Breast cancer detection system, breast cancer detection method, breast cancer detection program, and computer-readable recording medium having breast cancer detection program recorded thereon
A model calculating device calculates a mammary gland normal architecture model that scatters in a fan shape from the nipple toward the greater pectoral muscle with respect to the X-ray image of the breast. An orientation extracting device extracts linear components orientations of a region image texture that form a shape of a shadow in the breast X-ray image using a Gabor filter. A lesion determining device compares the mammary gland orientation in the normal architecture model calculated by the model calculating device and the orientation extracted by the orientation extracting device with respect to a region of interest in the X-ray image of the breast including architectural distortion candidates of the mammary gland detected by a preprocessing device to calculate a feature quantity based on a difference between the orientations and determines whether the candidates are an architectural distortion of the mammary gland based on the feature quantity.
1. A breast cancer detection system for detecting an architectural distortion of the mammary gland included in an X-ray image of the breast, the breast cancer detection system comprising:
a processor programmed to:
calculate a normal architecture model of the mammary gland that scatters in a fan shape from the nipple toward the greater pectoral muscle with respect to the X-ray image of the breast;
extract orientations of linear components of an image texture of a region that form a shape of a shadow in the X-ray image of the breast; and
compare the orientation of the mammary gland in the calculated normal architecture model and the extracted orientation with respect to a region of interest in the X-ray image of the breast including architectural distortion candidates of the mammary gland detected by preprocessing to: (i) calculate a feature quantity based on a difference between the orientations, and (ii) determine whether the candidates are an architectural distortion of the mammary gland based on the feature quantity.
2. The breast cancer detection system according to claim 1 , wherein
the processor calculates the feature quantity R according to the following equation in which, for a plurality of coordinates (x,y) in the region of interest, Φ(x,y) is the orientation of the mammary gland in the calculated normal architecture model, Θ(x,y) is the extracted orientation, N m is a parameter for determining an allowable range of matching orientations, and N ROI is the number of coordinates
[
Equation
1
]
R
=
∑
x
∑
y
h
(
x
,
y
)
N
ROI
where
h
(
x
,
y
)
=
{
0
,
Φ
(
x
,
y
)
-
Θ
(
x
,
y
)
≦
π
N
m
1
,
otherwise
.
3. The breast cancer detection system according to claim 2 , wherein
the processor extracts orientations of linear components of an image texture of a region in the X-ray image of the breast using a Gabor filter.
4. The breast cancer detection system according to claim 3 , wherein
the processor calculates the normal architecture model of the mammary gland using an exponential curve.
5. The breast cancer detection system according to claim 2 , wherein
the processor calculates the normal architecture model of the mammary gland using an exponential curve.
6. The breast cancer detection system according to claim 1 , wherein
the processor extracts orientations of linear components of an image texture of a region in the X-ray image of the breast using a Gabor filter.
7. The breast cancer detection system according to claim 6 , wherein
the processor calculates the normal architecture model of the mammary gland using an exponential curve.
8. The breast cancer detection system according to claim 1 , wherein
the processor calculates the normal architecture model of the mammary gland using an exponential curve.
9. A breast cancer detection method for detecting an architectural distortion of the mammary gland included in an X-ray image of the breast, comprising:
a model calculating step of calculating, by a computer, a normal architecture model of the mammary gland that scatters in a fan shape from the nipple toward the greater pectoral muscle with respect to the X-ray image of the breast;
an orientation extracting step of extracting, by the computer, an orientation of linear components of an image texture of a region that form a shape of a shadow in the X-ray image of the breast; and
a lesion determining step of comparing, by the computer, the orientation of the mammary gland in the normal architecture model calculated by the model calculating step and the orientation extracted by the orientation extracting step with respect to a region of interest in the X-ray image of the breast including architectural distortion candidates of the mammary gland detected by preprocessing to: (i) calculate a feature quantity based on a difference between the orientations, and (ii) determine whether the candidates are an architectural distortion of the mammary gland based on the feature quantity.
10. The breast cancer detection method according to claim 9 , wherein
the lesion determining step calculates the feature quantity R according to the following equation in which, for a plurality of coordinates (x,y) in the region of interest, Φ(x,y) is the orientation of the mammary gland in the normal architecture model calculated by the model calculating step, Θ(x,y) is the orientation extracted by the orientation extracting step, N m is a parameter for determining an allowable range of matching orientations, and N ROI is the number of coordinates
[
Equation
2
]
R
=
∑
x
∑
y
h
(
x
,
y
)
N
ROI
where
h
(
x
,
y
)
=
{
0
,
Φ
(
x
,
y
)
-
Θ
(
x
,
y
)
≦
π
N
m
1
,
otherwise
.
11. The breast cancer detection method according to claim 10 , wherein
the orientation extracting step extracts orientations of linear components of an image texture of a region in the X-ray image of the breast using a Gabor filter.
12. The breast cancer detection method according to claim 11 , wherein
the model calculating step calculates the normal architecture model of the mammary gland using an exponential curve.
13. The breast cancer detection method according to claim 10 , wherein
the model calculating step calculates the normal architecture model of the mammary gland using an exponential curve.
14. The breast cancer detection method according to claim 9 , wherein
the orientation extracting step extracts orientations of linear components of an image texture of a region in the X-ray image of the breast using a Gabor filter.
15. The breast cancer detection method according to claim 14 , wherein
the model calculating step calculates the normal architecture model of the mammary gland using an exponential curve.
16. The breast cancer detection method according to claim 9 , wherein
the model calculating step calculates the normal architecture model of the mammary gland using an exponential curve.
17. A non-transitory computer-readable medium storing a breast cancer detection program thereon, for detecting an architectural distortion of the mammary gland included in an X-ray image of the breast, the program causing a computer to perform steps comprising:
a model calculating step that calculates a normal architecture model of the mammary gland that scatters in a fan shape from the nipple toward the greater pectoral muscle with respect to the X-ray image of the breast;
an orientation extracting step that extracts orientations of linear components of an image texture of a region that form a shape of a shadow in the X-ray image of the breast; and
a lesion determining step that compares the orientation of the mammary gland in the normal architecture model calculated by the model calculating step and the orientation extracted by the orientation extracting step with respect to a region of interest in the X-ray image of the breast including architectural distortion candidates of the mammary gland detected by preprocessing to: (i) calculate a feature quantity based on a difference between the orientations, and (ii) determine whether the candidates are an architectural distortion of the mammary gland based on the feature quantity.