniques. The thresholding
is discussed in brief, Let w be the observed noisy data, 0' the estimated noise level, -1. the
threshold and DA
(.) denotes the shrinkage function, which determines how threshold is
applied to data. Then modified wavelet coefficients can be given as,
(13)
Denoising methods differ in the choices for DA
(.), A and 0'. Different denoisers consider
different shrinkage functions that determine how the threshold is applied, different noise
estimates and different shrinkage rules to determine the threshold a. A few shrinkage
functions, which are generally used for denoising, are listed below:
Hard threshold: for alllwi > -1.
otherwise
(14)
12
Soft threshold: D;(w) = sign(w) max(O;I~ - A) (15)
Garrot: D~(W)={(W-:} foralllwl>A
0, otherwise
(16)
Semisoft:
for/wI $; ~
for ~ < Iwl< A2
forlwl> ,12
(17)
The VisuShrink was proposed as a global rule for one-dimensional signals as disclosed in
David L. Donoho, "De-Noising by Soft-Thresholding", IEEE Trans. on Information Theory,
Vol. 41, No.3, pp. 613-627, May (1995). Regardless of the shrinkage function, for a signal
size n, with noise from a standard normal distribution N(O,I), the threshold is,
Au = ~210g(n) (18)
If data is not normalized w.r.t noise-standard deviation, first the (T using the equation below
is estimated
median {(Iwtl : k = 1, 2,3 ...~)}
(j = __ ~"':-- .:...L
0.6745
(19)
VisuShrink is found to yield an overly smoothed estimate. This is because the universal
threshold (UT) is derived under the constraint that with high probability the estimate should
be at least as smooth as the signal. So the VT tends to be high for large values of n, killing
13
Further reducing the overall computational complexity and the number of building blocks of
conventional ultrasound imaging system and it can be used with SR ultrasound image
reconstruction techniques.
Scan conversion means evaluating the pixel values at the grid points in rectangular coordinate
system. In ultrasound sector scanner the pixel values are available at the grid points
in polar co-ordinate system after scanning the object. Conventionally, in scan conversion
process the pixel values at the grid points in rectangular co-ordinate system are evaluated by
using interpolation techniques using the pixel values available in polar co-ordinate system.
This scan conversion makes it possible to display the ultrasound image in video monitor
which supports inputs in rectangular co-ordinate system only to display the image. After scan
conversion the speckle reduction technique is generally employed to remove the speckle
noise from the scan converted image. Tn the present technique we are avoiding this
interpolation.
In conventional techniques, the pixel values at Q are calculated by using a suitable
interpolation technique. Here, the values of P are first calculated along radial direction using
interpolation technique. After calculation of the pixel values at P, the pixel values at Q are
calculated using interpolation technique. This completes the scan conversion process. Speckle
reduction is applied after this scan conversion process Le. the pixel values at the grid points
(Q points) in the rectangular co-ordinate points are re-calculated by using the speckle
reduction filtering algorithms (e.g. Lee, Kuan, median, weighted median, adaptive weighted
median filters etc.). The drawback of this conventional procedure is that the interpolation in
the scan conversion stage makes the noise more coloured and the effect of noise is spread
from a smaller region to relatively bigger region. Moreover, we lose some information in the
scan conversion process due to low pass nature of interpolation operation. This degrades the
performance of the conventional procedure. In the present technique, we reduce the loss of
information in the in scan conversion process since scan conversion is performed through
filtering.
The present inventors have found an improved speckle reduction method where the prior
speckle reduction techniques can be used to obtain better quality of output image. The
20
inventors have used the already existing noise/speckle reduction filter, but the speckle
reduction filter are used before scan conversion or during scan conversion instead of using
them after scan conversion. Further the inventors also proposed an improved method for
speckle reduction using an improved filter which gives better quality of image if it is applied
in old speckle reduction technique. However applying it the improved speckle reduction
technique/method provides much better quality of image than that of old speckle reduction
technique.
OBJECTS OF THE INVENTION
An object of the present invention is to overcome the problems/disadvantages of the prior art.
Another object of the present invention is to provide an ultrasound imaging method for
speckle reduction/suppression in ultra sound imaging system adapted to eliminate the
interpolation stage in the prior art and hence decrease the loss of information.
Yet another object of the present invention is to provide an improved method for speckle
reduction using an improved filter in the said ultra sQund imaging system.
Yet another object of the present invention is to provide an improved method for speckle
reduction where the speckle reduction and scan conversion are performed simultaneously.
