Image analysis – Image transformation or preprocessing – General purpose image processor
Utility Patent
1999-04-26
2001-01-02
Tran, Phuoc (Department: 2721)
Image analysis
Image transformation or preprocessing
General purpose image processor
C382S257000, C382S266000, C382S276000
Utility Patent
active
06169823
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
This invention relates to an image processing method and apparatus. This invention particularly relates to an image processing method and apparatus, wherein a specific image portion, such as an abnormal pattern or a high-contrast image portion, which is embedded in an image, is emphasized selectively.
2. Description of the Prior Art
Image processing, such as gradation processing or frequency processing, has heretofore been carried out on an image signal, which represents an image and has been obtained with one of various image obtaining methods, such that a visible image having good image quality can be reproduced and used as an effective tool in, particularly, the accurate and efficient diagnosis of an illness. Particularly, in the field of medical images, such as radiation images of human bodies serving as objects, it is necessary for specialists, such as doctors, to make an accurate diagnosis of an illness or an injury of the patient in accordance with the obtained image. Therefore, it is essential to carry out the image processing in order that a visible image having good image quality can be reproduced and used as an effective tool in the accurate and efficient diagnosis of an illness.
As one of the image processing, frequency emphasis processing has been disclosed in, for example, Japanese Unexamined Patent Publication No. 61(1986)-169971. With the disclosed frequency emphasis processing, an image signal (i.e., an original image signal) Dorg representing the image density value of an original image is converted into an image signal Dproc with Formula (36).
Dproc=Dorg+&bgr;×(Dorg−Dus) (36)
wherein &bgr; represents the frequency emphasis coefficient, and Dus represents the unsharp mask signal. The unsharp mask signal Dus comprises super-low frequency components obtained by setting a mask, i.e. an unsharp mask, constituted of a picture element matrix, which has a size of N columns×N rows (wherein N represents an odd number) and has its center at the picture element represented by the original image signal Dorg, in a two-dimensional array of picture elements. The unsharp mask signal Dus is calculated with, for example, Formula (37).
Dus=(&Sgr;Dorg)/N
2
(37)
wherein &Sgr;Dorg represents the sum of the image signal values representing the picture elements located within the unsharp mask.
The value of (Dorg−Dus) in the parenthesis of the second term of Formula (36) is obtained by subtracting the unsharp mask signal, which represents the super-low frequency components, from the original image signal. Therefore, comparatively high frequency components can be extracted selectively by subtracting the super-low frequency components from the original image signal. The comparatively high frequency components are then multiplied by the frequency emphasis coefficient &bgr;, and the obtained product is added to the original image signal. In this manner, the comparatively high frequency components can be emphasized.
Also, iris filter processing (hereinbelow often referred to as the operation of the iris filter) has heretofore been known as the operation processing for selectively extracting only a specific image portion, such as an abnormal pattern, from an image. [Reference should be made to “Detection of Tumor Patterns in DR Images (Iris Filter),” Obata, et al., Collected Papers of The Institute of Electronics and Communication Engineers of Japan, D-II, Vol. J75-D-II, No. 3, pp. 663-670, March 1992.] The iris filter processing has been studied as a technique efficient for detecting, particularly, a tumor pattern, which is one of characteristic forms of mammary cancers. However, the image to be processed with the iris filter is not limited to the tumor pattern in a mammogram, and the iris filter processing is applicable to any kind of image having the characteristics such that the gradients of the image signal representing the image are centralized.
How the processing for detecting the image portion with the iris filter is carried out will be described hereinbelow by taking the processing for the detection of the tumor pattern as an example.
It has been known that, for example, in a radiation image recorded on a negative X-ray film (i.e., an image yielding an image signal of a high signal level for a high image density), the density values of a tumor pattern are slightly smaller than the density values of the surrounding image areas. The density values of the tumor pattern are distributed such that the density value becomes smaller from the periphery of an approximately circular tumor pattern toward the center point of the tumor pattern. Therefore, in the tumor pattern, gradients of the density values can be found in local areas, and the gradient lines (i.e., gradient vectors) centralize in the directions heading toward the center point of the tumor pattern.
With the iris filter, the gradients of image signal values, which are represented by the density values, are calculated as gradient vectors, the degree of centralization of the gradient vectors is calculated, and a tumor pattern is detected in accordance with the calculated degree of centralization of the gradient vectors. Specifically, the gradient vector at an arbitrary picture element in a tumor pattern is directed to the vicinity of the center point of the tumor pattern. On the other hand, in an elongated pattern, such as a blood vessel pattern, gradient vectors do not centralize upon a specific point. Therefore, the distributions of the directions of the gradient vectors in local areas may be evaluated, and a region, in which the gradient vectors centralize upon a specific point, may be detected. The thus detected region may be taken as a prospective tumor pattern, which is considered as being a tumor pattern. The processing with the iris filter is based on such fundamental concept. Steps of algorithms of the iris filter will be described hereinbelow.
(Step 1) Calculation of gradient vectors
For each picture element j among all of the picture elements constituting a given image, the direction &thgr; of the gradient vector of the image signal representing the image is calculated with Formula (38).
θ
=
tan
-
1
(
f
3
+
f
4
+
f
5
+
f
6
+
f
7
)
-
(
f
11
+
f
12
+
f
13
+
f
14
+
f
15
)
(
f
1
+
f
2
+
f
3
+
f
15
+
f
16
)
-
(
f
7
+
f
8
+
f
9
+
f
10
+
f
11
)
(
38
)
As illustrated in
FIG. 5
, f
1
through f
16
in Formula (38) represent the density values (i.e., the image signal values) corresponding to the picture elements located at the peripheral areas of a mask, which has a size of five picture elements (located along the column direction of the picture element array)×five picture elements (located along the row direction of the picture element array) and which has its center at the picture element j.
(Step 2) Calculation of the degree of centralization of gradient vectors
Thereafter, for each picture element among all of the picture elements constituting the given image, the picture element is taken as a picture element of interest, and the degree of centralization C of the gradient vectors with respect to the picture element of interest is calculated with Formula (39).
C
=
(
1
/
N
)
∑
j
=
1
N
COS
θ
j
(
39
)
As illustrated in
FIG. 6
, in Formula (39), N represents the number of the picture elements located in the region inside of a circle, which has its center at the picture element of interest and has a radius R, and &thgr;j represents the angle made between the straight line, which connects the picture element of interest and each picture element j located in the circle, and the gradient vector at the picture element j, which gradient vector has been calculated with Formula (38). Therefore, in cases where the directions of the gradient vectors of the respective picture elements j centralize upon the picture element of interest, the degree of centralization C represented by Formula (39) takes a large value.
The gradient vector of each picture element
Nakajima Nobuyoshi
Takeo Hideya
Yamada Masahiko
Fuji Photo Film Co. , Ltd.
Mariam Daniel G.
Sughrue Mion Zinn Macpeak & Seas, PLLC
Tran Phuoc
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