WO2021010277A1 - Dispositif d'identification de médicament, procédé d'identification de médicament, et programme - Google Patents

Dispositif d'identification de médicament, procédé d'identification de médicament, et programme Download PDF

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Publication number
WO2021010277A1
WO2021010277A1 PCT/JP2020/026850 JP2020026850W WO2021010277A1 WO 2021010277 A1 WO2021010277 A1 WO 2021010277A1 JP 2020026850 W JP2020026850 W JP 2020026850W WO 2021010277 A1 WO2021010277 A1 WO 2021010277A1
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drug
registered image
recognition
similarity
image
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PCT/JP2020/026850
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English (en)
Japanese (ja)
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横内　康治
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富士フイルム富山化学株式会社
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Priority to JP2021533015A priority Critical patent/JP7280361B2/ja
Publication of WO2021010277A1 publication Critical patent/WO2021010277A1/fr

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients

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  • the present invention relates to a drug identification device, a drug identification method and a program, and particularly to a drug identification device, a drug identification method and a program for identifying a drug having a similar drug.
  • Patent Document 1 for the purpose of preventing erroneous determination of drugs having similar shapes, colors, and sizes, a technique for inspecting whether or not a drug is dispensed according to a prescription using a photographed image of the drug.
  • a drug image registered image
  • a drug similar to the drug is acquired from a drug database and dispensed with the acquired drug image. It is collated with the photographed image of the drug.
  • the present invention has been made in view of such circumstances, and an object of the present invention is a drug identification device and a drug capable of accurately and efficiently identifying drugs having similar drugs. To provide identification methods and programs.
  • the drug identification device for achieving the above object is a drug identification device that identifies a first drug when the first drug has a similar second drug.
  • a first registered image which is an image of the first drug
  • a second registered image which is an image of the second drug when the second drug is present, are stored in relation to the first registered image.
  • the image input unit in which the target image of the drug to be identified is input the drug area extraction unit that extracts the drug area in which the drug appears from the target image, and the first drug from the drug database.
  • First recognition that performs similarity recognition between the registered image acquisition unit that acquires the first registered image and the second registered image and the first registered image that corresponds to the drug region extracted by the drug region extraction unit.
  • a second recognition unit, a first recognition unit, and a first recognition unit that perform similar recognition with the second registered image for a drug region recognized to be similar to the first registered image in the unit and the first recognition unit.
  • a drug identification unit that identifies whether or not the drug is the first drug based on the recognition result of the recognition unit 2 is provided.
  • identification is performed based on the result of similarity recognition between the first registered image and the second registered image, and the similarity recognition with the second registered image is the same as that of the second registered image. Since it is performed on drug areas that are considered to be similar, accurate and efficient identification can be performed.
  • the first recognition unit calculates the first similarity between the drug region and the first registered image, and performs similarity recognition by comparing the first similarity with the threshold value.
  • the second recognition unit calculates the second similarity between the drug region and the second registered image.
  • the drug identification unit identifies the drug in the drug region as the first drug when the first similarity is greater than the second similarity, and the first similarity is the first.
  • the degree of similarity is 2 or less, the drug shown in the drug area is identified as not the first drug.
  • the first agent has a plurality of similar second agents.
  • the second recognition unit calculates the second similarity between the drug region and each of the plurality of second registered images.
  • the drug identification unit identifies the drug in the drug region as the first drug when the first similarity is greater than each of the plurality of second similarities, and the first When the similarity is at least one or less of the plurality of second similarity, the drug shown in the drug region is identified as not the first drug.
  • the similarity recognition of the first recognition unit and the second recognition unit is performed by template matching.
  • the drug identification method is a drug identification method for identifying a first drug when the first drug has a similar second drug, and is an image of the first drug.
  • the first A registered image acquisition step for acquiring a first registered image and a second registered image corresponding to a drug
  • an image input step for inputting a target image of the drug to be identified, and a drug being captured from the target image.
  • the first recognition step for performing similarity recognition between the drug region extracted in the drug region extraction step and the corresponding first registered image, and the first recognition step.
  • a drug identification step for identifying whether or not the drug is a first drug.
