Analysis device, analysis method, and analysis program

Abstract

To provide an analyzer that can appropriately recognize the distribution of linear absorption coefficients represented by image information.SOLUTION: An analyzer 20 analyzes an object based on image information representing a linear absorption coefficient for each pixel position in an image region within a cross section of the object. The analyzer includes a component information receiving unit 201 and a linear absorption coefficient estimating unit 206. The component information receiving unit receives component information representing mass ratios of respective J components contained in the object. The linear absorption coefficient estimating unit acquires a frequency distribution of linear absorption coefficients in the image region based on the image information, estimates, based on the obtained frequency distribution, a component group existence ratio of each of I component group existence regions in which component groups different from each other are present, relative to the image region, and estimates the linear absorption coefficient in at least one of the I component group existence regions based on the estimated I component group existence ratios, the received component information, and the mass absorption coefficients of the respective J components.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present invention relates to an analysis device, an analysis method, and an analysis program. [Background technology]

[0002] There is known an analytical device that analyzes an object based on image information acquired by irradiating the object with X-rays having a predetermined wavelength. For example, the analytical device described in Non-Patent Document 1 analyzes an object based on image information that represents a linear absorption coefficient for each pixel position in an image area in a cross section of the object. [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] Masanao Sato and 2 others, "3D non-destructive observation of ice crystal structure in frozen foods by X-ray CT using synchrotron radiation", Journal of the Japan Society for Food Engineering, September 2016, Vol. 17, No. 3, pp. 83-89 Summary of the Invention [Problem to be solved by the invention]

[0004] Incidentally, among a plurality of components contained in an object, a certain component group (in other words, a group of unevenly distributed components) may be unevenly distributed. However, the above-mentioned analysis device cannot identify the group of unevenly distributed components. Therefore, there is a risk that the distribution of linear absorption coefficients represented by image information cannot be properly recognized.

[0005] One of the objects of the present invention is to appropriately recognize the distribution of linear absorption coefficients represented by image information. [Means for solving the problem]

[0006] In one aspect, the analysis device analyzes the object based on image information obtained by irradiating the object with X-rays having a predetermined wavelength and representing a linear absorption coefficient for each pixel position in an image area within a cross-section of the object.

[0007] The analysis apparatus includes a component information receiving unit and a linear absorption coefficient estimating unit. The component information receiving unit receives component information representing a mass ratio, which is the ratio of the mass of each of J (J is an integer of 2 or more) components contained in the object to the mass of the object. The linear absorption coefficient estimation unit acquires a frequency distribution of linear absorption coefficients in the image region based on the image information, and estimates a component group presence ratio, which is a ratio to the image region, for each of I (I represents an integer of 2 or more) component group presence regions that make up the image region and in which different component groups exist, based on the acquired frequency distribution, and the component group present in each of the I component group presence regions includes at least one of the J components, and estimates a linear absorption coefficient in at least one of the I component group presence regions based on the estimated I component group presence ratios, the received component information, and the mass absorption coefficients of each of the J components.

[0008] In another aspect, the analysis method analyzes the object based on image information obtained by irradiating the object with X-rays having a predetermined wavelength and representing a linear absorption coefficient for each pixel position in an image area within a cross section of the object.

[0009] The analysis method is as follows: receiving component information that indicates a mass ratio that is a ratio of the mass of each of J (J is an integer of 2 or more) components contained in the object to a mass of the object; A frequency distribution of linear absorption coefficients in an image region is obtained based on the image information; a component group existence ratio, which is a ratio to the image region, of each of I (I represents an integer of 2 or more) component group existence regions that constitute the image region and each of which has a different component group, is estimated based on the acquired frequency distribution, and a component group that exists in each of the I component group existence regions includes at least one of the J components; estimating a linear absorption coefficient in at least one of the I component group existence regions based on the estimated I component group existence ratios, the received component information, and the mass absorption coefficients of each of the J components; This includes:

[0010] In another aspect, the analysis program causes a computer to execute a process for analyzing an object based on image information obtained by irradiating an object with X-rays having a predetermined wavelength and representing a linear absorption coefficient for each pixel position in an image area within a cross section of the object.

[0011] The above process is receiving component information that indicates a mass ratio that is a ratio of the mass of each of J (J is an integer of 2 or more) components contained in the object to a mass of the object; A frequency distribution of linear absorption coefficients in an image region is obtained based on the image information; a component group existence ratio, which is a ratio to the image region, of each of I (I represents an integer of 2 or more) component group existence regions that constitute the image region and each of which has a different component group, is estimated based on the acquired frequency distribution, and a component group that exists in each of the I component group existence regions includes at least one of the J components; estimating a linear absorption coefficient in at least one of the I component group existence regions based on the estimated I component group existence ratios, the received component information, and the mass absorption coefficients of each of the J components; This includes: Effect of the Invention

[0012] The distribution of linear absorption coefficients represented by the image information can be appropriately recognized. [Brief description of the drawings]