Yet another object of the present invention is to provide an improved method and system
where the interpolation step is eliminated in the scan conversion.
Yet another object of the present invention is to provide with an improved system and
process for speckle reduction that has simplicity in computation of the speckle reduction, cost
effective and high quality ultra sound image.
These and other advantages of the present invention will become readily apparent from the
following detailed description read in conjunction with the accompanying drawings.
SUMMARY OF THE INVENTION
21
In accordance with one aspect of the present invention there is provided an improved method
for speckle reduction in an ultrasound imaging system, said method comprising steps of:
receiving in a processor means raw data samples as an input comprising image signals with
noises from a logarithmic amplifier;
processing said received image signals for scan conversion and speckle reduction in said
processor means so as to get pixel value from said raw data samples and to perform speckle
reduction to provide speckle filtered output image;
wherein said speckle reduction and scan conversion are performed/processed simultaneously;
wherein said pixel values at a raster grid points in a rectangular co-ordinate system are
determined using filtering technique/speckle reduction technique by means of speckle
reduction filter.
In accordance with another aspect of the present invention there is provide an improved ultra
sound imaging system for speckle reduction, said system comprising
a transducer means;
a transmitter means operatively connected with said transducer means;
a receiver means operatively connected with said a transducer means adapted to get raw
data/signal with speckle noise;
a time gain compensation means operatively connected with said receiver means;
a AID means operatively connected with said time gain compensation means;
a demodulator means operatively connected with said AID means adapted to provide
demodulated data as output from said raw data;
an envelope means operatively connected with said demodulator means comprises envelope
detected raw scan data;
a log compression means/logarithm amplification operatively connected with said envelope
means adapted to transform said envelope detected raw scan data to log compressed data;
22
a pre-processing means operatively connected with said log compression means;
a processor means operatively connected with said pre-processing means comprising a scan
conversion means and speckle reduction means to get scan converted data;
a post-processing means operatively connected with said processor means;
a display means operatively connected with said post-processing means adapted to display
the speckle filtered output image;
wherein said speckle reduction means is placed together/simultaneously with said scan
conversion means.
DETAILED DESCRIPTION OF THE INVENTION
The present invention relates to an improved ultrasound imaging method/technique for
speckle reduction/suppression in an ultra sound imaging system in which scan conversion and
speckle reduction is performed simultaneously in the scan conversion stage or speckle
reduction method are used before scan conversion instead of using them after scan
conversion avoiding any kind of conventional interpolation. The method reduces the overall
complexity of the building blocks of the ultrasound imaging system and enhances the quality
ofthe reconstructed image.
Further the ultrasound imaging system and ultrasound imaging method provide an adaptive
weighted based median filter algorithm for speckle reduction, which provide better visual
image quality than the other popular spatial filter based speckle reduction techniques.
In the present invention a few quantitative measurement parameters (quality metrics) are
employed to compare the reconstructed images generated by different existing popular
techniques with the proposed technique.
The present invention provides an improved method for speckle reduction where the speckle
reduction and scan conversion are performed simultaneously. (Speckle reduction during scan
conversion, preferably during scan conversion instead of using them after scan conversion. )
The present invention further provides an improved system for speckle reduction where
speckle reduction method and scan conversion are done simultaneously.
23
In the new technique the weight co-efficients are determined as follows:
w(i,j) = [W(K + 1, K + 1) -d _20_*_I---=Og=..:.lo=--.:(---=1+_m_+_(j--,-)]
10glO(m)
(29)
The proposed high-pass filter considers a trade-off between noise attenuation and edge
highlighting. This filter is efficient to enhance the positive-slope edges only. To enhance both
the positive and negative edges, the following procedure is used.
1. Calculating weights using equation (29)
2. Evaluating the weighted median of the image pixels within the window using the
weights obtained in step 1. This will extract the positive edges.
3. Inverting the pixel values of the window by subtracting the pixels from 255. Then
follow the step 2. This will extract the negative edges.
4. Combining the two images obtained from step 2 and step 3 appropriately
The above improved adaptive weighted median filtering can be implemented in the
preprocessing (before scan conversion) stage, post processing (after scan conversion) stage or
along with the scan conversion to show better results than the comventional filtering
techniques.
The main features of the present invention are:
The Filtering process is implemented along with scan conversion. It implements spatial linear
and non-linear speckle filtering techniques and the conventional interpolation is eliminated.
The ultrasound imaging system and method reduces the overall computational complexity
and the number of building blocks of the system. Further it outputs better speckle reduction
capability and adaptable with SR ultrasound image reconstruction.
