The features and advantages of the invention can be more clarified from the following detailed descriptions for the accompanying drawings. Wherein:
The preferred embodiment of the invention will now be described more fully hereinafter with reference to the accompanying drawings. In the drawings the same reference numerals are used for denoting the same or similar components that are shown in different figures. For clarify, the detailed description of the known function and structure incorporated herein will be omitted, which would otherwise weaken the subject of the invention.
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After the user inputs operation commands through the input device 65 such as keyboards and mouse, the instruction code of the computer programs will instruct the processor 66 to perform predetermined data processing algorithm. After the processing results are obtained, they will be displayed on the display device 67 such as LCD, or redirected in the form of a hard copy.
Besides, in the above procedures, the obtained initial environmental information of the liquid articles to be detected 20 contains the size of package, the material of package, the volume ratio of package to liquid articles, and so on. These information and radiation absorption coefficients of various liquid articles can be pre-classified by using neural network recognition algorithm to form a database. In the real detection procedure, the detection of the liquid articles 20 is implemented by comparing the measured features with the features in the database.
Thereafter, at step S20, the carrier mechanism 30 rotates under the control of the scan controller 50. When the carrier mechanism, 30 reaches the first angle, radiations will be emitted from the radiation source 10 to transmit through the liquid articles to be detected 20. The detection and to collection appliance 40 receives the transmitted radiations to obtain the projection data of the first angle, which is denoted as a 1×N dimensional vector g1 and stored in the memory 61 of the computer data processor 60, wherein N is the number of the detection units of one row in the detector.
At step S20′, the carrier mechanism 30 continues rotating under the control of the scan controller 50. When the carrier mechanism 30 reaches the second angle, radiations will be emitted from the radiation source 10 to transmit through the liquid article 20. The detection and collection appliance 40 receives the transmitted radiations to obtain the projection data of the second angle, which is denoted as 1×N dimensional vector g2 and stored in the memory 61 of the computer data processor 60.
The above steps are repeated in this manner. In step S20″, the carrier mechanism 30 continues rotating under the control of the scan controller 50. When the carrier mechanism 30 reaches the Mth angle, the projection data for Mth angle is obtained, which is denoted as 1×N dimensional vector gM and stored in the memory 61 of the computer data processor 60. After the above scan procedure, the multi-angle projection data of the liquid articles 20 is obtained, which is denoted as an M×N dimensional vector g. Thereby, the multi-angle projection data of the liquid article to be detected 20 can be sequentially acquired for one slice.
Herein, in order to increase multi-angle projection data, the amount of angle projection can be increased during the scanning, or the detector is mounted with an offset of ¼ size of one detection unit of the detector.
Suppose that the linear attenuating coefficient (the absorption coefficient) of the liquid article to be detected 20 is expressed as an I-dimensional vector f, wherein I is the dimension of discretized pixels of the liquid article. Based on the interaction between X-ray and substance, according to the Bill's Law, we can get:
g
1=exp(−H1f)
g
2=exp(−H2f)
g
M=exp(−HMf) (1)
Wherein the H1, . . . , HM each represents an N×I system matrix, whose element Hnj reflects the contribution of the discrete pixel j in the object image under the corresponding angle, to the signal collected by the nth detector. H1 . . . HM each is a single sparse matrix, which is determined by practical design of the scanning system. For example, these matrices can be determined by pre-computing and then being stored in the memory 61, or through a real time computation according to the temporal system parameters. Thus, the linear attenuating coefficient information of the liquid articles can be obtained through the inverse operation with regard to the formula (1).
The inverse operation is an inverse process of normal operation. The process of normal operation is that the original signal emitted by radiation source attenuates when transmitting through the liquid articles 20 and the detector receives the attenuated radiation signal. Accordingly, an inverse operation is to compute the information of radiation attenuation by the liquid articles on the basis of the signal received by the detector.
However, during the detection procedure of liquid articles, because the inverse operation is an ill-conditioned problem, other information needs to be incorporated, e.g. the geometry boundary information of the liquid articles to be detected 20, which is obtained at the former step S10, so as to improve the validity and stability of the solution.
At step S30, the boundary condition and uniformity condition for the inverse operation are set on the basis of the initial environmental information obtained in step S10, which contains the geometry boundary information of the liquid article 20. The space shape of the liquid articles can be expressed as a bounded function. The geometry boundary information of the liquid articles 20 can be determined by the above X-ray radiographic technology or X-ray scan imaging technology, thereby the valid active region Ω can be defined, which is fi=0, for i∉Ω. The introduction of the boundary condition can speed up the solution, and to some extent ameliorate its ill-condition. Secondly, as the target object of the detection system is the liquid part, the scanned object can be divided into two parts, i.e. the liquid region Ω1 and the non-liquid region Ωn. For the uniformity of the liquid part, fi=smooth function, for iεΩ1, will be lo obtained. The smooth function is characterized by that both the whole variance in the liquid region Ω1 and the local fluctuation in the non-liquid region Ωn are limited,. The use of the liquid articles uniformity greatly optimizes the extraction of the liquid article information, and improves the robustness of the system.
It is to be noted that the liquid articles having uniformity denotes those solutions, suspending liquids or emulsions that absorb the radiations uniformly. For example, in the above sense, the milk and the porridge etc are also liquid articles of uniformity, namely, the uniformity of these liquid articles will be exhibited when they absorb the radiation.
Therefore, at step S40, with the geometry boundary condition of the liquid articles 20 being the boundary condition and the uniformity of the liquid articles being the condition of convergence, using the above formula (1), the computer data processor 60 computes to get the radiation absorption coefficient of the liquid article 20. The valid radiation absorption coefficient of the liquid articles then can be worked out on the basis of the obtained statistical characteristics of the pixels within the region Ω1.
