JP2582666B2 - Abnormal shadow detector - Google Patents

Description

Description: BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an abnormal shadow detection device that detects an abnormal shadow appearing as a substantially circular pattern in a radiation image based on image data representing the radiation image of the subject. .

(Prior art) Reading a recorded radiation image to obtain image data,
After performing appropriate image processing on the image data, reproduction and recording of the image are performed in various fields. For example, an X-ray image is recorded using an X-ray film having a low gamma value designed to be compatible with later image processing, and this X-ray image is recorded.
An X-ray image is read from a film on which a line image is recorded, converted into an electric signal (image data), subjected to image processing, and then reproduced as a visible image in a copy photograph or the like, thereby providing contrast and sharpness. It has been practiced to obtain a reproduced image having good image quality performance such as graininess and the like (see Japanese Patent Publication No. 61-5193).

In addition, the applicant (X-ray, α-ray, β-ray,
Irradiation with gamma rays, electron beams, ultraviolet rays, etc. accumulates a part of this radiation energy, and then irradiation with excitation light, such as visible light, causes a stimulable phosphor (luminous) to emit stimulated emission according to the accumulated energy. Radiation image information of a subject such as a human body is temporarily recorded in a sheet-shaped stimulable phosphor using a stimulable phosphor, and the stimulable phosphor sheet is scanned with excitation light such as laser light to radiate the light. Generates luminescent light, obtains image data by photoelectrically reading the obtained stimulating luminescent light, and outputs a radiation image of the subject as a visible image to a recording material such as a photographic photosensitive material or a CRT based on this image data. A radiation image recording / reproducing system for causing the radiation image to be reproduced has already been proposed (Japanese Patent Laid-Open No. 55-1242).
No. 9, 56-11395, 55-163472, 56-104645
No. 55-116340, etc.).

This system has the practical advantage of being able to record images over a very large radiation exposure area compared to conventional radiographic systems using silver halide photography. That is, in the case of the stimulable phosphor, it has been recognized that the amount of emitted light that is stimulated by excitation after accumulation is proportional to the radiation exposure amount over an extremely wide range. Even if fluctuates considerably, the amount of the stimulating light emitted from the stimulable phosphor sheet is read by the photoelectric conversion means with the reading gain set to an appropriate value and converted into an electric signal. By outputting a radiation image as a visible image on a recording material such as a photographic light-sensitive material or a display device such as a CRT using, a radiation image which is not affected by a change in radiation exposure can be obtained.

In recent years, in a system using the X-ray film or the stimulable phosphor sheet, particularly a system configured for medical diagnosis of a human body, a reproduced image having good image quality suitable for observation (diagnosis) is simply obtained. In addition, automatic recognition of images has been performed (for example, see Japanese Patent Application Laid-Open No. Sho 62-1254).
No. 81).

Here, automatic image recognition refers to an operation of extracting a target pattern from a complex radiation image by performing various processes on image data. For example, various linear shapes such as a chest X-ray image of a human body are used. , For example, an operation of extracting a shadow corresponding to a tumor from a very complicated image mixed with a circular pattern.

Such a complicated radiographic image (for example, the chest X of the human body)
A target pattern (for example, tumor shadow) is extracted from the line image), and a visible image in which the extracted pattern is specified is reproduced and displayed, thereby assisting an observer in observation (for example, assisting a doctor in diagnosis). be able to.

(Problems to be Solved by the Invention) When automatically extracting, for example, an abnormal shadow such as a tumor shadow which appears as a substantially circular pattern on a chest X-ray image of a human body, the X-ray image is extremely complicated. Only a tumor shadow is not extracted. For example, a shadow of a blood vessel bifurcation where a blood vessel is bifurcated or a so-called tangent shadow of a blood vessel in which a blood vessel extends in a direction in which X-ray travels during imaging is almost a circular pattern. Therefore, the shadow of the blood vessel branch point, the shadow of the tangent of the blood vessel, and the like are often extracted as tumor shadows.

The present invention has been made in view of the above circumstances, and provides an abnormal shadow detection device that can prevent a part of a linear pattern such as a blood vessel shadow from being detected as an abnormal shadow and can detect an abnormal shadow with high accuracy. It is intended for.

(Means for Solving the Problems) A first abnormal shadow detecting device of the present invention is based on image data representing a radiation image of a subject.
In an abnormal shadow detection device that detects an abnormal shadow appearing as a substantially circular pattern on the radiation image, an abnormal shadow enhanced image in which the abnormal shadow is enhanced by scanning the radiation image using an abnormal shadow enhancement filter is provided. An abnormal shadow enhancement means to be obtained; a linear pattern enhancement means for obtaining a linear pattern enhanced image in which a linear pattern extending on the radiation image is enhanced by scanning the radiation image using a linear pattern enhancement filter; Difference calculating means for obtaining, from the abnormal shadow-enhanced image, a second abnormal shadow-enhanced image in which the regions emphasized in both the abnormal shadow-enhanced image and the linear pattern-enhanced image have been canceled; and An abnormal shadow extracting means for extracting the abnormal shadow from the abnormal shadow emphasized image is provided.

In addition, the second abnormal shadow detection device of the present invention, based on image data representing a radiation image of the subject,
An abnormal shadow detection device that detects an abnormal shadow appearing as a substantially circular pattern on the radiation image, wherein a linear pattern extending on the radiation image is enhanced by scanning the radiation image using a linear pattern enhancement filter. Linear pattern emphasizing means for obtaining a linear pattern emphasized image, difference calculating means for obtaining a linear pattern degenerate image which is an image of a difference between the radiation image and the linear pattern emphasized image, and an abnormal shadow emphasis filter. An abnormal shadow enhancing unit that obtains an abnormal shadow enhanced image in which the abnormal shadow is enhanced by scanning on the linear pattern degenerate image; and an abnormal shadow extracting unit that extracts the abnormal shadow from the abnormal shadow enhanced image. It is a feature.

