Oral image processing apparatus, oral image processing method, and recording medium

Abstract

An oral cavity image processing apparatus, an oral cavity image processing method, and a recording medium are disclosed according to embodiments. The disclosed oral cavity image processing method includes the following steps: acquiring an oral cavity image generated by scanning teeth; aligning a tooth model template including multiple template teeth with the teeth included in the oral cavity image; separating the teeth in the oral cavity image to obtain multiple tooth blocks; and collecting one or more tooth blocks corresponding to each aligned template tooth to individualize the teeth in the oral cavity image.

Description

Technical Field

[0001] The disclosed embodiments relate to an oral cavity image processing apparatus and an oral cavity image processing method.

[0002] Specifically, the disclosed embodiments relate to an apparatus and a method for processing oral images that individualize one or more teeth included in an oral image. Background Technology

[0003] For dental treatment, an oral scanner is inserted into the patient's mouth to acquire images of the oral cavity. These images can contain more than one tooth. For dental procedures such as fillings or orthodontic treatment, it is necessary to move or manipulate the teeth shown in the images. Therefore, it is essential to identify the positional information of each tooth in the images and to individualize each tooth, for example, by assigning them a number. Summary of the Invention

[0004] The problem the invention aims to solve

[0005] The disclosed embodiments provide a method for processing oral images that individualize one or more teeth included in an oral image, and an apparatus for performing the operation.

[0006] means for solving problems

[0007] According to one embodiment, an oral image processing method may include the following steps: acquiring an oral image generated by scanning teeth; aligning a tooth model template including multiple template teeth with the teeth included in the oral image; separating the teeth in the oral image to obtain multiple tooth blocks; and collecting one or more tooth blocks corresponding to each aligned template tooth to individualize the teeth in the oral image.

[0008] According to one embodiment, the step of individualizing the teeth in the oral cavity image may include the following steps: obtaining the mapping relationship between each tooth block in the oral cavity image and each template tooth by identifying tooth blocks corresponding to each aligned template tooth, and using the mapping relationship to collect one or more tooth blocks mapped to each template tooth.

[0009] According to one embodiment, the step of aligning a tooth model template including a plurality of template teeth with the teeth included in the oral cavity image may include the following step: positioning the plurality of template teeth of the tooth model template to positions corresponding to the teeth included in the oral cavity image.

[0010] According to one embodiment, the step of separating teeth from the oral cavity image to obtain multiple tooth blocks may include the following steps: separating the teeth according to a curvature distribution to separate each tooth into more than one tooth block.

[0011] According to one embodiment, the step of individualizing the teeth in the oral cavity image may include the following steps: identifying each tooth block corresponding to each template tooth using the orientation conditions of each template tooth and each tooth block.

[0012] According to one embodiment, the step of identifying each tooth block corresponding to each template tooth using the orientation condition may include the following steps: identifying the intersection points of the normal vectors at each vertex of the three-dimensional mesh constituting the template tooth and the three-dimensional mesh constituting the tooth block, and determining that the tooth block corresponds to the template tooth when the angle between the normal vector at the intersection point of the identified tooth block and the normal vector at the vertex is less than a critical value.

[0013] According to one embodiment, the step of individualizing the teeth in the oral cavity image may include the following steps: identifying each tooth block corresponding to each template tooth using the distance conditions between each template tooth and each tooth block; the step of identifying each tooth block corresponding to each template tooth may include the following steps: identifying the intersection points of the normal vectors at each vertex of the three-dimensional mesh constituting the template tooth and the three-dimensional mesh constituting the tooth block, and determining that the tooth block corresponds to the template tooth when the distance from the vertex to the intersection point is less than a critical value.

[0014] According to one embodiment, the step of individualizing the teeth in the oral cavity image may include the following steps: identifying each tooth block corresponding to each template tooth using distance and orientation conditions of each template tooth and each tooth block; the step of identifying each tooth block corresponding to each template tooth using the distance and orientation conditions may include the following steps: identifying the intersection points of the normal vectors at each vertex of the three-dimensional mesh constituting the template tooth and the three-dimensional mesh constituting the tooth block within a predetermined distance from the vertex, and determining that the tooth block corresponds to the template tooth when the angle between the normal vector at the intersection point of the identified tooth block and the normal vector at the vertex is less than a critical value.

[0015] According to one embodiment, an oral image processing apparatus includes: a processor and a memory; the processor can execute one or more instructions stored in the memory to perform the following operations: acquiring an oral image generated by scanning teeth, aligning a tooth model template including multiple template teeth with the teeth included in the oral image, separating the teeth in the oral image to obtain multiple tooth blocks, and collecting one or more tooth blocks corresponding to each aligned template tooth to individualize the teeth in the oral image.

[0016] According to an embodiment, a non-transitory computer-readable recording medium records a program including at least one instruction for performing an oral image processing method, the oral image processing method including the following steps: acquiring an oral image generated by scanning teeth; aligning a tooth model template including multiple template teeth with the teeth included in the oral image; separating the teeth in the oral image to obtain multiple tooth blocks; and collecting one or more tooth blocks corresponding to each aligned template tooth to individualize the teeth in the oral image.

[0017] Invention Effects

[0018] The oral image processing method and apparatus according to the disclosed embodiments enable more accurate and convenient individualization of teeth by disclosing a method for individualizing teeth included in an oral image using a tooth model template. Attached Figure Description

[0019] The present invention can be readily understood through the following detailed description and the accompanying drawings, wherein reference numerals denote constituent elements.

[0020] Figure 1 This is a diagram illustrating an oral image processing system according to a disclosed embodiment.

[0021] Figure 2 This is a reference diagram used to illustrate a method for individualizing teeth included in an oral cavity image using a template, based on an example.

[0022] Figure 3 This is a block diagram illustrating a data processing apparatus 100 according to a disclosed embodiment.

[0023] Figure 4 This is a flowchart illustrating a method for processing oral images using a data processing device according to a disclosed embodiment.

[0024] Figure 5 An example of a three-dimensional mesh structure of result data obtained using a three-dimensional scanner according to an embodiment is shown.

[0025] Figure 6 This is a reference diagram used to illustrate a method for dividing an oral cavity image into groups of teeth and gingiva according to an embodiment.

[0026] Figure 7 This is a reference diagram illustrating a method for dividing a group of teeth into multiple tooth blocks according to an embodiment.

[0027] Figure 8 According to one embodiment, the curvature distribution corresponding to an oral cavity image is represented by a map.

[0028] Figure 9 This is a reference diagram illustrating a method for aligning a dental model template with a group of teeth in an oral cavity image according to an embodiment.

[0029] Figure 10 This is a reference diagram illustrating a method for obtaining the mapping relationship between each tooth block of a tooth group and each template tooth according to an embodiment.

[0030] Figure 11 This shows a portion of the data aligned with the tooth group block and the tooth model template according to one embodiment.

[0031] Figure 12 This is a reference diagram illustrating a method for obtaining the mapping relationship between each tooth block and each template tooth of a tooth group block using orientation and distance conditions according to an embodiment.

[0032] Figure 13 This is a reference diagram illustrating a method for obtaining the mapping relationship between each tooth block and each template tooth of a tooth group block using orientation and distance conditions according to an embodiment.

[0033] Figure 14 This is a reference diagram illustrating, according to one embodiment, a case where a tooth block is repeatedly mapped onto multiple template teeth.

[0034] Figure 15 This is a reference diagram illustrating a method for individualizing teeth in an oral image by utilizing the mapping relationship between tooth blocks and template teeth, according to an embodiment. Detailed Implementation

[0035] This specification describes the principles of the invention and discloses embodiments to clarify the scope of the invention and enable those skilled in the art to implement it. The disclosed embodiments can be implemented in various forms.