[0013] [Figure 1] 1 is a block diagram illustrating a configuration of an analysis system according to a first embodiment. [Diagram 2] FIG. 1 is a block diagram illustrating a configuration of an analysis device according to a first embodiment. [Diagram 3] FIG. 2 is a block diagram illustrating the functions of the analysis device according to the first embodiment. [Figure 4] 4 is a flowchart showing a process executed by the analysis device of the first embodiment. [Diagram 5] 1 is a table showing components contained in an object, mass absorption coefficients, mass ratios, and linear absorption coefficients. [Figure 6] This is image information obtained by irradiating an object with X-rays. [Figure 7] FIG. 2 is an explanatory diagram illustrating information output by the analysis device of the first embodiment. [Figure 8] 1 is a table showing missing region mass ratios and missing region linear absorption coefficients estimated by the analysis device of the first embodiment. [Figure 9] 4 is a table showing existence region mass ratios and existence region linear absorption coefficients estimated by the analysis device of the first embodiment. [Figure 10] 1 is a graph showing an example of a change in linear absorption coefficient with respect to wavelength. [Figure 11] 1 shows image information obtained by irradiating an object of a reference example with X-rays. [Figure 12] FIG. 11 is an explanatory diagram illustrating a frequency distribution obtained based on image information of a reference example. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] Hereinafter, embodiments of an analysis device, an analysis method, and an analysis program according to the present invention will be described with reference to FIGS.

[0015] First Embodiment (overview) The analytical device of the first embodiment analyzes an object based on image information obtained by irradiating the object with X-rays having a predetermined wavelength and representing the linear absorption coefficient for each pixel position in an image area within a cross-section of the object.

[0016] The analysis apparatus includes a component information receiving unit and a linear absorption coefficient estimating unit. The component information receiving unit receives component information representing a mass ratio, which is the ratio of the mass of each of J (J is an integer of 2 or more) components contained in the object to the mass of the object. The linear absorption coefficient estimation unit acquires a frequency distribution of linear absorption coefficients in the image region based on the image information, and estimates a component group presence ratio, which is a ratio to the image region, for each of I (I represents an integer of 2 or more) component group presence regions that make up the image region and in which different component groups exist, based on the acquired frequency distribution, and the component group present in each of the I component group presence regions includes at least one of the J components, and estimates a linear absorption coefficient in at least one of the I component group presence regions based on the estimated I component group presence ratios, the received component information, and the mass absorption coefficients of each of the J components.

[0017] According to this, by comparing the estimated linear absorption coefficient with the frequency distribution of the linear absorption coefficient represented by the image information, or the linear absorption coefficient having a maximum value in the frequency distribution, it is possible to know the likelihood of the assumption that different component groups exist in each of the I component group existence regions. This makes it possible to identify each component group. As a result, it is possible to appropriately recognize the distribution of the linear absorption coefficient represented by the image information.

[0018] (composition) As shown in FIG. 1, the analysis system 1 includes an X-ray CT (Computed Tomography) device 10 and an analysis device 20.

[0019] The X-ray CT device 10 obtains image information by irradiating an object with X-rays having a predetermined wavelength (in other words, monochromatic X-rays). The image information represents a linear absorption coefficient for each pixel position in an image area in a cross section of the object. In this example, the wavelength is 0.05 nm or more. Specifically, the wavelength is 0.1 nm. For example, the wavelength is a length between 0.05 nm and 0.20 nm. In this example, the X-ray CT device 10 includes an X-ray beamline (e.g., a bending electromagnet beamline, etc.).

[0020] The target object is produced by adding an electrolyte to a colloid (in other words, a colloidal dispersion or a colloidal dispersion system). In this example, the target object is food. For example, the food is a frozen food (in this example, frozen kamaboko). The target object may also be a non-frozen food (for example, a room temperature food or a refrigerated food, etc.). The target object may also be an object other than food. For example, the target object may be a polymer, a synthetic resin, or a material into which ions have been injected (for example, a semiconductor material, etc.).

[0021] 2, the analysis device 20 includes a processing device 21, a storage device 22, an input device 23, and an output device 24, which are connected to each other via a bus BU1. For example, the analysis device 20 is configured by a computer (in other words, an information processing device). The computer may be a server computer, a desktop computer, a laptop computer, or a tablet computer. The computer may be at least a part of a smartphone or the like. The analysis device 20 may also be configured by a plurality of devices connected to each other so as to be able to communicate with each other.

[0022] The processing device 21 executes a program stored in the storage device 22 to control the storage device 22, the input device 23, and the output device 24. In this way, the processing device 21 realizes the functions described below.

[0023] In this example, the processing device 21 is a central processing unit (CPU). Note that the processing device 21 may include a micro processing unit (MPU), a graphics processing unit (GPU), or a digital signal processor (DSP) instead of or in addition to the CPU.