Further, the third abnormal shadow detection device according to the present invention, based on image data representing a radiation image of the subject,
In an abnormal shadow detection device that detects an abnormal shadow appearing as a substantially circular pattern on the radiation image, an abnormal shadow enhanced image in which the abnormal shadow is enhanced by scanning the radiation image using an abnormal shadow enhancement filter is provided. A second linear pattern in which only the linear pattern emphasized in the abnormal shadow-enhanced image is emphasized by scanning the abnormal shadow-enhanced image using a linear pattern emphasis filter. Linear pattern enhancement means for obtaining an enhanced image; difference calculation means for obtaining a third abnormal shadow-enhanced image that is an image of a difference between the abnormal shadow-enhanced image and the second linear pattern-enhanced image; and the third abnormality An abnormal shadow extracting means for extracting the abnormal shadow from the shadow emphasized image is provided.

(Operation) The first abnormal shadow detection device of the present invention creates an abnormal shadow emphasized image and a linear pattern emphasized image from a radiation image, and enhances the region emphasized in both images from the abnormal shadow emphasized image. In order to create a second abnormal shadow-enhanced image in which is canceled, in the second abnormal shadow-enhanced image, the emphasis of the linear pattern erroneously emphasized by the abnormal shadow emphasis filter is canceled, and the second abnormal shadow-enhanced image is canceled. An abnormal shadow can be detected with high accuracy based on the abnormal shadow emphasized image.

Further, the second abnormal shadow detection device of the present invention creates a linear pattern emphasized image from a radiation image, and subtracts the created linear pattern emphasized image from an original radiation image to create a linear pattern degenerate image. However, since an operation of enhancing the abnormal shadow is performed on the linear pattern degenerate image, even if the abnormal shadow enhancement filter simultaneously enhances a part of the linear pattern, the linear pattern is Only abnormal shadows are detected with high accuracy without being emphasized.

Further, the third abnormal shadow detection device of the present invention firstly generates an abnormal shadow-enhanced image from the radiographic image, and then enhances only the linear pattern emphasized in this abnormal shadow-enhanced image. A pattern favorable image is obtained, and the linear pattern emphasized in the abnormal shadow emphasized image is removed by subtracting the second linear pattern emphasized image from the abnormal shadow emphasized image. Is detected.

(Example) Hereinafter, an example of the present invention will be described with reference to the drawings. Here, an example will be described in which the above-described stimulable phosphor sheet is used to detect, as an abnormal shadow, a tumor shadow that typically occurs in the lungs of a human body as a substantially spherical shape. This tumor appears on the visible image reproduced based on the image data as an almost circular pattern that is whitish (has a lower density) than the surroundings.

 FIG. 9 is a schematic diagram of an example of an X-ray imaging apparatus.

The X-ray source 11 of the X-ray imaging apparatus 10 irradiates X-rays 12 toward the chest 13a of the human body 13 and irradiates the stimulable phosphor sheet 14 with the X-rays 12a transmitted through the human body 13 so that the human body The X-ray image of the chest 13a is stored and recorded on the stimulable phosphor sheet 14.

FIG. 10 is a perspective view showing an example of an X-ray image reading apparatus and a computer system which is an embodiment of the abnormal shadow detection apparatus of the present invention.

After the radiography is performed by the X-ray radiographing apparatus 10 shown in FIG.
The stimulable phosphor sheet 14 is set at a predetermined position of the X-ray image reading device.

The stimulable phosphor sheet 14, which is set at the predetermined position and on which the X-ray image is stored, is conveyed (sub-scanning) in the direction of arrow Y by sheet conveying means 22 such as an endless belt driven by a motor 21. You. Meanwhile, laser light source 23
Is reflected and deflected by a rotating polygon mirror 26 driven by a motor 25 and rotating at high speed in the direction of the arrow, and passes through a focusing lens 27 such as an fθ lens.
The optical path is changed by 28 to be incident on the stimulable phosphor sheet 14, and the main scanning is performed in an arrow X direction substantially perpendicular to the sub-scanning direction (arrow Y direction). From the portion of the stimulable phosphor sheet 14 irradiated with the light beam 24, a stimulable luminescent light 29 of a light amount corresponding to the accumulated and recorded X-ray image information is diverged, and this stimulable luminescent light is emitted.
29 is guided by a light guide 30 and is photoelectrically detected by a photomultiplier (photomultiplier tube) 31. The light guide 30 is formed by molding a light-guiding material such as an acrylic plate, and is arranged so that the linear incident end face 30a extends along the main scanning line, and is formed in an annular shape. The light receiving surface of the photomultiplier 31 is connected to the emission end face 30b. The stimulated emission light 29 that has entered the light guide 30 from the incident end face 30a travels through the inside of the light guide 30 by repeating total reflection, exits from the emission end face 30b, is received by the photomultiplier 31, and is received by the X-ray image. Is converted into an electric signal by the photomultiplier 31.

The analog output signal S _A output from the photomultiplier 31 is logarithmically amplified by a logarithmic amplifier 32, and the A / D converter 3
The image is digitized in step 3 to obtain image data _SD as a digital signal.

The image data _SD obtained in this manner is input to the computer system 40. This computer system
Reference numeral 40 denotes an example of the abnormal shadow detection device of the present invention. The main unit 41 includes a CPU and an internal memory, a drive unit 42 into which a floppy disk as an auxiliary memory is inserted and driven, and an operator. Keyboard 43, X for inputting necessary instructions and the like to computer system 40
It comprises a line image and a CRT display 44 for displaying necessary information.

Image data input to this computer system 40
Abnormal shadows on the X-ray image are detected based on the _SD .

In the present embodiment, each function obtained by combining the hardware constituting the computer system 40 and the software executed in the computer system 40 is regarded as an example of each means according to the present invention.

Here, the flow of processing performed in the computer system 40 will be described first, and then the tumor shadow enhancement filter and the blood vessel shadow enhancement filter used in the processing will be described.

FIG. 1A, FIG. 1B, and FIG.
It is a block diagram showing each aspect of the 2nd, 3rd abnormal shadow detection apparatus.

