CHAPTER 9 SPECKLE FILTERING TECHNIQUES FOR ...eprints.uthm.edu.my/id/eprint/11962/1/c9.pdfToshiba...

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Bioengineering Principle and Technology Applications Volume 2 ISBN 978-967-2306-26-9 2019

CHAPTER 9

SPECKLE FILTERING TECHNIQUES FOR DIFFERENT QUALITY LEVEL OF HEALTHY

KIDNEY ULTRASOUND IMAGES

Wan Nur Hafsha binti Wan Kairuddin Wan Mahani Hafizah binti Wan Mahmud

Ain binti Nazari

Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Batu Pahat, Johor, Malaysia

9.0 INTRODUCTION

The increasing reliance of modern medicine on diagnostic

techniques such as computerized tomography, histopathology, magnetic

resonance imaging, radiology and ultrasound imaging shows the

importance of medical images [1]. Ultrasound (US) imaging is an

imaging technique that is far the least expensive and most portable

comparing to other standard medical imaging modalities. US imaging is

a safe technique, easy to use, noninvasive nature and provides real time

imaging, hence it is used extensively. But on the downside, ultrasound

imaging has a poor resolution of image compared with other medical

imaging instrument like Magnetic Resonance Imaging (MRI). US has

wide spread application as a primary diagnostic aid of obstetrics and

gynecology, due to the lack of ionizing radiation or strong magnetic

fields. General US imaging applications include soft tissue organ and

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carotid artery [2].

The quality of US image is affected by the presence of the

speckle noise which leads to the difficulties in interpretation of US

image. Due to the presence of the Speckle noise in US images, the

enhancement of US image is extremely difficult in image of liver and

kidney whose

underlying structures are too small to be resolved by large wavelength

[3].They also complicate further image processing, such as image

segmentation and edge detection.

Filtering techniques used for image enhancement can be

classified as spatial filtering and frequency domain filtering. Spatial

filtering is defined by a neighbourhood and an operation that is

performed on the pixels inside the neighbourhood or can be derived as

an image operation where each pixel value I (u, v) is changed by a

function of the intensities of pixels in a neighbourhood of (u, v). Two

types of spatial filtering are linear spatial filtering and nonlinear spatial

filtering. A filtering method is linear when the output is a weighted sum

of the input pixels and methods that do not satisfy the linear property are

called non-linear which involving the neighbourhood encompassed by

the filter. Frequency domain is a space defined by values of the Fourier

transform and its frequency variables (u, v). Relation between Fourier

Domain and image is u = v = corresponds to the gray-level average. Low

frequencies means image’s component with smooth gray-level variation.

Frequency domain filtering requires some steps to do the operation. The

steps are shown in Figure 9.1. The pre-processing stage might encompass

procedures such as determining image size, obtaining the padding

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parameters and generating the filter. Post processing entails computing

the real part of the result, cropping the image and converting it to certain

class for storage.

The intensity transformation function based on information

extracted from image. Intensity histogram plays a central role in image

processing, in area such as enhancement, compression, segmentation and

description.

Mean Squared Error (MSE) is the average squared difference

between an original image and a filtered image. It is computed pixel-by-

pixel by adding up the squared differences of all the pixels and dividing

by the total pixel count. Peak Signal-to-Noise Ratio (PSNR) is the ratio

between the original image and the filtered image, given in decibels (dB).

The higher the PSNR, the closer the filtered image is to the original

image. In general, a higher PSNR value should correlate to a higher

quality image, but tests have shown that this isn't always the case.

However, PSNR is a popular quality metric because it's easy

and fast to calculate while still giving good results.