[0036] Throughout this specification, the same reference numerals refer to the same constituent elements. This specification does not describe all elements of the embodiments, and omit general content or repetition between embodiments within the scope of this invention. The term "part" (portion) used in this specification can be implemented in software or hardware, and according to embodiments, multiple "parts" can be implemented as one element, or one "part" can include multiple elements. The working principle and embodiments of the invention are described below with reference to the accompanying drawings.

[0037] In this specification, images may include images showing at least one tooth or an oral cavity including at least one tooth, or images showing a model of a tooth (hereinafter referred to as "oral cavity images").

[0038] Furthermore, the images in this specification may be two-dimensional images of the object, or three-dimensional models or images showing the object in three dimensions. Additionally, the images in this specification may refer to data required for two-dimensional or three-dimensional representation of the object, such as raw data acquired from at least one image sensor. Specifically, raw data is two-dimensional or three-dimensional data acquired to generate oral cavity images, and may be data acquired from at least one image sensor in an oral scanner (e.g., two-dimensional data) when scanning the patient's oral cavity as the object using a 3D scanner. The 3D scanner may include an oral scanner, a desktop scanner, a CT scanner, etc.

[0039] The following explanation will use an oral scanner as an example.

[0040] In this specification, "object" may include teeth, gums, at least a portion of the oral cavity, and / or artificial structures that can be inserted into the oral cavity (e.g., orthodontic appliances, dental implants, artificial teeth, orthodontic aids inserted into the oral cavity, etc.). Orthodontic appliances may include at least one of the following: brackets, attachments, orthodontic screws, lingual orthodontic appliances, and removable orthodontic maintenance devices.

[0041] The embodiments will now be described in detail with reference to the accompanying drawings.

[0042] Figure 1 This is a diagram illustrating an oral image processing system according to a disclosed embodiment.

[0043] Reference Figure 1 The oral scanner 10 is a medical device used to acquire images of the inside of the oral cavity.

[0044] Specifically, the oral scanner 10 can be a device that acquires images of the oral cavity, including at least one tooth, by inserting into the oral cavity and scanning the teeth non-contactly. Furthermore, the oral scanner 10 may have a shape capable of entering and exiting the oral cavity, and uses at least one image sensor (e.g., an optical camera, etc.) to scan the inside of the patient's oral cavity. In order to image at least one surface of the teeth, gums, and artificial structures that can be inserted into the oral cavity (e.g., orthodontic appliances including braces and wires, dental implants, artificial teeth, orthodontic aids inserted into the oral cavity, etc.), the oral scanner 10 can acquire surface information about the object as raw data. Furthermore, the oral scanner 10 can not only directly scan the patient's oral cavity, but also scan dental models or plaster models of the patient obtained through impressions to obtain oral cavity images.

[0045] Image data acquired from the oral scanner 10 can be transmitted to a data processing device 100 connected via a wired or wireless communication network.

[0046] The data processing device 100 may be any electronic device that connects to the oral scanner 10 via a wired or wireless network, receives two-dimensional images acquired by scanning the oral cavity from the oral scanner 10, and generates, processes, displays, and / or transmits oral cavity images based on the received two-dimensional images. In addition to receiving oral cavity images acquired by the oral scanner 10, the data processing device 100 may also receive dental model images acquired by the 3D model scanner by scanning a dental model.

[0047] The data processing device 100 can generate at least one of information generated by processing the two-dimensional image data and an oral cavity image generated by processing the two-dimensional image data, based on the two-dimensional image data received from the oral scanner 10, and display the generated information and the oral cavity image on the display 130.

[0048] exist Figure 1 In the illustration, the data processing device 100 is shown in the form of a laptop computer, but it is not limited to this. It can be a computing device such as a smartphone, desktop computer, PDA, or tablet PC, and is not limited to this.

[0049] Furthermore, the data processing device 100 may also exist in the form of a server (or server device) for processing oral images.

[0050] Furthermore, the oral scanner 10 can transmit the raw data acquired by scanning the oral cavity to the data processing device 100 as is. In this case, the data processing device 100 can generate a three-dimensional oral cavity image representing the oral cavity based on the received raw data. Moreover, the "three-dimensional oral cavity image" can be generated by three-dimensional modeling of the internal structure of the oral cavity based on the received raw data, and therefore can be referred to as a "three-dimensional oral cavity model" or "three-dimensional oral cavity image". Hereinafter, models or images displaying the oral cavity in two or three dimensions will be collectively referred to as "oral cavity images".

[0051] In addition, the data processing device 100 can analyze, process, display the generated oral cavity images, and / or transmit the oral cavity images to external devices.

[0052] As another example, the oral scanner 10 can acquire raw data by scanning the oral cavity, process the acquired raw data to generate an image corresponding to the oral cavity as the object, and transmit it to the data processing device 100. In this case, the data processing device 100 can analyze, process, display, and / or transmit the received image.

[0053] In the disclosed embodiments, the data processing device 100 is an electronic device capable of generating and displaying an image of an oral cavity including one or more teeth in three dimensions, which will be described in detail below.

[0054] Figure 2 This is a reference diagram used to illustrate a method for individualizing teeth included in an oral cavity image using a dental model template, based on an example.

[0055] The oral image 500 may be an oral image obtained by scanning the patient's oral cavity using an oral scanner 10 or an oral image obtained by scanning a dental model of the patient using a 3D model scanner or the like.

[0056] The oral image 500 provides a comprehensive image of the surface inside the patient's oral cavity, thus simultaneously imaging more than one tooth and the gingiva 520 surrounding the tooth.

[0057] The oral scanner 10 can acquire two-dimensional data representing surface information of the oral cavity as the object. Furthermore, the data processing device 100 can generate an oral cavity image 500 based on the two-dimensional data acquired from the oral scanner 10.

[0058] The oral cavity image 500 can form a polygonal mesh structure in a point cloud with the coordinates of points as vertices by polygonizing adjacent vertices, where the points are obtained by scanning the object using an oral scanner. The polygons constituting the mesh structure can be triangles, quadrilaterals, pentagons, etc.; as an example, they can be triangles. As described above, in the oral cavity image 500 with a polygonal mesh structure generated by the data processing device 100, the teeth are not separated from the gingiva 520, and the teeth can also be identified as a group of teeth 510 that are not separated from each other. Alternatively, according to another embodiment, the data processing device 100 can use artificial intelligence to divide the 2D image obtained during the scanning process into tooth and gingival regions, and the oral cavity image 500 generated in the data processing device 100 can automatically have a state of being divided into tooth groups and gingiva.

[0059] The tooth model template 200 represents a template model data in which the teeth have an ideal shape and are arranged in an ideal position, and each tooth is assigned a number. For example, the template teeth in the tooth model template 200 are assigned tooth numbers from left to right, such as No. 1, No. 2, and so on up to No. 14.

[0060] The data processing device 100 can use the tooth model template 200 to process the teeth in the oral cavity image 500, thereby individualizing the teeth in the oral cavity image and obtaining individualized teeth 550. Individualizing the teeth in the oral cavity image means separating the teeth from the gums in the oral cavity image and obtaining information about each tooth. This information may include information about the shape of each tooth, information about the position of each tooth, and information about the number of each tooth. Individualizing the teeth in the oral cavity image can also be referred to as segmentation or subdivision of the teeth in the oral cavity image. As described above, by individualizing the teeth in the oral cavity image, the data processing device 100 can use the individualized teeth 550 for processing, such as deleting or moving individual teeth, or inserting additional teeth.

