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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5837_Библиотеки_им_академика_М_И_Перельмана.pdf
2.4 Conclusions ........................................ 43
References
............................................. 44
3 Wavelet and Fast Bilateral Filter Based Despeckling Method
for Medical Ultrasound Images
............................. 47
3.1 Introduction of Wavelet
............................... 47
3.1.1 Backgrounds and Basics of Wavelet Transform
......... 47
3.1.2 Continuous Wavelet Transform
..................... 52
3.1.3 Two-Dimensional Wavelet Transform ................ 53
3.1.4 Discrete Wavelet Transform
....................... 54
3.1.5 Multi-resolution Analysis and Mallat Algori thm
........ 54
3.2 The Limitations of Traditional Wavelet Based
Denoising Method
................................... 55
3.3 Wavelet and Fast Bilateral Filter Based Despeckling Method
.... 56
3.3.1 The Model of Medical Ultrasound Image
............. 57
3.3.2 The Bilateral Filter and the Fa st Bilateral Filter
......... 59
3.3.3 A Despeckling Method Based on Wavelet and Fast
Bilateral Filter
................................. 62
3.4 Experimental Studies of the Despeckling Method ............. 66
3.4.1 Experiments for Simulated Ultrasound Images
.......... 66
3.4.2 Simulation Experiment 1
......................... 68
3.4.3 Simulation Experiment 2
......................... 68
3.4.4 Experiments for Medical Ultrasound Images
........... 69
3.4.5 Interpretations of Experimental Results ............... 69
3.5 Conclusions
........................................ 74
References
............................................. 75
4 Despeckle Filtering of Medical Ultrasonic Images Using Wavelet
and Guided Filter
....................................... 77
4.1 Backgrounds and Introductions
.......................... 77
4.1.1 Speckle Noise Models for Ultrasonic Medical Images
.... 79
4.2 Speckle Reduction of Ultrasound Images Based on Wavelet
and Guided Filter
.................................... 81
4.2.1 The Guided Filter
.............................. 81
4.2.2 Improvements on Wavelet Shrinkage Algorithms
........ 83
4.2.3 The Procedures of the Wavelet an d Guided Filter Based
Denoising Method
.............................. 90
4.3 Experimental Studies of the Wavelet and Guided Filter Based
Method
........................................... 91
4.3.1 Simulation Experiments: Synthetic Images Experiments
... 92
4.3.2 Experiments on Clinical Medical Ultrasound Images
..... 97
4.3.3 Discussion of the Experimental Results on Clinical
Medical Ultrasound Images
....................... 98
xii Contents
4.4 Conclusions ........................................ 101
References
............................................. 102
5 Despeckling Method for Medical Images Based on Wavelet
and Trilateral Filter
..................................... 103
5.1 Introduction
........................................ 103
5.2 A Despeckling Approach for Medical Ultrasound Images Based
on Wavelet and Trilateral Filter
.......................... 104
5.2.1 Model for Medical Ultrasound Images ............... 104
5.2.2 Some Backgrounds
............................. 105
5.2.3 Steps of the Proposed Despeckling Approach for Medical
Ultrasound Images Based on Wavelet
and Trilateral Filter
............................. 106
5.2.4 Main Aspects of the Despeckling Approach for Medical
Ultrasound Images
.............................. 107
5.2.5 Trilateral Filter
................................ 109
5.3 Experimental Studies of the Despeckling Method Based
on Wavelet and Trilateral Filter
.......................... 113
5.3.1 Experimental Results on Synthetic Images ............. 113
5.3.2 Experiments on Real Clinical Ultrasonic Images
........ 117
5.4 Conclusion
......................................... 121
References
............................................. 121
6 Nonsubsampled Shearlet and Guided Filter Based Despeckling
Method for Medical Ultrasound Images
...................... 123
6.1 Introduction of Shearlet
................................ 123
6.1.1 The Basic Principle of Shearlet Transformation
......... 124
6.1.2 Nonsubsampled- Shearlet Transformation and Its Inverse
Transformation
................................ 125
6.2 Nonsubsampled Shearlet and Guided Filter Based Despeckle
Method
........................................... 127
6.2.1 An Improved Shearlet Threshold Function
............ 127
6.2.2 Three-Variable Shrinkage Algorithm
................. 128
6.2.3 The Overall Steps of Nonsubsampled-Shearlet
and Guided Filter
............................... 132
6.3 Experiments and Analysis of Results
...................... 133
6.3.1 Experimental Verification
......................... 133
6.4 Conclusions
........................................ 141
References ............................................. 141
Contents xiii
About the Authors
Dr. Ju Zhang obtained his B.S. degree in Mechanical
Engineering from Zhejiang University of Technology
in 1994 and Ph.D. degree in Automatic Control
Engineering from Zhejiang University in 2005. From
2005 to 2006, he was Visiting Scholar at Stuttgart
University, Germany. From 2007 to 2010, he was
Associate Professor, and from 2011, he is Professor,
both at college of information Engineering, Zhejiang
University of Technology. From 2014 to 2015, he was
Visiting Scholar at Michigan State University, USA. His
research interests are in the areas of medical ima ge
signal processing and control, and model predictive
control. He has published research work in the Journal
of Franklin Institute, International Journal of Control,
Ultrasonics, International Journal of Automation control
and system, Asian Journal of Control, Circuits System
Signal Process, and Biomedical signal and control.