Moreover it focuses on new philosophy of ultrasound image formation, which does not
require any conventional interpolation during scan conversion and filtering techniques are
implemented along with the scan conversion.
24
The avoidance of one interpolation stage reduces the loss of information and provides better
output image quality and a new adaptive weighted median filter is implemented and adapted
with new image formation technique, which is implemented with Ultrasound SR
reconstruction technique from polar format data.
According to the first embodiment of the present invention there is provided an improved
method for speckle reduction in an ultrasound imaging system . The method comprising steps
of receiving in a processor means raw data samples as an input comprising image signals
with noises from a logarithmic amplifier , processing the received image signals for scan
conversion and speckle reduction in the processor means so as to get pixel value from the raw
data samples and to perform speckle reduction so as to provide speckle filtered output image.
The speckle reduction and scan conversion are performed/processed simultaneously . The
pixel values at a raster grid points in a rectangular co-ordinate system are determined using
filtering technique/speckle reduction technique by means of speckle reduction filter.
Further the step of processing/computation of the pixel value from the raw data as disclosed
above comprises steps of determining plurality of radial lines in the rectangular co-ordinate
system, determining plurality of rectangular grids comprising vertical and horizontal grid
lines in the rectangular co-ordinate system.
Such that the processing the plurality of pixel values from the raw data are at the plurality of
points where radial lines cut the horizontal grid lines.
Further step of processing plurality pixel values at each point where radial lines cut the
horizontal grid lines comprises determining the plurality of successive radial lines ,
evaluating plurality of nearest points on the radial lines with respect to point where a radial
line cut a horizontal grid line , assigning sample values to the nearest points where the
sample values lies substantially around the point where a radial line cut a horizontal grid
line, imposing speckle reduction techniques of single scale spatial filter to compute pixel
value at the point where a radial line cut a horizontal grid line and performing the above
steps to calculate all the pixel values at the plurality of points where the radial lines cut the
horizontal grid lines.
25
The step of processing the pixel value from the raw data at each raster grid point comprises
receiving plurality of points where a radial line cut a horizontal grid line from step as
disclosed in the above paragraphs, evaluating plurality of nearest points from the points
where a radial line cut a horizontal grid line with respect to the raster grid point ,assigning
sample values to the evaluated nearest points , imposing speckle reduction techniques of
single scale spatial filter to compute pixel value at the raster grid point, performing all the
above mentioned steps to calculate all the pixel values at the plurality of raster grid points
The single scale spatial filter comprises linear, non linear filter technique and like.
The speckle reduction filter comprises a high pass filter technique with edge enhancement.
The high pass filter technique having weight co-efficient of
~
20 * log (1+m +(T)] .. w(i, j) = IN w(K +I, K +1) - d 10 to enhance posItive edge slope or
10glO(m)
both positive and negative edge slope.
The step for the filter to enhance both positive and negative edge slope comprises steps of
(i) determining weights with filter technique as mentioned in the equation
above;
(ii) evaluating the weighted median of the image pixels within the window
using the weights obtained in step 1 adapted to obtain positive edge
slops;
(iii) controlling the sharpness in the positive slope directions by control
parameter 81;
(iv) inverting the pixel values of the window by subtracting the pixels
followed by the step 2 adapted extract the negative edges;
(v) controlling the sharpness in the negetive slope directions by control
parameter 8z and
(vi) combining the two images obtained from step ii and step iv .
26
According to the second embodiment of the present invention there is provided an improved
ultra sound imaging system for speckle reduction. the system comprises a transducer means,
a transmitter means operatively connected with the transducer means ,a receiver means
operatively connected with the a transducer means to get raw data/signal with speckle noise
,a time gain compensation means operatively connected with the receiver means , a AID
means operatively connected with the time gain compensation means, a demodulator means
operatively connected with the AID means to provide demodulated data as output from the
raw data, an envelope means operatively connected with the demodulator means comprises
envelope detected raw scan data , a log compression means/logarithm amplification
operatively connected with the envelope means to transform the envelope detected raw scan
data to log compressed data, a pre-processing means operatively connected with the log
compression means, a processor means operatively connected with the pre-processing
means comprises a scan conversion means and speckle reduction means to get scan converted
data, a post-processing means operatively connected with the processor means and a display
means operatively connected with the post-processing means to display the speckle filtered
output image. The speckle reduction means is placed together/simultaneously with the scan
conversion means.