[0061] Figure 3 This is a block diagram illustrating a data processing apparatus 100 according to a disclosed embodiment.

[0062] Reference Figure 3 The data processing device 100 may include a communication interface 110, a user interface 120, a display 130, an image processing unit 140, a memory 150, and a processor 160.

[0063] The communication interface 110 can communicate with at least one external electronic device via a wired or wireless communication network. Specifically, the communication interface 110 can communicate with the oral scanner 10 under the control of the processor 160. The communication interface 110 can also communicate with external electronic devices or servers connected via wired or wireless communication networks, depending on the control of the processor.

[0064] The communication interface 110 can communicate with external electronic devices (e.g., dental scanners, servers, or external medical devices) via wired or wireless communication networks. Specifically, the communication interface may include at least one near-field communication module that communicates according to communication standards such as Bluetooth, Wi-Fi, Bluetooth Low Energy (BLE), Near Field Communication / Radio Frequency Identification (NFC / RFID), Wi-Fi Direct, Ultra Wideband (UWB), or ZigBee.

[0065] Furthermore, the communication interface 110 may also include a remote communication module that communicates with a server supporting long-distance communication in accordance with remote communication standards. Specifically, the communication interface 110 may include a remote communication module that communicates via a network used for Internet communication. Additionally, the communication interface may include a remote communication module that communicates via a communication network conforming to communication standards such as 3G, 4G, and / or 5G.

[0066] Furthermore, the communication interface 110 may include at least one port that can be connected to an external electronic device via a wired cable for wired communication with the external electronic device (e.g., an oral scanner). Thus, the communication interface 110 is capable of communicating with an external electronic device that is wiredly connected via at least one port.

[0067] The user interface 120 can receive user input for controlling the data processing device. The user interface 120 may include: a touch panel for detecting user touch, buttons for receiving user press operations, and user input devices, including, but not limited to, a mouse or keyboard for specifying or selecting a point on the user interface screen.

[0068] Furthermore, the user interface 120 may include a voice recognition device for speech recognition. For example, the voice recognition device may be a microphone, capable of receiving voice commands or voice requests from the user. Thus, the processor can control the execution of operations corresponding to the voice commands or voice requests.

[0069] The display 130 displays a screen. Specifically, the display 130 may display a predetermined screen under the control of the processor 160. Specifically, the display 130 may display a user interface screen including a generated oral image based on data acquired by scanning the patient's oral cavity through the oral scanner 10. Alternatively, the display 130 may display a user interface screen including information related to the patient's dental treatment.

[0070] The image processing unit 140 can perform operations for image generation and / or processing. Specifically, the image processing unit 140 can receive raw data obtained from the oral scanner 10 and generate an oral image based on the received data. Specifically, the image processing unit 140 can individualize the teeth included in the patient's oral image using a dental model template, thereby obtaining individualized teeth. As described above, Figure 3 As shown, the image processing unit 140 can be disposed separately from the processor 160, or the image processing unit 140 can be included within the processor 160.

[0071] Memory 150 may store at least one instruction. Furthermore, memory 150 may store at least one instruction executed by a processor. Additionally, memory may store at least one program executed by processor 160. Furthermore, memory 150 may store data received from an oral scanner (e.g., raw data acquired by scanning the oral cavity). Alternatively, memory may store an oral cavity image representing a three-dimensional oral cavity. According to one embodiment, memory 150 may include more than one instruction for individualizing the teeth included in the oral cavity image using a dental model template to obtain individualized teeth. According to one embodiment, memory 150 may include more than one instruction for performing the method disclosed herein to individualize the teeth included in the oral cavity image using a dental model template to obtain individualized teeth.

[0072] The processor 160 executes at least one instruction stored in the memory 150 to control the execution of a intended operation. The at least one instruction may be stored in the internal memory of the processor 160 or in the memory 150, which is included separately from the processor within a data processing device.

[0073] Specifically, the processor 160 executes at least one instruction to control at least one structure within the data processing device, thereby performing a predetermined operation. Therefore, even though the example illustrates the processor performing a predetermined operation, it also implies that the processor controls at least one structure within the data processing device to perform the predetermined operation.

[0074] According to one embodiment, the processor 160 can acquire an oral cavity image generated by scanning teeth by executing one or more instructions stored in the memory 150, align a tooth model template including multiple template teeth with the teeth included in the oral cavity image, separate the teeth in the oral cavity image according to the curvature distribution to acquire multiple tooth blocks, and collect one or more tooth blocks corresponding to each aligned template tooth, thereby enabling the individualization of teeth in the oral cavity image.

[0075] According to one embodiment, the processor 160 can identify tooth blocks corresponding to each aligned template tooth by executing one or more instructions stored in the memory 150, thereby obtaining the mapping relationship between each tooth block and each template tooth in the oral cavity image, and using the mapping relationship, collecting one or more tooth blocks mapped to each template tooth.

[0076] According to one embodiment, the processor 160 can separate the teeth from the gums in an oral image and separate the teeth according to the curvature distribution by executing one or more instructions stored in the memory 150, thereby separating each tooth into one or more tooth blocks.

[0077] According to one embodiment, the processor 160 can execute one or more instructions stored in the memory 150 to position a plurality of template teeth of a dental model template at positions corresponding to the teeth included in an oral cavity image.

[0078] According to one embodiment, the processor 160 can identify each tooth block corresponding to each template tooth by executing one or more instructions stored in the memory 150, using the orientation conditions of each template tooth and each tooth block.

[0079] According to one embodiment, the processor 160 identifies the normal vectors of each vertex of the three-dimensional mesh constituting the template tooth and the intersection points of the three-dimensional mesh constituting the tooth block by executing one or more instructions stored in the memory 150. When the angle between the normal vector at the intersection point and the normal vector at the vertex in the identified tooth block is less than a critical value, it can be determined that the tooth block corresponds to the template tooth.

[0080] According to one embodiment, the processor 160 can identify each tooth block corresponding to each template tooth by executing one or more instructions stored in the memory 150, using the distance conditions between each template tooth and each tooth block.

[0081] According to one embodiment, the processor 160 can execute one or more instructions stored in the memory 150 to identify the intersection of the normal vector at each vertex of the three-dimensional mesh constituting the template tooth and the three-dimensional mesh constituting the tooth block in order to utilize the distance condition recognition operation. When the distance from the vertex to the intersection is less than a critical value, it can be determined that the tooth block corresponds to the template tooth.

[0082] According to one embodiment, the processor 160 can identify each tooth block corresponding to each template tooth by executing one or more instructions stored in the memory 150, using the orientation and distance conditions of each template tooth and each tooth block.

[0083] According to one embodiment, the processor 160 executes one or more instructions stored in the memory 150 to identify each tooth block corresponding to each template tooth using orientation and distance conditions. As the intersection point of the normal vector at each vertex of the three-dimensional mesh constituting the template tooth and the three-dimensional mesh constituting the tooth block, the processor identifies the intersection point located within a predetermined distance from the vertex. When the angle between the normal vector at the intersection point in the identified tooth block and the normal vector at the vertex is less than a critical value, it can be determined that the tooth block corresponds to the template tooth.

[0084] According to one embodiment, the processor 160 can ignore tooth blocks that are repeatedly mapped to multiple template teeth when collecting more than one tooth block mapped to each template tooth by executing one or more instructions stored in the memory 150.

[0085] According to one example, the processor 160 may be embodied in the form of at least one internal processor and a memory device (e.g., random access memory (RAM), read-only memory (ROM), etc.) for storing at least one of programs, instructions, signals, and data stored in the internal processor for processing or use.