Dr. Yun Cheng is Associate Professor in the Depart-
ment of Ultrasound and Associate Director of Research
and Education Administration of Zhejiang Hospital.
From 2012 to 2013, she worked as Visiting Researcher at
Echo Laboratory of SUNY Upstate Medical University,
Syracuse, USA, where she was engaged in research of
new technology of echocardiography. Her academic
positions are member of Committee, Echo Branch,
Zhejiang Province Medical Association, and
Committee of Zhejiang Province Precision Medicine
Association. She has focused on clinical application of
echocardiography and denoising the processing of
ultrasound image for several years.
xv
Chapter 1
Introduction
Abstract This chapter presents an overview of digital image processing, image
denoising technology, the general motivations of despeckling, introduction of recent
research work on denoising of medical ultrasonic images, basic principle of ultrasonic
image, several kinds of diasonograph and the structure and organization of this book.
1.1 Digital Image Processing
1.1.1 Digital Image Processing and Applications
Digital images have been widely used since 1920s, and the technology of digital
image processing is developing rapidly in recent 3 decades. Digital image process-
ing first appeared in the 1950s, and the aim of image processing was to improve the
visual effect of human beings. In 1988, Mallat applied wavelet analysis to image
decomposition and reconstruction. Image processing is one of the most important
technologies for mankind to understand and change the world. With the rapid devel-
opment of computer technology, a large number of images today are digital images
in computer, and digital image processing technology has been developed [1, 2].
Image processing technology is divided into two categories: analog image process-
ing and digital image processing. Digital image processing technology is generally
implemented by computer or real-time hardware, and thus it is also called computer
image processing. The purposes of digital image processing are as follows [3]:
(1) Improve the visual sensory quality of images and produce images that are more
suitable for human visual observation and recognition. Such as image bright-
ness transformation (Fig. 1.1), color transformation, image enhancement, image
geometric transformation.
(2) Features or special information contained in the image are extracted to facilitate
computer analysis of the image (Fig. 1.2).
(3) Image data is transformed, encoded and compressed to improve the efficiency of
information transmission and reduce the storage capacity of image information.
(4) Using graphics and image techniques and methods to help people understand
and analyze data, and that is usually known as information visualization.
© Springer Nature Singapore Pte Ltd. 2020
J. Zhang and Y. Cheng, Despeckling Methods for Medical Ultrasound Images,
https://doi.org/10.1007/978-981- 15-0516-4_1
1
2 1 Introduction
Fig. 1.1 Effects of a picture at three levels of brightness
Fig. 1.2 The Edge of character figure detected by canny operator
Images in natural form were transformed into a digital form that suitable for
computer processing by sampling and quantifying. These images are stored in a
computer in the form of a matrix. The element in the matrix is the pixel value of each
point in the image (Fig. 1.3).
Digital image processing is actually manipulating the elements in these matrices.
In the process of image ingestion, digital images always contain noise because of the
existence of many uncertain factors. That is, the distortion of some element values
in the matrix corresponding to the image. We will replace those pixel values, which
are less associated with the surrounding pixel values, with more appropriate values.
In the early 1970s, digital image processing technology began to be used in med-
ical imaging [4]. In order to make better use of image processing technology to help
medical treatment, medical image processing has been one of the focus of research
in the field of signal and image process. Because of the existence of noise, the
fusion, enhancement and restoration of medical image must go through the process
of denoising first. Image denoising refers to the removal of noisy parts from known
images containing noise using various filtering models.