The processor means receives ultrasound data samples for scan conversion in scan conversion
means and speckle reduction in the speckle reduction means so as to get the pixel value from
the raw data and to perform speckle reduction and provide speckle filtered output image.
The processor means for processing/computation the pixel value from the raw data
comprises a first computing means for computing plurality of radial lines in a rectangular coordinate
system , a second computing means for computing plurality of rectangular grids
comprising vertical and horizontal grid lines in the rectangular co-ordinate system.
The processor means for processing the plurality of pixel values from the raw data are at the
plurality of points where radial lines cut the horizontal grid lines. Further the processor means
for processing plurality of pixel values from the raw data are at the plurality of raster grid
points.
The processor for processing plurality pixel values at each point where radial lines cut the
horizontal grid lines where the processor means comprises a scan conversion means. The
27
conversion means comprises a first computing means for computing the plurality successive
radial lines , an evaluating means for evaluating plurality of nearest points on the radial lines
with respect to the point where a radial line cut a horizontal grid line , an assigning means for
assigning sample values to the nearest points where the sample values lies substantially
around the point where a radial line cut a horizontal grid line and a speckle reduction means
for imposing speckle reduction techniques of single scale spatial filter to compute pixel value
at the point where a radial line cut a horizontal grid line .
The processor means for processing the pixel value from the raw data at each raster grid
point. The processor means comprises a scan conversion means . the conversion means
comprises an inputting means for receiving plurality of points where a radial line cut a
horizontal grid line from evaluating means, an evaluating means for evaluating plurality of
nearest points from the points where a radial line cut a horizontal grid line with respect to the
raster grid point , an assigning means for assigning sample values to the evaluated nearest
points and a speckle reduction means for imposing speckle reduction techniques of single
scale spatial filter to compute pixel value at the raster grid point.
The speckle reduction filter means comprises linear and non-linear filter and the like. The
speckle reduction filter means comprises a high pass filter means.
The high pass filter means having
w(i,j) = INJ w(K +1, K +1)-d 20* 10glO(l +m +0")] ~l 10glO(m)
both positive and negative edge slope.
weight co-efficient of
to enhance positive edge slope or
The filter means to enhance both positive and negative edge slope comprises
(i) a processor means of filter means for calculating weights with filter
means as mentioned in the equation above;
(ii) a evaluating means of filter means for evaluating the weighted median
of the image pixels within the window using the weights obtained in
step 1 adapted to obtain positive edge slops;
28
(iii) a first controlling means of filter means adapted for controlling the
sharpness in the positive slope directions by control parameter ~;
(iv) an inverting means of filter means for inverting the pixel values of the
window by subtracting the pixels followed by the step 2 adapted
extract the negative edges;
(v) a second controlling means of filter means for controlling the
sharpness in the negative slope directions by control parameter 82 and
(vi) a combing means of filter means for combining the two images
obtained from step ii and step iv .
Advantages:
• Avoidance of interpolation reduces the extra loss of information during scan
conversion.
• A new speckle filtering technique is adapted with the new image formation technique
provide better image quality by removing speckles and preserving edges.
• It reduces the overall computational complexity and the number of building blocks of
conventional ultrasound imaging system.
• It can be used with SR ultrasound image reconstruction techniques.
BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWING
Other features as well as the advantages of the invention will be clear from the following
description.
In the appended drawing:
Fig.la illustrates schematic Block diagram ofB-mode ultrasound imaging system.
Fig Ib illustrates schematic block diagram of the proposed speckle reduction technique where
scan conversion and speckle reduction is performed simultaneously.
Fig.2 illustrates scan-conversion geometry.
29
Fig. 3 illustrates geometry of first stage computation
Fig. 4 illustrates pixel geometry for raster grid point computation.
Fig. 5 illustrates reconstructed phantom images it:l different stages for different filtering
methods.
Fig. 6 illustrates plot of Quality metrics of different methods for comparison of performance
Fig. 7 illustrates SR reconstructed images for different techniques.
Fig. 8 illustrates Plot of Quality metrics of different speckle reduction methods with SR
reconstruction.
Figure 9(a), (b) illustrates Final output images by applying median filtering technique on the
ultrasound simulated phantom image at different stages.
Fig 10 illustrates Image of the noisy scan data.
Fig 11 illustrates Scan converted noisy image after only scan conversion (without filtering).
Fig 12 illustrates High-pass adaptive weighted median filtering with edge enhancement
DETAILED DESCRIPTION OF THE ACCOMPANYING DRAWINGS
In the following detailed description, reference is made to the accompanying drawings that
form a part hereof, and illustrate the best mode presently contemplated for carrying out the
invention. The invention is described in reference to specific embodiment and such
description should not be considered to a limitation of the present invention. However, such
description should not be considered as any limitation of scope of the present mechanism.