[0086] Furthermore, the processor 160 may include a graphics processing unit (GPU) for processing graphics corresponding to the video. Additionally, the processor may be implemented as a system-on-a-chip (SoC) integrating a core and a graphics processing unit (GPU). Furthermore, the processor may include multiple cores beyond a single core. For example, the processor may include dual-core, triple-core, quad-core, hexa-core, octa-core, deca-core, dodecathlon, hexadecimal, and so on.

[0087] In the disclosed embodiments, the processor 160 may generate an oral image based on a two-dimensional image received from the oral scanner 10.

[0088] Specifically, the communication interface 110 can receive data acquired from the oral scanner 10, such as raw data acquired by scanning the oral cavity, under the control of the processor 160. Furthermore, the processor 160 can generate a three-dimensional oral cavity image representing the oral cavity based on the raw data received from the communication interface. For example, the oral scanner, in order to reconstruct a three-dimensional image using optical triangulation, may include at least one camera. As a specific embodiment, it may include an L camera corresponding to the left field of view and an R camera corresponding to the right field of view. Moreover, the oral scanner can acquire L image data corresponding to the left field of view and R image data corresponding to the right field of view from the L camera and the R camera, respectively. Subsequently, the oral scanner (not shown) can send the raw data including the L image data and the R image data to the communication interface of the data processing device 100.

[0089] Then, the communication interface 110 can transmit the received raw data to the processor, and the processor can generate a three-dimensional oral cavity image based on the received raw data.

[0090] Furthermore, the processor 160 can directly receive three-dimensional oral cavity images from external servers, medical devices, etc., via a control communication interface. In this case, the processor can acquire three-dimensional oral cavity images without generating three-dimensional oral cavity images based on raw data.

[0091] According to the disclosed embodiments, the processor 160 performing operations such as "extracting", "acquiring", and "generating" can refer to the case where the processor 160 directly performs the operations described above by executing at least one instruction in the processor 160, and the case where it controls other constituent elements to perform the operations described above.

[0092] To implement the embodiments disclosed in this specification, the data processing apparatus 100 may include Figure 3 A portion of the constituent elements shown may also include Figure 3 More constituent elements than those shown.

[0093] Furthermore, the data processing device 100 can store and execute dedicated software that works in conjunction with the dental scanner. This dedicated software can be referred to as a dedicated program, tool, or application. When the data processing device 100 and the dental scanner 10 operate in conjunction, the dedicated software stored in the data processing device 100 is connected to the dental scanner 10 and can receive data acquired through scanning the oral cavity in real time. For example, the i500 dental scanner from Medit Corporation contains dedicated software for processing data acquired through scanning the oral cavity. Specifically, Medit Corporation has created and released "Medit Link" software for processing, managing, using, and / or transmitting data acquired from a dental scanner (e.g., the i500). "Dedicated software" refers to an operable program, tool, or application that works in conjunction with the dental scanner; therefore, it can be used with various dental scanners developed and sold by various manufacturers. Furthermore, the dedicated software described above can be created and released separately from the dental scanner that performs the oral cavity scan.

[0094] The data processing device 100 can store and execute dedicated software corresponding to the i500 product. The transmission software can perform at least one operation for acquiring, processing, storing, and / or transmitting oral images. The dedicated software can be stored in a processor. Furthermore, the dedicated software can provide a user interface for using data acquired from the oral scanner. The user interface screen provided by the dedicated software can include oral images generated according to the disclosed embodiments.

[0095] Figure 4 This is a flowchart illustrating a method for processing oral cavity images using a data processing device, according to a disclosed embodiment. Figure 4 The oral cavity image processing method shown can be executed by the data processing device 100. Therefore, Figure 4 The method for processing the oral cavity image shown can be represented by a flowchart illustrating the operation of the data processing device 100.

[0096] Reference Figure 4 In step 410, the data processing device 100 can acquire an oral cavity image generated by scanning teeth.

[0097] According to one embodiment, the data processing device 100 can, from such... Figure 1 The oral scanner 10 shown receives two-dimensional data generated by scanning teeth and generates an oral image based on the received two-dimensional data.

[0098] According to one embodiment, the data processing device 100 can receive oral images from the oral scanner 10, the oral images being generated based on two-dimensional data acquired by scanning teeth.

[0099] According to one embodiment, the data processing device 100 can acquire oral cavity images stored in a memory.

[0100] In step 420, the data processing device 100 can obtain multiple tooth blocks by segmenting the teeth included in the oral cavity image according to the curvature distribution.

[0101] According to one embodiment, the data processing device 100 can separate an oral cavity image 500 into tooth clusters 510 and gingiva 520 based on curvature distribution. According to another embodiment, the data processing device 100 can automatically separate the oral cavity image 500 into tooth clusters 510 and gingiva 520 using a neural network employing artificial intelligence.

[0102] According to one embodiment, the data processing device 100 can separate the tooth group 510 into multiple tooth blocks based on the curvature distribution. In this case, even a single tooth can be separated into two or more tooth blocks based on the curvature distribution. Therefore, for example, the tooth group 510 can be separated into 30 to 50 tooth blocks.

[0103] In step 430, the data processing device 100 can align a tooth model template 200, which includes multiple template teeth, with the teeth in the oral cavity image.

[0104] According to one embodiment, when the data processing device 100 aligns the teeth of the oral cavity image with the tooth model template 200, it can utilize a variety of automatic alignment algorithms, such as the Iterative Closest Point (ICP) algorithm.

[0105] exist Figure 4 Although the embodiment shows the separation of the tooth block in step 420 and the alignment of the tooth model template in step 430, the embodiment is of course not limited to this. The operation of separating the tooth block and the operation of aligning the tooth model template can be performed in parallel, or the operation of separating the tooth block can be performed after the operation of aligning the tooth model template.

[0106] In step 440, the data processing device 100 can individualize the teeth in the oral cavity image by collecting one or more tooth blocks corresponding to each aligned template tooth.

[0107] According to one embodiment, the data processing device 100 identifies the template teeth corresponding to the tooth blocks based on template teeth aligned with teeth in an oral cavity image, using orientation conditions (or normal conditions) of the template teeth and tooth blocks. Orientation conditions (or normal conditions) can refer to conditions used to identify the tooth blocks corresponding to the template teeth using the normal vectors of the template teeth and the tooth blocks.

[0108] According to one embodiment, the data processing device 100 can use the normal vector of the template tooth and the normal vector of the tooth block as orientation conditions to identify the tooth block corresponding to the template tooth.

[0109] According to one embodiment, the data processing device 100 can use the angle between the normal vector at the vertex of each template tooth and the normal vector at the intersection point of the tooth block in the oral cavity image to identify whether the template tooth corresponds to the tooth block.

[0110] According to one embodiment, when the angle between the normal vector at the vertex of each template tooth and the normal vector at the intersection point of the tooth block in the oral cavity image is less than a critical angle, the data processing device 100 can identify that the template tooth corresponds to the tooth block; when the angle is above the critical angle, it is identified that the template tooth does not correspond to the tooth block.

[0111] According to one embodiment, the data processing device 100 can use a distance condition to identify whether a template tooth corresponds to a tooth block, wherein the distance condition is the distance from the vertex of each template tooth to the intersection point where the normal vector at that vertex intersects with the tooth block of the oral cavity image.

[0112] According to one embodiment, the data processing device 100 identifies the intersection points of the normal vectors at each vertex of the three-dimensional mesh constituting the template tooth and the three-dimensional mesh constituting the tooth block. When the distance from the vertex to the intersection point is less than a critical value, it can be determined that the tooth block corresponds to the template tooth.