[0024] In this example, the storage device 22 includes a volatile memory and a non-volatile memory. For example, the storage device 22 includes at least one of a random access memory (RAM), a read only memory (ROM), a semiconductor memory, an organic memory, a hard disk drive (HDD), and a solid state drive (SSD).

[0025] The input device 23 inputs information from outside the analysis device 20. In this example, the input device 23 includes a reading device capable of reading information from a storage medium or a recording medium (e.g., a semiconductor memory, an optical disk, a magneto-optical disk, or a magnetic disk, etc.), a keyboard, and a mouse. The input device 23 may also include a microphone.

[0026] The output device 24 outputs information to the outside of the analysis device 20. In this example, the output device 24 includes a display. The output device 24 may also include a speaker. The analysis device 20 may be equipped with a touch panel display that constitutes both the input device 23 and the output device 24.

[0027] (function) As shown in FIG. 3 , the functions of the analysis apparatus 20 include a component information receiving unit 201, an image information receiving unit 202, a mass absorption coefficient storage unit 203, a component group information receiving unit 204, a density information receiving unit 205, a linear absorption coefficient estimation unit 206, and a linear absorption coefficient output unit 207.

[0028] The component information receiving unit 201 receives component information. The component information includes information associating each of J components contained in the object with a mass ratio of the component (in other words, a constituent mass ratio). J represents an integer of 2 or more. The mass ratio of each component is the ratio of the mass of the component to the mass of the object. In this example, the J components contained in the object are each composed of J elements different from one another. Therefore, in this example, each of the J components contained in the object may be represented as an elemental component.

[0029] In this example, the component information is obtained using an elemental analyzer. In this example, the component information is input by a user of the analysis device 20 via the input device 23. The analysis system 1 may include a device for obtaining component information, such as an elemental analyzer, and may obtain the component information using the device.

[0030] The image information receiving unit 202 receives image information. The image information represents a linear absorption coefficient for each pixel position in an image region in a cross section of an object. In this example, as described above, the image information is acquired by the X-ray CT device 10. In this example, the image information is transmitted from the X-ray CT device 10 to the analysis device 20 via a storage medium or a recording medium. Note that the image information may be transmitted by communication between the X-ray CT device 10 and the analysis device 20.

[0031] The mass absorption coefficient storage unit 203 stores in advance in the storage device 22 the mass absorption coefficient for each component for the wavelength used to acquire the image information. The analysis device 20 may receive the mass absorption coefficient for each component. In this case, the mass absorption coefficient for each component may be input by a user of the analysis device 20 via the input device 23.

[0032] The mass absorption coefficient storage unit 203 may store in advance in the storage device 22 mass absorption coefficients for each component for each of a plurality of wavelengths different from each other. In this case, it is preferable that the analysis device 20 receives wavelength information representing the wavelength used to obtain the image information, and estimates the linear absorption coefficient described below using the mass absorption coefficient for the wavelength represented by the received wavelength information. The wavelength information may be input by a user of the analysis device 20 via the input device 23. In this case, the analysis device 20 may estimate the linear absorption coefficient described below using a value interpolated based on the wavelength used to obtain the image information and the plurality of stored mass absorption coefficients.

[0033] The component group information receiving unit 204 receives the component group information. The component group information indicates the number of component group existence regions I, which is the number of component group existence regions, and the component groups present in each of the I component group existence regions. I indicates an integer of 2 or more. The I component group existence regions constitute an image region represented by the image information received by the image information receiving unit 202, and each of the I component group existence regions contains a different component group. The component group present in each of the I component group existence regions contains at least one of the J components contained in the object. In this example, each component group consists of at least one of the J components contained in the object.

[0034] In this example, the component group present in the first component group existence region of the I component group existence regions includes components composed of elements having atomic numbers 1 to 10 (in other words, light element components). In addition, in this example, the component group present in the second component group existence region of the I component group existence regions includes components composed of elements having atomic numbers 11 to 20 (in other words, medium element components). Note that the component group present in the second component group existence region may include components composed of elements other than the elements having atomic numbers 11 to 20.

[0035] In this example, the component group information is input by a user of the analysis device 20 via the input device 23. The component group information receiving unit 204 may store component group candidate information representing a plurality of different component groups in advance in the storage device 22, receive selection information for selecting I pieces of component group candidate information from the plurality of pieces of component group candidate information, and receive component group information based on the I pieces of component group candidate information selected by the received selection information.

[0036] Furthermore, the component group information does not have to include information representing the number of component group existing regions I. In this case, the analysis device 20 may acquire information representing the number of component group existing regions I by using a frequency distribution described below. In this case, it is preferable that the analysis device 20 acquires, as the number of component group existing regions I, a number in which the second derivative of the frequency distribution has a negative minimum value.

[0037] The density information receiving unit 205 receives density information representing the density of the object in each of the I component group existence regions. However, the densities of the object in the I component group existence regions may have substantially the same value. In this case, the density information receiving unit 205 may receive density information representing the overall density of the object, and use the received density information as the density information for each component group existence region.

