JP2582667B2 - Linear pattern width calculator - Google Patents

Description

Description: BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a linear pattern width calculating device for calculating the width (thickness) of a linear pattern such as a blood vessel shadow or a rib shadow appearing on a radiographic image. is there.

(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.

As one of the above-mentioned pattern recognitions, a linear pattern in a radiation image, for example, a blood vessel shadow on an X-ray image of a human body may be recognized. Here, for example, recognition of a blood vessel shadow may be performed in order to make the blood vessel shadow itself an observation target,
It is also conceivable to perform the above processing, for example, when recognizing a tumor shadow (substantially circular pattern) on an X-ray image of a human body, instead of directly using a blood vessel shadow as an observation target. That is, the X of the human body
Since a line image is very complicated, only a tumor shadow is not extracted even if an attempt is made to extract a tumor shadow. For example, a shadow of a branch point where a blood vessel is bifurcated or a blood vessel has an X-ray source and an X-ray source at the time of imaging. The so-called tangent shadow of a blood vessel extending in the direction connecting the recording sheet such as an X-ray film for recording X-rays and a stimulable phosphor sheet also has a substantially circular pattern. Tangent shadows are often also extracted as tumor shadows. Therefore, when extracting the tumor shadow, the blood vessel shadow is also extracted together with it, and it is possible to identify whether the extracted tumor shadow is a true tumor shadow, a branch point of a blood vessel, a tangent, or the like, so that higher accuracy is obtained. It is conceivable to perform tumor shadow extraction.

(Problems to be Solved by the Invention) As a method for extracting the linear pattern such as the blood vessel shadow, for example, the method described in the above-mentioned Japanese Patent Application Laid-Open No. 62-125481 or "" Medical Electronics and Biotechnology " September 1984 36
Pp. 42 Identification of blood vessel shadows in indirect chest radiographs
Junichi Hasegawa et al.] Has been proposed, but no method has been proposed for obtaining the width (thickness) of a linear pattern extracted in this manner.

The width of this linear pattern is used for, for example, promoting automatic diagnosis of a disease in which a symptom appears on a linear pattern such as a blood vessel shadow, or for distinguishing a tumor shadow from a blood vessel shadow as shown in an example described later. It is necessary in.

The present invention has been made in view of the above circumstances, and has as its object to provide a linear pattern width calculation device that calculates the width of a linear pattern in a radiation image.

(Means for Solving the Problems) A linear pattern width calculating apparatus according to the present invention is configured to calculate the linear pattern width at a predetermined pixel position in the linear pattern based on image data representing a radiation image including the linear pattern. A linear pattern width calculating device for calculating a width of a pattern, wherein a predetermined number of two line segments extending in opposite directions are formed as one set from a predetermined pixel to an end of the radiation image. Distance calculating means for calculating each distance between each of the intersections of a large number of line segments extending and the boundary line inside and outside the linear pattern and the predetermined pixel, and adding the two distances for each set Second distance calculating means for determining the distance between intersection points for each pair by combining
And extracting means for extracting a minimum inter-intersection distance among a large number of inter-intersection distances as a width of the linear pattern at the position of the predetermined pixel.

(Operation) The linear pattern width calculating apparatus according to the present invention is configured such that each intersection between a number of line segments extending from a predetermined pixel in the linear pattern and boundary points inside and outside the linear pattern and each of the intersections between the predetermined pixel A predetermined pixel of the linear pattern is obtained by obtaining a distance, obtaining a distance between intersections for each set of two line segments extending in mutually opposite directions as a set, and obtaining a minimum distance between these intersections among the distances between the intersections. The width at the position can be determined.

(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 extract a blood vessel shadow when extracting a tumor shadow typically occurring as a substantially spherical shape in the lungs of a human body, and to determine the width of the blood vessel shadow. .

