Image analysis – Applications – Biomedical applications
Reexamination Certificate
2000-04-25
2004-01-13
Patel, Jayanti K. (Department: 2624)
Image analysis
Applications
Biomedical applications
C128S922000
Reexamination Certificate
active
06678400
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to an image processing apparatus for performing an image processing, for example, including a dynamic range compression processing for adding a high frequency component of an original image to the original image (dynamic range change processing), an image processing system, an image processing method, and a memory medium for storing processing steps to perform the processing in a computer readable manner.
2. Related Background Art
In recent years, with an advancement of digital technique, for example, a method is executed which comprises: digitizing a photographed image obtained by X-ray photographing (hereinafter also referred to as “X-ray image”), performing an image processing to the digitized image; and displaying the image on a monitor apparatus, or outputting the image onto an X-ray diagnosis film.
Here, the X-ray image is constituted of an image area in which it is easy to transmit X-rays, and an image area in which it is very difficult to transmit the X-rays. For example, the X-ray image of a chest (lungs) is constituted of a lung image area in which it is easy to transmit the X-rays, and a mediastinum image area in which it is very difficult to transmit the X-rays. In this manner, since the dynamic range in which pixel values are present (hereinafter also referred to as “density range”) is much broadened in the X-ray image, it has been difficult to obtain an X-ray image in which both the lung and mediastinum image areas far different in X-ray transmittance from each other can simultaneously be observed.
Therefore, in order to avoid the above-described problem, the following methods 1 to 5 of a dynamic range compression processing (hereinafter also referred to as “DRC processing”) have been proposed.
Method 1:
In method 1 described in “SPIE Vol. 626 Medicine XIV/PACSIV (1986)” or the like, assuming that the pixel value of the processed image is “S
D
”, the pixel value of the original image (input original image) is “S
org
”, and the pixel value of the low frequency component of the original image (pixel value of a smoothed image) is “S
US
”, equation (1) is represented with constants A, B, C (e.g., A=3, B=0.7).
S
D
=A[S
org
−S
US
]+B[S
US
]+C
(1)
Moreover, in the method 1, by changing weighting (constants A and B) of the high frequency component (first term) and low frequency component (second term) in the equation (1) and, for example, assuming A=3, B=0.7, the high frequency component is emphasized, and the dynamic range of the entire image can be compressed. This is evaluated by many radiotherapists, et al. such that the image subjected to the present processing is effective for diagnosis as compared with the image not subjected to the present processing.
Method 2:
In a method 2 described in Japanese Patent Publication No. 6-046409, assuming that the pixel value of the processed image is “S
D
” , the pixel value of the original image is “S
org
”, and the pixel value of the low frequency component of the original image (pixel value of the smoothed image) is “S
US
”, equation (2) is represented with a monotonous decrease function f(X).
S
D
=S
org
+f
(
S
US
) (2)
Also in this method 2, similarly to the above-described method 1, the dynamic range of the entire image can be compressed based on the low frequency component of the original image.
Method 3:
In a method 3 described in Japanese Patent No. 2509503, assuming that the pixel value of the processed image is “S
D
”, and the pixel value of the original image is “S
org
”, equation (3) is represented with the average profile Py of the Y-directional profile of the original image, and the average profile Px of the X-directional profile.
S
D
=S
org
+F[G
(
Px,Py
)] (3)
Here, the property of the function F(x) in the equation (3) will be described. First, in “x>Dth”, F(0) is “0”. Moreover, in “0≦x≦Dth”, F(x) monotonously decreases with an intercept “E”, and inclination “E/Dth”, and is represented by equation (4).
F[x]=E
−(
E/Dth
)
X
(4)
Moreover, the average profiles Py and Px in the equation (3) are represented by equations (5) and (6) with profiles pyi and pxi (i=1 to n).
Py
=(&Sgr;
Pyi
)/
n
(5)
Px
=(&Sgr;
Pxi
)/
n
(6)
Moreover, “G(Px,Py)” in the equation (3) is represented by equation (7).
G
(
Px,Py
)=max(
px,py
) (7)
Therefore, in the method 3, the dynamic range is compressed when the pixel value of the low frequency component is Dth or less.
Method 4:
A method 4 similar to the method 2 described in the Japanese Patent Publication No. 6-046409 and the method 3 described in the Japanese Patent No. 2509503 is described in “Journal of Japanese Society of Radiological Technology, Vol. 45, No. 8, August, 1989, p. 1030, Anan et al”.
Assuming that the pixel value of the processed image is “S
D
”, the pixel value of the original image is “S
org
”, and the average pixel value (pixel value of the smoothed image) obtained by taking the moving average of the original image with a mask size of M×M pixels is “S
US
”, the method 4 is represented by equations (8) and (9) with the monotonous decrease function f(X).
S
D
=S
org
+f
(
S
US
) (8)
S
US
=&Sgr;S
org
/M
2
(9)
Moreover, the equation (8) can be changed to equation (10).
S
D
=
(
S
org
-
S
US
)
+
(
f
(
S
US
)
+
S
US
)
=
(
S
org
-
S
US
)
+
f1
(
S
US
)
(10)
Here, the method 4 is different from the method 3 represented by the equation (3) in method of generating the low frequency component, the low frequency component is generated with one-dimensional data in the method 3, and the low frequency component is generated by two-dimensional data in the method 4.
Also in the method 4, similarly to the above-described method 3, the dynamic range is compressed when the pixel value of the low frequency component is Dth or less.
Method 5:
For a method 5 described in Japanese Patent No. 2663189, assuming that the pixel value of the processed image is “S
D
”, the pixel value of the original image is “S
org
”, and the average pixel value (pixel value of the smoothed image) obtained by taking the moving average of the original image with the mask size of M×M pixels is “S
US
” equations (11) and (12) are represented with the monotonous decrease function f
2
(X).
S
D
=
S
org
+
f
2
(
S
US
)
=
(
S
org
-
S
US
)
+
f
3
(
S
US
)
f
3
(
S
US
)
=
f
2
(
S
US
)
+
S
US
(
11
)
S
US
=
∑
S
org
/
M
2
(
12
)
Here, the property of the function f
2
(x) in the equation (11) will be described. First, in “x<Dth”, f
2
(
0
) is “0”. Moreover, in “Dth≦x”, f
2
(x) monotonously decreases with the intercept “E”, and inclination “E/Dth”, and is represented by equation (13).
f
2
[
x]=E
−(
E/Dth
)
X
(13)
Therefore, in the method 5, the dynamic range is compressed when the low frequency component pixel value is Dth or less.
Additionally, the compression algorithm of the dynamic range in the method 5 is similar to the algorithm in the method 4 described in the “Journal of Japanese Society of Radiological Technology, Vol. 45, No. 8, August, 1989, p. 1030, Anan et al”.
However, the conventional image processing method using the above-described methods 1 to 5 of the DRC processing has at least the following problems 1 and 2.
Problem 1:
For example, as shown in
FIG. 14
, when the original image (input original image) is subjected to the DRC processing represented by the “Journal of Japanese Society of Radiological Technology, Vol. 45, No. 8, August, 1989, p. 1030, Anan et al.”, particularly to the DRC processing including the processing (original image—smoothed image) of subtracting the pixel value S
US
of the smoothed image fro
Azarian Seyed
Patel Jayanti K.
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