[0038] In this example, the density information is input by a user of the analysis device 20 via the input device 23. For example, the density information is obtained based on the mass ratio of each food component to the mass of the object. In this example, each food component contained in the object is moisture, protein, lipid, carbohydrate, or ash. The food components may be expressed as general components of food. The mass ratio of each food component is the ratio of the mass of the food component to the mass of the object. In this example, the density information is obtained based on a formula that expresses the relationship between temperature and density for each food component, as described in Non-Patent Document 2. (Non-Patent Document 2) Dennis R. Heldman, Daryl B. Lund, Cristina Sabliov, "Handbook of Food Engineering", 2nd edition, CRC Press, 2006, p.439

[0039] The linear absorption coefficient estimation unit 206 acquires a frequency distribution of linear absorption coefficients in an image region in a cross section of an object based on image information received by the image information receiving unit 202. In this example, the frequency distribution represents the change in the number of pixel positions having a linear absorption coefficient included in each of a plurality of ranges different from each other (in other words, the frequency of occurrence of a linear absorption coefficient in an image region) relative to the linear absorption coefficient. In this example, the sizes of the plurality of ranges are the same.

[0040] The linear absorption coefficient estimation unit 206 acquires an approximation function specifying information group for specifying an approximation function f(x) expressed by Equation 1 so that the acquired frequency distribution is approximated by the approximation function f(x). x represents a variable representing a linear absorption coefficient. f represents a frequency. In this example, the approximation function specifying information group includes the i-th coefficient a i , the i-th maximum position parameter b i , and the i-th dispersion parameter c i In this example, the i'th maximum position parameter b i’ is the (i'+1)th maximum position parameter b i’+1 i' represents an integer from 1 to I-1.

number

[0041] In this example, the linear absorption coefficient estimation unit 206 acquires the approximate function specifying information group using the least squares method. Note that the linear absorption coefficient estimation unit 206 may acquire the approximate function specifying information group using a method other than the least squares method (for example, a maximum likelihood estimation method, etc.).

[0042] The linear absorption coefficient estimation unit 206 estimates the component group existence ratio for each of I component group existence regions (in other words, the first component group existence region to the Ith component group existence region) based on the acquired frequency distribution. The component group existence ratio for each component group existence region is the ratio of that component group existence region to the image region represented by the image information accepted by the image information accepting unit 202. In this example, the component group existence ratio for each component group existence region is the ratio of the number of pixel positions constituting that component group existence region to the number of pixel positions constituting the image region.

[0043] In this example, the linear absorption coefficient estimation unit 206 estimates the component group existence ratio for each of the I component group existence regions based on the acquired approximate function identification information group. In this example, as expressed in Equation 2, the linear absorption coefficient estimation unit 206 estimates the component group existence ratio for each of the acquired first coefficient a1 to I-th coefficient a I Based on this, the component group existence ratio P i Estimate.

number

[0044] The linear absorption coefficient estimation unit 206 estimates a linear absorption coefficient in each of the I component group existence regions based on the estimated I component group existence ratios, the component information accepted by the component group information accepting unit 201, the component group information accepted by the component group information accepting unit 204, the density information accepted by the density information accepting unit 205, and the mass absorption coefficients of the J components contained in the object stored in the mass absorption coefficient storage unit 203. The linear absorption coefficient estimation unit 206 may estimate the linear absorption coefficient in a part of the I component group existence regions.

[0045] Specifically, as shown in Equation 3, the linear absorption coefficient estimation unit 206 calculates the linear absorption coefficient μ i Estimate. Here, ρ i represents the density of the object in the i-th component group existence region, represented by the density information accepted by the density information accepting unit 205. μ M,j represents the mass absorption coefficient of the j-th component stored in the mass absorption coefficient storage unit 203. j represents an integer from 1 to J. W i,j represents the mass ratio of the jth component in the i-th component group existence region (in other words, the component group existence region mass ratio).

number

[0046] The mass ratio of the jth component in the i-th component group existence region is the component group existence region mass ratio W i,j is expressed by Equation 4. W j represents the mass ratio of the j-th component included in the component information received by the component information receiving unit 201. e i,j represents an existence coefficient that indicates whether the j-th component exists in the i-th component group existence region. The existence coefficient e i,j represents "1" when the jth component exists in the i-th component group existence region, and represents "0" when the jth component does not exist in the i-th component group existence region. In this example, the existence coefficient e i,j is included in the component group information received by the component group information receiving unit 204.

number

[0047] The linear absorption coefficient output unit 207 outputs the first maximum position parameter b1 to the I-th maximum position parameter b2 acquired by the linear absorption coefficient estimation unit 206. I and the linear absorption coefficients μ1 to μ2 in the I component group existence regions estimated by the linear absorption coefficient estimation unit 206. Iand output via the output device 24 (in this example, displayed on the display of the output device 24). In this example, the linear absorption coefficient output unit 207 also outputs via the output device 24 the frequency distribution acquired by the linear absorption coefficient estimation unit 206 and the approximate function f(x) specified by the approximate function specifying information group acquired by the linear absorption coefficient estimation unit 206.