FIG. 10 is a schematic view of an example of an X-ray imaging apparatus. An X-ray 12 from an X-ray source 11 of the X-ray imaging apparatus 10 is applied to a chest 13 of a human body 13.
The X-rays 12a that have been radiated toward the human body 13 and transmitted through the human body 13 are radiated to the stimulable phosphor sheet 14, thereby forming the chest 13 of the human body.
The X-ray image a is accumulated and recorded on the stimulable phosphor sheet 14.

FIG. 11 is a perspective view showing an example of an X-ray image reading device and a computer system which is an embodiment of the abnormal shadow detection device of the present invention.

After imaging is performed by the X-ray imaging 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 a linear pattern width detecting apparatus according to the present invention, which includes a main body 41 having a built-in CPU and an internal memory, a drive section 42 into which a floppy disk as an auxiliary memory is inserted and driven, and an operator. A keyboard 43 for inputting necessary instructions and the like to the computer system 40, a CRT for displaying X-ray images and necessary information
It comprises a display 44.

Image data input to this computer system 40
Based on the _SD , the detection of the tumor shadow and the detection of the blood vessel shadow on the X-ray image are performed, and the width of the blood vessel shadow is obtained.

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.

In the computer system 40, the tumor shadow appearing in the X-ray image is extracted by scanning the X-ray image using the 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 on an X-ray image using a filter as shown here, a tumor shadow appearing in the X-ray image is extracted. 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 pixel P ₀ is a determination is made whether a pixel of tumor Kagenai. These average values M ₁₅ , M ₂₆ , M
₃₇ the determination is not limited to a particular decision method based on, M _48, but for example, the following methods are employed.

4 is a diagram for explaining an example of a method of obtaining characteristic values C ₁ used in the determination. The horizontal axis is the average value M ₁₅ , M ₂₆ , M ₃₇ , M ₄₈ determined as described above, and the vertical axis is the average value M ₁₅ , M
Evaluation values C ₁₅ , C ₂₆ , C ₃₇ , and C ₄₈ corresponding to ₂₆ , M ₃₇ , and M ₄₈ , respectively.

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 the characteristic value C ₁ . That is, this characteristic value C ₁ is the minimum value 0.0
And any value between 4.0 and the maximum value.

This characteristic value C ₁ is compared with a predetermined threshold value Th ₁ , and C ₁ ≧ T
whether it is h1, by either a C ₁ <Th1, a given pixel P ₀ is whether the pixel of each tumor Kagenai is determined.

Upon obtaining the evaluation values _{C 15, C 26, C 37} , C 48, fourth each evaluation value by using a conversion equation as saturated at as indicated by the chain line smaller value M ₂ 'in Figure C ₁₅ , C ₂₆ , C ₃₇ , and C ₄₈ , the characteristic values obtained according to the equation (3) are obtained.
C ₁ is a characteristic value C ₁ having a large value in the case of a tumor shadow that is more circular, and uses a conversion formula that does not saturate to a large value M ₂ ″ as shown by a two-dot chain line in FIG. Characteristic value
When obtaining the C _1, the characteristic value C ₁ is a characteristic value C ₁ having a large value for large tumors shadow contrast with the surroundings. Therefore, an appropriate conversion formula is selected according to the purpose.

Note that the determination based on the average values M ₁₅ , M ₂₆ , M ₃₇ , and M ₄₈ is not limited to the method using the characteristic value C ₁ , and for example, thresholds M ₁₅ , M ₂₆ , M ₃₇ , and M ₄₈ are used. M ₁₅ , M ₂₆ , M
_37, M ₄₈ Kyo該the predetermined pixel P ₀ if the threshold Th2 or more may be determined that the pixel of tumor Kagenai, and M
_15, may be determined as M _26, M _37, M the predetermined pixel P ₀ when three or more is the threshold Th2 or more of the ₄₈ is a pixel of tumor Kagenai, further M ₁₅ , M ₂₆ , M ₃₇ , M ₄₈ sum M = M ₁₅ +
M ₂₆ + M ₃₇ + M ₄₈ may be obtained, and the sum M may be compared with a threshold Th 3 to determine that the predetermined pixel P ₀ is a pixel in the tumor shadow when M ≧ Th 3.