The structure of the system thus conceived is susceptible of numerous modifications and
variations, all the details may furthermore be replaced with elements having technical
equivalence. In practice the materials and dimensions may be any according to the
requirements, which will still be comprised within its true spirit.
Figure la discloses the simplified schematic block diagram of a typical diagnostic
conventional B-mode ultrasound imaging system. The speckle reduction filter is employed
here after log compression of the demodulated output. Interpolation is then performed on the
filtered log compressed signal for scan conversion and the signal is prepared for display after
some post processing tasks. The speckle reduction techniques are applied on envelope
30
detected raw scan-data, log compressed data at the preprocessing stage before scan
conversion or scan converted data at post-processing stage.
Fig Ib is the simplified block diagram of the proposed new paradigm of the speckle reduction
technique. In the new technique, all the blocks perform same operations as in the case of old
conventional technique except the preprocessing, post-processing and the scan conversion
block. Here the speckle reduction is shifted from the preprocessing or post-processing block
to scan conversion block since speckle reduction is performed simultaneously with scan
conversion.
Fig 2. : In the present technique/method, the speckle reduction scan conversion method is
employed simultaneously avoiding the conventional interpolation. A few so-called single
scale spatial speckle-reduction filtering methods (linear and nonlinear such as Lee, Kuan,
Median) are chosen to test the performance of the improved method/technique. The method
for speckle reduction scan-conversion is described with the help of a diagram of scanconversion
geometry as in Fig. 2. Ultrasound datil samples obtained from the logarithm
amplifier are placed on rectangular raster along radial lines. A few sample points are placed
in Fig.2 as solid triangular points for the ease of illustration. Now, for scan conversion it
needs to be found the pixel value on the rectangular grids from the available data. To perform
this, the inventors have first found out the pixel value at the points where radial lines cut the
horizontal grid lines. For example, three successive radial lines (Line j -1 , Line j, and Line
j +1) are considered. The pixel value at point P is found out, where the radial line, Line j
cuts the horizontal grid line. The three nearest points around P along the Line j is found.
These points are D, E and F. Suppose E is the nearest point of P along Line j . Hence, next
two nearest points are F and D, respectively. The nearest sample value as s(nearest, j) is
assigned. Consequently, the other two point~ D and F as s( nearest -1, j) and
s( nearest + 1,j) respectively is also assigned. In a similar the other six points (A, B, C, G, H
and I), three from each Line j -I and Line j + I is found out. These six points are:
s(nearest -I, j -1), s(nearest, j -1), s(nearest + 1,j -1), s(nearest -I, j + 1),
s(nearest, j +1), s(nearest + 1,j +1)respectively. Around the point P we get nine sample
values as a local window from which the pixel value at P is calculated.
31
To calculate the pixel value at P, different single scale spatial filter (linear or nonlinear) based
so-called popular speckle reduction algorithm is imposed. For illustration, the Lee filter
technique is used. Lee filter technique is already discussed in the literature survey. The
parameter k of Lee filter can be determined from the variance and the mean of the local
window. Then the pixel value p at the point P can be calculate as,
p = s +k[ s(nearest, j) - s] (26)
where s is the average value of the pixels within the local window. s is calculated by adding
all the pixel values within the window which contains the pixels designated by s( nearest, j) ,
s(nearest + 1, j), ..... etc. as described and dividing the result by the number of the pixels
within the windows.
k is different for different linear filtering techniques (such as Lee, Kuan etc.) and it can be
calculated from the statistics of the local window. For nonlinear filters such as median,
weighted median or adaptive weighted median filters k is not defined. For these filters, the
median value is calculated from the pixel values of the local window using simple median
calculation technique or weighted median calculation technique and it is mentioned earlier
section of this document.
After computation of all the pixel values at the points where radial lines cut the horizontal
grid lines, the geometry will be converted as shown in Fig. 3 below: The computed points are
denoted as solid circles. Pt, P2, P3 ... are such points.
Now, with available of the points PI, P2, P3 ... the raster grid points of the raster scan is
computed.
Fig. 4 discloses the procedure of computation of the pixel values at the raster grid points. In
Fig. 4, pixel values at the points Ph P2, P3... are already calculated in the first stage. In the
next step, the pixel values at the raster grid points Qt, Q2, Q3... etc is computed. In the
example the raster grid point Qs in the i'" row and j''' column is considered. Also the pixel
values at the points Pk, k = 1,2,3 ... are represented with two index variables is considered.