[0048] When a specific component exists in each of a plurality of component group existence regions, the concentration of the component (the mass of the component contained per unit volume) may differ for each component group existence region. In this case, the existence coefficient e i,j Preferably, represents a real number between 0 and 1.

[0049] In addition, when the number I of component group existence regions is "2", if the first component group exists in the first component group existence region and the first component group and the second component group exist in the second component group existence region, the second component group may be expressed as a maldistributed component group. In this case, the first component group existence region may be expressed as a missing region that is a region in the image region where the maldistributed component group is missing. Also, in this case, the second component group existence region may be expressed as a presence region that is a region in the image region where the maldistributed component group exists.

[0050] The description of the functions of the analysis device 20 may be supplemented by a description of the operation of the analysis device 20 described below.

[0051] (operation) Next, an example of the operation of the analysis device 20 will be described with reference to FIGS. In this example, the object is a frozen kamaboko (fish paste) that is produced by repeatedly freezing in a freezer at -25°C and thawing naturally multiple times (four times in this example).

[0052] First, the X-ray CT apparatus 10 obtains image information by irradiating an object with X-rays having a predetermined wavelength (in other words, monochromatic X-rays). Next, the analysis device 20 executes the process shown in FIG. 4 to analyze the target object according to an execution instruction input by a user of the analysis device 20 via the input device 23.

[0053] First, the analysis device 20 waits until it receives the component information (determines "No" in step S101). Next, the user of the analysis device 20 inputs the component information via the input device 23. In this example, the component information includes information in which, for each of J components (in this example, J represents 12) each composed of H, C, N, O, Na, Mg, P, S, Cl, K, Ca, and Zn, the component is associated with the mass ratio of the component (in other words, the constituent mass ratio), as shown in FIG. 5. As a result, the analysis device 20 receives the input component information, determines "Yes" in step S101, and proceeds to step S102.

[0054] Next, the analysis device 20 waits until it receives image information (determines "No" in step S102). Next, a user of the analysis device 20 inputs image information via the input device 23. In this example, the image information is information as shown in FIG. 6. FIG. 6 illustrates that the closer to white the color is, the higher the linear absorption coefficient becomes. As a result, the analysis device 20 receives the input image information, determines "Yes" in step S102, and proceeds to step S103.

[0055] Next, the analysis device 20 waits until it receives the component group information (determined as "No" in step S103). Next, the user of the analysis device 20 inputs the component group information via the input device 23. In this example, the number of component group existence regions I in the component group information represents "2". In this example, in the component group information, the component group existing in the first component group existence region consists of seven components each composed of H, C, N, O, P, S, and Zn. In this example, in the component group information, the component group existing in the second component group existence region consists of 12 components each composed of H, C, N, O, Na, Mg, P, S, Cl, K, Ca, and Zn. Note that the component group existing in the first component group existence region may consist of five components each composed of H, C, N, O, and Zn. Thus, the analyzer 20 accepts the input component group information, determines "Yes" in step S103, and proceeds to step S104.

[0056] Next, the analysis device 20 waits until it receives density information for each of the I component group existence regions (determined as "No" in step S104). Next, the user of the analysis device 20 inputs each piece of density information via the input device 23. In this example, the density information for each of the I component group existence regions is 1.00 g / cm 3 In this way, the analysis device 20 accepts the input density information for each of the I component group existence regions, determines "Yes" in step S104, and proceeds to step S105.

[0057] Next, the analysis device 20 acquires a frequency distribution of linear absorption coefficients in an image region in a cross section of the object based on the image information received in step S102. In this example, the frequency distribution is represented by a circle labeled H1 in FIG. 7. Next, the analysis device 20 acquires an approximation function identification information group that identifies an approximation function f(x) expressed by Equation 1 so that the acquired frequency distribution is approximated by the approximation function f(x) (step S105). In this example, the approximation function f(x) is represented by a solid line labeled F1 in FIG. 7.

[0058] Next, the analysis device 20 calculates the first coefficient a1 to the I-th coefficient a2 included in the approximate function specification information group acquired in step S105. I Based on the above and Equation 2, the component group presence ratio P i (Step S106) In this example, the component group existence ratio P1 for the first component group existence region and the component group existence ratio P2 for the second component group existence region are 0.7 and 0.3, respectively.

[0059] Next, the analysis device 20 calculates the I component group abundance ratios P i Based on the component information received in step S101, the component group information received in step S103, the density information received in step S104, the mass absorption coefficients for each component stored in the storage device 22, and Equation 3 and Equation 4, a linear absorption coefficient μ i is estimated (step S107).