The algorithm of the filter for 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 As but feature amount C _2, the feature amount C ₂ a predetermined threshold value Th4
And whether C ₂ ≧ Th4 or C ₂ <Th4 determines whether each of the predetermined pixels P ₀ is a pixel in the tumor shadow.

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 between surrounding feature amount C ₂ which has a large 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 When the distance is almost constant, the distance may be one (in this case, the calculation for obtaining the representative value is unnecessary). To extract the tumor shadows of various sizes with higher precision,
It may perform operations for almost continuous number of distances from r ₁ to a distance r _3.

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 compared with a predetermined threshold value Th1, in the case of C ₃ ≧ Th5 relatively average value Is large and the variance σ ² is small, the predetermined pixel P ₀ is determined to be a pixel in the tumor shadow, and if C ₃ <Th5, the predetermined pixel P
₀ is determined to be a pixel outside the tumor shadow.

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 is employed, the characteristic value C ₄ is compared with a predetermined threshold value Th6, or a C ₄ ≧ Th6, by either a C ₄ <Th6, the pixel
It is determined whether each of P ₀ is a pixel in 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 detects tumor shadows that typically appear as circular patterns on the X-ray image However, whether the tumor shadow detected in this way is a true tumor shadow is determined by the determination unit described later, and thus the tumor shadow detected in this manner is used as a candidate tumor shadow. Shall be called.

Immediately before and after the detection of the tumor shadow candidate, the computer system 40 scans the X-ray image using the vascular shadow extraction filter shown below based on the image data _SD , thereby forming an X-ray image. The appearing blood vessel shadow is also 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.

Given pixel P ₀ is whether the pixel of the vessel Kagenai is recognized. By scanning on an X-ray image using a filter as shown here, a blood vessel shadow appearing in the X-ray image is recognized.

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
_{L i (i = 1,2, ...} , 8) assumed, and further assuming a circle R of radius r centered at the given pixel P _0, also the central region Q ₀ which includes the given pixel P _0, Each peripheral area Q _i including each pixel P _i (i = 1, 2,..., 8) on the intersection of each of the line segments L _i (i = 1, 2,. Think. The radius r, the central area Q ₀ ,
The Suto of each peripheral area of the region Q _i, and the peripheral area, considering the thickness of the blood vessel shadow of interest recognized, the magnitude of the noise component mixed in the X-ray image, the accuracy of the recognition, the operation speed, etc. It is determined appropriately.

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. 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.

When the average values Q ₀ , Q _i of the image data of the central area Q ₀ and the peripheral areas Q _i are obtained in this manner, each difference Δ _i is calculated based on the following equation: Δ _i = Q _i −Q ₀ (17) sought Te, then these maximum values .DELTA.max = MAX of the differential _{Δ i (Δ i) ... (} 18) is obtained, the output value the maximum value .DELTA.max corresponds to a predetermined pixel P _0. This implements a blood vessel shadow recognition filter. The maximum value Δmax is set as the image data value of the predetermined pixel P ₀ , and the same operation is performed for each pixel of the X-ray image to obtain an X-ray image in which the blood vessel shadow is emphasized.し き い 値 max
The blood vessel shadow is extracted by extracting only the pixel that becomes Th7.

However, in the above embodiment even when the profile 3 shown in broken lines even when the profile 2 the profile of the image shown in Figure 7 is indicated by the solid line, the same output values when the .DELTA.max = delta _5. That is, the predetermined pixel P ₀ is the blood vessel shadow 1
And the case near the boundary line 3a, the result is similarly recognized. Even if the boundary line 3a is extracted as a blood vessel shadow, there is no problem because the boundary line 3a is excluded from the determination of the tumor shadow by the determination unit described below, but the boundary line 3a is extracted as a blood vessel shadow as another example of the blood vessel shadow extraction filter. 5 shows a blood vessel shadow extraction filter that does not have any problem.