1.1 Digital Image Processing 3
Fig. 1.3 a An image, b image’s storage form in a computer, c a three-dimensional image that takes
the pixel value of each point in the image as a vertical coordinate
Digital image processing now has many kinds of usage:
(1) Image Fusion
We take medical images as an example. According to the information provided by
medical images, images can be divided into two categories: anatomical images and
functional images. These two kinds of images have their own advantages and disad-
vantages: anatomical images provide anatomical information of viscera with higher
resolution, but cannot reflect the function of organs; the resolution of functional
images is poor, but the information provided on organ function metabolism cannot
4 1 Introduction
Fig. 1.4 a An anatomical medical image, b a functional medical images, c an medical image fused
by image (a)andimage(b)
be replaced by anatomical images. Obviously, it would make a lot of sense to com-
bine the characteristics of these two images together. All we need to do for image
fusion is to combine the useful information from the two images together (Fig. 1.4).
(2) Denoising
Because of the uncontrollable factors in the environment, the digital image will
inevitably have noise. The presence of noise degrades image quality and make it
difficult to image recognition. And thus, before doing other image processes, we
must first denoise the image (Fig. 1.5).
(3) Image Segmentation
Image segmentation is the technique and process of dividing the image into sev-
eral specific and unique regions and proposing the object of interest (Fig. 1.6). It
is a key step from image processing to image analysis. The existing image seg-
mentation methods can be divided into four categories: threshold-based segmenta-
tion, region-based segmentation, edge-based segmentation and specific theory based
segmentation.
Fig. 1.5 Image with noise and the denoised image
1.1 Digital Image Processing 5
Fig. 1.6 The segmentation of characters and background
1.1.2 Image Denoising Technology
There are inevitable external and internal interference in the process of formation,
transmission, reception and processing of the obtained images, for example, sensitive
element in photoelectric conversion process with sensitivity inhomogeneity, quanti-
zation noise of digitized process, error in transmission process and human factors,
etc. All cases above can cause a certain degree of noise interference.
The image denoising process belongs to the preprocessing stage of the image
from the whole process of image analysis. The significances of image denoising are
as follows:
(1) Because of the different imaging mechanism, the original image contains a lot of
noise of different properties. The presence of these noises affects the observation
of images and interferes with their understanding of image information. When
the noise is serious, the image is almost deformed and thus the image loses its
essential meaning of storing information.
(2) In addition to improving the accuracy of human visual recognition information,
the significance of image denoising is that it is a reliable guarantee for further
processing of images. Image processes of feature extraction, registration or
image fusion directly from a noisy image will not be satisfactory, and thus
image denoising is necessary.
With two different filters, Fig. 1.7 shows the different result of denoising an image
which is contaminated by varying noise. We can see that the image Fig. 1.7b preserves
the true information of the image better, while the i mage Fig. 1.7c with Gaussian
filter blurs the entire image. The blurring phenomenon is one of the most impor-
tant problems in image denoising domain, especially in medical image denoising.
Removing the noise in an image without destroy the useful information is what many
scholars have made a lots effort to achieve.
6 1 Introduction
Fig. 1.7 a An image contaminated by varying noise, b the image denoised by bitonic filter, c the
image denoised by Gaussian filter
1.1.3 Signal Convolution
Convolution is widely used in digital image processing. Convolution is a mathemati-
cal method of integral transformation, and it has also been widely used in many other
aspects and fields. For image denoising, convolution is almost inevitable.
For 1-D signal, the convolution operations has such a characteristic that the result
of convolution will be large for places with similarities of two convolution functions,
and vice versa. In a sense, convolution operations reflect the degree of similarity
between two convolution functions at a certain area.
In digital image processing, a kernel, a convolution matrix, or a mask is a small
matrix. It is used for blurring, sharpening, embossing, edge detection, and so on. This
is accomplished by doing a convolution between a kernel and an image. Convolution
is the process of adding each element of the image to its local neighbors, weighted
by the kernel as in Fig. 1.8.
Figure 1.9 is a curve consisting of three sinusoidal functions with different fre-
quencies. The three frequencies are w1, w2 and w3 respectively. Let’s convolution it
Fig. 1.8 f (x, y) is an image, h(x, y)isthekernel,g(x, y) is the result of convolution
1.1 Digital Image Processing 7
Fig. 1.9 A curve consisting of three sinusoidal functions with different frequencies
with a curve with a frequency of w3 (Fig. 1.10) and see what happens, and the result
is shown in Fig. 1.11.
We can see from Fig. 1.11 that the part of the original function with a frequency
of w3 is highlighted. This property can be completely extended from the convolution
of 1-dimensional signal to the 2-dimensional signals.
In a sense, image convolution is filtering the image. The result of image convolu-
tion is to highlight the part of an image that is similar to the nature of a nucleus. With
-4 -3 -2 -1 0
1
2
3
4
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
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kernel,frequency =w3
X
Y
Fig. 1.10 A curve with a frequency of w3