Three nearest points of Qs along ilk row are determined. P7, P6 and Pg are such three nearest
points. P7 is the nearest one and P6 and 8 are the next two successive nearest points. The pixel
value of P7 as p(i, nearest) is assigned. Then other two nearest points can be assigned as
32
p(i, nearest -1) and p(i, nearest +1), respectively. Similarly, the three nearest points from
previous row other three from next row is found out. For finding three nearest points from the
previous row i.e.(i -It row, the grid point Q20fthe same column and (i -It row and search
three nearest points around Q2 along the row is found out. These points are assigned as
p(i -1, nearest I) , p(i -1, nearestl-l) and p(i -1, nearestl + 1). And in a similar way, three
nearest points from next row i.e. (i + l)'h row is found out. The points as p(i + 1, nearest 2) ,
p(i + 1, nearest2 -1) and p(i + 1,nearest 2 + 1) are assigned. Finally, the pixel value at the
grid point Qs can be computed from these nine points as
q = p +k[PU, nearest) - p] (27)
where p average value of the pixels within the window.
The average value p is calculated by adding the pixel values within the windows which are
designated by p(i, nearest), pC;, nearest + 1), ...... etc. and dividing the result by the number
of pixels within the local window.
Different single scale spatial filtering techniques are applied within this improved method
where filtering and scan conversion is done simultan~ously.
The pixel values at the grid points in rectangular co-ordinate system are calculated using
filtering technique from the neighbor pixel values. It fulfills the requirement of scan
conversion, and at the same time, it gives the speckle filtered output image. Hence
interpolation stage in the scan conversion process is avoided.
In the geometrical portrait, Q points are the grid points in the rectangular co-ordinate system.
To generate a speckle filtered ultrasound image that is displayed in the conventional video
monitor which supports rectangular co-ordinate system and therefore, first the pixel values at
the grid points Q is calculated. To evaluate the values at the pixel points Q, in the present
technique the pixel values at the points P is calculated as an intermediate stage using filtering
algorithm avoiding interpolation. After calculating the pixel values at the point P the pixel
values at Q is calculated by using the pixel values at the points P applying filtering algorithm
again.
33
In the present invention, to evaluate the pixel value of a grid point on the rectangular raster,
the nearest pixel value from the raw data is used and the noise reduction algorithm on that
nearest pixel value is applied. Hence scan conversion and speckle reduction are performed
simultaneously.
Fig. 5 discloses Simulation results: The reconstructed image of a simulated phantom for each
case. The comparisons of quality metrics for the evaluation of the quality of the reconstructed
images are shown in Fig. 6 according to table 1.
It is observed that the quality of the reconstructed image is the best if filtering and scan
conversion are performed simultaneously. This present improved technique also reduces the
functional blocks of the ultrasound imaging systems. It is verified that it is also valid in case
of super-resolution. SR reconstructed images by the above methods are shown in Fig. 7 and
the performance in terms of quality metrics is shown in Fig. 8. Filtering operation for all the
reconstructed images is done with 3x 3 window.
The present adaptive weighted median filtering technique is also applied to noiseless signal to
verify whether the present filter provides a considerable good output or not.
Figure 9(a) shows the original noiseless image and fig 9(b) shows the output of the present
filter.
Figure 12 demonstrate the high-pass filtering with positive slope and negative slope edge
enhancement. This algorithm increases the sharpness and the contrast of the image. The
parameters 81 and 82 are the controls parameters which controls the sharpness in the positive
and negative slope directions as per requirement.
Since it is high pass in nature, it is able to preserve image details, which is most important
criteria in the medical ultrasound image. The negative values of the weights make the filter
high pass in nature. The filter preserves both positive and negative-slope edges of the image.
The sharpness control factor controls sharpness and the positive and negative-slope edge
enhancing capability.
The quality metrics of the output of the present filter with noiseless image is given in table 1.
Table 1: Quality Metrics of the output image of the present filter when input image is
noise free image
34
MSE
PSNR
Q
71.0386
29.6159
0.9986
The quality metric also confirms that the filter does not hamper much the noise free image.
MSE: Mean Square Error
PSNR: peak signal to noise ratio
Q: Universal quality index
The different techniques are compared with the help of quality metrics. The value of the
quality metrics imply that the invention provides the better quality of the image and the
reconstructed image is closer to the original image than the other methods.