[0060] In this example, the component group presence region mass ratio W 1,j , and the linear absorption coefficient μ1 in the first component group existence region is estimated as shown in Fig. 8. In Fig. 8, the linear absorption coefficient μ1 in the first component group existence region is 2.54, which is the sum of the linear absorption coefficients for each of the components constituting the component group present in the first component group existence region.

[0061] In this example, the component group presence region mass ratio W 2,j , and the linear absorption coefficient μ2 in the second component group existence region are estimated as shown in Fig. 9. In Fig. 9, the linear absorption coefficient μ2 in the second component group existence region is 3.41, which is the sum of the linear absorption coefficients for each of the components constituting the component group present in the second component group existence region.

[0062] Next, as shown in FIG. 7, the analysis device 20 calculates the first maximum position parameter b1 to the I-th maximum position parameter b2 included in the approximate function identification information group acquired in step S105. I and the linear absorption coefficient μ1 to the linear absorption coefficient μ in the I component group existence region estimated in step S107. I The frequency distribution acquired in step S105 and the approximate function f(x) specified by the approximate function specifying information group acquired in step S105 are displayed on the display of the output device 24.

[0063] In this example, as shown in Fig. 7, the first maximum position parameter b1 and the second maximum position parameter b2 are displayed in a column marked with a symbol B1 and a column marked with a symbol B2, respectively. In this example, the linear absorption coefficient μ1 in the first component group existence region and the linear absorption coefficient μ2 in the second component group existence region are displayed in a column marked with a symbol B3 and a column marked with a symbol B4, respectively. In this example, the frequency distribution is represented by a circle marked with a symbol H1. In this example, the approximation function f(x) is represented by a solid line marked with a symbol F1. In this example, the two Gaussian functions constituting the approximation function f(x) are represented by a solid line marked with a symbol F11 and a solid line marked with a symbol F12, respectively.

[0064] In this example, as shown in FIG. 7, the first maximum position parameter b1 and the linear absorption coefficient μ1 in the first component group existence region have sufficiently close values, and the second maximum position parameter b2 and the linear absorption coefficient μ2 in the second component group existence region have sufficiently close values.

[0065] Therefore, in this example, it may be assumed that the component group consisting of seven components each consisting of H, C, N, O, P, S, and Zn exists in the first component group existence region, and the component group consisting of 12 components each consisting of H, C, N, O, Na, Mg, P, S, Cl, K, Ca, and Zn exists in the second component group existence region. In other words, it may be assumed that the unevenly distributed component group consisting of five components each consisting of Na, Mg, Cl, K, and Ca is missing in the missing region, and the unevenly distributed component group exists in the existence region. As a result, it can be recognized that the distribution of linear absorption coefficients represented by the image information shown in FIG. 6 is such that the unevenly distributed component group is missing in the region close to black, and the unevenly distributed component group exists in the region close to white.

[0066] Then, the analysis device 20 ends the execution of the process shown in FIG.

[0067] The analysis device 20 may execute the processes of steps S101 to S104 in parallel. Also, the analysis device 20 may execute the processes of steps S101 to S104 in an order different from the order illustrated in FIG. Furthermore, the analysis device 20 may output at least one of a plurality of tables (three in this example) shown in each of FIG. 5, FIG. 8, and FIG.

[0068] Furthermore, the analysis device 20 may estimate the density of the object in each of the I component group presence regions. In this example, the analysis device 20 may estimate the density of the object in each of the first component group presence region and the second component group presence region based on the assumption that food components other than ash (in other words, water, protein, lipid, and carbohydrate) are present in the first component group presence region, and food components including ash (in other words, water, protein, lipid, carbohydrate, and ash) are present in the second component group presence region. For example, the analysis device 20 may estimate the density of the object in the first component group presence region as 0.991 g / cm 3and the density of the object in the second component group existence region is estimated to be 1.02 g / cm 3 It can be presumed that:

[0069] As described above, the analysis device 20 of the first embodiment analyzes an object based on image information that is obtained by irradiating the object with X-rays having a predetermined wavelength and that represents the linear absorption coefficient for each pixel position in an image area within a cross section of the object.

[0070] The analysis device 20 includes a component information receiving unit 201 and a linear absorption coefficient estimating unit 206 . The component information receiving unit 201 receives component information indicating a mass ratio, which is the ratio of the mass of each of J (J is an integer of 2 or more) components contained in the object to the mass of the object.

[0071] The linear absorption coefficient estimation unit 206 acquires a frequency distribution of linear absorption coefficients in the image region based on the image information, estimates a component group presence ratio, which is a ratio to the image region, of each of I (I represents an integer of 2 or more) component group presence regions that constitute the image region and in which different component groups exist, based on the acquired frequency distribution, and estimates a linear absorption coefficient in at least one of the I component group presence regions, where the component group present in each of the I component group presence regions includes at least one of the J components, based on the estimated I component group presence ratios, the received component information, and the mass absorption coefficients of each of the J components.