Thereafter, at step S50, the computer data processor 60 outputs the relevant information of the liquid article to be detected 20, by comparing the computed radiation absorption coefficient with those of the known liquids in the database. For example, the radiation absorption coefficient of alcohol is −280, if the detected result for an unknown liquid article falls into the range of −270 to −290, this unknown liquid article in all probability is alcohol. Afterwards, the identification information of the detected liquid article will be shown on the display device 67 or directly printed out.
At the above step S40, the Bayesian method can be adopted to compute the radiation absorption coefficient of the liquid article 20 with the geometry boundary information and the uniformity as conditions. Also the non-statistical method can be adopted, wherein first solve the above formula (1) to obtain a preliminary radiation absorption coefficient, then after optimizing using the boundary condition and uniformity, estimate the linear attenuation coefficient of the liquid article 20 on the basis of distribution of fi, for i εΩ1, to improve the vialidity and the stability of the computation. The computation of the radiation absorption coefficient with the Bayesian method and the non-statistical method will be described below as examples.
[An example of computation of the linear absorption coefficient of liquid article with the Bayesian method]
1. Determine the target function:
Φ(f)=Φ1(g;f)+λP(f) (2)
Wherein Φ1(g;f) is a likelihood function determined by the noise characteristics of the collected data, P(f) is the metric of the uniformity for f1εΩ1, e.g. P(f)=−variance(f)|rεΩ, λ is a regulation parameter preset empirically;
2. Solve {circumflex over (f)} arg max[Φ(f)] using the numerical optimization method. During the process of solution, keep fi=0, for i∉Ω;
3. Calculate the probability distribution p(μliquid) of fεΩ1 to get the linear absorption coefficient of the liquid article, e.g.
μliquid=mean(f)|fεΩ
[An example of computation of the linear absorption coefficient of liquid article with the non-statistic method]
1. Acquire a preliminary estimate of the radiation absorption coefficient f by an analytic method, e.g. filter-back-projection reconstructing method or ART method;
2. Compute the uniformity of fiεΩ1
a) If the preset uniformity demand is satisfied, say, the local variance is lower than a certain threshold, then acquire the absorption coefficient of the liquid article on the basis of the statistical characteristics of fεΩ1 such as μliquid=mean(f)|fεΩ
b) If the uniformity demand is not satisfied, then conduct a boundary condition processing and a smoothing processing with regard to the radiation absorption coefficient f to acquire f′. Compare the orthographic projection of the processed f′ with the collected data g, analyze the difference between again to reconstruct and modify f, and then return step 2.
During the implementation of the non-statistical method, the operational speed and precision can be adjusted by setting different uniformity demands. In some extreme cases, the absorption coefficient of liquid article can be obtained just by one step, without iteration.
Besides, at the above step S10, if the liquid article 20 is of a sandwiched structure or layered e.g. it has two layers. The geometry boundary information of these two layers can be obtained using the above method, respectively, then conduct the same subsequent procedures with regard to the liquid article of the respective layers, and finally output the identification information of the two types of liquid articles, which serves as the ultimate identification information of the detected liquid article 20.
For example, in the case of a two-layer liquid article, the liquid article region comprises the first liquid article region Ω1A and the second liquid article region Ω1B. The linear attenuation coefficient of the first liquid article region Ω1A is denoted as fA, the linear attenuation coefficient of the second liquid article region Ω1B is denoted as fB. Then fA=smooth function 1, for AεΩ1A, fB=smooth function 2, for BεΩ1B.
Thus, the above-described step S10˜S50 are conducted with regard to the first liquid article region Ω1A and the second liquid article region Ω1B, respectively As mentioned above, based on the information such as the i of package, the material of package, the size ratio of the package to the liquid article, making use of the recognition algorithms such as the ANM (Artificial Neural Network), SVM (Support Vector Machine), BNN (Bayesian Neural Network), a classification table for the known various liquid articles can be established and stored into a database. As stated above, at steps S10 and S40, after acquiring the initial environmental information as well as the radiation absorption coefficient of the liquid article 20, the classification of the liquid article 20 in the database can hence be determined with the same neural network recognition algorithm, thereby the identification information of the liquid article 20 can be obtained.
In this embodiment of the invention, the scanning is implemented by rotating the detected liquid article 20. By means of scanning, both the volume and the cost of the device are reduced. However, another manner of scanning, that the detected liquid article 20 stays still while the radiation source 10 with the detection and collection appliance 40 rotates, can also be adopted.
Besides, the radiation source 10 may comprise one or more X-ray machines, as well as one or more isotope sources, and the radiation energy of the X-ray machines is adjustable. In the case that the radiation source 10 comprises a plurality of X-ray machines or isotope sources, there may be the same number of detectors as the X-ray machines or isotope sources, and these X-ray machines or isotope sources are set correspondingly. Herein, the detectors may be gas collectors, liquid detectors, solid detectors or semiconductor detectors, and may have an energy switching function. Besides, the detectors can work under the mode of one-dimensional array or two-dimensional arrays, i.e. the line array detector or the area array detector.
The computation procedure of the radiation absorption coefficient and the acquiring procedure of the identification information of the detected liquid article 20 are described above in the form that the computer data processor 60 runs the programs containing the predetermined data processing algorithm. However, the computer data processor 60 may be embodied in other forms.
As shown in
Although exemplary embodiments of the present invention have been described hereinabove, it should be clear to those skilled in the field that any variations and/or modifications of the basic inventive concepts will still fall within the scope of the present invention, as defined in the appended claims.
Number | Date | Country | Kind |
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200610127652.7 | Sep 2006 | CN | national |