The invention explores a new paradigm where the old popular speckle reduction techniques
can be used to obtain better quality of output imag~. The same equations for Lee, Kuan or
Median filtering techniques are used. But they must be used before scan conversion or during
scan conversion instead of using them after scan conversion. The MSE, PSNR, Q shows
better results when the conventional filtering techniques are used during scan conversion.
Further it is found that the improved AWM based speckle reduction technique which gives
better quality of image if it is applied in old popular speckle reduction techniques like Lee,
Kuan or Median filtering algorithm.
It is observed that though speckle reduction before scan conversion and during scan
conversion performs better than the speckle reduction after scan conversion, the best
technique is the speckle reduction during scan conversion. This is because it gives the best
noise reduction capability and decrease in computational burden.
Expectedly, the method for speckle reduction in an ultrasound imaging system and system for
speckle reduction disclosed herein will find many useful applications in diverse technical
fields. Examples of such applications include not only: ultrasound imaging for medical
diagnostic and non-destructive evaluation but also SAR imaging, PET/SPECr and other
modalities, etc.
35
It is understood that the systems and methods of the illustrative embodiments may be
modified in a variety of ways which will become readily apparent to those skilled in the art,
and having the benefit of the novel teachings disclosed herein. All such modifications and
variations of the illustrative embodiments thereof shall be deemed to be within the scope and
spirit of the present invention as defined by the claims to invention appended hereto ..
36
WE CLAIM
1. An improved method for speckle reduction in an ultrasound imaging system , said
method comprising steps of:
receiving in a processor means raw data samples as an input comprising image
signals with noises from a logarithmic amplifier;
processing said received image signals for scan conversion and speckle reduction
in said processor means so as to get pixel value from said raw data samples and to
perform speckle reduction to provide speckle filtered output image;
wherein said speckle reduction and scan conversion are performed/processed
simultaneously;
wherein said pixel values at a raster grid points in a rectangular co-ordinate
system are determined using filtering technique/speckle reduction technique by
means of speckle reduction filter.
2. Method as claimed in claim I wherein said step of processing/computation of the
pixel value from the raw data comprises steps of:
determining plurality of radial lines in said rectangular co-ordinate system and
determining plurality of rectangular grids comprising vertical and horizontal grid lines
in said rectangular co-ordinate system ;
such that step of processing the plurality of pixel values from the raw data are at the
plurality of points where radial lines cut the horizontal grid lines.
37
3. Method as claimed in claim2 wherein said step of processing plurality pixel values at
each point where radial lines cut the horizontal grid lines comprising:
determining said plurality of successive radial lines ;
evaluating plurality of nearest points on said radial lines with respect to said point
where a radial line cut a horizontal grid line ;
assigning sample values to said nearest points ; said sample values lies substantially
around said point where a radial line cut a horizontal grid line;
imposing speckle reduction techniques of single scale spatial filter to compute pixel
value at said point where a radial line cut a horizontal grid line and
performing the above steps to calculate all the pixel values at the plurality of points
where the radial lines cut the horizontal grid lines.
4. Method as claimed in claims 2 and 3 wherein said step of processing the pixel value
from the raw data at each raster grid point comprising:
receiving plurality of points where a radial line cut a horizontal grid line from step of
claim 3;
evaluating plurality of nearest points from said points where a radial line cut a
horizontal grid line with respect to said raster grid point;
assigning sample values to said evaluated nearest points;
imposing speckle reduction techniques of single scale spatial filter to compute pixel
value at said raster grid point and
performing the above steps to calculate all the pixel values at the plurality of raster
grid points .
5. Method as claimed in claims 3 and 4 wherein said single scale spatial filter comprises
linear, non linear filter technique and like.
38
6. Method as claimed in claim 1 wherein said speckle reduction filter comprising a high
pass filter technique with edge enhancement.
7. Method as claimed in claim 6 wherein said high pass filter technique having weight
~
20*IOglO(l+m+0')] co-efficient of w(i,}) = IN w(K +1, K +1)-d adapted to
10glO(m)
enhance positive edge slope or both positive and negative edge slope.
8. Method as claimed in claim 7 wherein said step for filter to enhance both positive
and negative edge slope comprises steps of .
(i) determining weights with filter technique as claimed in claim 9;
(ii) evaluating the weighted median of the image pixels within the window
using the weights obtained in step I adapted to obtain positive edge
slops;
(iii) controlling the sharpness in the positive slope directions by control
parameter 81 ;
(iv) inverting the pixel values of the window by subtracting the pixels
followed by the step 2 adapted extract the negative edges;
(v) controlling the sharpness in the negetive slope directions by control
parameter 82 and
(vi) combining the two images obtained from step ii and step iv .