  • a program according to another aspect of the present invention is a program that causes a computer to perform a drug identification step of identifying a first drug when the first drug has a similar second drug, and is a first drug. From the drug database in which the first registered image, which is an image of, and the second registered image, which is an image of the second drug when the second drug is present, are stored in relation to the first registered image. From the registered image acquisition step of acquiring the first registered image and the second registered image corresponding to the first drug, the image input step of inputting the target image of the drug to be identified, and the target image.
  • identification is performed based on the result of similarity recognition between the first registered image and the second registered image, and the similarity recognition with the second registered image is the same as that of the second registered image. Since it is performed on drug regions that are considered to be similar, accurate and efficient identification can be performed.
  • FIG. 1 is a diagram showing a similarity distribution.
  • FIG. 2 is a diagram showing a similarity distribution.
  • FIG. 3 is a diagram showing the configuration of a packaged audit support system.
  • FIG. 4 is a diagram showing a state of acquiring an image of a sachet under illumination.
  • FIG. 5 is another diagram showing how an image of the packaging bag is acquired under illumination.
  • FIG. 6 is a block diagram showing a configuration example of the drug identification device.
  • FIG. 7 is a flowchart illustrating a drug identification method.
  • FIG. 8 is a diagram showing an example of a memory configuration of a drug database.
  • FIG. 9 is a diagram showing a target image.
  • FIG. 10 is a flowchart illustrating the calculation of the degree of similarity.
  • FIG. 10 is a flowchart illustrating the calculation of the degree of similarity.
  • FIG. 11 is a flowchart showing the similarity recognition of the first recognition unit, the similarity recognition of the second recognition unit, and the drug identification.
  • FIG. 12 is a diagram showing a target image.
  • FIG. 13 is a flowchart showing the similarity recognition of the first recognition unit, the similarity recognition of the second recognition unit, and the drug identification.
  • Drug ⁇ has drug ⁇ , which is a similar drug with similar size, color, and marking (printing). At this time, the drug ⁇ should be prescribed, but even if the drug ⁇ is mistakenly contained in the sachet, the drug ⁇ may be mistakenly identified as the drug ⁇ .
  • FIGS. 1 and 2 are diagrams showing the similarity distribution between the image of the drug to be identified and the registered image (master image).
  • the vertical axis of FIGS. 1 and 2 indicates the application frequency, and the horizontal axis indicates the degree of similarity with the registered image.
  • the registered image is an image of a drug prepared in advance for collating the drug.
  • the registered image is stored in the drug database, and the drug can be identified by collating the registered image with the captured image of the drug or the image of the enhanced drug.
  • FIG. 1 is a diagram showing the similarity distribution between the drug ⁇ and the drug ⁇ that is not similar to the drug ⁇ .
  • the similarity distribution 53 between the image of the drug ⁇ (drug region) and the registered image ⁇ of the drug ⁇ is shown, and the similarity between the image of the drug ⁇ (drug region) and the registered image ⁇ of the drug ⁇ is shown.
  • Distribution 51 is shown.
  • the registered image ⁇ and the image obtained by taking the drug ⁇ have a certain range of calculated similarity values, but the similarity is calculated to be relatively high (see similarity distribution 53).
  • the registered image ⁇ and the image obtained by photographing the drug ⁇ have a certain range of calculated similarity values, but the similarity is calculated to be relatively low (see the similarity distribution 51).
  • the similarity distribution 51 and the similarity distribution 53 do not overlap.
  • FIG. 2 is a diagram showing the similarity distribution between the registered image ⁇ and the drug ⁇ , and the registered image ⁇ and the drug ⁇ .
  • the similarity distribution 57 shows the similarity distribution between the image (drug region) in which the drug ⁇ is photographed and the registered image ⁇
  • the similarity distribution 55 is the image (drug region) in which the drug ⁇ is photographed and the registered image ⁇ . Shows the similarity distribution.
  • the registered image ⁇ and the image obtained by taking the drug ⁇ have a certain range in the calculated similarity values, but the similarity is calculated to be relatively high (see the similarity distribution 57).
  • the registered image ⁇ and the image obtained by photographing the drug ⁇ have a certain range of calculated similarity values, but the similarity is calculated to be relatively low (see the similarity distribution 55). Further, since the drug ⁇ and the drug ⁇ are similar in size, color, and marking (printing), the similarity distribution 55 and the similarity distribution 57 partially overlap.
  • the threshold Th1 cannot be set between the two similarity distributions as shown in FIG.