As shown in FIG. 1A, when the image data _SD is input to the computer system 40, the tumor shadow enhancement means 51 in the computer system uses a tumor shadow enhancement filter described later based on the input image data _SD. Scanning on the X-ray image to generate a tumor shadow emphasized image in which the tumor shadow is enhanced on the X-ray image. Here, in the tumor shadow emphasized image, the shadow of a branch point of a blood vessel, the shadow of a tangent of a blood vessel, and the like are also emphasized similarly to the tumor shadow. The vascular shadow enhancing means 52 in the computer system 40 uses the vascular shadow enhancing filter, which will be described later, based on the input image data _SD.
A line image is scanned, and a blood vessel shadow enhanced image in which the blood vessel shadow is enhanced on the X-ray image is generated. When the tumor shadow-enhanced image and the blood vessel shadow-enhanced image are generated in this manner, the difference calculation unit 53 cancels the emphasis of the shadow emphasized by the blood vessel shadow-enhanced image among the shadows emphasized by the tumor shadow-enhanced image. And
A second tumor shadow emphasized image in which only the shadow of the tumor shadow is truly enhanced is generated. In the tumor shadow extracting means 54, only the true tumor shadow is extracted with high accuracy based on the second tumor shadow emphasized image.

In the block configuration shown in FIG. 1B, when image data _SD is input into the computer system 40, the image data _SD
SD is input to the blood vessel shadow enhancing means 55 and the difference calculating means 56,
The vascular shadow enhancing means 55 generates a vascular shadow enhanced image in which the vascular shadow on the X-ray image is enhanced based on the image data _SD . Image data S _D1 representing the blood vessel shadow enhanced image is also input to the differential operation circuit 56. The difference calculating means 56 subtracts the blood vessel shadow enhanced image from the original X-ray image based on the two input image data S _D and S _D1 to obtain the original X data.
A blood vessel shadow degenerated image in which the blood vessel shadow has been removed from the line image is obtained. Image data S _D2 representing the blood vessel shadow degenerate image is input to the tumor shadow enhancement means 57. This tumor shadow enhancing means 57
In performs the process of enhancing tumor shadow on the basis of the image data S _D2 entered, without the image that the image data S _D2 is supported vascular shadow for an image vascular shadow is removed is emphasized, Therefore, only the true tumor shadow is extracted with high accuracy by the tumor shadow extracting means 58.

In the block configuration shown in FIG. 1C, the image data _SD input to the computer
, A tumor shadow enhanced image is generated, and image data _SD3 carrying the tumor shadow enhanced image is input to the blood vessel shadow enhancing means 60 and the difference calculating means 61. The vascular shadow enhancement means 60 performs vascular shadow enhancement processing on the tumor shadow enhanced image. The blood vessel shadow among the shadows emphasized in the tumor shadow emphasized image by this processing is emphasized by both the tumor shadow emphasized filter and the blood vessel shadow emphasized filter. It becomes possible to raise the shadow of the blood vessel. The image data _SD4 representing the second blood vessel shadow enhanced image generated by the blood vessel shadow enhancing means 60 is also different from the image data _SD3 output from the tumor shadow enhanced means 59 by the difference calculating means 6.
Entered into 1. Based on the input image data S _D3 and S _D4 , the difference calculating means 61 subtracts the blood vessel shadow emphasized in the tumor shadow emphasized image to generate a third abnormal shadow emphasized image. , The tumor shadow is extracted with high accuracy based on the third abnormal shadow emphasized image.

By using any of the above configurations, the tumor shadow is separated from the blood vessel shadow, and the true tumor shadow is extracted with high accuracy.

Next, the tumor shadow enhancement filter and the blood vessel shadow enhancement filter used in the abnormal shadow enhancement means and the blood vessel shadow enhancement means will be described. Here, a case will be described in which a tumor shadow and a blood vessel shadow are enhanced based on the image data _SD as shown in the block diagram shown in FIG. 1A.However, a tumor shadow and a blood vessel shadow based on the other image data S _D2 and S _D3 are described. The same can be applied to enhance the shadow.

Abnormal shadow enhancing means In the abnormal shadow enhancing means in the computer system 40,
A tumor shadow appearing in the X-ray image is extracted by scanning the X-ray image using a tumor shadow extraction filter based on the image data _SD .

FIG. 2, for explaining an example of a real space filter for extracting tumor shadow, said around a predetermined pixel P ₀ on the X-ray image X
It is the figure drawn virtually on the line image.

Given pixel P ₀ is whether the pixel of the tumor Kagenai is determined. By scanning the X-ray image using the filter as shown here, a tumor shadow emphasized image in which the tumor shadow appearing in the X-ray image is enhanced is generated. The filter described first below is a filter described in Japanese Patent Application No. 1-162904.

Figure 3 is centered on the given pixel P _0, which is a diagram showing an example of a profile of the X-ray image of the second view of the line segment L ₁ and L ₅ of extending direction (x-direction).

As shown in FIG. 2, a plurality of (eight in this case) line segments extending from a predetermined pixel P _{0 in the} X-ray image to the periphery of the X-ray image
Assuming L _i (i = 1, 2,..., 8), circles R _j (j = 1, 2, and ₃ ) having radii r ₁ , r ₂ , and r ₃ centered on a predetermined pixel P ₀ , respectively.
Assume 3). The image data of the predetermined pixel P ₀ is f ₀ ,
Each pixel P _ij located at each intersection of each line segment _Li and each circle R _j (in FIG. 2, symbols are shown for P ₁₁ , P ₁₂ , P ₁₃ , P ₅₁ , P ₅₂ , and P ₅₃ . ) _Is image data f _ij .

Here, the difference delta _ij between the image data f ₀ of a given pixel P ₀ and the image data f _ij of each pixel P _ij is calculated according to the following equation (1).

_{Δ ij = f ij -f 0 ...} (1) (i = 1,2, ......, 8; j = 1,2,3) then for each line segment L _i, the difference obtained in (1) The maximum value of _Δij is obtained. That is, when an example line segments L _1, L _5, for the line segment L _1, pixel P _11, P _12, P each difference corresponding to _{13 Δ 11 = f 11 -f 0} Δ 12 = f 12 - The maximum value of f ₀ Δ ₁₃ = f ₁₃ −f ₀ is obtained. In this example, Δ ₁₃ > Δ ₁₂ > ₁₁ as shown in FIG. 3, and therefore Δ ₁₃ is the maximum value.