9. An improved ultra sound imaging system for speckle reduction , said system
comprising
a transducer means;
a transmitter means operatively connected with said transducer means;
a receiver means operatively connected with said a transducer means adapted to get
raw data/signal with speckle noise;
39
a time gain compensation means operatively connected with said receiver means;
a AID means operatively connected with said time gain compensation means;
a demodulator means operatively connected with said AID means adapted to provide
demodulated data as output from said raw data;
an envelope means operatively connected with said demodulator means comprises
envelope detected raw scan data;
a log compression means/logarithm amplification operatively connected with said
envelope means adapted to transform said envelope detected raw scan data to log
compressed data;
a pre-processing means operatively connected with said log compression means;
a processor means operatively connected with said pre-processing means comprising
a scan conversion means and speckle reduction means to get scan converted data;
a post-processing means operatively connected with said processor means and
a display means operatively connected with said post-processing means adapted to
display the speckle filtered output image;
wherein said speckle reduction means is placed together/simultaneously with said
scan conversion means.
10. System as claimed in claim 9 wherein said processor means is adapted to receive
ultrasound data samples for scan conversion in scan conversion means and speckle
reduction in said speckle reduction means so as to get the pixel value from the raw data
and to perform speckle reduction and provide speckle filtered output image.
11. System as claimed in claim 10 wherein said processor means for processing/computation
the pixel value from the raw data comprising:
a first computing means for computing plurality of radial lines in a rectangular coordinate
system;
40
a second computing means for computing plurality of rectangular grids comprising
vertical and horizontal grid lines in said rectangular co-ordinate system.
12. System as claimed in claim 9 wherein said processor for processing the plurality of pixel
values from the raw data are at the plurality of points where radial lines cut the
horizontal grid lines.
13. System as claimed in claim 9 wherein said processor for processing plurality of pixel
values from the raw data are at the plurality of raster grid points.
14. System as claimed in claim 12 wherein said processor for processing plurality pixel
values at each point where radial lines cut toe horizontal grid lines , said processor
means comprising:
a scan conversion means, said conversion means comprising:
a first computing means for computing said plurality successive radial lines ;
an evaluating means for evaluating plurality of nearest points on said radial lines with
respect to said point where a radial line cut a horizontal grid line;
an assigning means for assigning sample values to said nearest points ; said sample
values lies substantially around said point where a radial line cut a horizontal grid
line and
a speckle reduction means for imposing speckle reduction techniques of single scale
spatial filter to compute pixel value at said point where a radial line cut a horizontal
grid line.
15. System as claimed in claims 12 and 13 wherein said processor means for processing the
pixel value from the raw data at each raster grid point, said processor means comprising:
a scan conversion means, said conversion means comprising
an inputting means for receiving plurality of points where a radial line cut a horizontal grid
line from evaluating means;
41
an evaluating means for evaluating plurality of nearest points from said points where a
radial line cut a horizontal grid line with respect to said raster grid point;
an assigning means for assigning sample values to said evaluated nearest points;
a speckle reduction means for imposing speckle reduction techniques of single scale
spatial filter to compute pixel value at said raster grid point;
16. System as claimed in claim 9 wherein said speckle reduction filter means comprises
linear and non-linear filter and the like.
17. System as claimed in claim 16 wherein said speckle reduction filter means comprises a
high pass filter means.
18. System as claimed in claim 17 wherein said high pass filter means having weight co-
. . ~ 20*log (l+m+CT)] effiCient of wet,}) = IN w(K +1,K +1)-d 10 adapted to enhance
10glO(m)
positive edge slope or both positive and negative edge slope.
19. System as claimed in claim 18 wherein said filter means to enhance both positive and
negative edge slope comprises
(i) a processor means of filter means for calculating weights with filter
means as claimed in claim 19;
(ii) a evaluating means of filter means for evaluating the weighted median
of the image pixels within the window using the weights obtained in
step 1adapted to obtain positive edge slops;
(Hi) a first controlling means of filter means adapted for controlling the
sharpness in the positive slope directions by control parameter 81;
(iv) an inverting means of filter means for inverting the pixel values of the
window by subtracting the pixels followed by the step 2 adapted
extract the negative edges;
42
(v) a second controlling means of filter means for controlling the
sharpness in the negative slope directions by control parameter 82 and
(vi) a combing means of filter means for combining the two images
obtained from step ii and step iv .