  • the threshold value Th2 for identifying the drug ⁇
  • the drug ⁇ can be identified as the drug ⁇ , but the drug ⁇ may also be erroneously identified as the drug ⁇ . It ends up.
  • the similarity threshold Th3 see FIG. 2 is set, there may be a case where the drug A is not identified as the drug A even though it is the drug A.
  • the present invention even when identifying a drug having a similar drug, the identification can be performed accurately and efficiently.
  • FIG. 3 is a diagram showing a configuration of a packaged audit support system 1 equipped with the drug identification device 2 (FIG. 6) of the present invention.
  • the one-pack audit support system 1 includes a processing unit 10, a storage unit (drug database 20A (see FIG. 6)) 20, a display unit 30, an operation unit 40, a transport mechanism 50, an illumination unit 12, and a camera 15A.
  • a camera 15B and a prescription reader 16 are provided.
  • the one-pack audit support system 1 provides support for auditing whether or not the drug is properly packaged according to the prescription.
  • the drug identification device 2 identifies the packaged drug, and the identification result is displayed on the display unit 30 (monitor 32) to support the user's audit.
  • the example in which the drug identification device 2 is used is not limited to the one-pack audit.
  • the drug identification device 2 may be used to discriminate the drug brought.
  • the drug identified by the drug identification device 2 is not limited to the drug prescribed by the prescription.
  • the drug identification device 2 can use the drug. Can be identified.
  • the camera 15A and the camera 15B are composed of a digital camera. As shown in FIG. 4, the camera 15A is arranged on the upper side (+ Z side of FIG. 4) of the medicine bandage PB in which the sachet TP (medicine package) is continuously formed, and the camera 15A is arranged on the lower side of the medicine bandage PB (FIG. 4).
  • the camera 15B is arranged on the -Z side of the bag, and the medicines packaged in one packaging bag TP are photographed from above and below (plurality in different directions).
  • the sachet TP (medicine bandage PB) is transported by the transport mechanism 50 in the + X direction of FIG. 4 (the axis along the longitudinal direction of the drug bandage PB; the arrow direction of FIG.
  • a plurality of light sources 13 having the light sources illuminate the packaging bag TP from four directions.
  • the prescription reader 16 reads the prescription information.
  • OCR Optical Character Recognition
  • information such as the prescribed drug and its quantity may be read from the bar code.
  • the user may read the prescription and input the prescription information by an input device such as a keyboard included in the operation unit 40.
  • FIG. 6 is a block diagram showing a configuration example of the drug identification device 2.
  • the drug identification device 2 is mainly composed of a processing unit 10 and a storage unit 20.
  • the processing unit 10 includes an image input unit 10A, a drug region extraction unit 10B, a registered image acquisition unit 10C, a first recognition unit 10D, a second recognition unit 10E, and a drug identification unit 10F.
  • the CPU Central Processing Unit
  • devices such as various electronic circuits refer to the data stored in EEPROM (Electronically Erasable and Programmable Read Only Memory: non-temporary recording medium), etc. Will be realized.
  • EEPROM Electrically Erasable and Programmable Read Only Memory: non-temporary recording medium
  • these functions are performed by executing a drug identification program stored in a ROM (Read Only Memory: non-temporary recording medium) or the like of the storage unit 20.
  • RAM Random Access Memory
  • processing unit 10 is connected to the storage unit 20, and can freely retrieve and rewrite the data of the program and drug database 20A stored in the storage unit 20.
  • FIG. 7 is a flowchart illustrating a drug identification method (drug identification step) using the drug identification device 2. A detailed explanation of each step will be given later.
  • the prescription is read by the prescription reader 16 and the prescription information is acquired. Then, based on the prescription information, the registered image acquisition unit 10C acquires the first registered image and the second registered image of the drug that can be dispensed by the prescription (registered image acquisition step: step S10).
  • the first registered image is a registered image of the first drug dispensed based on the prescription information.
  • the second registered image is a registered image of the second drug similar to the first drug.
  • the target image PI (see FIG. 9) obtained by photographing one packaging bag TP is input to the image input unit 10A (image input step: step S11).
  • the drug region extraction unit 10B extracts the drug region R1 and the drug region R2 in the target image PI (see FIG. 9) (drug region extraction step: step S12).
  • the first recognition unit 10D performs similar recognition with the first registered image with respect to the drug region R1 and the drug region R2 (first recognition step: step S13).