For as in the above embodiments the central region Q ₀ and the peripheral region Q _i the average value Q _0, Q _i of the image data, further the above (1
7) After obtaining each difference delta _i based on the formula, the line segment L _{i (i} = 1,2 as shown in Figure 6, ..., 2 that extend in opposite directions from a given pixel P ₀ of 8) Two peripheral regions on the line segment of
That is, as a set of Q ₁ and Q ₅ , Q ₂ and Q ₆ , Q ₃ and Q ₇ , Q ₄ and Q ₈ , each difference Δ _{i, i + 4} (i = 1,2,3,4) is expressed by the following equation Δ _{i, i + 4 = Δ i} + Δ i + 4 - | Δ i -Δ i + 4 | ... determined according to (19), the maximum value _{Δmax = MAX (Δ i, i} + 4) of this difference Δ _{i, i + 4} ... ( 20) is required. Here, considering the case of i = 1, (19)
The equation is as follows: Δ _1,5 = Δ ₁ + Δ ₅ − | Δ ₁ −Δ ₅ | (21) Here, in the case of the profile 2 shown by the solid line in FIG. 7, since Q ₁ ≒ Q ₅ , Δ ₁ ５Δ ₅ and thus |
Δ 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 Figure 7, because it differs significantly from the Q _1, Q _5, for example, Q ₅ ≒ 0
Then, Δ ₅ ≒ 0, and thus Δ _1,5 ≒ 0 (23). That is, by performing the calculations of the equations (19) and (20), 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 equal to the width of the blood vessel shadow shown in FIG. And a circular pattern is similarly recognized.
An example of this point will be described below. Referring to FIGS. 7 and 8, when the predetermined pixel P ₀ is substantially at the center of the blood vessel shadow 1, Q ₀ = Q ₂ = Q ₃ = Q ₇ = Q ₈ (24) Q ₁ = Q ₄ = Q ₅ = the Q ₆ ... considered (25), (22) as shown formula _{Δ 1,5 ≒ 2Q 1 -2Q 0 ...} (22) , and the addition _{Δ 26 = Δ 37 = Δ 48} = 0 .. (26), and 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, (19) 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. The same applies to the above-described embodiment. That is, the blood vessel shadow extraction filter also extracts a tumor shadow having a diameter approximately equal to the thickness of the blood vessel shadow as a blood vessel shadow.

In each of the embodiments described above, 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, these central regions Q ₀ and the peripheral region Q The area of _i is X
The average value Q ₀ , Q _i is used when the noise is small or when noise removal processing is performed separately, for example, when the noise is small. Instead, the aforementioned predetermined pixel P ₀ and each pixel
Using the image data itself corresponding to P _i is intended may be.

In the above embodiment, the predetermined distance r (see FIG. 6) is considered to be fixed. The blood vessel shadow may be thin or thick, and when only the blood vessel shadow of a specific width is recognized, the predetermined distance r may be fixed. A plurality of output values are obtained by performing an operation of performing recognition for various predetermined distances r, and the maximum value among the plurality of output values is newly set as an output value. May be recognized with a width of.

In the computer system 40, after the candidate for the tumor shadow and the blood vessel shadow are extracted as described above, in an area where both the candidate and the blood vessel shadow on the X-ray image are extracted,
It is determined as follows whether the extracted pattern is a tumor shadow or a blood vessel shadow.

FIG. 1 is a diagram showing a blood vessel shadow and a diagram virtually drawn on the blood vessel shadow in order to explain a method for obtaining the thickness (width) of the blood vessel shadow, which is necessary for performing this determination. is there. As it follows width of vascular Pictures 1 at the position of a given pixel P ₀ is calculated.

First, a large number of two line segments (L ₁ and L ₅ , L ₂ and L ₆ , L ₃ and L ₇ , L ₄ and L _{8 in} the example shown in FIG. 1) extending in opposite directions It sets a number of line segments extending toward the end of a predetermined pixel P ₀ in X-ray images so as to form a (this four sets in the example)
Each intersection point C ₁ , C ₂ , L ₁ , L ₂ ,..., L ₈ of the blood vessel shadow 1
…, C ₈ is required.

