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CHAPTER 1

INTRODUCTION TO ULTRASOUND IMAGING

Ultrasound imaging is one of the most widely used imaging

technologies in medicine. This imaging modality has achieved excellent

patient acceptance because it is safe, fast, painless and relatively inexpensive

when compared with the other imaging modalities. It has the additional

advantage of portability and may be used at bedside, which is very useful for

intensive care unit patients. Ultrasound provides detailed imaging of soft

tissues that is usually obscured in X-ray images. Unlike other tomographic

techniques, ultrasound imaging offers interactive visualization of the

underlying anatomy in real time.

Although ultrasound imaging has reached a high level of technical

sophistication, the usefulness of this imaging is degraded by the presence of

signal dependent noise known as speckle. It arises from the constructive and

destructive interference of ultrasound scattered from very small structures

within a tissue. Speckle noise deteriorates the image quality, fine details and

edge definition. It also tends to mask the presence of low-contrast lesions.

Only a skilled radiologist can make an effective diagnosis. In addition, the

presence of speckle complicates the image processing tasks like segmentation

(Hiransakolwong et al 2003), and pattern recognition. Hence, speckle

suppression is essential to improve the visual quality of ultrasound image and

possibly the diagnostic potential of medical ultrasound imaging.

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1.1 PHYSICS OF ULTRASOUND

Diagnostic ultrasound uses high frequency ultrasound waves to view

tissue structures and their motion. Ultrasound imaging is basically a non-

reconstructive imaging process wherein image information is obtained by

localizing an ultrasonic echo signal reflected from a scattering medium.

1.1.1 Nature of Ultrasound

Ultrasound is a mechanical wave, with a frequency for clinical use

between 1 and 15 MHz. The speed of sound in tissue is 1540 m/s. Medical

diagnostic ultrasound imaging is performed using a pulse-echo approach. A

probe is placed on the skin surface, and a transducer in the probe transmits

small pulses of ultrasound. The transducer (transmitter) works on the

piezoelectric principle, and converts electrical energy to mechanical energy.

The same transducer can be used to receive the returning echoes (receiver).

1.1.2 Propagation in Tissue

As ultrasound waves travel through tissues, they are partly

transmitted to deeper structures, partly reflected back to the transducer as

echoes, partly scattered, and partly transformed as heat.

For imaging purposes, the echoes reflected back to the transducer

are considered. When an ultrasound wave encounters a boundary between two

tissues with different values of acoustic impedances, a certain fraction of the

acoustic energy is reflected towards the transducer. The amount of reflection

depends upon the acoustic impedance. Refraction occurs when there is a

mismatch in acoustic impedance and the angle of incidence is not 90 degrees.

Refraction causes artifacts on an ultrasound image. Scattering of the

ultrasound beam occurs when it strikes structures which are approximately the

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same size as, or smaller than the wavelength. The magnitude and direction of

the scattered beam depend upon the shape, size, physical and acoustical

properties of the structure. As an ultrasound beam passes through the body, its

energy is attenuated by a number of mechanisms including reflection,

scattering and absorption. The signals received from the tissue boundaries

deep in the body are highly attenuated than those from boundaries which lie

close to the surface. In addition to backscattering from boundaries and small

structures, the intensity of the beam is reduced by absorption, which converts

the energy of the beam in to heat.

1.2 ULTRASOUND IMAGING & INSTRUMENTATION

In ultrasound imaging, the transducer uses an array of piezoelectric

elements to transmit an ultrasound pulse into the body and to receive the

echoes that return after reflection or scattering at tissue interfaces. The time of

arrival of the echo from a given interface depends on its depth. The ultrasound

after the transmission. The brightness of the echo on the display is determined

by the amplitude of the echo.

1.2.1 Ultrasound Modes

The various modes show the returning echoes in different ways.

Amplitude (A) mode scan acquires a one dimensional line image

which plots the amplitude of the back scattered echo versus time.

In brightness (B) mode the echoes are displayed as 2D gray scale

image. The amplitude of the returning echoes is displayed as points of

different gray-scale brightness corresponding to the intensity (amplitude) of

each signal.

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Motion (M) mode scanning acquires a continuous series of A-mode

lines and displays them as a function of time. The brightness of the displayed

M-mode signal represents the amplitude of the backscattered echo.

Doppler can be used to demonstrate the blood flow in the peripheral

vessels of adults.

1.2.2 Instrumentation

A block diagram of the basic instrumentation used for ultrasound

imaging is shown in Figure 1.1. The input signal to the transducer comes from

a frequency generator. The frequency generator is gated on for short time

durations and then gated off, thus providing short periodic voltage pulses.

These pulsed voltage signals are amplified and fed via transmit/receive switch

to the transducer. Since the transducer transmits both high power pulses and

also receives low intensity signals, the transmit and receive circuits must be

very well isolated from each other. The transmit/receive switch serves this

purpose. The amplified voltage is converted by the transducer into a

mechanical pressure wave which is transmitted into the body. Reflection and

scattering from boundaries and structures within a tissue occur. The

backscattered pressure waves reach the transducer at different times dictated

by the depth in tissue from which they originate, and are converted into

voltage by the transducer. These voltages have relatively small values, and so

pass through a very low-noise preamplifier before being digitized. Time gain

compensation is used to reduce the dynamic range of the signals, and after

appropriate amplification and signal processing, the images are displayed in

real time on the computer monitor.

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1.3 IMAGE QUALITY AND RESOLUTION

The quality of the produced ultrasound image depends on the image

resolution, axial and lateral. Resolution is defined as the smallest distance

between two features such that the features can be individually resolved.