The maximum value of the pixel P _51, P _52, P ₅₃ each difference corresponding to _{Δ 51 = f 51 -f 0 Δ} 52 = f 52 -f 0 Δ 53 = f 53 -f 0 for line L ₅ delta ₅₃ is obtained, which is the difference delta _51, delta
₅₂ is a representative value representing the delta _53.

The line segments L _i for each predetermined pixel P ₀ as a plurality of pixels P
the maximum value of the difference delta _ij with _ij, the maximum value this that the obtained representative values for the line segment.

Next, the person sets the two line segments extending in opposite directions from a given pixel P _0, i.e. the line segment L ₁ and the line segment L _5, a line segment L ₂ and the line segment L _6, a line segment L ₃ With the line segment L ₇ and each of the line segment L ₄ and the line segment L ₈ as one set, the average value of two representative values (M ₁₅ , M ₂₆ , M ₃₇ , and M ₄₈ , respectively) is obtained for each set. . The set of the segment L ₁ and the line segment L _5, the average value M ₁₅ is Is required.

By handling this way the two line segments extending in opposite directions from a given pixel P ₀ as human pair, the distribution of the image data tumor shadow 7 is in the position where a concentration gradient is not asymmetrical Can reliably detect the tumor shadow.

If the average value _{M 15, M 26, M 37} , M 48 as described above is determined, on the basis of these average values _{M 15, M 26, M 37} , M 48 , as described below, predetermined image data corresponding to the pixel P ₀ is calculated. This method of obtaining is not limited to a specific method. For example, the following method is adopted.

4 is a diagram for explaining an example how obtaining the image data C ₁ corresponding to a predetermined pixel P _0. Average M ₁₅ horizontal axis obtained as described _{above, M 26, M 37, M} 48, these average M ₁₅ is the vertical axis, M _26, M _37, the evaluation value C ₁₅ corresponding to M _48, C ₂₆ ,
C ₃₇ and C ₄₈ .

If the average value M ₁₅ , M ₂₆ , M ₃₇ , M ₄₈ is smaller than a certain value M _{1, the} evaluation value is zero, and if it is larger than a certain value M _{2, the} evaluation value is 1.0, M _{1 to} M _1
In the middle of _2, the evaluation value is a value between 0.0 and 1.0 depending on the magnitude of the value. In this way, the average values M ₁₅ , M ₂₆ ,
Evaluation values C ₁₅ , C ₂₆ , C ₃₇ , C ₄₈ corresponding to M ₃₇ , M ₄₈ are obtained, and the sum of these evaluation values C ₁₅ , C ₂₆ , C ₃₇ , C ₄₈ C ₁ = C ₁₅ + C ₂₆ + C ₃₇ + C ₄₈ (3) is taken as the image data C ₁ of the pixel P ₀ . That is, the image data C ₁ has any value between the minimum value of 0.0 and a maximum value 4.0. Such an operation is performed by converting each pixel of the X-ray image into a predetermined pixel.
By performing a P _0, the image data representing the tumor shadow enhanced image is generated. Incidentally, comparing the image data C ₁ with a predetermined threshold Th1, so that the tumor shadow is extracted by this when extracting only pixels of C ₁ ≧ Th1.

In obtaining each of the above evaluations C ₁₅ , C ₂₆ , C ₃₇ , and C ₄₈ ,
When the above evaluation values C ₁₅ , C ₂₆ , C ₃₇ , and C ₄₈ are obtained using a conversion equation that saturates at a small value M ₂ ′ as shown by a dashed line in the figure, the following equation (3) is obtained. The obtained image data C ₁ becomes image data C ₁ having a large value in the case of a tumor shadow closer to a circle, and conversely does not saturate to a large value M ₂ ″ as shown by a two-dot chain line in FIG. When obtaining the image data C ₁ using the conversion equation, the image data C ₁ is the image data C ₁ having a large value for large tumors shadow contrast with the surroundings. Thus appropriate conversion according to the purpose The formula is selected.

The algorithm of the filter for enhancing or extracting the tumor shadow is not limited to the above algorithm.
Hereinafter, another example will be described (see Japanese Patent Application No. 1-162905).

The gradient ▽ f _ij of the image data f _ij of each pixel P _ij (i = 1, 2,... 8; j = 1, 2, 3) in FIG. 2 is obtained.

FIG. 5 is a diagram showing the gradient and the calculation method described below.

After the gradient ▽ f _ij is obtained, the length of the vector of these gradients ▽ _ij is adjusted to 1.0. That is, _{assuming that} the magnitude of the gradient ▽ f _ij is | ▽ f _ij |, the normalized gradient ▽ f _ij / | ▽ f _ij | is obtained.

Then, the normalized gradient _{▽ f ij / | ▽ f ij} | , the direction component of the line segment L _i is calculated. That is, a unit vector from each pixel P _ij toward a predetermined pixel P ₀ is And when (Where * represents the inner product).

Thereafter, the components a inward (direction of the predetermined pixels P ₀₎ positive for, when the outward and negative, each line segment L _i (i = 1, 2,
…, 8) each maximum value Is required.

Furthermore, each of these maximum values Value obtained by adding Is required. By dividing the added value by the number of line segment L _i (8 in this embodiment) becomes an average value. Therefore, this added value is obtained by simply multiplying the average value by a constant, and can be identified with the average value.

This sum There are image data C _2. The image data C ₂ is compared with a predetermined threshold value Th4, the tumor shadow is extracted by extracting an image of a C ₂ ≧ Th4.

This filter has the magnitude of the gradient ▽ f _ij | ▽ f _ij
| Normalized, by careful only in that direction (degree direction difference between the line segment L _i), the shape regardless of the contrast and surrounding image data C ₂ having a larger value by a circular And thereby extract the tumor shadow with great certainty.