[0072] According to this, by comparing the estimated linear absorption coefficient with the frequency distribution of the linear absorption coefficient represented by the image information, or the linear absorption coefficient having a maximum value in the frequency distribution, it is possible to know the likelihood of the assumption that different component groups exist in each of the I component group existence regions. This makes it possible to identify each component group. As a result, it is possible to appropriately recognize the distribution of the linear absorption coefficient represented by the image information.

[0073] Furthermore, in the analysis device 20 of the first embodiment, the linear absorption coefficient estimation unit 206 specifies an approximation function f(x) expressed by Equation 1 so that the acquired frequency distribution is approximated by the approximation function f(x). i , the i-th maximum position parameter b i , and the i-th dispersion parameter c i The i-th coefficient a i Based on this, the abundance ratio of I component groups is estimated.

[0074] Incidentally, the frequency distribution in a region where each component is homogeneous is approximated with high accuracy by a Gaussian function. Therefore, in each of I component group existence regions, when each component is homogeneous, the frequency distribution is approximated with high accuracy by an approximation function that is a linear sum of I Gaussian functions, as expressed by Equation 1. Furthermore, in this case, the i-th coefficient a i There is a strong correlation between the abundance ratio of the I component groups and the I number of component groups. Therefore, the analysis device 20 can estimate the abundance ratio of the I component groups with high accuracy.

[0075] Furthermore, the analysis device 20 of the first embodiment calculates the i-th maximum position parameter b i and a linear absorption coefficient output unit 207 that outputs the estimated linear absorption coefficient.

[0076] The i-th maximum position parameter b i reflects the linear absorption coefficient in the I component group presence region with high accuracy. Therefore, according to the analysis device 20, the i-th maximum position parameter b i By comparing the estimated linear absorption coefficient with the estimated linear absorption coefficient, it is possible to know with high accuracy the likelihood of the assumption that different component groups exist in each of the I component group existence regions. This makes it possible to identify each component group with high accuracy.

[0077] Incidentally, for X-rays with a wavelength shorter than 0.05 nm, the difference in mass absorption coefficient between components consisting of elements with atomic numbers 1 to 10 (in other words, light element components) and components consisting of elements with atomic numbers 11 to 20 (in other words, medium element components) is considerably small. Therefore, even if the medium element components are unevenly distributed in an object containing light element components and medium element components, the uneven distribution of the unevenly distributed component group is unlikely to be reflected in the image information.

[0078] FIG. 10 shows an example of the change in linear absorption coefficient with respect to wavelength. In FIG. 10, the object includes components having the mass ratio shown in FIG. 5. The curve represented by the symbol C1 shows the sum of the linear absorption coefficients of the light element components in the object. The curve represented by the symbol C2 shows the sum of the linear absorption coefficients of the medium element components in the object. The curve represented by the symbol C3 shows the medium element contribution. The medium element contribution is the ratio of the sum of the linear absorption coefficients of the medium element components in the object to the linear absorption coefficient of the object. As shown in FIG. 10, when the wavelength of the X-ray is shorter than 0.05 nm, the intermediate element contribution decreases rapidly as the wavelength becomes shorter.

[0079] In contrast, in the analysis device 20 of the first embodiment, the component group present in a first component group existence region among the I component group existence regions includes components composed of elements having atomic numbers 1 to 10. Furthermore, the component group present in a second component group existence region among the I component group existence regions includes components composed of elements having atomic numbers 11 to 20. In addition, the wavelength of the X-rays used to acquire the image information is 0.05 nm or more.

[0080] Therefore, according to the analysis device 20, when a medium element component is unevenly distributed in an object containing light element components and medium element components, the uneven distribution of the unevenly distributed component group is reflected in the image information with sufficiently high accuracy. As a result, the unevenly distributed component group can be identified with high accuracy. Therefore, for example, in food, it can be determined with high accuracy whether a component composed of Na, Mg, Cl, K, Ca, etc., which is likely to affect the quality, is included in the unevenly distributed component group.

[0081] <Reference example> 11 and 12 show image information and frequency distribution for an object according to a reference example. The object according to the reference example is a frozen kamaboko fish paste. In this example, the object is produced by repeatedly freezing with liquid nitrogen and naturally thawing multiple times (four times in this example).

[0082] Fig. 11 shows that the linear absorption coefficient increases as the color approaches white. As shown in Fig. 12, the frequency distribution is represented by a circle labeled H2. In this example, the approximation function h(x) represented by Equation 5 is represented by a solid line labeled F2. a1 represents the first coefficient, b1 represents the first maximum position parameter, and c1 represents the first dispersion parameter.

number

[0083] In this example, the first maximum position parameter b1 is 2.81. The linear absorption coefficient μ calculated based on Equation 6 and the mass ratio shown in Fig. 5 is also 2.81. ρ represents the density of the entire object.

number

[0084] The present invention is not limited to the above-described embodiment. For example, various modifications that can be understood by a person skilled in the art may be made to the above-described embodiment without departing from the spirit of the present invention.