Axial resolution refers to the ability of representing two points that lie along

the direction of ultrasound propagation. It depends on the wavelength of the

beam. In B-mode ultrasound pulses consist of one to two sinusoidal

wavelengths and the axial resolution is dependent on the wavelength of the

waveforms, and lies in the range of the ultrasound wavelength. The resolution

depends on the frequency of the beam waveforms. Since this value is

reciprocal to the ultrasound frequency, the axial resolution improves with

increasing frequency.

Figure 1.1 Basic ultrasound imaging system (Smith & Webb 2010)

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Lateral resolution refers to the ability to represent two points that

lie at right angle to the direction of ultrasound propagation. This is dependent

on the width of the ultrasound wave (beam). To be able to resolve points that

lie close together, the width of the ultrasound beam has to be kept reasonably

small and the diameter of the transducer is kept as large as possible (i.e. small

phase-array transducers have a worse lateral resolution than large linear or

curved-array transducers).

1.3.1 Image Artifacts

The term artifact refers to any feature in the image which does not

correspond to actual tissue structures, but rather to errors introduced by the

imaging technique or instrumentation. Such artifacts must be recognized to

avoid incorrect image interpretation. The main artifact in ultrasound imaging

is speckle, which arises from the constructive and destructive interference of

ultrasound scattered from very small structures within a tissue. This

complicated wave pattern gives rise to high and low intensities within the

tissue which are not correlated directly with any particular structure.

There are several other artifacts that appear in ultrasound images.

Reverberation artifact occurs when the ultrasound wave encounters a very

strong reflector in its path. The wave bounces back and forth between the

surface of the transducer and the reflector and causes multiple reflections.

These reflections cause multiple lines in the image. These artifacts can easily

be detected due to the equidistant nature of the lines. Acoustic enhancement

occurs when there is an area of low attenuation relative to the surrounding

tissue, and therefore structures lying deeper and in line with this area show

artificially high signal intensity. Areas which contain a high proportion of

water such as cysts can show this effect. The opposite phenomenon, termed

acoustic shadowing, occurs when a highly attenuating medium results in a

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dark area deeper below the attenuating medium. Solid tumours are one

example of tissues which cause acoustic shadowing.

1.3.1.1 Noise in Digital Images

Noise in digital images occurs during image acquisition or

transmission process or even during reproduction of the image. Removal of

noise from an image is one of the most important tasks in image processing.

The knowledge about the imaging system and the visual perception of the

image helps in generating the noise model and estimating of the statistical

characteristics of noise embedded in an image. The four important classes of

noise encountered in digital images are additive noise, impulse noise,

quantization noise and multiplicative noise (Acharya & Ray, 2005).

Additive Noise: Sometimes the noise generated from sensor is thermal white

Gaussian, which is essentially additive and signal independent and it is

represented as in Equation (1.1)

(1.1)

where is the result of the original image function corrupted by

the additive Gaussian noise .

Impulse Noise: Quite often the noisy sensors generate impulse noise.

Sometimes the noise generated from digital image transmission system is

impulsive in nature, which can be modelled as in Equation (1.2)

(1.2)

where is the impulse noise and p is a binary parameter that assumes

the values of either 0 or 1. The impulse noise is also known as salt and pepper

noise.

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Quantization Noise: The quantization noise is a signal dependent noise and

is characterized by the size of quantization interval. It produces image like

artifacts and may produce false contours around the objects. The quantization

noise removes the image details which are of low contrast.

Multiplicative Noise: The graininess noise from photographic plates is

multiplicative in nature. Speckle noise occurs in coherent imaging systems

such as LASER, SAR and ultrasound imaging is also multiplicative in nature,

which may be modelled as in Equation (1.3).

(1.3)

where is the multiplicative noise. It is a signal dependent noise whose

magnitude is related to the value of the original pixel.

Speckle noise is difficult to reduce, since it is multiplicative and

signal dependent in nature. This study aims to reduce the system noise artifact

speckle in ultrasound images.

1.3.2 Speckle Noise Model

Speckle is described as one of the most complex image noise

models. The block diagram in Figure 1.2 (Loizou & Pattichis 2008) explains

the entire track of the RF-signal from the transducer to the screen inside the

ultrasound imaging system. The statistics of the signal is affected since it is

subjected to several transformations. The most important of these is the log-

compression of the signal which is employed to reduce the dynamic range of

the input signal to match the lower dynamic range of the display device. The

input signal could have a dynamic range of the order of 50-70 dB whereas a

typical display could have a dynamic range of the order of 20-30 dB.

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Figure 1.2 The processing steps of the RF signal inside the ultrasound scanner (Loizou & Pattichis 2008)

The speckle reduction methods described in this work are based on

the noise model as proposed by Loizou & Pattichis (2008). The speckle noise

model may be approximated as multiplicative if the envelope detected signal

is captured before logarithmic compression and may be defined as in

Equation (1.4).

(1.4)

where represents the noisy pixel in the middle of the moving window,

represents the noise-free pixel, and represent the

multiplicative and additive noise respectively, and are the indices of the

spatial locations. This model is considered in this work, as it can be applied

on the images as displayed by the ultrasound machine rather than the

envelope detected echo signal. Speckle reduction algorithms estimate the true

intensity , as a function of the intensity of the pixel , and some

local statistics calculated within the neighbourhood of this pixel. Since

Wagner et al (1983) showed that the histogram of amplitudes of the envelope

detected RF-signal backscattered from a uniform area has a Rayleigh

Screen

ScanConverter

Demodulation

logcompression

Pulser

Transducer

Ultrasound Scanner

ReceiverOver all gain

TGC

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speckle could be modelled as multiplicative noise.

Nonlinear processing such as logarithmic compression, employed

on ultrasound echo images, affects the speckle statistics in such a way that the

local mean becomes proportional to the local variance rather than the standard

deviation. More specifically, logarithmic compression affects the high

intensity tail of the Rayleigh and Rician Probability Density Functions (PDF)

more than the low intensity part. As a result the speckle noise becomes very

close to white Gaussian noise corresponding to the uncompressed Rayleigh

signal (Dutt 1995). The envelope at the output of the demodulator before

logarithmic compression may thus be approximated as in Equation (1.4).