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In this work, three different specifications of ultrasound

machine are used in order to get different quality of kidney ultrasound

image as each of the ultrasound machine used have different technology

and specifications. The image quality of the ultrasound image may varies

from one ultrasound machine to another. The ultrasound machines used

are:

Machine 1: Toshiba Nemio XG (SSA-580A) Machine 2: GE Healthcare (LOGIQ P5) Machine 3: Philips (HDII XE) This research is applying four well-known filtering techniques

mostly used to smoothing the speckle noise in ultrasound image. These

techniques include Median filtering, Wiener filtering, Gaussian low-pass

filtering and Histogram equalization. These conventional techniques for

denoising the speckle noise is chosen to be used in this project because

some of the advanced technology of ultrasound machines already been

built with a speckle reduction technology. So that, it may no need to use

advanced filtering techniques to remove the speckle noise because it may

eliminates some of the important edges which contains valuable features.

9.1 METHODOLOGY

The work starts with acquiring a B-mode of healthy kidney

images. These images are gathered from volunteer students and staff

from Faculty of Electrical & Electronics, Universiti Tun Hussein Onn

Malaysia (UTHM). 20 images from each ultrasound machines are

collected. All of images are then cropped to the region of interest (ROI)

before performing the enhancement for each of the image.

A good image enhancement technique will suppress the noise but still

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maintaining the edge and the texture of the image. In medical purposes,

choosing the suitable image filtering technique is important to secure the

critical diagnostic information for further diagnosis analysis. In this

work, the efficiency of all the filtering techniques is evaluated using

MSE and PSNR by comparing the filtered image with the original image

as reference. MSE and PSNR have inverse relationship where higher

value of PSNR will give a lower value of MSE.

MSE is the average squared difference between an original image and

a filtered image. It is computed pixel-by-pixel by adding up the squared

differences of all the pixels and dividing by the total pixel count. MSE

of the output image is defined as:

𝑀𝑀𝑀𝑀𝑀𝑀 =∑ ∑ |𝑥𝑥 (𝑖𝑖,𝑗𝑗)− 𝑥𝑥 � (𝑖𝑖,𝑗𝑗)|2𝑁𝑁

𝑗𝑗=1𝑀𝑀𝑖𝑖=1

𝑀𝑀𝑀𝑀 (1)

where 𝑥𝑥 (𝑖𝑖, 𝑗𝑗) is the original image, 𝑥𝑥 � (𝑖𝑖, 𝑗𝑗) is the output image and MN is the size of the image.

PSNR is the ratio between the original image and the filtered

image, given in decibels (dB). PSNR is defined as:

𝑃𝑃𝑀𝑀𝑃𝑃𝑃𝑃 = 20 log10 �2𝑛𝑛− 1√𝑀𝑀𝐹𝐹𝑀𝑀

� dB (9.2) where n is the number of bits used in representing the pixel of the image.

For grayscale image, n is 8.

9.1.1 Image Acquisition

For image acquisition, three different specification of

ultrasound machines are used. The convex probe transducer is set to

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frequency of 3.75MHz while the ultrasound machine frequency is set to

6MHz. These three machines are compared using three technologies that

have the most impact on ultrasound image quality. They are Tissue

Harmonics Imaging (THI), Compound Imaging and Speckle Reduction

Imaging [4]. The difference technologies used for each machine giving

variation of the image quality. THI is a technology that having a function

of the harmonic imaging used to reduce artifacts and noise by sending

and receiving signals at two different frequencies. This will help to

improve image quality because the body tissue will reflects sound at

twice the frequency that was initially sent. With that, a clean image with

reduce artifacts can be produced. Speckle Reduction Imaging works by

evaluating the image on a pixel-by-pixel basis where it can identify

tissue, so that it can reduce the speckle noise that occurs in the ultrasound

image. This technology uses some algorithm to identify weak and strong

signals. The weak signal will be removed while the strong signals will

be enhanced. A better and clear image can be produced.

Compound imaging is a technique that combines multiple

images from different angle to be a single image. The ultrasound sends

signals at multiple angles, so that the tissues can be seen at the different

angles. This can help to reduce artifacts in the image and produce a

clearer image. The ultrasound images of healthy kidney from the three

different ultrasound machines used are shown in Fig. 1 (a) – (c). Each

machine provides a different quality of images depending on the

technologies used for each machine.