  • the first recognition unit 10D performs similar recognition between the drug region R1 and the drug region R2 and the first registered image (step S14).
  • the drug identification unit 10F identifies the drug based on the recognition result of the first recognition unit 10D.
  • the second recognition unit 10E recognizes that the drug region is similar to the first registered image. Similar recognition with the registered image of (second recognition step: step S15). After that, the drug identification unit 10F identifies the drug based on the recognition results of the first recognition unit 10D and the second recognition unit 10E (drug identification step: step S16).
  • the registered image acquisition step (step S10) is performed by the registered image acquisition unit 10C.
  • the registered image acquisition unit 10C acquires the first registered image and the second registered image corresponding to the first drug from the drug database 20A based on the prescription information.
  • the registered image acquisition unit 10C acquires the prescription information acquired by the prescription reader 16, and is similar to the first registered image of the first drug dispensed by the prescription information and the first drug.
  • a second registered image of the second drug is obtained from the drug database 20A.
  • the registered image acquisition unit 10C has described an example in which the prescription reader 16 acquires the first registered image and the second registered image corresponding to the first drug based on the prescription information.
  • the mode in which the registered image acquisition unit 10C acquires the first registered image and the second registered image from the drug database 20A is not limited to the above example. For example, even if the user inputs the prescription information in the operation unit 40 and the registered image acquisition unit 10C acquires the first registered image and the second registered image from the drug database 20A based on the input prescription information. Good.
  • FIG. 8 is a diagram showing an example of a memory configuration of the drug database 20A.
  • drug name "drug name”, "registered image” and “similar drug” are stored in association with each other.
  • the drug name drug A is remembered in relation to the surface image of drug A and the similar drug being drug X.
  • the surface image of the drug A is marked with "A" on the surface.
  • the similar drug of the drug A is the drug X.
  • the drug name drug B is remembered in relation to the surface image of drug B and the absence of similar drugs.
  • the surface image of the drug B is marked with "B" on the surface.
  • the drug name drug C is remembered in relation to the surface image of drug C and the similar drugs being drug Y and drug Z.
  • the surface image of the drug C is marked with "C” on the surface.
  • the similar drugs of the drug C are the drug Y and the drug Z.
  • the drug name, the registered image, and the similar drug are stored in association with each other.
  • the memory structure of the drug database 20A shown in FIG. 8 is an example and is not limited thereto.
  • the registered image may have a front surface image of the drug and a back surface image of the drug. By having the front side image and the back side image in the registered image in this way, it is possible to perform similar recognition in more detail.
  • the registered image acquisition unit 10C may use the registered image A (first registered image) of the drug A. And the registered image B (first registered image) of the drug B is acquired. Further, since the drug X is similar to the drug A, the registered image acquisition unit 10C acquires the registered image X (second registered image) of the drug X.
  • the image input step (step S11) is performed by the image input unit 10A.
  • the target image of the drug to be identified is input to the image input unit 10A.
  • the target image is an image of one sachet TP containing the drug in order to identify that the drug is packaged according to the prescription.
  • the target image may be composed of one photographed image obtained by photographing the sachet TP, or an image in which the marking or printing of the drug A and the drug B is emphasized by image processing in one or more photographed images. There may be.
  • the drug region extraction step (step S12) is performed by the drug region extraction unit 10B.
  • the drug region extraction unit 10B extracts (cuts out) the drug region in which the drug to be identified is shown from the target image for each drug.
  • the drug region extraction unit 10B extracts the drug region, for example, based on the difference in color between the drug and the background.
  • FIG. 9 is a diagram showing a target image PI input to the image input unit 10A.
  • the target image PI is photographed by the camera 15A, and enhancement processing is performed by the marking (printing) enhancement processing unit (not shown) of the processing unit 10.
  • the drug region extraction unit 10B extracts the drug region R1 of the drug A and the drug region R2 of the drug B, respectively.
  • the camera 15A and the camera 15B can photograph the packaging bag TP from both sides, and images of the front and back surfaces of the drug can be acquired without omission.
  • the example shown in FIG. 9 was taken with the camera 15A, and is an example in which the images of the surfaces of the drug A and the drug B could be acquired.
  • the first recognition step (step S13) is performed by the first recognition unit 10D.