Since the effect of additive noise, such as sensor noise is

considerably smaller and less significant when compared with that of the

multiplicative noise component (Achim et al 2001) and it is expressed in

Equation (1.5)

(1.5)

The multiplicative model in Equation (1.1) can be approximated as

in Equation (1.6)

(1.6)

The logarithmic compression transforms the model in

Equation (1.6) into an additive noise form as given in Equation (1.7)

(1.7)

(1.8)

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After the logarithmic compression the noisy pixel is denoted

as , the noise free pixel , and the multiplicative noise component

are represented as , and respectively.

1.3.3 Ultrasound Image Database

The performance of the proposed methods is investigated using

ultrasound images of breast, liver, gall bladder, and kidney. The ultrasound

images are collected from KG Hospitals and MM scan centre, Coimbatore.

The ultrasound image of liver is obtained from public image database

Medison available at http://www.medison.ru/uzi. All the real images are

obtained after logarithmic transformation. In the case log transformed images,

quantitative evaluation of speckle reduction methods is problematic due to the

absence of noise free image. So, for the quantitative evaluation, artificial

speckled images are simulated using imnoise command in MATLAB.

1.4 IMAGE QUALITY EVALUATION METRICS

The quality of a despeckled image is examined by the following

standard image quality assessment metrics. The original image is represented

by and the despeckled image is represented by .

1.4.1 Peak Signal to Noise Ratio

Peak Signal to Noise Ratio (PSNR) (Sakrison 1997) is used to

measure the difference between the original and despeckled images of size

M x N, and is estimated using Equation (1.9). It is expressed in decibel (dB).

(1.9)

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where 255 is the maximum intensity in the gray scale image and MSE is the

Mean Squared Error and is given in Equation (1.10).

(1.10)

1.4.2 Root Mean Square Error

Root Mean Square Error (RMSE) (Gonzalez &Woods 2008) is the

square root of the squared error averaged over M x N window and is

calculated using Equation (1.11).

(1.11)

1.4.3 Edge Preservation Index

The edge preservation ability of the filter is assessed using Edge

Preservation Index (EPI) (Sattar et al 1997) and is computed as in

Equation (1.12).

(1.12)

where and represents the edge images of original image

and the despeckled image respectively. The edge images are the

high pass filtered versions of images x and , obtained with a 3x3 pixel

standard approximation of the Laplacian operator. The and are the

mean intensities of and respectively. If the edge is preserved well

during despeckling process, the edge preservation index will be close to unity.

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1.4.4 Correlation Coefficient

Correlation Coefficient (CoC) (Sattar et al 1997) is used to measure

the similarity between the original image and despeckled image, which is

given in Equation (1.13).

(1.13)

where and are the mean of the original and despeckled image

respectively.

1.4.5 Feature Similarity Index

Feature Similarity (FSIM) Index (Zhang et al 2011) calculates the

similarity between the original image and the despeckled image . It

computes the local similarity map and then pools the similarity map into a

single similarity score. Phase Congruency (PC) and Gradient Magnitude

(GM) are the two features extracted to obtain FSIM. The PC maps and

are extracted from and , and and are the GM maps extracted

from them.

The similarity measure for and is defined as in

Equation (1.14).

(1.14)

where is a positive constant to increase the stability of . The similarity

measure for and is given by Equation (1.15).

(1.15)

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where is a positive constant. and are combined to get the

overall similarity and is given in Equation (1.16)

(1.16)

FSIM is formulated as in Equation (1.17)

(1.17)

where and k is the spatial location.

1.4.6 Speckle Suppression Index

Speckle Suppression Index (SSI) is used to measure the quality of

the real ultrasound images and is given in Equation (1.18)

(1.18)

This index tends to be less than 1 if the filter performance is

efficient in reducing the speckle noise (Sheng & Xia 1996, Riyadi et al 2009).

Lower values indicate better performance of speckle filtering.

1.5 LITERATURE REVIEW

Several approaches have been proposed over the years for speckle

reduction in ultrasound imaging and other coherent imaging systems. They

include local statistics filtering, transform domain filtering, non local

approaches, total variation and partial differential equation based approaches.

A survey was done on the existing methods for speckle reduction in

medical ultrasound images, and some of them are described below.

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The speckle reduction techniques can be classified into:

Spatial domain techniques

Transform domain techniques

1.5.1 Spatial Domain Techniques

The image enhancement techniques in the spatial domain are based

on the operations performed on local neighbourhoods of input pixels. The

image is usually convolved with a spatial mask also known as kernel or

window. The size of the window must be odd. The simplest linear filter in

spatial domain is the mean filter (Gonzalez & Woods 2008). It replaces each

pixel in an image by the average value of the intensity values in the

neighbourhood. Since it introduces blurring effect, it is not suitable for the

removal of speckle noise. To overcome the drawbacks of the mean filter, the

adaptive mean filters have been proposed. These filters perform averaging in

homogeneous areas, and when the edges are detected, the filter passes the

original signal unchanged. The standard adaptive mean filters for speckle

reduction are Lee (Lee 1980), Kuan (Kuan et al 1985), and Frost (Frost et al

1982).

