Analysis of quantitative properties of ultrasound images

11 Pages·0.483 MB·English

by  Yankov К.

15 th International Conference SAER-2001 109
ANALYSIS OF QUANTITATIVE PROPERTIES OF
ULTRASOUND IMAGES
Kaloyan Yankov, Ph.D.
Medical Faculty, Thracian University,
Armejska str., 11, Stara Zagora 6000, Bulgaria
e-mail: [email protected]
Abstract: A system for ultrasound image processing and image evaluation is discussed. The
image examination has been carried out of three stages. Iimage resampling includes linear,
polynomial and harmonic interpolation. Image processing gives aids for filtering and
densitometric scaling. Image analysis calculates numerical properties of a sonograph. The
system is realized on Borland Delphi. It is used in Medical faculty, Thracian university, Stara
Zagora, Bulgaria.
Key words: Interactive Computer Graphics, Image processing, Ultrasound diagnostic, Sonography.
1. INTRODUCTION
Image analysis in medicine is a significant tool in diagnostic. Visual observation of human
organs and systems gives an information about possible changes and aids for precise diagnosis.
Medical imaging includes nuclear magnetic resonance, emission and transmission tomography, X-
ray and ultrasound. The ultrasound examinations are performed in a very wide range of medical
disciplines. The main are probably: obstetrics and gynaecology, ophthalmology, cardiology and
other areas of internal medicine.
The information which is obtained using imaging systems can be categorized under three
headings – morphological, functional and analytical [1].
Morphological information techniques are predominantly qualitative. They are used for
mapping normal and abnormal anatomy and for making interferencies about the presence and
nature of pathology. It is very difficult to estimate the real status to the pacient. Recently a modern
three-dimensional imaging techniques are used in scanning of organs and systems. These
techniques generate realistic images which benefits the precise diagnostic.
Functional information is concerned to the normal or pathological functional parameters of
human organs.
Analytical information is obtained after quantitative image analysis. Preliminary processing
facilitates successful qualitative measurements [2]. In [3] after scanning of radiographic image it is
processed using Adobe Photoshop (Auto FX Software) to improve the image quality and then
Autocad (AutoDesk) is used for metric evaluations. In this case two programs are used to solve the
task for image analysis. These programs are expensive and they require training to work with them.
Common characteristic of systems for image processing and image analysis is that they require
specialized education, even programming skills. They are difficult to use and practically unusable
for general practitioners and specialists.
In this paper a software KORELIA-SONOGRAPH for processing and analytical analysis of
images obtaained ultrasound scanning technique is presented. The main principles in designing of
the system are: user-friendly interface; reduction of the ways and the means for image processing to
a minimal set, easy learning from physicians without computer skills; output of the end results in a
proper form for afterward analysis.
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2. ULTRASOUND ANALYTICAL EXAMINATION OF TISSUES
Tissues in a human body are four groups: epitelial, nervous, muscular and connective. These
basic tissue groups are divided into subgroups. Each of them has various density. Organs and
systems are formed from different tissues which are presented in various relations. Interactions
between ultrasound waves and tissues depend on nature of the information that is coded by the
tissue structure. That perturbes ultrasound wave and on the computer display it reflect on different
gray values.
The changes in tissues or in their normal relation in organs lead to changes in gray value
density and its statistical parameters and distribution. These numerical values may be used for exact
and objective estimation of observed image.
The process of analytical examination of tissues passes over three stages: image resampling,
image processing and image analysis.