  • the first recognition unit 10D performs similar recognition of the drug region extracted by the drug region extraction unit 10B and the corresponding first registered image. Specifically, the first recognition unit 10D calculates the first similarity between the drug region and the first registered image, and compares the first similarity with the threshold value to perform similarity recognition.
  • the second recognition step (step S15) is performed by the second recognition unit 10E.
  • the second recognition unit 10E performs similar recognition with the second registered image for the drug region recognized by the first recognition unit 10D to be similar to the first registered image. Specifically, the second recognition unit 10E calculates the second similarity between the drug region and the second registered image.
  • the drug identification step (step S16) is performed by the drug identification unit 10F.
  • the drug identification unit 10F discriminates whether or not the drug is the first drug based on the recognition results of the first recognition unit 10D and the second recognition unit 10E. Specifically, the drug identification unit 10F identifies the drug shown in the drug region as the first drug when the first similarity is larger than the second similarity, and the first similarity. When is less than or equal to the second similarity, the drug shown in the drug area is identified as not the first drug.
  • FIG. 10 is a flowchart illustrating the calculation of the degree of similarity performed by the first recognition unit 10D.
  • the second recognition unit 10E also calculates the similarity, the description will be omitted because the similarity is calculated in the same manner as the first recognition unit 10D.
  • the first recognition unit 10D recognizes whether or not the size or shape (circumscribed rectangle) of the drug region and the first registered image is different (step S20). Here, if either the size or shape (circumscribed rectangle) of the drug region and the first registered image is different, the similarity is calculated as "0" zero (step S24).
  • the first recognition unit 10D recognizes whether or not the size or shape (circumscribed rectangle) is different based on the difference in the feature amount between the drug region and the first registered image. For example, the first recognition unit 10D recognizes that the difference between the drug region and the first registered image is different when the difference between the feature amounts is equal to or larger than the threshold value.
  • the first recognition unit 10D recognizes whether or not the brightness (brightness) of the drug region and the first registered image is different (step S21).
  • the first recognition unit 10D calculates that the similarity is “0” zero if the brightness (brightness) of the drug region and the first registered image are different (step S24).
  • the first recognition unit 10D recognizes whether or not the brightness is different based on the difference in brightness between the drug region and the first registered image. For example, the first recognition unit 10D recognizes that when the difference in brightness between the drug region and the first registered image is equal to or greater than the threshold value, the two are different.
  • the first recognition unit 10D recognizes whether or not the color of the drug region and the first registered image are different (step S22).
  • the first recognition unit 10D recognizes that the similarity is “0” zero (step S24).
  • the first recognition unit 10D recognizes whether or not the colors are different based on the difference in the R (red), G (green), and B (blue) values between the drug region and the first registered image. To do. For example, the first recognition unit 10D recognizes that the two are different when the color difference between the drug region and the first registered image is equal to or greater than the threshold value.
  • the first recognition unit 10D calculates the degree of similarity between the drug region and the first registered image (step S22).
  • the first recognition unit 10D calculates the similarity by template matching. For example, the similarity is when 1.0 is the most similar (same case), and is calculated lower than 1.0 depending on the degree of different points.
  • the first recognition unit 10D calculates the first similarity between the drug region and the first registered image.
  • the second recognition unit 10E may calculate the similarity in the same manner as the first recognition unit 10D, but the second recognition unit 10E is a drug region recognized to be similar by the first recognition unit 10D. Since the similarity is calculated for the above, the similarity may be directly calculated by template matching from the beginning (step S23 in FIG. 10).
  • FIG. 11 is a flowchart showing the similarity recognition of the first recognition unit 10D, the similarity recognition of the second recognition unit 10E, and the drug identification.
  • the first recognition unit 10D performs similar recognition between the drug region R1 and the registered image A and the registered image B (step S30). Specifically, since the drug region R1 and the registered image A have the same size (shape), brightness (brightness), and color, the first recognition unit 10D calculates the similarity by template matching ( See step S23 in FIG. 10). On the other hand, the first recognition unit 10D calculates the similarity as zero because the size of the drug region R1 and the registered image B are different.
  • the first recognition unit 10D compares the calculated similarity with the threshold value ThA and performs similarity recognition. Since the degree of similarity between the drug region R1 and the registered image A is equal to or higher than the threshold value ThA, it is recognized that the drug region R1 is similar to the registered image A.
  • the threshold ThA is preferably set as low as possible from the viewpoint of extracting the drug A without omission. For example, the threshold ThA is set as the threshold Th2 described with reference to FIG.