Median filter, a well known nonlinear filter (Nixon & Aguado 2002)

is used to reduce speckle noise in ultrasound images. It replaces the original

value of the pixel by the median of the gray level values of the pixels in a

specific neighbourhood. It retains edges and produces less blurring than the

mean filters. An Adaptive Weighted Median Filter (AWMF) was proposed to

achieve maximum speckle reduction in ultrasonic images and to preserve

edges and features (Loupas et al 1989). However, this algorithm uses an

operator which is fixed in shape (i.e. round: two points at equal distance from

the central pixel receive identical weight) and can cause difficulties in

enhancing image features such as line segments. Czerwinski et al (1995)

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presented a median filter which is comprised of a bank of oriented one

dimensional median filters. These filters were applied to the image and each

pixel in the image was replaced by the largest value among all the filter bank

outputs. The filter was able to preserve the thin bright streaks, which tend to

occur along boundaries between tissue layers. The performance was found to

be superior to block median filtering. To reduce speckle noise in ultrasound

images and to improve their suitability later for feature extraction, mode filter

(Evans & Nixon 1995) was proposed. The mode of the distribution of

brightness values in each neighbourhood is defined as the most likely value.

However, for small neighbourhoods, the mode is poorly defined, and an

approximation to this can be obtained with a truncated median filter. The test

results confirmed the edge preserving properties of the filter.

Qiu et al (2004) proposed a local adaptive median filter for speckle

noise reduction in SAR imagery. The filter uses the local statistics to detect

speckle noise and to replace it with the local median value. The local standard

deviation of the moving window helped to improve the differentiation of

speckle noise from valid pixels by incorporating the local instead of global

statistics. The local mean is used to define the valid pixel range. The filter

updates only the pixels that are identified as noisy with the local median

value, while the valid central pixels are kept unchanged. The major problem

of the median filter is the selection of window size; a larger window size

suppresses noise effectively but fails to preserve edges. In multi-scale median

filter (Wang 2010) though a larger window size was employed the edges were

still preserved. Mohammed Mansoor Roomi & Jayanthirajee (2011) presented

a technique using Particle Swarm Optimization (PSO) for removing speckle

noise from ultrasound images. Modified Hybrid Median Filter (MHMF)

(Vanithamani et al 2010) was initially used to reduce the speckle noise

present in the image. PSO was used to compute the weighting factors to

convolve with the median values computed using MHMF to recover the

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corrupted image. The variance of the estimated uniform block was used as the

objective function of the PSO.

Tomasi & Manduchi (1998) introduced the concept of bilateral

filtering, which is a nonlinear filter. It performs spatial weighted averaging

without smoothing edges. The method is non iterative, local, and simple. It

combines gray levels based on both their geometric closeness and their

photometric similarity, and prefers near values to distant values in both

domain and range. An important issue with the bilateral filter is, there is no

theoretical work on optimization of the filter parameters. It was implemented

for medical image denoising (Bhonsle et al 2012), since it can help the

physicians or radiologists to diagnose the diseases effectively. The algorithm

was tested using medical images like MRI, CT, X-ray and ultrasound

corrupted by Additive White Gaussian Noise (AWGN) and salt and pepper

noise. The results helped them to conclude that the bilateral filter was

effective for the removal of AWGN and not the salt and pepper noise. An

adaptive bilateral filter was introduced by Farzana et al (2010) for

despeckling of medical ultrasound images. The main focus of the approach

was on adaptive estimation of range parameter of the bilateral filter. It is

estimated from block based intensity homogeneity measurements. For each

pixel, the local neighbours in different directions are used to identify the most

homogeneous blocks. The range parameter is then estimated from the

variance of these blocks and thus, the filter automatically gets adapted

according to the variations of the speckle noise. The performance of this

method was studied using both synthetically speckled and real ultrasound

images.

In homomorphic Wiener filter (Jain 1989), logarithmic

transformation is used to convert the multiplicative noise into additive noise,

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and then Wiener filter is applied. At the last stage exponent of the filtered

image is taken.

Nonlinear Diffusion removes speckles by modifying the image via

solving a PDE. Yu & Acton (2002) presented a Speckle Reducing Anisotropic

Diffusion (SRAD) method for ultrasonic and radar imaging applications. The

SRAD was derived based on the context that Lee and Frost filters can be

casted as partial differential equations. Similar to Lee and Frost filters, SRAD

also utilizes the instantaneous coefficient of variation, which was shown to be

a function of the local gradient magnitude and Laplacian operators. The

algorithm was tested for both SAR and ultrasound images and it provided

superior performance in comparison to anisotropic diffusion and other

standard filters for speckle reduction. A new Non-linear Coherent Diffusion

(NCD) model (Abd-Elmoniem et al 2002), was proposed for speckle

reduction and coherence enhancement of ultrasound images. The NCD model

was a combination of three different models, and changed progressively from

isotropic diffusion through anisotropic diffusion to, finally curvature motion

according to the speckle extent and image anisotropy. This structure used low

pass filter to the parts of the image that correspond to fully developed speckle

and substantially preserved the information associated with resolved-object

structures.

Chen et al (2003) proposed an adaptive algorithm for speckle

reduction. In that algorithm, homogeneous regions were processed with an

arithmetic mean filter, and edge pixels were filtered using a non linear median

filter. The performance of the filter was compared with the adaptive weighted

median filter and the homogeneous region growing mean filter.

Zhang et al (2004) proposed an adaptive filter based on the Two

Dimensional adaptive Least Mean Square filter (TDLMS) design for speckle

reduction. They have used simple weighted local dynamics to verify the local

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between the weighted local dynamics and the ideal local dynamics. The filter

structure was easy to implement and performed well in both speckle

suppression and details preservation.

A new algorithm referred to as Anisotropic Savitzky - Golay filter

was developed by Chinrungrueng & Toonkum (2004) for real-time speckle

reduction and coherence enhancement of ultrasound images. It is the two

dimensional weighted Savitzky - Golay filter enhanced with a mechanism for

adjusting both the degree and direction of the smoothing so that they

both match the anisotropic properties of each loca1 regions in the image.