[0113] According to one embodiment, the data processing device 100 can identify the template tooth corresponding to the tooth block based on the template tooth and the distance and orientation conditions between the template tooth and the tooth block, using a template tooth aligned to an oral cavity image.

[0114] According to one embodiment, the data processing device 100 can identify a tooth block that is located within a predetermined distance at the apex of each template tooth and satisfies the orientation conditions described above as the corresponding tooth block.

[0115] According to one embodiment, when multiple template teeth are mapped to a single tooth block, the data processing device 100 may exclude tooth blocks that are jointly mapped to multiple template teeth from tooth block collection and ignore them.

[0116] The method for processing oral images according to the disclosed embodiments will be referred to below. Figures 5 to 15 Detailed explanation.

[0117] The data processing device 100 separates the teeth included in the oral cavity image according to the curvature distribution to obtain multiple tooth blocks. First, the data processing device 100 separates the teeth included in the oral cavity image according to the curvature distribution to separate them into tooth groups and gingiva. Furthermore, the data processing device 100 separates the tooth groups according to the curvature distribution, thereby enabling it to separate them into multiple tooth blocks.

[0118] First, we will explain the concepts of three-dimensional mesh structure and curvature distribution based on the data obtained using an oral scanner.

[0119] Figure 5 An example of a three-dimensional mesh structure of result data obtained using a three-dimensional scanner according to an embodiment is shown.

[0120] When acquiring two-dimensional data using an oral scanner, the data processing device 100 can calculate the coordinates of multiple illuminated surface points using triangulation methods. By scanning while moving the oral scanner across the surface of the object, the coordinates of the surface points can accumulate as the amount of scanned data increases. As a result of this image acquisition, a point cloud of vertices can be identified and the surface extent can be displayed. Points within the point cloud can represent actual measured points on the three-dimensional surface of the object. The surface structure can be approximated by forming a polygonal mesh of adjacent vertices of the point cloud connected by line segments. The polygonal mesh can be defined as various types such as triangular, quadrilateral, and pentagonal meshes. As described above, the relationships between the polygons and adjacent polygons in the mesh model can be used to extract features of the tooth boundary, such as curvature, minimum curvature, edge and spatial relationships.

[0121] Reference Figure 5 Region 501 of the oral cavity image 500 can be composed of a triangular mesh, which is generated by connecting multiple vertices that constitute the point cloud with adjacent vertices using lines.

[0122] Figure 5 and Figure 8 This is a reference diagram used to describe the separation of teeth and gums and the segmentation of tooth groups using curvature distribution. The principal curvatures at a given point P on the surface can represent the degree to which the surface bends in different directions from that given point P. Specifically, a normal vector can be defined at each point P on the three-dimensional surface, and the plane that includes the normal vector at point P is called the normal plane, where all said normal planes have a tangent normal vector on the surface at point P.

[0123] When the surface near point P is cut into different normal vector planes, the curvature of the curve formed by each section can have different values. Among these curvatures, the principal curvatures k1 and k2 are inherently characteristic.

[0124] The principal curvatures k1 and k2 at point P can be defined as follows.

[0125] k1 represents the value with the largest absolute value of curvature among the countless cross sections formed by the normal vector plane and the curve, where the normal vector plane is the plane that includes the normal vector of any point P on the surface.

[0126] k2 represents the curvature evaluated on a normal vector plane orthogonal to the normal vector plane that has been evaluated k1.

[0127] Each principal curvature corresponds to 1 / curvature radius on each normal vector plane.

[0128] Figure 8 According to one embodiment, the curvature distribution corresponding to an oral cavity image is represented by a map.

[0129] Reference Figure 8 Using the principal curvatures k1 and k2 evaluated at each vertex of the mesh structure constituting the oral cavity image, the distribution of small values ​​(min(k1, k2)) in k1 and k2 is represented as a map. For example, the data processing device 100 can represent this distribution as a map. Figure 8 The diagram shows that the parts with min(k1, k2) values ​​less than -2 are cut and divided into fragments to separate them into teeth and gums, and the tooth group is further separated into multiple tooth blocks.

[0130] Figure 6 This is a reference diagram used to illustrate a method for dividing an oral cavity image into groups of teeth and gingiva according to an embodiment.

[0131] Reference Figure 6 Since the oral image 500 obtained by the oral scanner includes tooth blocks 510 and gingiva 520, the data processing device 100 can segment the oral image 500 according to the curvature distribution, thereby separating the tooth blocks 510 from the gingiva 520. The data processing device 100 can determine an appropriate curvature threshold value for the boundary that enables the separation of the tooth blocks 510 and the gingiva 520, and separate the tooth blocks 510 and the gingiva 520 by separating portions having curvature values ​​smaller than the determined curvature threshold value.

[0132] The data processing device 100 can separate tooth clusters and gingiva according to a predetermined curvature value, and then use the data of the region corresponding to the tooth clusters for subsequent processing.

[0133] Figure 7 This is a reference diagram illustrating a method for dividing a group of teeth into multiple tooth blocks according to an embodiment.

[0134] Reference Figure 7 In the oral cavity image 500, a group of teeth 510 separated from the gingiva 520 can be separated into multiple tooth blocks according to curvature. When the group of teeth 510 is separated into multiple tooth blocks, not only can each tooth included in the group of teeth 510 be divided into tooth blocks, but a single tooth can also be separated into multiple tooth blocks according to curvature. The data processing device 100 can determine an appropriate curvature threshold value for the boundaries that can separate each tooth block in the group of teeth 510, and separate the group of teeth 510 into multiple tooth blocks by separating portions having curvature values ​​smaller than the determined curvature threshold value. For example, refer to Figure 7 Tooth 511 can be divided into 5 tooth blocks, namely C1, C2, C3, C4, and C5. Therefore, tooth block 510 can be separated into 30 to 50 tooth blocks.

[0135] According to one embodiment, the data processing device 100 can determine the same curvature threshold value for separating tooth blocks and gingiva in an oral cavity image as well as the same curvature threshold value for separating multiple tooth blocks in a tooth block, so that multiple tooth blocks can be separated from the tooth block 510 at one time.

[0136] According to another embodiment, the data processing device 100 can determine a curvature threshold value for separating tooth blocks and gingiva in an oral image as a first value, and a curvature threshold value for separating tooth blocks into multiple tooth blocks in a tooth block as a second value. First, tooth blocks and gingiva are separated in an oral image using the first value of the curvature threshold value, and then multiple tooth blocks are separated in a tooth block using the second value of the curvature threshold value.

[0137] Figure 9 This is a reference diagram illustrating a method for aligning a dental model template with a group of teeth in an oral cavity image according to an embodiment.

[0138] The tooth model template 200 represents 3D tooth model data showing the ideal tooth arrangement. The tooth model template 200 contains tooth data where each template tooth has an ideal shape and an ideal arrangement, and each template tooth in the tooth model template 200 is assigned a tooth number. The tooth model template 200 may include the shape data of each template tooth, the position data of each tooth, and the tooth number of each tooth. (See reference...) Figure 9 For example, the tooth model template 200 consists of 14 teeth, each tooth is numbered starting from the left molar and numbered sequentially from number 1 to number 14.