  • the first recognition unit 10D recognizes that the drug region R1 and the registered image B are not similar to each other because the similarity is zero and is smaller than the threshold value ThA, and the drug identification unit 10F recognizes that the drug region R1 is not similar. It is identified as not drug B (step S35).
  • the second recognition unit 10E determines the presence or absence of a similar drug of the drug A (step S31), and if there is a similar drug, calculates the second degree of similarity between the drug region R1 and the registered image X. (Step S32). Then, the drug identification unit 10F discriminates whether or not the drug region R1 is the drug A based on the first similarity and the second similarity (step S33). Specifically, the drug identification identifies the drug region R1 from the drug A when the first similarity is greater than the second similarity (step S34). On the other hand, when the first similarity is equal to or less than the second similarity, the drug region R1 is identified as not the drug A (step S34).
  • the first recognition unit 10D performs similar recognition between the drug region R2 and the registered image A and the registered image B (step S30). Specifically, since the drug region R2 and the registered image B have the same size (shape), color, and marking (printing), the first recognition unit 10D calculates the similarity by template matching (FIG. 10). (See step S23). On the other hand, since the size of the drug region R2 and the registered image A are different in the first recognition unit 10D, the similarity is calculated as zero.
  • the first recognition unit 10D compares the calculated similarity with the threshold value ThA and performs similarity recognition. Since the degree of similarity between the drug region R2 and the registered image B is equal to or higher than the threshold value ThA, it is recognized that the drug region R2 is similar to the registered image B. Then, the second recognition unit 10E determines the presence or absence of a similar drug of the drug B (step S31), and since there is no similar drug in the drug B, the second recognition unit 10E calculates the second similarity degree. do not do. After that, the drug identification unit 10F identifies the drug shown in the drug region R2 from the drug B based on the similarity recognition of the first recognition unit 10D (step S34).
  • the first recognition unit 10D recognizes that the drug region R2 and the registered image A are not similar because the similarity is zero and is smaller than the threshold value ThA
  • the drug identification unit 10F recognizes that the drug region R2 is not similar. It is identified as not drug A (step S35).
  • the first recognition unit 10D performs similar recognition of the drug region and the corresponding first registered image. Then, in this aspect, the first recognition unit 10D performs similar recognition with the second registered image for the drug region recognized to be similar to the first registered image. Thereby, in this aspect, since the similarity recognition with the second registered image is performed for the drug region considered to be similar to the second registered image, accurate and efficient identification can be performed.
  • FIG. 12 is a diagram showing a target image PJ input to the image input unit 10A.
  • the target image PJ is acquired in the same manner as the target image PI described with reference to FIG. Then, the drug region extraction unit 10B extracts the drug region R3 of the drug C.
  • FIG. 13 is a flowchart showing the similarity recognition of the first recognition unit 10D, the similarity recognition of the second recognition unit 10E, and the drug identification of the drug identification unit 10F.
  • the identification of the drug C will be described with reference to the flowchart of FIG.
  • the drug C which is the first drug
  • the registration image acquisition unit 10C is registered to correspond to the first drug based on the prescription information.
  • Image C, registered image Y, and registered image Z are acquired from the drug database 20A.
  • the first recognition unit 10D performs similar recognition between the drug region R3 and the registered image C (step S40). Specifically, since the drug region R3 and the registered image C have the same size (shape), brightness (brightness), and color, the first recognition unit 10D calculates the similarity by template matching ( See step S23 in FIG. 10).
  • the first recognition unit 10D compares the calculated similarity with the threshold value ThA and performs similarity recognition. Since the degree of similarity between the drug region R3 and the registered image C is equal to or higher than the threshold value ThA, it is recognized that the drug region R3 is similar to the registered image C.
  • the second recognition unit 10E calculates the second similarity (1) between the drug region R3 and the registered image Y (step S41). In addition, the second recognition unit 10E calculates the second similarity (2) between the drug region R3 and the registered image Z (step S42). In this way, the second recognition unit 10E calculates the degree of similarity for each of the registered images of similar drugs. Then, the drug identification unit 10F performs identification based on each of the first similarity and the second similarity. Specifically, the drug identification unit 10F determines whether or not the first similarity is greater than the second similarity (1) (step S43). The drug identification unit 10F identifies that the drug region R3 is not drug C when the first similarity is not greater than the second similarity (1).