The authors concluded that their method was more effective in both reducing

speckle noise and coherence enhancement than both adaptive speckle filter

and adaptive weighted median filter.

Yang & Fox (2004) proposed a new compound scheme using

median and anisotropic diffusion to reduce speckle noise and enhance

structures in ultrasound images. The median-filter-based reaction term acted

as a source to enhance the structures in the image, and also regularizes the

diffusion equation to ensure the existence and uniqueness of a solution. In

addition a decimation and back reconstruction scheme was introduced to

further enhance the processing result. The image is first decimated and then

the diffusion process starts. This allowed the speckle noise to be broken into

impulsive or salt-pepper noise, which is easy to remove by median filtering.

Zhang et al (2007) proposed a Laplacian Pyramid-based Nonlinear

Diffusion (LPND), for speckle reduction in medical ultrasound imaging. The

ultrasound images in Laplacian pyramid domain. For non linear diffusion in

each layer, a gradient threshold is estimated automatically by Median

Absolute Deviation (MAD) estimator. The performance of LPND was

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compared with SRAD and NCD. The filter improved the image quality and

A kernel anisotropic diffusion method (Yu et al 2008) was

proposed for low Signal to Noise Ratio (SNR) images for robust noise

reduction and edge detection. A kernelized gradient operator was incorporated

in the diffusion for more effective edge detection. To enhance the robustness

of the method adaptive diffusion threshold estimation and automatic diffusion

termination criterion were also introduced.

Rui et al (2008) discussed about the statistical Nakagami

distribution and analytical multiplicative noise models of speckles in

ultrasound images, and then proposed an adaptive filter, named as Nakagami

Multiplicative Adaptive Filter (NaMAF), based on these models for effective

speckle reduction and feature preservation. The parameters that measure the

speckle noise strength in ultrasound images were derived, and an adaptive

filter was designed based on these parameters. An unsharp masking filter

could well serve this purpose. Adaptive windowing technique was applied for

both noise reduction and detail preservation. Large windows were used to

suppress noise in homogeneous regions, and in heterogeneous regions small

windows were employed for filtering. Performance of the adaptive filter was

compared with that of standard speckle reduction filters, showing that the

NaMAF performed the best in terms of best visual effect and largest SNR.

Guo et al (2008) proposed a novel approach for speckle reduction

in ultrasound images using two dimensional homogeneity and directional

average filters. They built a two dimensional homogeneity histogram and

obtained a threshold using maximal entropy principle and based on the

threshold, the pixels were divided into two groups as homogeneous and non

homogenous sets. The pixels in the homogeneous set were unchanged, and

the non homogeneous set was handled iteratively using the directional

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average filters, and clinical breast ultrasound images were used to assess the

performance of this method.

Sanches et al (2008) presented a Bayesian denoising algorithm for

the removal of white Gaussian and multiplicative noise described by Poisson

and Rayleigh distribution. The algorithm was based on Maximum A

Posteriori (MAP) criterion, and edge preserving priors. The Sylvester

Lyapunov equation, which was developed in the context of control theory,

was used in the denoising algorithm.

To smooth out speckle noise and preserve edge information in

noisy images, Liu et al (2009) proposed a novel algorithm called speckle

reduction by adaptive window anisotropic diffusion. This method can

structure. The instantaneous coefficient of variation for the edge area was

calculated with more accuracy than a fixed window method. The authors also

presented a novel method for finding the diffusion coefficient.

Coupe et al (2009) proposed a Non Local (NL) means based filter

for speckle reduction in ultrasound images. Bayesian frame work was used to

derive a NL means filter adapted to a relevant ultrasound model, and was

denoted as Optimized Bayesian Non Local Means with block selection

(OBNLM). Guo et al (2011) proposed a modified version of NL filter known

as Modified Non Local (MNL) means filter for despeckling of medical

ultrasound images. The Rayleigh distribution was employed to model the

speckle. The MNL was implemented in two steps. First Maximum Likelihood

(ML) is employed to calculate the noise free intensity. In the second step NL

filter was used to restore the details. The authors tested the algorithm

experimentally using synthetic data and clinical ultrasound images, with the

help of the results the authors concluded that the proposed algorithm

outperformed some of the accepted state-of the art filters in despeckling.

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As the presence of speckle in the ultrasonic image adversely

impacts the contrast and resolution in the image, it poses serious problems in

the interpretation of B mode images of internal organs such as breast, liver,

kidney and so on. Classification of these regions of interest also becomes

error prone. In order to enhance the contrast of speckled images, Shankar

(2009) proposed a new class of spatial filters based on cylindrical Bessel

function of the first kind. The author identified the optimum window size as

9x9 even though in some cases 7x7 might be sufficient. The resolution and

contrast decide the window size, the former suggested the use of a smaller

window size and the latter suggested the use of a larger window size. The

ability of the Bessel filter was compared with Gabor filters, the results

demonstrated that the filter reduced speckle both visually and quantitatively.

1.5.2 Transform Domain Techniques

Image transforms play an important role in digital image processing.

They are employed in applications like image denoising, image compression,

object recognition, etc.

Riyadi et al (2009) proposed a method to suppress the speckle noise

while attempting to preserve the image content using combination of

Gaussian filter and Discrete Cosine Transform (DCT). Karhunen Loeve

(KL) transform with overlapping segments was used to filter out the

multiplicative noise from ultrasound images (Al-Asad et al 2009).A

multilevel thresholding technique in curvelet transform domain using cycle-

spinning was proposed for effective speckle reduction (Binh & Khare 2010).

Liu & Jiang (2013) proposed a novel despeckling algorithm for medical

ultrasound images based on edge detection of the images using directional

information of the contourlet transform. A method based on the combination

of contourlet transform and anisotropic diffusion was presented to remove

noise from ultrasound images effectively with less loss of edge details

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(Xuhuiet al 2013). Hashemi Berenjabad & Mahloojifar (2013) proposed a

new contourlet based compression and speckle reduction method for

ultrasound images.