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Figure 9.2: B-mode ultrasound images for healthy kidney using (a) Toshiba Nemio XG (SSA-580A), (b) GE Healthcare (LOGIQ P5) and (c) Philips (HDII XE)

9.1.2 Image Cropping

Cropping is an operation, which is performed on acquired

images to accentuate the ROI and to remove all the unwanted artifacts

such as written labels and background noise from them. Image cropping

is needed to speed up further image processing. In this work, manual

cropping is used where the image is cut in a rectangular shape which

consist only the ROI.

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9.1.3 Image Enhancement

Four enhancement techniques used in this work are Wiener

filter, Median filter, Gaussian LPF and Histogram Equalization. These

enhancement techniques are commonly used for enhance the US image

that is degraded by the speckle noise. These techniques have different

fundamental theories which include spatial filtering, frequency domain

filtering and histogram processing. The efficiency of all the filtering

techniques will be evaluated using MSE (Mean Square Error) and PSNR

(Peak Signal to Noise Ratio) by comparing the filtered image with the

original image as reference. MSE and PSNR have inverse relationship

where higher value of PSNR will give a lower value of MSE.

The description of each filtering technique is described as

followings:

i. Wiener Filter:

The Wiener filtering is a linear type of filter. It helps in inverting

the blur. Wiener filter removes additive noise in the image. It

optimally minimizes the overall mean square error in the

process of inverse filtering and noise smoothing.

ii. Median Filter:

The median filter is one of the nonlinear filter types. The

filtering is performed by replacing the median of the gray values

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of pixels into its original gray level of a pixel in a specific

neighborhood. The speckle noise as well as salt and pepper

noise can be reduced by using the median filter. The

neighborhoods’ spatial extent and the number of pixels

involved in the median calculation decide to what extent the

noise can be reduced.

iii. Gaussian Lowpass Filter:

Gaussian filtering is a frequency domain filtering. In Gaussian

filtering, the smoother cutoff process is used rather cutting the

frequency coefficients abruptly. It also takes advantage of the

fact that the discrete Fourier Transform (DFT) of a Gaussian

function is also a Gaussian function. The Gaussian low-pass

filter varies frequency components that are further away from

the image center.

iv. Histogram Equalization:

It is used for improving the contrast where it brightens the

image appearance and gives an improve quality of the image.

The histogram equalization is applied to identify the maximum

of the intensity value.

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Figure 9.3- 9.5 shows the US kidney image with original image

before enhancement and after enhancement with different enhancement

techniques.

Figure 9.3: US images before and after enhancement (Machine 1)


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9.2 RESULT & DISCUSSION

20 images from each US machine were used for measurement.

The assessment between the techniques is using the traditional distortion

measurements, MSE and PSNR as discussed earlier. The results for

image enhancement for all subjects are shown in Table 9.1. Based on

Based on Table 9.1, using Gaussian Lowpass Filtering technique has the

highest PSNR value for all US machine followed by Wiener Filtering,

Median Filtering and Histogram Equalization. Higher PSNR means that

the image contains more valuable signal compared to the noise in the

image. Based on the value of PSNR, it shows that Gaussian Lowpass

Filtering eliminates more noise compared to other enhancement

techniques while still preserve the important features of the images.

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Table 9.1: Mean value of MSE and PSNR [dB] for different enhancement techniques

ULTRASOUND MACHINE

TECHNIQUE MSE PSNR[dB]

1

Wiener Filter 95.21 28.47 Median Filter 175.95 25.78 Gaussian LPF 52.18 31.08 Histogram 2878.9 13.6

2


3


9.3 CONCLUSION

In this paper the aim is to de-noise a healthy kidney ultrasound

images with different level of image quality corrupted by speckle noise

using several enhancement techniques. Based on the result, by measuring

the MSE and PSNR, it shows that Gaussian LPF is the best technique in

enhancing the ultrasound kidney image regardless of any image quality

compared to other techniques used and Wiener filtering is the second

best technique. This method may be beneficial for future work to do the

segmentation and classification of kidney image where later can be used

to design a computer aided diagnosis (CAD) system for classification of

kidney to detect various kidney disease.

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