When the intersections C ₁ , C ₂ ,…, C ₈ are obtained in this way,
Then each intersection and a predetermined pixel _{P C 1, C 2, ...} , the distance d ₁ between C _8,
d ₂ ,..., d ₈ are obtained, and then these distances d ₁ , d ₂ ,
…, D ₈ are added. That is, d ₁ + d ₅ , d ₂ + d ₆ , d ₃ + d ₇ ,
d ₄ + d ₈ is required. In this way, the distances d ₁ + d ₅ , d ₂ + d ₆ , d ₃ + d ₇ , d ₄ + d ₈ between the intersections of each pair are obtained, and the minimum value of the distances between the intersections, that is, in this example, d ₁ + d ₅ is obtained, and the minimum value d ₁ + d ₅ is a predetermined pixel of the blood vessel shadow.
It is the width (thickness) at the position of P _0.

Although FIG. 1 shows eight line segments, the number of line segments is not limited to eight, and may be, for example, 16, 32, or the like.

FIGS. 9A, 9B, and 9C schematically show an X-ray image pattern in an enlarged candidate area that includes a region extracted as both a tumor shadow candidate and a blood vessel shadow and spreads near the region. 9A, 9B, and 9C show a tumor shadow, a shadow of a branch point of a blood vessel, and a shadow of a tangent of a blood vessel, respectively.

When the tumor shadow extraction filter is used, the shadow of the branch point of the blood vessel shown in FIG. 9B and the shadow of the tangent of the blood vessel shown in FIG. 9C may be extracted as a tumor shadow because they form a substantially circular pattern.

On the other hand, when the blood vessel shadow extraction filter is used, a tumor shadow as shown in FIG. 9A, which has a diameter substantially equal to the thickness of the blood vessel shadow as described above, is also extracted as a blood vessel shadow.

Therefore, the enlargement candidate region 4 is considered to include the regions extracted as both the tumor shadow candidate 7 and the blood vessel shadow 1, and the area of the blood vessel shadow 1 in this enlargement candidate region is set to S. Further, the thickness (width) of the blood vessel shadow 1 is obtained according to the above-described algorithm for obtaining the thickness of the blood vessel shadow in each pixel device while moving the predetermined image P ₀ within the enlarged candidate region 4 and within the extracted blood vessel shadow 1. And the maximum width d is obtained. Note that the tumor shadow as shown in FIG. 9A is also extracted as a blood vessel shadow, and therefore, the diameter of the tumor shadow is also obtained as the blood vessel shadow thickness according to the algorithm for obtaining the width (thickness) of the blood vessel shadow.

Thus, the area S of the blood vessel shadow 1 in the enlargement candidate area 4
Is determined, whether or not the candidate for the tumor shadow is truly a tumor shadow is determined based on the area S and the maximum width d. Here, the arithmetic expression for determination is not limited to a specific one, but for example, , And this value is compared with a predetermined threshold value Th8. If E ₁ ≦ Th8 (32), it is determined to be a tumor shadow, and if E ₁ > Th8 (33), the shadow of the vascular tangent or It is determined that the shadow is a shadow of a branch point of a blood vessel and is not a tumor shadow.

As an arithmetic expression for this determination, for example, E ₂ = S / d (34) may be used.

In the computer system 40 shown in FIG. 11, whether or not the once extracted tumor shadow is truly a tumor shadow is determined as described above.

After the determination as described above, for example, when a visible image is reproduced and displayed on the CRT display 44, it is possible to assist the observer by specifying the region determined as a true tumor shadow.