Achim et al (2001) presented a novel method for suppression of

speckle noise in medical ultrasound images. The subband decompositions of

ultrasound images have significant non-Gaussian statistics, and were using

alpha-stable. A Bayesian estimator that makes use of these statistics was

designed. Then the Alpha-stable model was used to develop a nonlinear blind

noise-removal processor. Results showed that the processor developed was

more effective than thresholding methods, but this approach was

computationally expensive due to the fact that the prior distribution

parameters need to be estimated at each scale of interest. However it was

suggested that this method can be used in off-line processing.

Rangsanseri & Prasongsook (2002) proposed a speckle reduction

algorithm based on the Stationary Wavelet Transform (SWT). High-

frequency subband coefficients were filtered using Wiener filter. The

despeckled image was obtained by reconstruction from the filtered

coefficients. SAR image was used to test the effectiveness of the algorithm.

Pizurica et al (2003) proposed a robust wavelet domain medical

image denoising technique which adapted itself to various types of image

noise as well as to the preference of the medical experts. A single parameter

was used to balance the feature preservation against the degree of noise

reduction. They have demonstrated its usefulness for noise suppression in

ultrasound and Medical Resonance Imaging (MRI) images

A thresholding scheme NeighShrink was proposed by Chen et al

(2004), which incorporate neighbouring wavelet coefficients for image

denoising. The thresholding of wavelet coefficients was carried out according

24

to the magnitude of the squared sum of all the wavelet coefficients within the

neighborhood window. They also investigated for different Neighborhood

window sizes and found that a size of 3x3 is the best among all the window

sizes. Dengwen & Wengang (2008) improved the NeighShrink by

determining an optimal threshold and neighbouring window size for every

URE).

Thakur & Anand (2005) presented a study on the most suitable

wavelet shapes for ultrasound image denoising. In this study, they compared

the different wavelets based on the parameters PSNR, Normalized Mean

Squared Error (NMSE) and CoC; these objective measures helped them in

selecting the best wavelet, the level of decomposition for efficient denoising.

They tested for different ultrasound images and tabulated only for ultrasound

image of liver and identified that the bi-orthogonal filters performed well in

almost all cases and decomposition of images up to five levels resulted in the

best compromise between NMSE and CoC.

Mastriani & Giraldez (2005) developed a wavelet domain method

SmoothShrink for removing speckle of unknown variance from SAR images.

The SAR image was decomposed into wavelet subbands, and a Directional

Smoothing (DS) was applied within each high subband. The denoised image

was obtained by reconstructing the modified detail coefficients. The DS

performed spatial filtering in a square moving window and was based on the

statistical relationship between the central pixel and its surrounding pixels.

The size of the window used for DS filter can range from 3x3 to 33x33, but

the studies show that 3x3 or a 7x7 window yielded better results when

compared to the others.

Yue et al (2005) introduced Multi-scale Nonlinear Wavelet

Diffusion (MNWD) method for speckle reduction and edge enhancement of

ultrasound images. MNWD took the advantage of the sparsity and multi

25

resolution properties of wavelet, and the iterative edge enhancement feature

of non linear diffusion. The authors validated the method using synthetic and

real echocardiographic images.

Gupta et al (2005) presented a novel technique for despeckling the

medical ultrasound images using lossy compression. The logarithmic

transformation was applied to the input image, and was decomposed to more

than one level. The subband coefficients were modelled using the generalized

Laplacian distribution and based on this model a uniform threshold quantizer

was proposed to achieve simultaneous speckle reduction and quantization.

Simulation results using a contrast detail phantom image and several real

ultrasound images were also presented.

Lee & Rhee (2005) proposed a simple and efficient algorithm for

adaptive noise reduction by combining the linear filtering and thresholding

methods in the wavelet transform domain. The image is decomposed into

subbands. Constrained Least Squares (CLS) filter was applied to the LL

subband and soft thresholding was applied to the detail subbands LH, HL,

HH. Finally the image is reconstructed from the denoised subbands. The

algorithm was tested for different noise levels and compared with the standard

wavelet shrinkage techniques and Wiener filter. It exhibited much better

performance in both Peak Signal to Noise Ratio and visual effect.

A speckle reduction technique for Synthetic Aperture Radar using

Fuzzy thresholding in wavelet domain was proposed by Mastriani (2006). In

the wavelet transform based despeckling methods; an initial threshold was

estimated according to the noise variance for thresholding the coefficients of

the detail subband. This method used an additional fuzzy thresholding

approach for automatic determination of the rate threshold level around the

initial threshold and used for soft or hard thresholding. This process was

applied to the wavelet coefficients of the first level. With the help of

26

simulation results obtained, the author concluded that the performance of the

Fuzzy Thresh algorithm was superior when compared to the most commonly

used statistical filters and wavelet thresholding techniques for SAR imagery.

Wang & Zhou (2006) proposed a denoising algorithm for medical

images by combining Total Variation (TV) minimization scheme and the

wavelet scheme. In addition to noise removal the proposed method

maintained sharpness of the objects. More importantly, the scheme also

allowed implementing an effective automatic stopping time criterion. The

optimal results were obtained through trial and error experiments with the

threshold.

In order to make the noise in the log-transformation domain behave

very close to that of white Gaussian noise, Michailovich et al (2006)

introduced a simple pre-processing procedure. The pre-processing modified

the acquired radio-frequency images (without affecting the anatomical

information they contain), and allowed filtering methods based on assuming

the noise to be white and Gaussian. Three different nonlinear filters, wavelet

denoising, total variation filtering and anisotropic diffusion were evaluated. In

all these cases, the proposed pre-processing significantly improved the quality

of resultant images.