[0139] The data processing device 100 can align the tooth model template 200 to the tooth block 510. The tooth model template 200 has position information based on its own coordinate system, and the tooth block 510 also has position information based on its own coordinate system determined from oral images acquired by an oral scanner. Aligning the tooth model template 200 to the tooth block 510 means positioning the tooth model template 200 at the corresponding position within the tooth block 510 using the position information of both the tooth block 510 and the tooth model template 200. When the data processing device 100 aligns the tooth model template 200 to the tooth block 510, various alignment algorithms can be used, such as the known Iterative Closest Point (ICP) algorithm. ICP is an algorithm that minimizes the gap between two point clouds and reconstructs a 2D or 3D surface from different scan data. The ICP algorithm fixes a point cloud, referred to as the reference, and transforms a point cloud, referred to as the source point cloud, to best match the reference. The ICP algorithm aligns 3D models by iteratively modifying the deformation (a combination of translation and rotation) required to minimize the error metric representing the distance from the source point cloud to the reference. Besides ICP, various other alignment algorithms can be used, such as the Kabsch algorithm.

[0140] When the data processing device 100 aligns the tooth model template 200 to the tooth block 510 extracted from the oral cavity image 500, when using the Iterative Closest Point (ICP) algorithm, the point cloud corresponding to the tooth block 510 can be used as a reference, and the point cloud corresponding to the tooth model template 200 can be used as the source point cloud.

[0141] The data processing device 100 locates the tooth in the tooth model template 200 that has the shape closest to the first tooth of the tooth group 510, thereby determining that tooth number 1 in the tooth model template 200 is the tooth with the shape closest to the first tooth of the tooth group 510. As described above, by locating and positioning the tooth closest to each tooth of the tooth group 510 in the tooth model template 200, and by aligning the tooth model template 200 to the tooth group 510, alignment data 900 between the tooth group and the tooth model template can be obtained. (Refer to...) Figure 9 The alignment data 900 shows that the tooth model template 200 is aligned to the tooth block 510 in an overlay manner. Therefore, the tooth block 510 is displayed as a solid line and the tooth model template 200 is displayed as a dashed line in the alignment data 900.

[0142] Figure 10This is a reference diagram illustrating a method for obtaining the mapping relationship between each tooth block of a tooth group and each template tooth according to an embodiment.

[0143] According to one embodiment, the data processing device 100 can use the orientation conditions (normal conditions) of template teeth and tooth blocks to identify template teeth corresponding to tooth blocks, thereby obtaining the mapping relationship between each tooth block of a tooth group and each template tooth. The orientation conditions can represent the conditions for identifying tooth blocks corresponding to template teeth using the normal vectors of the template teeth and the tooth blocks. More specifically, the data processing device 100 can use the angle between the normal vector at the vertex of each template tooth and the normal vector at the intersection point of the tooth block in the oral image to identify whether the template teeth and tooth blocks correspond. When the angle between the normal vector at the vertex of each template tooth and the normal vector at the intersection point of the tooth block in the oral image is less than a critical angle, the data processing device 100 identifies that the template teeth and tooth blocks correspond; when the angle is above the critical angle, it can identify that the template teeth and tooth blocks do not correspond.

[0144] According to one embodiment, in order to obtain the mapping relationship between each tooth block of a tooth group and each template tooth pair, the data processing device 100 can use the distance conditions and orientation conditions between the template teeth and the tooth blocks to identify the template teeth corresponding to the tooth blocks. According to one embodiment, the data processing device 100 can identify tooth blocks that are located within a predetermined distance at the vertices of each template tooth and satisfy the orientation conditions described above as the corresponding tooth blocks.

[0145] The data processing device 100 can utilize, for example, all vertices of the template teeth. Figure 10 The orientation conditions or orientation conditions and distance conditions are used to identify the template teeth corresponding to the tooth blocks, thereby obtaining the mapping relationship between each tooth block of the tooth group and all template teeth of the tooth model template.

[0146] Below, refer to Figures 11 to 15 This section explains in detail the method of obtaining the mapping relationship between each tooth block of the tooth group and each template tooth of the tooth model template using orientation and distance conditions.

[0147] Figure 11 This diagram illustrates a portion of the data aligning a tooth group block and a tooth model template according to one embodiment. To illustrate the method for obtaining the mapping relationship between the template teeth and the tooth group block, some regions of the alignment data are considered, including... Figure 11 The tooth template model shown includes template teeth T1, T2, T3 and tooth blocks C1, C2, C3, C4, C5, C6, C7.

[0148] Figure 11The template tooth shown is illustrated as including a root and a crown, the crown being the portion of the tooth exposed above the gum line. However, template teeth used for alignment, in addition to including... Figure 11 In addition to the root form shown, forms may also include the crown portion exposed on the gum line, excluding the root. (See reference...) Figure 11 Since the portion used for alignment corresponds to the crown of the template tooth, the root portion of the template tooth is likely to act as noise during the alignment process. Therefore, using a template tooth that includes only the crown and not the root can reduce the likelihood of alignment errors caused by the root portion.

[0149] Figure 12 This is a reference diagram illustrating a method for obtaining the mapping relationship between each tooth block and each template tooth of a tooth group block using orientation and distance conditions according to an embodiment.

[0150] Reference Figure 12 This paper illustrates a method for obtaining a mapping relationship using template tooth T1 and tooth block C1, based on an example.

[0151] For all vertices of the polygonal mesh structure constituting the template tooth T1, the data processing device 100 can find the intersection points where the normal vector N at each vertex intersects with the polygonal mesh structure constituting the tooth block. Furthermore, the angle formed by the normal vector at the intersection point of the found tooth block mesh structure and the normal vector at the vertex of the template tooth T1 can be determined. If the angle formed by the normal vector at a vertex of the tooth template and the normal vector at the tooth block is less than a critical value, then the vertex portion of the tooth template can be determined to represent a mapping relationship corresponding to the found tooth block. By performing the above-described operation on all vertices of the polygonal mesh structure constituting the template tooth T1, the data processing device 100 can identify tooth blocks that have a mapping relationship with all vertices of the template tooth T1. Figure 12 The image shows some of the vertices of the polygonal mesh structure that makes up the template tooth T1, namely V1 to V16.

[0152] Referring to part A containing vertex V7 that constitutes template tooth T1, the method of obtaining the mapping relationship using orientation and distance conditions is explained in detail.

[0153] The data processing device 100 can find the intersection point CR7 where the normal vector N1 at the vertex V7 of the polygonal mesh structure constituting the template tooth T1 intersects the mesh surface constituting the tooth block C1 in a first direction. The intersection point CR7 where the normal vector N1 intersects the mesh surface constituting the tooth block C1 can be a vertex of the mesh structure constituting the tooth block C1 or a polygonal surface of the mesh structure constituting the tooth block C1.

[0154] The data processing device 100 can acquire the normal vector N2 at the intersection point CR7 where the normal vector N1 intersects with the mesh surface constituting the tooth block C1. If the intersection point CR7 is located at a vertex of the tooth block C1, then the normal vector N2 will be the normal vector at the vertex CR7; if the intersection point CR7 is located on the triangular surface of the tooth block C1, then the normal vector N2 will be the normal vector at the triangular surface.

[0155] The data processing device 100 identifies the angle formed by normal vectors N1 and N2. When the angle is less than a critical value, it can determine that vertex V7 of the template tooth T1 is mapped to tooth block C1. Therefore, the data processing device 100 can obtain a mapping relationship such as (V7:C1) for vertex V7 of the template tooth T1. For example, the critical value for determining whether the angle is mapped can be determined based on at least one of the similarity information between the template tooth and the actual scanned tooth shape and the reliability (accuracy) of the alignment algorithm. The critical value for determining whether the angle is mapped can be determined in various ways considering the above conditions, for example, it can be determined in the range of 20 degrees to 45 degrees. For example, when the critical value for determining whether the angle is mapped is determined to be 30 degrees, if the angle is less than 30 degrees, it can be determined that the template tooth is mapped to the corresponding tooth block. And if the angle is greater than 30 degrees, it can be determined that the template tooth is not mapped to the corresponding tooth block.