In the above embodiment, as shown in FIG.
Each image data f corresponding to each pixel P _ij on the line segments L _{1 to} L _{8
Although ij} is used, it is needless to say that the number of line segments does not need to be eight, and may be, for example, sixteen. In addition, the calculation was performed for three distances r ₁ , r ₂ , and r ₃ with respect to the distance from the predetermined pixel P ₀ , but the calculation is not limited to the three distances, and the size of the tumor shadow to be extracted is not limited to three. When the distance is almost constant, the distance may be one (in this case, the calculation for obtaining the representative value is unnecessary). In order to enhance or extract the tumor shadows of various sizes with higher accuracy, from the distance r ₁ to a distance r ₃ may perform an operation for almost continuous number of distances.

Next, a filter having a different algorithm will be described (see Japanese Patent Application No. 1-162909).

Figure 6, in order to illustrate this algorithm, a diagram depicting virtually on the image around a predetermined pixel P ₀ on the X-ray image.

As shown in FIG. 6, a plurality of (eight in this case) line segments extending from a predetermined pixel P _{0 in the} X-ray image to the periphery of the X-ray image
Assume L _i (i = 1, 2,..., 8), and further assume a circle R having a radius r and centered on a predetermined pixel P ₀ . Further, each pixel P _i (ip1,2,..., 8) at the intersection of the center region Q ₀ including the predetermined pixel P ₀ and each of the line segments L _i (i = 1, 2,. ) _Are considered. Incidentally, the radius r, the center region Q _0, Suto of each peripheral area of the region Q _i, and assuming that the peripheral region, the size of the tumor shadow of interest, determination accuracy, to properly consider the operation speed, etc. Determined. In the present embodiment, a predetermined pixel
It is assumed that each pixel P _i is equidistant r from P ₀ ,
For example, if to be extracted tumor shadow having major axis in the X direction of FIG. 6, etc., a predetermined pixel for each pixel P _i for selecting the pixels that are far from the pixel P ₀ as a pixel P _1, P ₅ it may be different from the distance from P _0.

Average values Q ₀ , Q _i (i = 1, 2,..., 8) of a large number of image data corresponding to a large number of pixels in the central region Q ₀ and the peripheral regions Q _i assumed as described above are obtained. Can be For the sake of simplicity, the same symbol is used for a symbol indicating each area Q ₀ , Q _i (i = 1, 2,..., 8) and a symbol indicating the average value of image data in each area. ing.

Here, the average value of these differences Δ _i (i = 1, 2,..., 8) And the distribution Is required.

Next, these average values The ratio C ₃ of the variance σ ² is Obtained as this ratio C ₃ is the image data C ₃ corresponding to the predetermined pixel P _0. In this case, the image data
C _1, as in the case of C _2, is compared with a predetermined threshold value Th5, C ₃
≧ Th5, relatively average The predetermined pixel P ₀ for variance sigma ² is smaller increase is determined to be a pixel of tumor Kagenai.

Another different real space filter will be described with reference to FIG.

As shown in FIG. 2, a plurality of (eight in this case) line segments extending from a predetermined pixel P _{0 in the} X-ray image to the periphery of the X-ray image
Assuming L _i (i = 1, 2,..., 8), three circles R _j (j = 1, ₂ ) with respective radii r ₁ , r ₂ , r ₃ centered on a predetermined pixel P ₀
2,3) is assumed. The central region including the predetermined pixel P ₀ is defined as Q _0, and each pixel element P located at each intersection of each line segment _Li and each circle R _j
ij (in FIG. 2, symbols are shown for P ₁₁ , P ₁₂ , P ₁₃ , P ₅₁ , P ₅₂ , P ₅₃ ), and Q _ij (i = 1, 2 _,.
8; j = 1,2,3) (However, in FIG. 2, Q ₀ and
Only Q ₁₁ , Q ₁₂ , Q ₁₃ , Q ₅₁ , Q ₅₂ and Q ₅₃ are shown. ).

For each of the regions Q ₀ and Q _ij (i = 1, 2,..., 8; j = 1, 2, 3), a large number of pixels corresponding to a large number of pixels in each of the regions Q ₀ , Q _ij Average values Q ₀ , Q _ij (i = 1, 2,..., 8; j = 1,
2,3) is required. For simplicity, each area Q
_The same symbol is used for the symbol indicating ₀ , Q _ij (i = 1, 2,..., 8; j = 1, 2, 3) and the symbol indicating the average value of the image data in each area.

Next, each difference Δ _ij (i = 1, 2,..., 8; j = 1, 2, 3) between the average value Q _{0 of the} central region and the average value Q _ij of each peripheral region is Δ _ij = Q _ij -Q ₀ ... determined as (5), further each line segment L _i, the maximum value delta _i of the difference delta _ij is calculated.

Next, a second characteristic value V representing the variation of the maximum value Δ _{i (i} = 1~8) first characteristic value representative of the U and the maximum value Δ _{i (i} = 1~8) is obtained. For this purpose, first, the characteristic values U _{1 to} U ₄ and V _{1 to} V ₄ are obtained according to the following arithmetic expressions.

_{U 1 = (Δ 1 + Δ} 2 + Δ 5 + Δ 6) / 4 ... (6) U 2 = (Δ 1 + Δ 2 + Δ 6 + Δ 7) / 4 ... (7) U 3 = (Δ 3 + Δ 4 + Δ 7 + Δ 8 ) / 4 ... (8) U 4 = (Δ 4 + Δ 5 + Δ 8 + Δ 1) / 4 ... (9) V 1 = U 1 / U 3 ... (10) V 2 = U 2 / U 4 ... (11) V ₃ = U ₃ / U ₁ (12) V ₄ = U ₄ / U ₂ (13) Here, for example, a case where the characteristic value U ₁ is obtained according to the equation (6) will be described. delta ₁ and delta ₂ or delta adding about ₅ and delta ₆₎ means smoothing, region of opposite sides sandwiching the pixel P ₀ (delta _1,
The addition of + Δ ₂ and Δ ₅ + Δ ₆ ) is performed so that even if there is a tumor shadow in an area having a concentration gradient, the tumor shadow can be detected.