  • the drug identification unit 10F determines whether the first similarity is larger than the second similarity (2). Is determined (step S44). The drug identification unit 10F identifies that the drug region R3 is not drug C when the first similarity is not greater than the second similarity (2) (step S46). On the other hand, when the first similarity is larger than the second similarity (2), the drug region R3 is distinguished from the drug C (step S45).
  • the drug is accurately identified based on the recognition results of the first recognition unit 10D and the second recognition unit 10E. Efficient drug identification can be performed.
  • Drug identification device 10 Processing unit 10A: Image input unit 10B: Drug area extraction unit 10C: Registered image acquisition unit 10D: First recognition unit 10E: Second recognition unit 10F: Drug identification unit 12: Lighting unit 13: Light source 15A: Camera 15B: Camera 16: Prescription reader 20: Storage unit 20A: Drug database 30: Display unit 40: Operation unit 50: Conveyance mechanism

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  • Image Analysis (AREA)

Abstract

Dispositif d'identification de médicament, procédé d'identification de médicament et programme avec lesquels il est possible d'effectuer une identification efficace avec précision, même lors de l'identification de médicaments comprenant des médicaments qui sont similaires les uns aux autres. Un dispositif d'identification de médicament (2) est pourvu : d'une base de données de médicaments (20A); d'une unité d'entrée d'image (10A) dans laquelle une image cible est entrée; d'une unité d'extraction de région de médicament (10B) qui extrait de l'image cible une région de médicament dans laquelle un médicament apparaît; d'une unité d'acquisition d'image enregistrée (10C) qui acquiert une première image enregistrée et une seconde image enregistrée; d'une première unité de reconnaissance (10D) qui effectue une reconnaissance de similarité entre la région de médicament et la première image enregistrée correspondante; d'une seconde unité de reconnaissance (10E) qui effectue une reconnaissance de similarité avec la seconde image enregistrée, pour une région de médicament qui a été reconnue par la première unité de reconnaissance comme étant similaire à la première image enregistrée; et d'une unité d'identification de médicament (10F) qui identifie si le médicament est un premier médicament sur la base des résultats de reconnaissance provenant de la première unité de reconnaissance et de la seconde unité de reconnaissance.
PCT/JP2020/026850 2019-07-16 2020-07-09 Dispositif d'identification de médicament, procédé d'identification de médicament, et programme WO2021010277A1 (fr)

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WO2018173649A1 (fr) * 2017-03-23 2018-09-27 富士フイルム株式会社 Dispositif de reconnaissance de médicament, procédé de reconnaissance de médicament et programme de reconnaissance de médicament
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WO2019039016A1 (fr) 2017-08-25 2019-02-28 富士フイルム株式会社 Dispositif d'assistance d'inspection de médicament, dispositif de traitement d'image, procédé de traitement d'image et programme
JP6853891B2 (ja) 2017-08-31 2021-03-31 富士フイルム富山化学株式会社 薬剤監査装置、画像処理装置、画像処理方法及びプログラム

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2004167158A (ja) * 2002-11-22 2004-06-17 Glory Ltd 薬剤認識装置
JP2005056252A (ja) * 2003-08-06 2005-03-03 Casio Comput Co Ltd 画像照合装置、画像照合方法および画像照合プログラム
WO2011145351A1 (fr) * 2010-05-20 2011-11-24 パナソニック株式会社 Dispositif de détermination d'une solution de médicament et procédé de détermination d'une solution de médicament
WO2012147907A1 (fr) * 2011-04-28 2012-11-01 株式会社湯山製作所 Dispositif de vérification de médicament et appareil pour le conditionnement séparé de médicaments
JP2014064836A (ja) * 2012-09-27 2014-04-17 Fujifilm Corp 薬剤検査装置及び方法
JP2018128931A (ja) * 2017-02-09 2018-08-16 キヤノン株式会社 画像処理装置、画像処理方法、及びプログラム
WO2018173649A1 (fr) * 2017-03-23 2018-09-27 富士フイルム株式会社 Dispositif de reconnaissance de médicament, procédé de reconnaissance de médicament et programme de reconnaissance de médicament
WO2018221065A1 (fr) * 2017-05-30 2018-12-06 富士フイルム株式会社 Dispositif et procédé d'aide à l'inspection de médicaments

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