In the above embodiment, a blood vessel shadow is extracted to extract a tumor shadow that typically appears as a circle in a chest X-ray image of a human body obtained using a stimulable phosphor, and the width of the blood vessel shadow is obtained. However, it is needless to say that the purpose of obtaining the width of the blood vessel shadow need not be to extract the tumor shadow. In the above embodiment, the blood vessel shadow is automatically extracted. However, in the present invention, it is not always necessary to automatically extract the blood vessel shadow or the like. For example, an X-ray image is displayed on the CRT display 44 (see FIG. 11). Then, the operator determines a predetermined pixel P _{0 in the} blood vessel shadow.
The specified may be obtained the width of the vessel shadow in the specified position in a given pixel P _0. Further, the present invention is not limited to obtaining only the width of the blood vessel shadow, but may be, for example, a rib shadow, and has a configuration that can be widely used when obtaining the width of a linear pattern appearing in a radiographic image. Is what it is.

(Effects of the Invention) As described in detail above, the linear pattern width calculating apparatus according to the present invention is configured such that the predetermined pixels are formed so as to form a large number of sets each including two line segments extending in opposite directions. Intersection detection means for finding each intersection between a large number of line segments extending toward the end of the radiation image and boundary lines inside and outside the linear pattern; and for each line segment, the intersection point on each line segment and the predetermined A first distance calculating means for obtaining each distance between the pixels of the first and second, and a second distance calculating means for obtaining the distance between the intersections for each set by adding the two distances for each set, Since there is provided extraction means for extracting the minimum distance between intersections among a large number of the distances between intersections as the width of the linear pattern at the position of the predetermined pixel, the width of the linear pattern in the radiation image is accurately determined. Desired.

[Brief description of the drawings]

FIG. 1 is a diagram showing a blood vessel shadow and a diagram virtually drawn on the blood vessel shadow to explain a method of obtaining the thickness (width) of the blood vessel shadow. FIG. to illustrate an example of a real space filter for extracting, virtually drawn figure on the image around a predetermined pixel P ₀ on the X-ray image, FIG. 3 is the predetermined pixel P ₀ mainly, shows 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), FIG. 4, the predetermined pixels P ₀ is the tumor Kagenai FIG. 5 is a diagram for explaining an example of a method of obtaining a characteristic value used to determine whether a pixel is a pixel, FIG. 5 is a diagram showing a vector such as a gradient ▽ f _ij of image data f _ij , and FIG. examples and tumor shadow extracting virtually drawn figure on the X-ray image in order to explain an example of a filter in a blood vessel shadow extraction filter, FIG. 7 is a central predetermined pixel P ₀ And, in Figure 6 the line L ₁
And illustrates an example of a profile of the X-ray image on the direction (x-direction) of extension of the L _5, FIG. 8 is a vascular shadows and drawing and was schematically shown superimposed filter shown in FIG. 6, the 9A to 9C are diagrams schematically showing an X-ray image pattern of an enlarged candidate region including a region extracted as both a tumor shadow candidate and a blood vessel shadow and extending in the vicinity of the region, FIG. FIG. 11 is a schematic diagram of an example of an X-ray imaging apparatus. FIG. 11 is a diagram showing an example of an X-ray image reading apparatus and a computer system including an example of an abnormal shadow detection apparatus of the present invention. 1 ... Vessel shadow 2,3 ... X-ray image profile 3a ... Boundary line, 4 ... Tumor shadow 10 ... X-ray imaging device, 11 ... X-ray source 14 ... Storable phosphor sheet 20 ... ... X-ray image reader, 23 ... Laser light source 29 ... Stimulated luminescence 31 ... Photomultiplier 40 ... Computer system 44 ... CRT display

Claims (1)

(57) [Claims] An apparatus for calculating a width of a linear pattern at a position of a predetermined pixel in the linear pattern based on image data representing a radiation image including the linear pattern, the apparatus comprising: Numerous line segments extending from the predetermined pixel toward the end of the radiographic image and boundary lines inside and outside the linear pattern so as to form a large number of sets each including two line segments extending in opposite directions. A first distance calculating means for calculating each distance between each intersection point of the predetermined pixel and the predetermined pixel; and a second calculating means for calculating the distance between intersection points for each pair by adding the two distances for each pair. Linear calculating means, and extracting means for extracting a minimum inter-intersection distance among a large number of inter-intersection distances as a width of the linear pattern at the position of the predetermined pixel. Width operation Location.