Further, for example, (10) will be described for obtaining the characteristic value V ₁ in accordance with formula, is a characteristic value determined for directions perpendicular to each other and the characteristic value U ₁ and the characteristic value U _3, thus the tumor shadow as shown in FIG. 3 If 57 is circular, V ₁ ≒ 1.0, and if it deviates from the circle, that is, if pixel P ₀ is within a linear shadow, V ₁ will deviate from 1.0.

As a first characteristic value U representing the maximum value Δ _{i (i} = 1~8) of the difference, the maximum value U = MAX of _{U 1 ~U 4 (U 1,} U 2, U 3, U 4) ... (14) is adopted, and as the second characteristic value V representing the variation of the maximum value Δ _i (i = 1 to 8) of the difference, the maximum value V _{1 to} V ₄ V = MAX (V ₁ , V _{2, V 3, V 4)} ... (15) is employed. Thus, the first and second characteristic values
U, the V is obtained, as a characteristic value C ₄ for a given pixel P ₀ to determine whether the pixel of tumor Kagenai, the ratio of these first and second characteristic values There are employed, the ratio C ₄ is an image data of a predetermined pixel P _0. By comparing the image data C ₄ with a predetermined threshold value Th 6, if C ₄ ≧ Th 6, the predetermined pixel P ₀
Can be determined to be pixels within the tumor shadow.

In the above filter example, as shown in FIG. 2, the average value Q _ij of the pixel data corresponding to each peripheral area Q _ij including the pixels P _ij on the _eight line segments L _{1 to} L ₈ was used. However, the number of the line segments does not need to be eight, and may be, for example, sixteen. The same applies to the embodiment described with reference to FIG. Further, in the above-described embodiment described with reference to FIG. ₂ , the calculation is performed for the three distances r ₁ , r ₂ , and r _3. However, the calculation is not limited to the three distances and may be of various sizes. to extract more accurately Shuyokage, distance may be performed continuously varied computed from r ₁ to r _3.

Scanning the x-ray image using any of the above real space filters or a combination thereof or other known filters enhances tumor shadows that typically appear as circular patterns on the x-ray image. An enhanced tumor shadow image is generated. In addition, since the tumor shadow enhancement filter does not actually enhance the tumor shadow but enhances the circular pattern, the shadow of the branch point of the blood vessel shadow and the shadow of the tangent of the blood vessel are also enhanced in the same manner as the tumor shadow. Will be done.

Vessel shadow enhancing means The vascular shadow extracting means in the computer system 40 scans the X-ray image on the basis of the image data _SD using a vascular shadow extraction filter described below, thereby obtaining a blood vessel appearing in the X-ray image. Shadows are extracted.

An example of a blood vessel shadow extraction filter will be described with reference to FIG. 6 virtually drawn on an X-ray image.

By scanning on the X-ray image using the filter as shown here, the blood vessel shadow appearing in the X-ray image is emphasized.

Figure 7 is centered on the given pixel P _0, shows an example of a profile of the X-ray image of the 6 view line L ₁ and L ₅ of extending direction (x-direction), FIG. 8 FIG. 7 is a diagram schematically depicting a blood vessel shadow and a filter shown in FIG. Here, the predetermined pixel P ₀ is
It is assumed that the shadow is substantially at the center of the blood vessel shadow 1.

As shown in FIG. 6, a plurality of (eight in this case) line segments extending from a predetermined pixel P _{0 in the} X-ray image to the periphery of the X-ray image
Assuming L _i (i = 1, 2,..., 8), further assuming a circle R with a radius r centered on a predetermined pixel P ₀ , and a line segment L _i (i = 1, 2,
, 8) and each pixel P _i (i
= 1, 2, ..., I think of each peripheral area Q _i, including 8). Incidentally, when referring to the number of the radius r, the area of each peripheral region Q _i, and the peripheral region,
It is appropriately determined in consideration of the thickness of the blood vessel shadow to be recognized, the size of the noise component mixed in the X-ray image, the recognition accuracy, the calculation speed, and the like.

Here, the average values Q ₀ , Q _i (i = 1, 2,..., 8) of a large number of image data corresponding to a large number of pixels in the central region Q ₀ and the peripheral regions Q _i assumed as described above. Is required. Here, for the sake of exclamation, the same symbol is used for a symbol indicating each area Q ₀ , Q _i (i = 1, 2,..., 8) and a symbol indicating the average value of image data in each area. ing.

Here, after calculating the average values Q ₀ and Q _i of the image data for the central region Q ₀ and each peripheral region Q _i , the line segment L _i shown in FIG. 6 is obtained.
(I = 1, 2,..., 8), two peripheral regions on two line segments extending in opposite directions from a predetermined pixel P ₀ , that is, Q ₁ and Q ₅ , Q ₂ and Q ₆ , Q ₃ and Q _7, Q ₄ and the Q ₈ as a human group, each difference delta _{i, i + 4} are _(i = 1, 2, 3, 4), the following equation _{Δ i, i + 4 = Δ} i + Δ i + 4 - | Δ _i -Δ _{i + 4} | ... determined according to (17), the difference Δ _{i, i} = ₊ maximum value of _{4 Δmax MAX (Δ i, i} + 4) ... (18) and the minimum value .DELTA.min = MIN (delta _{i, i + 4} ) (19) is obtained, and the difference Δ = Δmax−Δmin (20) between these maximum value Δmax and minimum value Δmin is obtained.

 Here, the significance of the above calculation will be described.