Arivazhagan et al (2007) analyzed the performance of soft

thresholding for four levels of wavelet decomposition. They have tested for

images corrupted by speckle noise using Meyer wavelet filter. After

conducting exhaustive experiments the conclusion was, the highest PSNR was

obtained for first level of decomposition in case of most of the speckled

images and second level of decomposition was required for only higher level

of noise densities.

27

A versatile wavelet domain despeckling technique for visual

enhancement of the medical ultrasound images was presented (Gupta et al

2007) in order to improve the clinical diagnosis. The speckle wavelet

coefficients were modelled using two-sided Generalized Nakagami

Distribution (GND) and the signal wavelet coefficients are approximated

using the Generalized Gaussian Distribution (GGD). The thresholding

estimators were derived by combining these statistical priors with the

Bayesian MAP criterion for processing the wavelet coefficients of detail

subbands. Consequently, the authors have implemented two blind speckle

suppressors named as GNDThresh and GNDShrink and evaluated on both the

artificial speckle simulated images and real ultrasound images. The algorithm

could deal directly with either envelope detected speckle image or log

compressed medical image without any pre-transform. To adapt the estimator

to the local image statistics (homogeneous to highly-heterogeneous areas),

signal variance was estimated from the local neighborhood using scale-space

adaptive window. A tuning parameter was used to make the method user-

interactive to suppress the speckle according to the preference of medical

expert. The authors have demonstrated the performance superiority of the

proposed algorithm over well-known spatial domain filters and state-of-the-

art wavelet based denoising techniques in terms of different quantitative

metrics.

Bhuiyan et al (2007) developed a closed-form Bayesian wavelet-

based maximum a posteriori denoiser in a homomorphic framework for

despeckling medical ultrasound images. It modelled the wavelet coefficients

of the log-transform of the reflectivity with a Symmetric Normal Inverse

Gaussian (SNIG) prior. The method was made spatially adaptive by

estimating the parameters of the SNIG prior using local neighbours, and was

tested using both synthetically speckled and real ultrasound images.

28

Zhang & Gunturk (2008) presented a multi-resolution bilateral

filtering for image denoising. The authors first identified the optimal

parameter values for the bilateral filter. The multi-resolution image denoising

frame work proposed by the authors integrates bilateral filtering and wavelet

thresholding. The image was first decomposed into approximation and detail

subbands. At each decomposition level, approximation subbands are denoised

using bilateral filter and wavelet thresholding is applied on detail subbands.

The bilateral filter is applied after reconstruction also. The optimal value of

r was found to be linearly proportional to the standard

d was relatively

independent of noise power. The parameters used for the bilateral filter were

d r n and the window size was 11×11. BayesShrink was used for

wavelet thresholding.

In order to suppress the Gaussian noise, the noisy image undergoes

several iterations in case of TV based denoising. The more number of

iterations lead to blurring effect. Hence the major disadvantage of TV based

denoising was estimation of number of iterations. To overcome the drawbacks

of TV filter, Bhoi & Meher (2008) proposed TV based wavelet domain filter.

The image was first decomposed to a single level using wavelet transform and

the LL subband was used to find the horizontal, vertical and diagonal edges.

The pixel positions of these edges were used to retain the wavelet coefficients

in the respective subbands and other coefficients were made to zero. The LL

subband was filtered using TV method for single iteration only. The image

was reconstructed using the modified wavelet coefficients with little noise and

was filtered by applying TV method for a single iteration. From the

experimental results the authors observed that their method outperformed

even the wavelet based bench mark filtering schemes.

29

An efficient and adaptive method of threshold estimation

(Gnanadurai et al 2009) was developed for removal of speckle noise from

SAR images based on Undecimated Double Density Wavelet Transform

(UDDWT).The threshold value for the wavelet coefficient was estimated by

analyzing the statistical parameters of the wavelet coefficients like Arithmetic

mean, Geometric mean and Standard Deviation. The estimated threshold was

used in soft thresholding technique to remove the noisy wavelet coefficients.

Nasri & Nezamabadi-pour (2009) proposed a new nonlinear

thresholding function for image denoising in wavelet domain. The function

was suitable for Gaussian noise and speckle noise reduction. To improve the

efficiency of the denoising algorithm, it was used in a new subband adaptive

Thresholding Neural Network (TNN). A new adaptive learning method was

also introduced for TNN based image denoising, in which the threshold and

the thresholding function effects were considered simultaneously.

A context based adaptive wavelet thresholding method was

proposed (Sudha et al 2009) for speckle reduction in ultrasound images. The

noisy image was decomposed up to two levels using db8 wavelet. The

adjacent wavelet scale coefficients were multiplied to achieve the inter scale

dependency and which could sharpen the important structures while reducing

noise. The authors proposed a background support threshold selection scheme

for a coefficient dependent choice of the threshold and defined weighted

variance for threshold estimation. To identify important features, the

thresholding was applied to the multi-scale products instead of the wavelet

coefficients. Soft thresholding was employed for thresholding of all subband

coefficients.

Daubechies complex wavelet transform (Khare et al 2010) was used

for despeckling of medical ultrasound images due to its approximate shift

invariance property and extra information in imaginary plane of complex

30

wavelet domain when compared to real wavelet domain. A wavelet shrinkage

factor was derived to estimate the noise free wavelet coefficients. Strong

edges were detected using imaginary component of complex scaling

coefficients and shrinkage was applied on magnitude of complex wavelet

coefficients in the wavelet domain at non edge points.

The key challenge of wavelet shrinkage is to find an appropriate

threshold value, which is typically controlled by the signal variance. To tackle

this challenge, a new image shrinkage approach AntShrink was proposed

(Tian et al 2010), which exploits the intra scale dependency of the wavelet

coefficients to estimate the signal variance using the homogeneous

neighbouring coefficients, rather than using all local neighbouring

coefficients in the conventional shrinkage approaches. Ant Colony

Optimization (ACO) was used to determine the homogeneous local

neighbouring coefficients.