[0085] For example, the component may be a component other than an elemental component, for example, the component may be a food component (such as moisture, protein, lipid, carbohydrate, or ash).

[0086] Furthermore, the analysis device 20 may estimate the density of the object in each of the I component group existence regions. In this case, the analysis device 20 may estimate the density of the object in each of the I component group existence regions based on the component group information and the component group existence ratio for each of the I component group existence regions. [Explanation of symbols]

[0087] 1. Analysis system 10 X-ray CT device 20 Analyzer 21 Processing equipment 22 Storage device 23 Input Devices 24 Output Devices 201 Ingredient Information Reception Department 202 Image Information Reception Department 203 Mass absorption coefficient memory unit 204 Unevenly distributed component group information reception unit 205 Density Information Reception Department 206 Linear absorption coefficient estimation section 207 Linear absorption coefficient output section BU1 Bus

Claims

1. 1. An analysis apparatus for analyzing an object based on image information that is acquired by irradiating an object with X-rays having a predetermined wavelength and that represents a linear absorption coefficient for each pixel position in an image area in a cross section of the object, comprising: a component information receiving unit that receives component information representing a mass ratio that is a ratio of the mass of each of J (J represents an integer of 2 or more) components contained in the object to the mass of the object; a component group information receiving unit that receives component group information including an existence coefficient indicating whether each of the J components is present in each of I (I is an integer equal to or greater than 2) component group existence regions that constitute the image region and in which different component groups are present; a linear absorption coefficient estimating unit that acquires a frequency distribution of the linear absorption coefficient in the image region based on the image information, estimates a component group existence ratio, which is a ratio of each of the I component group existence regions to the image region, based on the acquired frequency distribution, and a component group existing in each of the I component group existence regions includes at least one of the J components, and estimates a linear absorption coefficient in at least one of the I component group existence regions based on the estimated I component group existence ratios, the accepted component information, the accepted component group information, and a mass absorption coefficient of each of the J components; An analysis device comprising:

2. The analysis device according to claim 1 , The linear absorption coefficient estimation unit specifies an approximation function f(x) expressed by Equation 1 so that the acquired frequency distribution is approximated by the approximation function f(x), and calculates an i-th (i represents an integer from 1 to I) coefficient a ｉ , the i-th maximum position parameter b ｉ , and the i-th dispersion parameter c ｉ and obtaining the i-th coefficient a ｉ The analysis apparatus estimates the abundance ratios of the I component groups based on the above. [0010]

3. The analysis device according to claim 2, The i-th maximum position parameter b ｉ and a linear absorption coefficient output unit that outputs the estimated linear absorption coefficient.

4. The analysis device according to any one of claims 1 to 3, A component group existing in a first component group existence region among the I component group existence regions includes components composed of elements having atomic numbers 1 to 10, The component group present in the second component group existence region among the I component group existence regions includes components composed of elements having atomic numbers 11 to 20, The wavelength is 0.05 nm or greater.

5. 1. A method for analyzing an object based on image information that is obtained by irradiating an object with X-rays having a predetermined wavelength and that represents a linear absorption coefficient for each pixel position in an image area in a cross section of the object, comprising: receiving component information representing a mass ratio that is a ratio of masses of J (J represents an integer of 2 or more) components contained in the object to a mass of the object; receiving component group information including an existence coefficient indicating whether each of the J components is present in each of I (I is an integer equal to or greater than 2) component group existence regions that constitute the image region and in which different component groups are present; acquiring a frequency distribution of the linear absorption coefficient in the image region based on the image information; a component group existence ratio, which is a ratio of each of the I component group existence regions to the image region, is estimated based on the acquired frequency distribution, and a component group existing in each of the I component group existence regions includes at least one of the J components; estimating a linear absorption coefficient in at least one of the I component group existence regions based on the estimated I component group existence ratios, the received component information, the received component group information, and the mass absorption coefficients of each of the J components; The analytical method includes:

6. An analysis program that causes a computer to execute a process for analyzing an object based on image information that is obtained by irradiating an object with X-rays having a predetermined wavelength and that represents a linear absorption coefficient for each pixel position in an image area in a cross section of the object, the analysis program comprising: The process comprises: receiving component information representing a mass ratio that is a ratio of masses of J (J represents an integer of 2 or more) components contained in the object to a mass of the object; receiving component group information including an existence coefficient indicating whether each of the J components is present in each of I (I is an integer equal to or greater than 2) component group existence regions that constitute the image region and in which different component groups are present; acquiring a frequency distribution of the linear absorption coefficient in the image region based on the image information; a component group existence ratio, which is a ratio of each of the I component group existence regions to the image region, is estimated based on the acquired frequency distribution, and a component group existing in each of the I component group existence regions includes at least one of the J components; estimating a linear absorption coefficient in at least one of the I component group existence regions based on the estimated I component group existence ratios, the received component information, the received component group information, and the mass absorption coefficients of each of the J components; (c) analyzing the data;

Images