Considering the case of i = 1, the expression (17) is as follows: Δ _1,5 = Δ ₁ + Δ ₅ − | Δ ₁ −Δ ₅ | (21) Here in the case of solid line profile 2 shown in FIG. 7 Q ₁ ≒ Q ₅ a is for delta ₁ ≒ ₅ becomes, therefore | delta
₁₋ Δ ₅ | ≒ 0, and the above equation (21) becomes Δ _1,5 ≒ 2Q ₁ −2Q ₀ ... (22). On the other hand when the dashed profile 3 shown in FIG. 7, i.e., a given pixel P ₀ border 3a rather than into the vessel Pictures 1
If in the vicinity of, because it differs significantly from the Q _1, Q _5, for example, considering that Q ₅ = Q _0, the delta ₅ ≒ 0, and the thus _{Δ 1,5 ≒ 0 ... (23)} . That is, by performing the calculations of the equations (17) and (18), the boundary line is prevented from being recognized as a blood vessel shadow. However, in this state, a similar output value is obtained for a substantially circular pattern such as a tumor shadow having a diameter substantially the same as the width of the blood vessel shadow shown in FIG. 8. The circular pattern will be similarly emphasized. An example of this point will be described below. Fig. 7,
Referring to FIG. 8, when a predetermined pixel P ₀ is in the approximate center of the blood vessel shadow _{1, Q 0 = Q 2 =} Q 3 = Q 7 = Q 8 ... (24) Q 1 = Q 4 = Q 5 = Considering Q ₆ … (25), Δ _1,5 ≒ 2Q ₁ −2Q ₀ … (22) as shown in equation (22), and Δ ₂₆ = Δ ₃₇ = Δ ₄₈ = 0 (26) Δmax = Δ ₁₅ = 2Q ₁ −2Q ₀ (27)

On the other hand, if the given pixel P ₀ is in the approximate center of the tumor shadow 4 shown in FIG. _{8, Q 1 = Q 2 = Q} 3 = Q 4 = Q 5 = Q 6 = Q 7 = Q 8 ... (28) Considering that, (18) by _{Δ 15 = Δ 26 = Δ 37} = Δ 48 = 2Q 1 -2Q 0 ... (29) , and the same Δmax = 2Q ₁ -2Q maximum .DELTA.max the above (27) ₀ … (30) That is, the blood vessel shadow 1 and the tumor shadow 4 shown in FIG. That is, with this blood vessel shadow extraction filter, a tumor shadow having a diameter approximately equal to the thickness of the blood vessel shadow is also extracted as a blood vessel shadow.

Thus, by performing the calculations shown in the above-described equations (19) and (20), it is possible to prevent the circular pattern from being recognized as a blood vessel shadow.

In this case, since the predetermined pixel P ₀ is at the center of the blood vessel shadow 1 and is considered as in the above equations (24) and (25), Δmin = 0... (31) from equation (26). Equations (30) and (31) are substituted into the equation, and Δ = Δmax−Δmin = 2Q ₁ −2Q ₀ −0 2Q ₁ −2Q ₀ (32) On the other hand, the predetermined pixel P ₀ is at the center of the tumor shadow 4,
Considering the above equation (28), from equation (29), Δmin = 2Q ₁ −Q ₀ (33), and substituting equations (30) and (33) into equation (20), Δ = Δmax−Δmin = (2Q ₁ -2Q ₀₎ - a _{(2Q 1 -2Q 0) = 0} (34). That is, in the case of the blood vessel shadow 1 (Equation (32)), the tumor shadow 4
In the case of (Expression (34)), different values are obtained as image data, so that the tumor shadow 4 can be excluded and only the blood vessel shadow 1 extending on the X-ray image can be emphasized.

In the above blood vessel shadow extraction filter, was used as the average value Q ₀ of the image data of the central area Q ₀ and the mean value Q _i of the image data of each peripheral area Q _i, each peripheral region and these central regions Q ₀ Q _i
Is set according to the magnitude of the noise superimposed on the X-ray image. Therefore, when the noise is small or when noise removal processing is performed separately, the average value
Instead of using Q ₀ and Q _i , the above-described predetermined pixel P ₀ and the image data itself corresponding to each pixel P _i may be used.

In the above-described blood vessel shadow extraction filter, a predetermined distance r (see FIG. 1) is considered to be fixed. There are also thin and thick vascular shadows, and when only vascular shadows of a specific width are recognized, the predetermined distance r may be fixed.
The operation of recognizing the blood vessel shadow with respect to the predetermined distance r as described above is performed for various predetermined distances r to obtain a plurality of image data, and the maximum value of the plurality of image data is used as new image data. Thus, the blood vessel shadow may be enhanced with various widths from a thin blood vessel shadow to a thick blood vessel shadow.

The blood vessel shadow extraction filter is not limited to the above two examples, and various known blood vessel shadow extraction filters may be used unless the blood vessel shadow enhancement filter emphasizes a circular pattern as a blood vessel shadow. It goes without saying that you can do it.

As described above, a tumor shadow-enhanced image and a blood vessel shadow-enhanced image can be generated, and a blood vessel shadow can be excluded from enhancement by obtaining a difference between image data for each corresponding pixel, thereby enabling a true tumor shadow to be generated. Only the true tumor shadow can be detected with high accuracy by, for example, the threshold processing described above.

After detecting the tumor shadow as described above, for example, CRT
When a visible image is reproduced and displayed on the display 44 (see FIG. 9), it is possible to assist the observer by specifying the area detected as the tumor shadow.

The above embodiment is an example of extracting a tumor shadow that typically appears as a circle in a human chest X-ray image obtained using a stimulable phosphor, but the present invention is limited to a chest X-ray image. However, the present invention is not limited to a system using a stimulable phosphor, and has a configuration that can be widely used when detecting an abnormal shadow on a radiation image based on image data representing the radiation image of the subject. Things.

(Effects of the Invention) As described in detail above, the first abnormal shadow detection device of the present invention scans the radiation image using an abnormal shadow emphasis filter, thereby enhancing the abnormal shadow. Abnormal shadow enhancing means for obtaining an enhanced image, and linear pattern enhancing means for obtaining a linear pattern enhanced image in which a linear pattern extending on the radiation image is enhanced by scanning the radiation image using a linear pattern enhancing filter And a difference calculating means for obtaining, from the abnormal shadow-enhanced image, a second abnormal shadow-enhanced image in which the regions emphasized in both the abnormal shadow-enhanced image and the linear pattern-enhanced image have been canceled. Since an abnormal shadow extracting means for extracting an abnormal shadow from an abnormal shadow emphasized image is provided, it is possible to prevent a part of a linear pattern from being extracted as an abnormal shadow, and An ordinary shadow is extracted with high accuracy.