[0156] According to one embodiment, when the intersection point of the normal vector N at the vertex of the polygonal mesh structure constituting the template tooth T1 and the polygonal mesh structure constituting the tooth block is found, the data processing device 100 can find the intersection point not only in a first direction but also in a second direction. For example, from the perspective of region B, the surface of the tooth block C1 is located inside the vertices V11 to V16 of the mesh constituting the template tooth T1. Therefore, for region B, the data processing device 100 can find the intersection point of the normal vector at the vertex of the template tooth and the mesh constituting the tooth block C1 in the second direction.

[0157] According to one embodiment, when acquiring the mapping relationship between the template tooth and the tooth block, the data processing device 100 can utilize not only the orientation condition using the normal vector as described above, but also the distance condition. That is, when the intersection point where the normal vector at the vertex of the template tooth T1 intersects with the mesh surface of the tooth block C1 is within a preset distance threshold, the data processing device can determine whether the orientation condition is met. If the intersection point is not within the preset distance threshold, it can be excluded from the mapping relationship without determining whether the orientation condition is met. The distance threshold can be determined based on at least one of the similarity information between the template tooth and the actual scanned tooth shape and the reliability (accuracy) of the alignment algorithm. The distance threshold can be determined in various ways considering the above conditions. For example, when the data processing device 100 determines the distance threshold to be 0.5 mm, the data processing device 100 can find intersection points within 0.5 mm of the vertex of the template tooth T1. The data processing device 100 can exclude intersection points not within 0.5 mm of the vertex of the template tooth T1 from the mapping relationship, even if the intersection point meets the orientation condition. As mentioned above, 0.5mm is one example, and the distance threshold can be determined in various ways based on experience or experimental values. This indicates that even if the vertices of the template tooth T1 and a portion of the tooth block have similar orientations, these tooth block portions will not be considered in the mapping relationship if the distance is too great.

[0158] Figure 13 This is a reference diagram illustrating a method for obtaining the mapping relationship between each tooth block and each template tooth of a tooth group block using orientation and distance conditions according to an embodiment.

[0159] Reference Figure 13 In an oral cavity image, some teeth can be separated into tooth blocks C2, C3, and C4. In this case, illustrate the mapping relationship between the template tooth T2 and the tooth blocks C2, C3, and C4.

[0160] Although the mesh surface of template tooth T2 can be constructed with more vertices, in Figure 13 For ease of explanation, vertices V1 to V14 of the template tooth T2 are shown. The data processing device 100 organizes the mapping relationship based on the angle between the normal vector at each vertex of the template tooth T2 and the normal vector at the intersection point of the mesh surfaces of tooth blocks C2, C3, and C4, and can obtain mapping relationships such as (V1:C2), (V2:C2), (V3:C2), (V4:C2), (V5:C2), (V6:C2), (V7:C3), (V8:C3), (V9:C3), (V10:C4), (V11:C4), (V12:C4), (V13:C4), and (V14:C4).

[0161] By organizing the mapping relationship between each vertex of the template tooth T2 and the tooth blocks, it can be confirmed that tooth blocks C2, C3, and C4 are mapped to the template tooth T2 respectively.

[0162] Figure 14 This is a reference diagram illustrating, according to one embodiment, a case where a tooth block is repeatedly mapped onto multiple template teeth.

[0163] Certain tooth blocks in an oral cavity image may have a mapping relationship to multiple template teeth. The data processing device 100 can exclude tooth blocks that are repeatedly mapped to multiple template teeth from the mapping relationship.

[0164] Reference Figure 14 This indicates that tooth block C4 is mapped to both template tooth T2 and template tooth T3. In other words, when the angle between the normal vector at vertex V1 of template tooth T2 and the normal vector at the intersection point CR1 of tooth block C4 is less than a critical value, the data processing device 100 can determine the mapping between template tooth T2 and tooth block C4. Furthermore, when the angle between the normal vector at vertex V2 of template tooth T3 and the normal vector at the intersection point CR2 of tooth block C4 is less than a critical value, the data processing device 100 can determine the mapping between template tooth T3 and tooth block C4.

[0165] As described above, in the case of repeated mapping of a tooth block C4 to both template tooth T2 and template tooth T3, the data processing device 100 can exclude the tooth block C4 that is repeatedly mapped to multiple template teeth from the mapping relationship.

[0166] Optionally, the data processing device 100 may not exclude tooth blocks that are repeatedly mapped to multiple template teeth, but instead map them to template teeth that are considered more similar.

[0167] The aforementioned similarity can be evaluated by appropriately combining mapping frequency, region, angle, and distance.

[0168] Figure 15 This is a reference diagram illustrating a method for individualizing teeth in an oral image by utilizing the mapping relationship between tooth blocks and template teeth, according to an embodiment.

[0169] Reference Figure 15The data processing device 100 can utilize the orientation and distance conditions described above to obtain mapping relationships such as C1:T1, C2:T2, C3:T2, C4:T2, T3, C5:T3, C6:T3, and C7:T3 for tooth blocks C1, C2, C3, C4, C5, C6, and C7 in an oral cavity image. Alternatively, the data processing device 100 can use the reverse of this mapping relationship, taking a template tooth as a reference, and individualize the teeth corresponding to each template tooth by collecting more than one tooth block mapped to each template tooth. That is, by organizing the above mapping relationships based on template teeth, the data processing device 100 can obtain correspondences such as T1:C1, T2:C2, C3, T3:C5, C6, and C7. This indicates that template tooth T1 corresponds to tooth block C1, template tooth T2 corresponds to tooth blocks C2 and C3, and template tooth T3 corresponds to tooth blocks C5, C6, and C7. On the other hand, for tooth block C4, since it is repeatedly mapped to multiple different template teeth T2 and T3, tooth block C4 may not be used in the operation of collecting more than one tooth block mapped to each template tooth, and may be excluded. As mentioned above, the empty spaces that may appear when individualizing teeth due to the tooth blocks excluded in the collection of tooth blocks can be naturally filled by surface reconstruction, mesh hole filling, mesh reconstruction, etc.

[0170] As described above, the data processing device 100 can determine the teeth corresponding to each template tooth by collecting one or more tooth blocks corresponding to each template tooth. After determining the teeth corresponding to each template tooth, the data processing device 100 can complete the tooth model through appropriate post-processing. After determining the teeth corresponding to each template tooth, the data processing device 100 uses relevant information of the template teeth to determine the information of the corresponding teeth, such as the tooth's position, orientation, posture, or tooth number. Specifically, the data processing device 100 can transcribe the orientation information of the template teeth, such as buccal, lingual, distal, and mesial surfaces, to the corresponding scanned teeth.

[0171] A method for processing oral images according to an embodiment of the present disclosure is implemented in the form of program commands executable by various computer mechanisms and recorded in a computer-readable medium. Furthermore, embodiments of the present disclosure may use a computer-readable storage medium containing one or more programs including at least one instruction for performing the method for processing oral images.

[0172] The computer-readable storage medium may include, individually or in combination, program instructions, data files, data structures, etc. Examples of computer-readable storage media include magnetic media such as hard disks, floppy disks, and magnetic tapes; optical media such as CD-ROMs and DVDs; magneto-optical media such as floppy disks; and hardware devices such as ROMs, RAMs, and flash memory for storing and executing program instructions.

[0173] The device-readable storage medium may be provided in the form of a non-transitory storage medium. "Non-transitory storage medium" can mean a tangible device. Furthermore, "non-transitory storage medium" may include buffers for temporary data storage.