A hybrid model (Roy et al 2010) based on wavelet and bilateral

filter was presented for denoising of standard images, like X-ray images,

ultrasound and astronomical telescopic images. Application of bilateral filter

before and after decomposition of the image using discrete wavelet transform

enhanced the performance. In the case of wavelet thresholding, soft

thresholding was used with a threshold value of 0.01 using db8 filters. The

d, r and w) were varied to find the optimal

d ranges from 0.01 to 2.2, the window

r from 10 to70.

Rizi et al (2011) presented a comparative study of alternative

wavelet based ultrasound image denoising methods, particularly the

contourlet and curvelet techniques with dual tree complex and real and double

density wavelet transform. With the help of quantitative results obtained, the

31

authors concluded that curvelet based method performed superior as

compared to the other methods.

Li et al (2011) proposed an adaptive algorithm for speckle reduction

and feature enhancement of SAR images based on curvelet transform and

PSO. A gain function was developed by integrating the speckle reduction with

feature enhancement to nonlinearly shrink and stretch the curvelet

coefficients. An objective criterion was presented for the quality of

despeckled and enhanced images and improved PSO algorithm to adaptively

adjust the parameters of the gain function. They also introduced a new

learning scheme and a mutation operator for the classic PSO algorithm to

make its convergence speed fast and to avoid premature convergence.

In an edge preserved image enhancement method (Bhutada et al

2011) features of wavelet and curvelet transform utilized separately and

adaptively for homogeneous and non-homogeneous regions. As the

information of these different regions had been fused adaptively, the true

edges were not affected in denoising process and hence edge information was

preserved. The better smoothness in background was also achieved due to the

removal of fuzzy edges from homogeneous regions.

Andria et al (2012) proposed a set of denoising filters to improve the

quality of ultrasound images affected by speckle noise. The image was

decomposed to one level using discrete wavelet transform and the vertical and

diagonal details were filtered using a Gaussian filter with a kernel size based

upon the amplitude of speckle noise.

Gupta et al (2012) proposed a nonlinear filtering based on Rational

Dilation Wavelet Transform (RADWT) for the enhancement of medical

ultrasound images. The noisy image was decomposed up to four levels using

RAWDT. The bilateral filter was applied on the low frequency RADWT

32

coefficients of the final stage, and thresholding was applied to all other high

frequency coefficients. Finally the inverse rational dilation wavelet transform

was applied on the filtered coefficients to reconstruct the image.

TV method and wavelet thresholding were hybridized for speckle

reduction in ultrasound images (Abrahim et al 2012).The noisy image was

first decomposed into four subbands using db8 wavelet. The noise in the low

frequency subband LL was eliminated using TV based method and the

wavelet based soft thresholding was applied to the other three subbands. TV

method was used again in the last step to get the denoised image.

Gao et al (2013) proposed an algorithm for speckle reduction in

SAR images based on directionlet transform. The directionlet transform

coefficients of the logarithmically transformed reflectance image were

modelled using Cauchy PDF, while the distribution of speckle noise was

modelled as an additive Gaussian distribution with zero-mean. Then based on

the assumed priori models a MAP estimator was designed. And to estimate

the parameters from the noisy observations a regression-based method was

also proposed. The algorithm was tested on both synthetic speckled images

and real SAR images.

Deka & Bora (2013) presented a new wavelet domain technique for

despeckling of medical ultrasound images for improved clinical diagnosis.

The speckle in the detailed subbands of log transformed ultrasound images

was modelled using Generalized Gamma Distribution (GGAD) and

generalized Gaussian distribution was used for the signal. Combining these, a

priori distributions with the Bayesian maximum a posterior criterion,

shrinkage estimators were designed for processing the coefficients of the

detail subband.

33

1.6 SCOPE OF THE THESIS

Speckle noise reduces the contrast resolution in ultrasound imaging,

and thus affect human interpretation and accuracy of the computer aided

techniques.

Speckle reduction is always a trade-off between noise suppression

and loss of information and is therefore essential to retain as much of the

diagnostic information as possible. It is one of the important pre-processing

steps in a Computer Aided Diagnosis (CAD) system for breast cancer

detection and classification (Cheng et al 2010).

For CAD system, it is very important to have very low

computational complexity so that the filtering operation is performed in a

short time. Hence, there is enough scope to develop better speckle reduction

algorithms with low computational complexity that may yield higher noise

reduction as well as preservation of edges and fine details in an ultrasound

were considered for achieving better performance. The processing may be

done in spatial domain or in transform domain.

Therefore, the main objective of this doctoral research work is to

analyze and propose robust and effective methods for speckle reduction and

feature preservation of ultrasound images.

1.7 THESIS ORGANISATION

The chapter wise organization of the thesis is as follows. Chapter 1

gives an introduction to physics of ultrasound, instrumentation, image quality,

and quality evaluation metrics and also provides a literature review of various

speckle suppression methods in ultrasound imaging.

34

Chapter 2 discusses the drawbacks of the linear and nonlinear filters

in spatial domain. Based on hybrid median filter, a modified hybrid median

filter and an adaptive window hybrid median filter are developed and

discussed in detail.

In Chapter 3, a comparative study has been made to identify a

wavelet thresholding technique for effective despeckling of ultrasound

images.

Chapter 4 discusses the importance of bilateral filter and wavelet

thresholding. The proposed algorithms using bilateral filter and wavelet

thresholding are explained in detail. The performances of the proposed

algorithms are compared with the existing algorithms based on the objective

measures.

Chapter 5 concludes the thesis with a summary of the results and the

directions for future work.