Image analysis – Applications – Biomedical applications
Reexamination Certificate
1998-08-26
2001-08-07
Mancusco, Joseph (Department: 2721)
Image analysis
Applications
Biomedical applications
C128S922000
Reexamination Certificate
active
06272233
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
This invention relates to a method and apparatus for detecting a prospective abnormal pattern. This invention particularly relates to a method and apparatus for detecting a prospective abnormal pattern in accordance with radiation images of the right and left mammae.
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 acquiring 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.
In such image processing, the processing is often carried out on the entire area of the image. Alternatively, in cases where the purpose of examination or diagnosis is clear to a certain extent, the emphasis processing is often carried out selectively on a desired image portion, which is adapted to the purpose of examination or diagnosis.
Ordinarily, when an image portion to be processed is to be selected, the person, who views the radiation image, views the original image before being processed and manually selects the image portion to be processed. However, there is the risk that the selected image portion or the specified image range will vary, depending upon the level of the experience or the image understanding capability of the person, who views the radiation image, and the selection cannot be carried out objectively.
For example, in cases where a radiation image has been recorded for the examination of breast cancer, it is necessary to find a tumor pattern, which is one of features of a cancerous portion, from the radiation image. However, the range of the tumor pattern cannot always be specified accurately. Therefore, there is a strong demand for techniques for accurately detecting an abnormal pattern, such as a tumor pattern, without depending upon the skill of the person, who views the radiation image.
In order to satisfy the demand described above, extensive research has been carried out to make a computer aided diagnosis of medical images (CADM).
Specifically, with the CADM techniques, prospective abnormal patterns, which are considered as being abnormal patterns, such as tumor patterns or small calcified patterns, are detected automatically by carrying out detection processing with computers in accordance with features of image density distribution or forms of the abnormal patterns. As the processing for the detection of prospective abnormal patterns, iris filter processing, which is suitable for the detection of, primarily, prospective tumor patterns, has heretofore been proposed. [Reference should be made to “Detection of Tumor Patterns in DR Images (Iris Filter),” Kobatake, 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 portion having the characteristics such that the gradients of the image signal (the image density, or the like) representing the image are centralized.
How the processing for detecting a prospective abnormal pattern 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 X-ray film (i.e., an image yielding an image signal of a high signal level for a high image density), the image density values of a tumor pattern are slightly smaller than the image density values of the surrounding image areas. The image density values of the tumor pattern are distributed such that the image density value becomes smaller from the periphery of an approximately circular tumor pattern toward the center point of the tumor pattern. Thus the distribution of the image density values of the tumor pattern has gradients of the image density values. Therefore, in the tumor pattern, the gradients of the image 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.
The iris filter calculates the gradients of image signal values, which are represented by the image density values, as gradient vectors and feeds out the information representing the degree of centralization of the gradient vectors. With the iris filter processing. a tumor pattern is detected in accordance with the degree of centralization of the gradient vectors.
Specifically, by way of example, as illustrated in
FIG. 18A
, a tumor pattern P
1
may be embedded in a mammogram P. As illustrated in
FIG. 18B
, the gradient vector at an arbitrary picture element in the tumor pattern P
1
is directed to the vicinity of the center point of the tumor pattern P
1
. On the other hand, as illustrated in
FIG. 18C
, in an elongated pattern P
2
, such as a blood vessel pattern or a mammary gland 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. As illustrated in
FIG. 18D
, in a pattern P
3
, in which elongated patterns, such as mammary gland patterns, intersect each other, gradient vectors are liable to centralize upon a specific point. Therefore, the pattern P
3
may be detected as a false positive (FP).
The processing with the iris filter is based on the fundamental concept described above. 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 (1) shown below.
θ
=
tan
-
1
(
f
3
+
f
4
+
f
5
+
f
6
+
&AutoLeftMatch;
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
)
(
1
)
As illustrated in
FIG. 9
, f
1
through f
16
in Formula (1) represent the picture element 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, for example, 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 (2) shown below.
C
=
(
1
/
N
)
∑
j
=
1
N
cos
θ
j
(
2
)
Fuji Photo Film Co. , Ltd.
Mancusco Joseph
Sughrue Mion Zinn Macpeak & Seas, PLLC
Tabatabai Abolfazl
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