[0174] According to one embodiment, methods for processing oral images according to various embodiments disclosed in this specification can be provided by being included in a computer program product. The computer program product can be distributed in the form of a device-readable storage medium (e.g., a compact disc read-only memory, CD-ROM). Alternatively, it can be distributed (e.g., downloaded or uploaded) directly or online between two user devices (e.g., smartphones) through an app store (e.g., a game store). Specifically, the computer program product according to the disclosed embodiments may include a storage medium recording a program comprising at least one instruction to perform the methods for processing oral images according to the disclosed embodiments.

[0175] The embodiments have been described in detail above, but the scope of the present invention is not limited thereto. Various modifications and improvements made by those skilled in the art using the basic concepts of the present invention as defined in the claims are also within the scope of the present invention.

Claims

1. A method for processing oral cavity images, wherein, Includes the following steps: Acquire oral images generated by scanning teeth. Align a tooth model template, which includes multiple template teeth, with the teeth included in the oral cavity image. The oral cavity image is separated into tooth clusters and gingiva based on the curvature distribution, and each tooth within the tooth cluster is further separated into multiple tooth blocks based on the curvature distribution to obtain multiple tooth blocks. Identify each tooth block corresponding to each template tooth and collect one or more tooth blocks corresponding to each aligned template tooth to individualize the teeth in the oral image.

2. The oral cavity image processing method according to claim 1, wherein, The step of individualizing the teeth in the oral cavity image includes the following steps: By identifying tooth blocks corresponding to each aligned template tooth, the mapping relationship between each tooth block in the oral cavity image and each template tooth is obtained. Using the mapping relationship, collect one or more tooth blocks mapped to each of the template teeth.

3. The oral cavity image processing method according to claim 1, wherein, The step of aligning a tooth model template, which includes multiple template teeth, with the teeth included in the oral cavity image includes the following steps: Position the plurality of template teeth of the dental model template to the positions corresponding to the teeth included in the oral cavity image.

4. The oral cavity image processing method according to claim 1, wherein, The step of separating the teeth from the oral cavity image to obtain multiple tooth blocks includes the following steps: The teeth are separated according to the curvature distribution to separate each tooth into more than one tooth block.

5. The oral cavity image processing method according to claim 1, wherein, The step of individualizing the teeth in the oral cavity image includes the following steps: Using the orientation conditions of each template tooth and each tooth block, each tooth block corresponding to each template tooth is identified.

6. The oral cavity image processing method according to claim 5, wherein, The step of identifying each tooth block corresponding to each template tooth using the orientation conditions includes the following steps: Identify the intersection points of the normal vectors at each vertex of the 3D mesh constituting the template tooth and the 3D mesh constituting the tooth block, and When the angle between the normal vector at the intersection of the identified tooth block and the normal vector at the vertex is less than a critical value, it is determined that the tooth block corresponds to the template tooth.

7. The oral cavity image processing method according to claim 1, wherein, The step of individualizing the teeth in the oral cavity image includes the following steps: Using the distance conditions between each template tooth and each tooth block, identify each tooth block corresponding to each template tooth; The step of identifying each tooth block corresponding to each template tooth includes the following steps: Identify the intersection points of the normal vectors at each vertex of the 3D mesh constituting the template tooth and the 3D mesh constituting the tooth block, and When the distance from the vertex to the intersection is less than a critical value, it is determined that the tooth block corresponds to the template tooth.

8. The oral cavity image processing method according to claim 1, wherein, The step of individualizing the teeth in the oral cavity image includes the following steps: Using the distance and orientation conditions of each template tooth and each tooth block, identify each tooth block corresponding to each template tooth; The operation steps for identifying each tooth block corresponding to each template tooth using the distance and orientation conditions include the following steps: As the intersection points of the normal vectors at each vertex of the 3D mesh constituting the template tooth and the 3D mesh constituting the tooth block, the intersection points within a predetermined distance from the vertex are identified, and When the angle between the normal vector at the intersection of the identified tooth block and the normal vector at the vertex is less than a critical value, the tooth block is determined to correspond to the template tooth.

9. The oral cavity image processing method according to claim 1, wherein, The steps to obtain multiple tooth fragments include the following: The tooth mass and the gingiva are separated in the oral cavity image by using a first curvature threshold value for separating the boundaries of the tooth mass and the gingiva; as well as The tooth group is separated into multiple tooth blocks by using a second curvature threshold value for the boundary used to separate the individual tooth blocks in the tooth group.

10. The oral cavity image processing method according to claim 9, wherein, The second curvature threshold value may be the same as or different from the first curvature threshold value.

11. An oral cavity image processing device, in, include: Processor and memory; The processor executes one or more instructions stored in the memory to perform the following operations: Acquire oral images generated by scanning teeth. Align a tooth model template, which includes multiple template teeth, with the teeth included in the oral cavity image. The oral cavity image is separated into tooth clusters and gingiva based on the curvature distribution, and each tooth within the tooth cluster is further separated into multiple tooth blocks based on the curvature distribution to obtain multiple tooth blocks. Identify each tooth block corresponding to each template tooth and collect one or more tooth blocks corresponding to each aligned template tooth to individualize the teeth in the oral image.

12. The oral cavity image processing apparatus according to claim 11, wherein, In order to individualize the teeth in the oral cavity image, the processor executes one or more instructions stored in the memory to perform the following operations: By identifying tooth blocks corresponding to each aligned template tooth, the mapping relationship between each tooth block in the oral cavity image and each template tooth is obtained. Using the mapping relationship, collect one or more tooth blocks mapped to each of the template teeth.

13. The oral cavity image processing apparatus according to claim 11, wherein, In order to align a dental model template comprising multiple template teeth with the teeth included in the oral cavity image, the processor executes one or more instructions stored in the memory to perform the following operations: Position the plurality of template teeth of the dental model template to the positions corresponding to the teeth included in the oral cavity image.

14. The oral cavity image processing apparatus according to claim 11, wherein, In order to separate the teeth from the oral cavity image to obtain multiple tooth blocks, the processor executes one or more instructions stored in the memory to perform the following operations: The teeth are separated according to the curvature distribution to separate each tooth into more than one tooth block.

15. The oral cavity image processing apparatus according to claim 11, wherein, In order to individualize the teeth in the oral cavity image, the processor executes one or more instructions stored in the memory to perform the following operations: Using the orientation conditions of each template tooth and each tooth block, each tooth block corresponding to each template tooth is identified.

16. The oral cavity image processing apparatus according to claim 15, wherein, In order to identify each tooth block corresponding to each template tooth using the orientation conditions, the processor executes one or more instructions stored in the memory to perform the following operations: Identify the intersection points of the normal vectors at each vertex of the 3D mesh constituting the template tooth and the 3D mesh constituting the tooth block, and When the angle between the normal vector at the intersection of the identified tooth block and the normal vector at the vertex is less than a critical value, it is determined that the tooth block corresponds to the template tooth.

17. A computer-readable recording medium, wherein, A program containing at least one instruction for performing an oral cavity image processing method in a computer is recorded. The oral cavity image processing method includes the following steps: Acquire oral images generated by scanning teeth. Align a tooth model template, which includes multiple template teeth, with the teeth included in the oral cavity image. The oral cavity image is separated into tooth clusters and gingiva based on the curvature distribution, and each tooth within the tooth cluster is further separated into multiple tooth blocks based on the curvature distribution to obtain multiple tooth blocks. Identify each tooth block corresponding to each template tooth and collect one or more tooth blocks corresponding to each aligned template tooth to individualize the teeth in the oral image.

Images