3. GEOMETRICAL OPERATIONS FOR IMAGE RESAMPLING
The purpose of geometrical operations is to ensure image scaling. The image is presented in
BitMap format [4]. When decreasing size some pixels are lost but they hold information. This
requires to specify the pixel value, substituting vanished pixels. When increasing the image
neibourhood pixels are remoted (fig.1). A square p p p p is obtained and we determine the new
1 2 3 4
pixel values inside this square. To shift an image by a subpixel offset, it is necessary to use
resampling methods [5], i.e. construct a new image using appropriate interpolation filter. The
resampling operation (also known as Image Warping) is a classical problem in image processing
[6]. There is no best filter for all purposes, but rather a set of possible filters. The output quality
depends on the frequency contents of the input signal.
3.1. Nearest Neighbour filter. (Box filter). Fills in half of the heighbourhood of a pixel p(A)
(fig.1). It is not possible to get acceptable results with this filter for subsampling. It copies the pixel
value onto its neighbourhood.
P(x ,x ) =p(A), if -0.5 ≤ x ≤ 0.5, -0.5 ≤ x ≤ 0.5 (1)
1 2 1 2
This filter generates an image with local block structure. The image quality is not raised and
statistical parameters are the same as the basic ones.
3.2. Piecewise linear interpolation. The diagonal line p p separates the square into two
1 3
triangular regions and the density of a new pixel p(x ,x ) is a linear interpolation bеtween three
1 2
pixels:
p +(p -p )x +(p -p )x , if 0 ≤х ≤х ≤1
1 2 1 1 3 2 2 2 1
p(x ,x ) = (2)
1 2
p +(p -p )x +(p -p )x , if 0 ≤х ≤х ≤1
1 4 1 2 3 4 1 1 2
3.3. Hermite filter [7,8]. A third-degree polynomial is used. First derivative must be smooth.
p(t) = 2|t|3 - 3|t|2 + 1, -1 ≤ t ≤ 1 (3)
Hermite curves are very easy to calculate and very powerfull to use. They are used to
interpolate smoothly data between key-points. Relatively fast calculation speed.
3.4. Bell filter. Smooth image, bordering on soft. Relatively fast.
0.75 – t2 0< t < 0.5
p(t) = (t-0.75)2/2 0.5 < t < 1.5 (4)
0 otherwize
3.5. B-spline filter. Spline-interpolation gives a smooth second derivative [9,10], but also
produces the softest image. One of the slower filters to calculate.
t3/2-t+2/3 0< t < 1
p(t) = (2-t3)/6 1 < t < 2 (5)
0 otherwize
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3.6. Michell filter. One of the smoother and slower polynomial filters:
((12-9b-6c)t3 +(-18+12b+6c)t2 +(6-2b))/6 0< t < 1
p(t) = ((b-6c)t3 +(6b+30c)t2 +(12b-48c)t+(8b+24c)/6 1 < t < 2 (6)
0 otherwise
The main problem with polynomial filters is that evaluation complexity is linear in a number of
basic functions. Another problem is numerical stability and oscillations in generated image.
3.7. Lanczos filter [11]. The Lanczos function is defined by:
Sinc(t) * sinc(t/2) |t| < 2
p(t)= (7)
0 otherwise
where: sinc(t) = sin(πt)/ πt
It is possible to extend this function to superior orders if the filter is enlarged to use more
neighbouring pixels. A little smarter than a simple resize, but also the slowest filter. Minimal
smoothing.

Fig.1. Image Warping Fig.2 Densitometric normalization

4. IMAGE PROCESSING
The goal is to increase the image resolution, remove noninformative parts from the image and
this way to prepare it for analytical examination. A local grid filters are applied in this case. They
use pixels from a local neighbourhood to specify an image of one pixel at a time [12]. The program
offers about 20 filters. Some of them are listed below.
4.1. Neighbourhood averaging. The simplest approach is neighbourhood averaging, where
each pixel is replaced by the average value of the pixels contained in some neighbourhood about it.
This tends to blur the image.
4.2. Median filter. A neighbourhood around the pixel under consideration is used, but this time
the pixel value is replaced by the median pixel value in the neighbourhood.
4.3. Sobel Filter. The Sobel filter consists of two kernels which detect horizontal and vertical
changes in an image. If both are applied to an image, the results can bе used to compute the
magnitude and direction of the edges in the image. If the application of the Sobel kernels results in
two images G , G , the magnitude of the edge passing through the pixel p(x, y) is given by:
h v
M(x,y)=sqrt(G 2(x,y) + G 2(x,y)) (8)
h v
The direction can also be determined from G (x,y) and G (x,y):
h v
φ(x,y)=tan-1(G (x,y)/ G (x,y)) (9)
v h
4.4. Scale gray value. Performs a linear gray value scaling of the image defined by Input and
Output gray value range.
4.5. Densitometric normalization. Increases the contrast of an image by scaling the gray
values linearly into the full image range. That is a scaling with Input range between NLow and
Nhigh and Output range is from 0 to 255. The dark and bright tails of the gray values histogram
can be clipped in order to exclude scattered noise pixels from the rescaling (Fig.2).
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4.6 Invert. Inverts the gray value in an image according to:
Z’(x,y) = 255-Z(x,y)
4.7 Thresold level. Discriminates objects from the background by setting two thresolds. The
discriminated pixels are set to 255(white).
4.8. User defined filters. It is possible to create user defined local grid filters using 3x3, 5x5
and 7x7 mask and save it in a file.

Fig.3. Sonograph of a 35 weekend boy.

5. OPERATIONS FOR ANALYSIS OF IMAGE ELEMENTS
Image analysis is based on statistical parameters of an image. There is possibility for calculation
of:
• Mean level parameters: mean , mode, median, standard error of mean;
• Dispersion parameters: standard deviation, confidence interval, variation, range, skewness,
kurtosis;
• Gray value histogram. Displays a histogram of the gray value frequencies of an image. The
input parameters for histogram vizualizations are:
♦ lower bound gray value;
♦ upper bound gray value;
♦ number of intervals (upper-lower+1);
• Metric relations between image elements. They are calculated according to [13].