Further, the second abnormal shadow detection apparatus of the present invention scans a radiographic image using a linear pattern emphasis filter to obtain a linear pattern enhanced image in which a linear pattern extending on the radiographic image is emphasized. Pattern emphasis means,
Difference calculation means for obtaining a linear pattern degenerate image which is an image of the difference between the radiation image and the linear pattern emphasized image, and an abnormality in which an abnormal shadow is enhanced by scanning the linear pattern degenerate image using an abnormal shadow enhancement filter Since there is provided an abnormal shadow enhancing means for obtaining a shadow enhanced image and an abnormal shadow extracting means for extracting an abnormal shadow from the abnormal shadow enhanced image, a part of the linear pattern is similar to the first abnormal shadow detecting apparatus. It is prevented from being extracted as an abnormal shadow, and the abnormal shadow is extracted with high accuracy.

Further, the third abnormal shadow detection device of the present invention is an abnormal shadow enhancing means for obtaining an abnormal shadow enhanced image in which the abnormal shadow is enhanced by operating on the radiation image using an abnormal shadow enhancing filter, Linear pattern emphasis for obtaining a second linear pattern emphasized image in which only the linear pattern emphasized in the abnormal shadow emphasized image is scanned by scanning over the abnormal shadow emphasized image using the pattern emphasis filter Means for calculating a third abnormal shadow-enhanced image which is an image of a difference between the abnormal shadow-enhanced image and the second linear pattern-enhanced image; and extracting an abnormal shadow from the third abnormal shadow-enhanced image. Since abnormal shadow extraction means is provided, similarly to the first and second abnormal shadow detection devices, the linear pattern is prevented from being extracted as an abnormal shadow, and the abnormal shadow is extracted. It can be extracted to the accuracy.

[Brief description of the drawings]

FIGS. 1A, 1B, and 1C are block diagrams showing respective embodiments of the first, second, and third abnormal shadow detecting devices of the present invention, and FIG. to illustrate an example of a spatial filter, virtually drawn figure on the image around a predetermined pixel P ₀ on the X-ray image, Fig. 3 is centered on the given pixel P _0, diagram showing an example of a profile of the X-ray image on the direction (x-direction) of extension of the line segments L ₁ and L ₅ of FIG. 2, Fig. 4, the image data corresponding to a predetermined pixel P ₀ Determination of FIG. 5 is a diagram for explaining an example, FIG. 5 is a diagram showing a vector such as gradient _{ij ij} of image data f _ij , and FIG. 6 is a diagram for explaining an example of a blood vessel shadow extraction filter and an example of a tumor shadow extraction filter. For a given pixel on the X-ray image
FIG. 7 is a diagram virtually drawn on the image centered on P _0. FIG. 7 is a line segment L _{i of} FIG. 6 centered on a predetermined pixel P ₀ .
Figure, showing an example of a profile in the direction of X-ray images of extension of L ₅ and FIG. 8 is a vascular shadows and drawing overlapping the filter depicted schematically shown in FIG. 6, FIG. 9 is FIG. 10 is a schematic view showing an example of an X-ray image photographing apparatus. FIG. 10 is a perspective view showing an example of an X-ray image reading apparatus and a computer system which is an embodiment of the abnormal shadow detecting apparatus according to the present invention. 10 X-ray imaging device, 14 stimulable phosphor sheet 20 X-ray image reading device 23 Laser light source 26 Rotating polygon mirror 29 Photostimulated emission light 30, Light guide 31 …… Photomultiplier 40 …… Computer system 51,57,59 …… Tumor shadow enhancement means 52,55,60 …… Vessel shadow enhancement means 53,56,61 …… Difference calculation means 54,58,62 …… Tumor Shadow extraction means

Claims (3)

(57) [Claims] 1. An abnormal shadow detecting apparatus for detecting an abnormal shadow appearing as a substantially circular pattern on a radiographic image based on image data representing a radiographic image of a subject. Abnormal shadow enhancement means for obtaining an abnormal shadow-enhanced image in which the abnormal shadow is enhanced by scanning a linear pattern extending on the radiation image by scanning the radiation image using a linear pattern enhancement filter. Linear pattern enhancement means for obtaining an enhanced linear pattern enhanced image, from the abnormal shadow enhanced image, the emphasis of a region enhanced in both the abnormal shadow enhanced image and the linear pattern enhanced image is canceled. Difference calculating means for obtaining a second abnormal shadow emphasized image; and extracting the abnormal shadow from the second abnormal shadow emphasized image. An abnormal shadow detection device, comprising: an abnormal shadow extraction unit. 2. An abnormal shadow detecting apparatus for detecting an abnormal shadow appearing as a substantially circular pattern on a radiation image based on image data representing a radiation image of a subject. A linear pattern enhancement unit that obtains a linear pattern enhancement image in which a linear pattern extending on the radiation image is enhanced by scanning the linear image; a linear image that is an image of a difference between the radiation image and the linear pattern enhancement image Difference calculation means for obtaining a reduced pattern image; abnormal shadow enhancement means for obtaining an abnormal shadow enhanced image in which the abnormal shadow is enhanced by scanning the linear pattern reduced image using an abnormal shadow enhancement filter; and An abnormal shadow detecting device, comprising: an abnormal shadow extracting means for extracting the abnormal shadow from an image. Place. 3. An abnormal shadow detecting apparatus for detecting an abnormal shadow appearing as a substantially circular pattern on a radiation image based on image data representing a radiation image of a subject. Abnormal shadow enhancing means for obtaining an abnormal shadow enhanced image in which the abnormal shadow is enhanced by scanning the abnormal shadow enhanced image by scanning the abnormal shadow enhanced image using a linear pattern enhancement filter. Linear pattern emphasizing means for obtaining a second linear pattern emphasized image in which only the linear pattern is emphasized, a third image being a difference image between the abnormal shadow emphasized image and the second linear pattern emphasized image Difference calculating means for obtaining the abnormal shadow-enhanced image, and abnormal shadow extracting means for extracting the abnormal shadow from the third abnormal shadow-enhanced image An abnormal shadow detection device, comprising: