CN115830287B - Tooth point cloud fusion method, device and medium based on laser mouth scanning and CBCT reconstruction - Google Patents
Tooth point cloud fusion method, device and medium based on laser mouth scanning and CBCT reconstruction Download PDFInfo
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- 238000007500 overflow downdraw method Methods 0.000 title claims abstract description 31
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Abstract
The application relates to the technical field of image processing, in particular to a tooth point cloud fusion method, equipment and medium based on laser mouth scan and CBCT reconstruction, wherein the method comprises the steps of dividing a laser mouth scan tooth model and extracting laser mouth scan crown point cloud data; dividing a CBCT tooth reconstruction model, and extracting a CBCT tooth crown point cloud and a CBCT tooth root point cloud; performing first registration on the CBCT dental crown point cloud and the laser mouth scanning dental crown point cloud based on PCA; performing second registration on the CBCT dental crown point cloud and the laser mouth-scan dental crown point cloud after the first registration based on an ICP and KD-tree algorithm; performing interactive registration on the CBCT dental crown point cloud after the second registration and the laser mouth scanning dental crown point cloud; and fusing the laser mouth scanning dental crown point cloud data with the CBCT dental root point cloud data, and correcting the fusion result. The method effectively solves the problems of low accuracy and low efficiency of fusion results of the laser scanning tooth model and the CBCT tooth reconstruction model, has the advantages of high efficiency, accuracy, high robustness and the like, and effectively improves the registration and fusion effects of the laser scanning tooth model and the CBCT tooth reconstruction model.
Description
Technical Field
The application relates to the technical field of image processing, in particular to a tooth point cloud fusion method, device and medium based on laser mouth scanning and CBCT reconstruction.
Background
According to oral disease surveys reports, nearly 90% of people worldwide have a degree of oral problems, with many people requiring dental treatment.
However, the CBCT tooth reconstruction model and the laser scanning tooth model have large differences in spatial positions and shapes, and the conventional point cloud processing algorithm has low robustness and accuracy and relies on manual priori information, so that an efficient and accurate registration and fusion method is difficult to form. For example, a research institute of Shandong university proposes a semi-automatic laser scanning and CBCT tooth fusion method based on ICP (Iterative Closest Point) algorithm, wherein feature point pairs corresponding to positions are manually selected on two point cloud models, a transformation matrix is calculated through ICP algorithm, so that registration of the CBCT teeth and the laser scanning teeth is realized, then crown point clouds of the laser scanning teeth are intercepted through Boolean operation, and the crown point clouds and the CBCT tooth point clouds are fused to obtain a final three-dimensional tooth model. Although the method can effectively realize the point cloud fusion of the laser scanning tooth model and the CBCT tooth reconstruction model, the obtained fusion result has lower accuracy and lower efficiency and is not suitable for processing large-scale medical data.
For another example, the publication number of the comparison document 1 is: the CN108629839A solves the technical problem that the traditional method can not well acquire the full-tooth model in the tooth occlusion state by utilizing the oral cavity CT image in the tooth occlusion state, thereby providing a reliable basis for a doctor to formulate orthodontic treatment and occlusion reconstruction schemes. However, in this solution, the user is required to manually segment the oral CT, which is inefficient, and to interactively operate, three points are selected to determine a segment plane, intercept the crown portion of the tooth, which is inefficient. The accuracy of the tooth fusion result is lower and the efficiency is lower.
For another example, reference 2 discloses No.: CN105447908B performs preliminary registration according to the feature point pairs, based on the dentition model generation method of the oral scan data and CBCT data; calculating a model point cloud error after preliminary registration and setting accurate registration parameters by combining medical parameter requirements; performing accurate registration by adopting an optimized ICP algorithm; carrying out interactive pick-up points on the registered intraoral scanning model, and adopting the pick-up points to create a B-spline curve as a cutting curve to extract a dental crown part; adopting a point fitting plane on a clipping curve as a characteristic plane of the clipping curve, biasing the clipping curve to a negative normal direction of the characteristic plane by a certain distance, and clipping CBCT data by using the biasing curve to obtain a tooth root model; smooth splicing is carried out on the data of the dental crowns and the dental roots by adopting a filling and splicing method meeting continuous conditions; outputting the spliced complete tooth or dentition model. However, in the patent scheme, the CBCT tooth point cloud is directly registered to the laser mouth sweeping point cloud, the influence of mismatching of the CBCT tooth point cloud and the laser mouth sweeping gum point cloud is not considered, the matching effect is seriously influenced, and secondly, the scheme is based on the fact that the CBCT tooth point cloud realized by manually picking up characteristic point pairs and an ICP algorithm is registered to the laser mouth sweeping point cloud, the efficiency is low, and the registering effect is poor.
Therefore, there is a need for further improvements in dental point cloud fusion methods, apparatus and media based on laser oroscan and CBCT reconstruction to address the above-described problems.
Disclosure of Invention
The purpose of the application is that: the method, the device and the medium for fusing the tooth point cloud based on the laser mouth scanning and the CBCT reconstruction are provided for solving and overcoming the defects of the prior art and application, the problems that the fusion result of a laser scanning tooth model and a CBCT tooth reconstruction model is low in accuracy and low in efficiency are effectively solved, and the method, the device and the medium have the advantages of being efficient, accurate, high in robustness and the like, and the registration and fusion effects of the laser scanning tooth model and the CBCT tooth reconstruction model can be effectively improved.
The application aims to accomplish the following technical scheme, and discloses a tooth point cloud fusion method based on laser mouth scanning and CBCT reconstruction, which is characterized by comprising the following steps of: the method comprises the following steps:
s1: dividing a laser mouth-sweeping tooth model, and extracting laser mouth-sweeping crown point clouds;
s2: dividing a CBCT tooth reconstruction model, and extracting a CBCT tooth crown point cloud and a CBCT tooth root point cloud;
s3: performing point cloud optimization on the CBCT root point cloud obtained in the step S2;
s4: performing first registration on the CBCT dental crown point cloud in the S2 and the laser mouth scanning dental crown point cloud in the S1 based on PCA to obtain registration parameters of the CBCT dental crown point cloud and the PCA of the first registration;
s5: performing second registration on the CBCT crown point cloud after the first registration in the S4 and the laser mouth scanning crown point cloud in the S1 based on an ICP algorithm and a KD-tree algorithm to obtain registration parameters of the CBCT crown point cloud and ICP of the second registration;
s6: performing interactive registration on the CBCT dental crown point cloud subjected to the second registration in the step S5 and the laser mouth scanning dental crown point cloud in the step S1 to obtain a final registered CBCT dental crown point cloud and three-point registration parameters;
s7: based on PCA registration parameters, registering the CBCT single-tooth point cloud to a single-tooth laser mouth-sweeping crown point cloud, calculating the distance between each tooth center point cloud of the registered CBCT and the center coordinates of the laser mouth-sweeping crown point cloud to obtain a nearest neighbor CBCT single-tooth point cloud, and registering the nearest neighbor CBCT single-tooth point cloud to the laser mouth-sweeping single-crown point cloud by utilizing an ICP algorithm; searching the distance between the nearest neighbor CBCT single tooth point cloud and the nearest neighbor point cloud of the laser single tooth crown point cloud by using a Kd-tree algorithm, generating a CBCT single tooth root point cloud according to the distance, and fusing the CBCT tooth root point cloud and the laser mouth scanning tooth crown point cloud by using a poisson algorithm;
s8: and (3) registering the fusion result in the step (S7) with the laser mouth scanning crown point cloud in the step (S1) to obtain a complete tooth model after position correction.
Preferably, the step S1 specifically includes: different CBCT data are collected, and the CBCT fracture slices are marked, so that the outline marking information of each tooth and the upper and lower alveolar bones and the category information of each tooth are obtained.
Preferably, the step S1 specifically includes: the laser mouth sweep tooth model comprises point cloud data of the dental crowns and gums of the upper and lower jaws, and the dental crown point cloud data of each tooth of the upper jaw and the lower jaw of the laser mouth sweep tooth model is extracted through a tooth point cloud segmentation algorithm.
Preferably, the step S2 specifically includes:
s21, counting the highest value and the lowest value of the three-dimensional coordinate value of the point cloud of the teeth on the Z axis for the maxillary teeth, classifying the point cloud data of the maxillary teeth into a crown and a tooth root by using a threshold value method, wherein the point cloud with the Z axis coordinate value smaller than or equal to the threshold value is the crown or the tooth root, and storing the point cloud as an STL file by using a Poisson curved surface reconstruction algorithm;
s22, counting the highest value and the lowest value of the three-dimensional coordinate value of the point cloud of the teeth on the Z axis for the mandibular teeth, classifying the point cloud data of the mandibular teeth into two parts of a crown and a root by using a threshold method, wherein the point cloud with the Z axis coordinate value being greater than or equal to the threshold value is the crown or the root, and storing the point cloud as an STL file by using a Poisson curved surface reconstruction algorithm.
Preferably, the step S3 specifically includes: and analyzing the connection relation between the point clouds by using a traversal and search algorithm of the graph model to obtain a plurality of point cloud communication areas, and obtaining an optimized root point cloud result by counting the number of the point clouds of each communication area and reserving the point cloud communication area with the largest number of the point clouds.
Preferably, the S4 specifically includes:
s41, calculating CBCT dental crown point cloud V C ＝{[x 1 ,y 1 ,z 1 ],…,[x m ,y m ,z m ]Mean v of } C ＝[x c ,y c ,z c ]Performing mean value removal processing on the point cloud data;
s42, calculating a covariance matrix through an SVD singular value decomposition methodThe characteristic values and the characteristic vectors of the (a) are sequenced from big to small, and the position of the characteristic vectors is adjusted according to the sequence to obtain a characteristic vector matrix P C ；
S43, scanning laser mouth with crown point cloud V by using PCA method L Calculating to obtain corresponding mean value v L ＝[x L ,y L ,z L ]And a feature vector matrix P L ；
S44, for CBCT dental crown point cloud V C Cloud V of laser mouth scanning dental crown point L The first registration is performed as follows:
v T ＝v L -v L R C
V T ＝V C R C +v T
wherein R is C For transfer matrix, v T For displacing the bias term, V T And (3) registering CBCT dental crown point cloud to the laser mouth-scanned dental crown model for the first time, wherein the registration of the upper and lower jaw dental crowns is respectively carried out in the registration process.
Preferably, the step S5 specifically includes:
s51, registering the CBCT dental crown point cloud registered for the first time onto the laser mouth-scan dental crown point cloud by utilizing an ICP algorithm to obtain a point cloud transfer matrix M 1 CBCT dental crown point cloud V registered with ICP algorithm for the first time C1 ；
S52, constructing laser mouth-sweeping dental crown point cloud V by utilizing KD-tree algorithm L Extracting CBCT dental crown point cloud V from nearest neighbor search model of (1) C1 Dental crown V is swept with laser to well L Is used for filtering CBCT dental crown point cloud V C1 Intermediate redundancy and false crown point cloud to obtain CBCT crown point cloud
S53, utilizing ICP algorithm to enable CBCT dental crown point cloudRegistered to laser oral scan crown V L Obtaining a point cloud transfer matrix M 2 And CBCT dental crown point cloud V after secondary registration C2 。
Preferably, the step S6 specifically includes:
s61, adopting interactive three-point registration correction registration results to respectively register CBCT dental crown point cloud V in the second time C2 And laser mouth scanning dental crown point cloud V L Respectively selecting three groups of corresponding point clouds
V s ＝{[x s1 ,y s1 ,z s1 ],[x s2 ,y s2 ,z t2 ],[x s3 ,y s3 ,z s3 ]And
V t ＝{[x t1 ,y t1 ,z t1 ],[x t2 ,y t2 ,z t2 ],[x t3 ,y t3 ,z t3 ]}；
wherein V is s Is CBCT dental crown point cloud V C2 Three key point coordinates of (2)，V t Dental crown point cloud V for laser mouth sweeping L Is defined by the three key point coordinates;
s62, calculating V by utilizing ICP algorithm s Mapping to V t Is a transfer matrix M of (2) 3 ；
S63, based on a transfer matrix M 3 CBCT dental crown point cloud V with second registration C2 Laser mouth scan crown point cloud V registered into S1 L Obtaining CBCT dental crown point cloud and laser dental crown point cloud V L Results V of three registrations of (2) C3 。
Preferably, the step S7 specifically includes:
s71, based on PCA registration parameters, CBCT single tooth point cloud { V } 1 ,V 2 ,…,V n Registration to single tooth laser mouth scan crown point cloud { U } 1 ,U 2 ,…,U m Obtaining a registered CBCT single tooth point cloud { Vp } 1 ,Vp 2 ,…,Vp n }；
S72, scanning single-tooth crown point cloud U for laser mouth i First, the point cloud center coordinates [ x ] of the model are calculated i ,y i ,z i ]Then calculate the coordinate value and CBCT single tooth point cloud { Vp 1 ,Vp 2 ,…,Vp n The distance between the central point cloud coordinates of each tooth is used for obtaining the space position and the single tooth crown point cloud U of the laser mouth sweep i Nearest neighbor CBCT single tooth point cloud Vp j ；
S73, utilizing ICP algorithm to enable CBCT single tooth point cloud Vp j Registering to laser mouth scanning single tooth crown point cloud U i Construction of laser mouth scanning single-tooth crown point cloud U by using Kd-tree algorithm i Searching a search model of CBCT single tooth point cloud Vp j Each point cloud of the laser single-crown point cloud U i Is considered to be a CBCT single tooth point cloud Vp if the distance is greater than 0.5 j Finally, reserving all the root point clouds to generate CBCT single root point cloud Vr j ；
S74, performing CBCT (Console computed tomography) on single-root point cloud Vr j Point cloud and laser mouth scanning single dental crown model U i Combining the point clouds of the laser mouth scan, and reconstructing a curved surface for generating a fused point cloud by using the Poisson surfaceFusion point cloud Uc of dental crown point cloud and CBCT dental root point cloud i ；
S75, counting point cloud communication areas of the fusion result, reserving a maximum communication area, filtering discrete point cloud data, and obtaining optimized fusion point cloud
S76, utilizing ICP algorithm to fuse the resultsRegistering to laser mouth scan single crown model U i Obtaining the registered fusion point cloud Up i And saved as a ". Stl" file.
The application also provides an electronic device, comprising: one or more processors; a storage means for storing one or more programs; the one or more programs, when executed by the one or more processors, cause the one or more processors to implement a dental point cloud fusion method based on laser mouth scan and CBCT reconstruction as provided by the present application.
The application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a computer processor to realize the tooth point cloud fusion method based on laser mouth scanning and CBCT reconstruction.
Compared with the prior art, the application has at least the following obvious advantages and effects:
according to the application, the CBCT tooth reconstruction model is registered into the laser scanning tooth model, and then the crown model of the laser scanning tooth and the tooth root of the CBCT tooth reconstruction model are fused to obtain a complete tooth model, so that the method has the advantages of high efficiency, accuracy, high robustness and the like, and the registration and fusion effects of the crown model and the tooth root of the laser scanning tooth can be effectively improved.
Drawings
Fig. 1 is an overall flow chart in the present application.
Fig. 2 is a graph showing the effect of the tooth root and crown registration and fusion in the present application.
Fig. 3 is a flow chart of CBCT tooth point cloud processing in the present application.
Fig. 4 is a schematic structural view of an electronic device in the present application.
Reference numerals in the present application:
processor 101, memory device 102, output device 103, output device 104, and bus 105.
Detailed Description
The application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present application are shown in the drawings.
Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart depicts operations (or steps) as a sequential process, many of the operations (or steps) can be performed in parallel, concurrently, or at the same time. Furthermore, the order of the operations may be rearranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figures. The processes may correspond to methods, functions, procedures, subroutines, and the like.
The following embodiments and alternatives thereof are used to describe the tooth point cloud fusion method, device and medium based on laser mouth scan and CBCT reconstruction in detail.
Fig. 1 is an overall flowchart of a tooth point cloud fusion method based on laser mouth scan and CBCT reconstruction provided in an embodiment of the present application. The embodiment of the application can be applied to the situation of a tooth point cloud fusion method of laser mouth scanning and CBCT reconstruction. The method can be performed by a tooth point cloud fusion device based on laser mouth scan and CBCT reconstruction, and the device can be realized in a software and/or hardware mode and integrated on any electronic device with a network communication function. As shown in fig. 1, the tooth point cloud fusion method for laser mouth scanning and CBCT reconstruction provided in the embodiment of the present application may include the following steps:
s1: and (5) cutting the laser mouth-sweeping tooth model, and extracting laser mouth-sweeping crown point cloud data. The step S1 specifically comprises the following steps: the laser mouth sweep tooth model comprises point cloud data of the dental crowns and gums of the upper and lower jaws, and the dental crown point cloud data of each tooth of the upper jaw and the lower jaw of the laser mouth sweep tooth model is extracted through a tooth point cloud segmentation algorithm.
In the embodiment of the application, the laser mouth-sweeping tooth model comprises point cloud data of the crowns and the gums of the upper and lower jaws, the format is 'stl', the point cloud data of the crowns of each tooth of the upper jaw and the lower jaw of the laser mouth-sweeping tooth model is extracted through interactive operation software or a tooth point cloud segmentation algorithm, and the point cloud coordinate data of the crowns of the upper jaw and the lower jaw are stored, namely, the crown point cloud of each tooth s is stored as a 'stl' file and is used as a registration target of the CBCT teeth. In order to reduce the calculation amount and avoid the error condition of the fusion of the maxillary dental crowns and the mandibular dental crowns, the maxillary and mandibular dental crown point clouds are respectively stored as a 'xx_uppercrown. Stl' file and a 'xx_lowercrown. Stl' file, wherein 'xx' is a tooth ID number, so that the maxillary and mandibular differences of the dental crown point clouds are distinguished.
S2: and (3) segmenting the CBCT tooth reconstruction model, and extracting CBCT dental crown point cloud and CBCT dental root point cloud. The step S2 specifically comprises the following steps:
s21, counting the highest value and the lowest value of the three-dimensional coordinate value of the point cloud of the teeth on the Z axis for the maxillary teeth, classifying the point cloud data of the maxillary teeth into a crown and a tooth root by using a threshold method, wherein the point cloud with the Z axis coordinate value smaller than or equal to the point cloud is the crown or the tooth root, and storing the point cloud as an STL file by using a Poisson curved surface reconstruction algorithm;
s22, counting the highest value and the lowest value of the three-dimensional coordinate values of the point cloud of the teeth on the Z axis for the mandibular teeth, classifying the point cloud data of the mandibular teeth into two parts of a crown and a root by using a threshold method, wherein the point cloud with the Z axis coordinate value being greater than or equal to the crown is the root or else is the crown, and storing the point cloud as an STL file by using a Poisson curved surface reconstruction algorithm.
In an embodiment of the present application, the CBCT dental reconstruction model contains crown and root point cloud data for each individual toothHowever, the point cloud data of the crown is not accurate, and thus it is necessary to fuse the root of the CBCT tooth with the crown of the laser dental brush tooth. In order to effectively register a CBCT tooth model into a laser mouth scan tooth model, it is necessary to separate the crown and the point cloud of teeth of the tooth model. For the maxillary teeth V i ＝{[x 1 ，y 1 ，z 1 ]，...，[x m ，y m ，z m ]Firstly, counting the highest value Z of three-dimensional coordinate values of the tooth point cloud in the Z axis max And a minimum value z min Then calculate the threshold z th ＝z min +0.2(z max -z min ) The maxillary teeth V are treated by a thresholding method i Is classified into two parts of a dental crown and a dental root, and the Z-axis coordinate value is less than or equal to Z th The point cloud of (1) is a dental crown or else is a dental root, and is stored as an xx_UpPERRoot. Stl file; for the mandibular teeth V j ＝{[x 1 ，y 1 ，z 1 ]，...，[x n ，y n ，z n ]Firstly, counting the highest value Z of three-dimensional coordinate values of the tooth point cloud in the Z axis max And a minimum value z min Then calculate the threshold z th ＝z max -0.2(z max -z min ) The maxillary teeth V are treated by a thresholding method j Is classified into two parts of a dental crown and a dental root, and the Z-axis coordinate value is greater than or equal to Z th The point cloud of (1) is the crown or else the root, and is stored as an xx_lowerroot.
S3: and (3) performing point cloud optimization on the CBCT root point cloud obtained in the step (S2). In the embodiment of the present application, step S3 specifically includes: and analyzing the connection relation between the point clouds by using a traversal and search algorithm of the graph model to obtain a plurality of point cloud communication areas, and obtaining an optimized root point cloud result by counting the number of the point clouds of each communication area and reserving the point cloud communication area with the largest number of the point clouds.
In the embodiment of the application, as the shape of the tooth root is complex and the robustness of the threshold method is low, the CBCT tooth root point cloud extracted by the point cloud coordinate threshold has the condition of excessively high false positive, and part of the point cloud is mutually separated from the tooth root main body, so that the subsequent point cloud fusion effect is seriously influenced. Aiming at the problem, the method analyzes the connection relation between point clouds based on the traversal and search algorithm of the graph model to obtain a plurality of point cloud communication areas, and only the point cloud communication area with the largest point cloud quantity is reserved by counting the point cloud quantity of the plurality of communication areas, so that the discrete root point clouds are filtered, and an optimized root point cloud result is obtained.
S4: and (3) carrying out first registration on the CBCT dental crown point cloud in the S2 and the laser mouth scanning dental crown point cloud in the S1 based on PCA to obtain registration parameters of the CBCT dental crown point cloud and the PCA of the first registration. The CBCT tooth reconstruction model has a greater similarity to the crown point cloud of the laser mouth scan tooth model, so the two can be registered for the first time using a Principal Component Analysis (PCA) algorithm. The method comprises the following specific steps:
s41, calculating CBCT dental crown point cloud V C ＝{[x 1 ，y 1 ，z 1 ]，...，[x m ，y m ，z m ]Mean v of } C ＝[x c ，y c ，z c ]Performing mean value removal processing on the point cloud data;
s42, calculating a covariance matrix through an SVD singular value decomposition methodThe characteristic values and the characteristic vectors of the (a) are sequenced from big to small, and the position of the characteristic vectors is adjusted according to the sequence to obtain a characteristic vector matrix P C ；
S43, scanning laser mouth with crown point cloud V by using PCA method L Calculating to obtain corresponding mean value v L ＝[x L ，y L ，z L ]And a feature vector matrix P L ；
S44, for CBCT dental crown point cloud V C Cloud V of laser mouth scanning dental crown point L The first registration is performed as follows:
v T ＝v L -v L R C
V T ＝V C R C +v T
wherein R is C For transfer matrix, v T For displacing the bias term, V T For the first registration to the CBCT dental crown point cloud of the laser mouth-scan dental crown model, the registration of the upper and lower jaw dental crowns is respectively carried out in the registration process.
S5: performing second registration on the CBCT dental crown point cloud and the laser mouth-scan dental crown point cloud which are subjected to the first registration in the S4 based on an ICP algorithm and a KD-tree algorithm to obtain registration parameters of the CBCT dental crown point cloud and the ICP of the second registration; in the embodiment of the application, the PCA algorithm maps the point cloud coordinates of the CBCT dental crown reconstruction model to the laser mouth-scan dental crown model from a global angle, but the accuracy is lower, and the registration accuracy is required to be improved by using a local registration method, and the method specifically comprises the following steps:
s51, registering the CBCT dental crown point cloud registered for the first time onto the laser mouth-scan dental crown point cloud by utilizing an ICP algorithm to obtain a point cloud transfer matrix M 1 CBCT dental crown point cloud V registered with ICP algorithm for the first time C1 ；
S52, constructing laser mouth-sweeping dental crown point cloud V by utilizing KD-tree algorithm L Extracting CBCT dental crown point cloud V from nearest neighbor search model of (1) C1 Dental crown V is swept with laser to well L Is used for filtering CBCT dental crown point cloud V C1 Intermediate redundancy and false crown point cloud to obtain CBCT crown point cloud
S53, utilizing ICP algorithm to enable CBCT dental crown point cloudRegistered to laser oral scan crown V L Obtaining a point cloud transfer matrix M 2 And CBCT dental crown point cloud V after secondary registration C2 。
S6: and (3) performing interactive registration on the CBCT dental crown point cloud subjected to the second registration in the step (S5) and the laser mouth scanning dental crown point cloud in the step (S1) to obtain a final registered CBCT dental crown point cloud. In the embodiment of the application, although the steps S4 and S5 can efficiently and automatically register most of CBCT dental crown reconstruction models to the laser mouth-scan dental crown model, the possibility of registration errors exists for partial point cloud data with larger shape complexity. To improve the robustness of the method, the interactive three-point registration method is used for correcting registration errors, and the method comprises the following specific steps of
S61, adopting interactive three-point registration correction registration results to respectively register CBCT dental crown point cloud V in the second time C2 And laser mouth scanning dental crown point cloud V L Respectively selecting three groups of corresponding point clouds
V s ＝{[x s1 ,y s1 ,z s1 ],[x s2 ,y s2 ,z t2 ],[x s3 ,y s3 ,z s3 ]And
V t ＝{[x t1 ,y t1 ,z t1 ],[x t2 ,y t2 ,z t2 ],[x t3 ,y t3 ,z t3 ]}；
wherein V is s Is CBCT dental crown point cloud V C2 Three key point coordinates of V t Dental crown point cloud V for laser mouth sweeping L Is defined by the three key point coordinates;
s62, calculating V by utilizing ICP algorithm s Mapping to V t Is a transfer matrix M of (2) 3 ；
S63, based on a transfer matrix M 3 CBCT dental crown point cloud V with second registration C2 Laser mouth scan crown point cloud V registered into S1 L Obtaining CBCT dental crown point cloud and laser dental crown point cloud V L Results V of three registrations of (2) C3 。
S7: based on PCA registration parameters, registering the CBCT single-tooth point cloud to a single-tooth laser mouth-sweeping crown point cloud, calculating the distance between each tooth center point cloud of the registered CBCT and the center coordinates of the laser mouth-sweeping crown point cloud to obtain a nearest neighbor CBCT single-tooth point cloud, and registering the nearest neighbor CBCT single-tooth point cloud to the laser mouth-sweeping single-crown point cloud by utilizing an ICP algorithm; searching the distance between the nearest neighbor CBCT single tooth point cloud and the nearest neighbor point cloud of the laser single tooth crown point cloud by using a Kd-tree algorithm, generating a CBCT single tooth root point cloud according to the distance, and fusing the CBCT tooth root point cloud and the laser mouth scanning tooth crown point cloud by using a poisson algorithm; the method comprises the following specific steps:
s71, based on PCA registration parameters, CBCT single tooth point cloud { V } 1 ,V 2 ,…,V n Registration to single tooth laser mouth scan crown point cloud { U } 1 ,U 2 ,…,U m Obtaining a registered CBCT single tooth point cloud { Vp } 1 ,Vp 2 ,…,Vp n }；
S72, scanning single-tooth crown point cloud U for laser mouth i First, the point cloud center coordinates [ x ] of the model are calculated i ,y i ,z i ]Then calculate the coordinate value and CBCT single tooth point cloud { Vp 1 ,Vp 2 ,…,Vp n The distance between the central point cloud coordinates of each tooth is used for obtaining the space position and the single tooth crown point cloud U of the laser mouth sweep i Nearest neighbor CBCT single tooth point cloud Vp j ；
S73, utilizing ICP algorithm to enable CBCT single tooth point cloud Vp j Registering to laser mouth scanning single tooth crown point cloud U i Construction of laser mouth scanning single-tooth crown point cloud U by using Kd-tree algorithm i Searching a search model of CBCT single tooth point cloud Vp j Each point cloud of the laser single-crown point cloud U i Is considered to be a CBCT single tooth point cloud Vp if the distance is greater than 0.5 j Finally, reserving all the root point clouds to generate CBCT single root point cloud Vr j ；
S74, performing CBCT (Console computed tomography) on single-root point cloud Vr j Point cloud and laser mouth scanning single dental crown model U i Combining the point clouds of the laser dental plaque and the CBCT dental root point cloud to obtain a fusion point cloud Uc of the laser dental plaque and the CBCT dental root point cloud by reconstructing a curved surface of the fusion point cloud by using the Poisson surface i ；
S75, counting point cloud communication areas of the fusion result, reserving a maximum communication area, filtering discrete point cloud data, and obtaining optimized fusion point cloud
S76, utilizing ICP algorithm to fuse the resultsRegistering to laser mouth scan single crown model U i Obtaining the registered fusion point cloud Up i And saved as a ". Stl" file.
S8: and (3) correcting the fusion result in the step S7 to obtain a complete tooth model. In the embodiment of the application, as the fusion result of the tooth root and the crown point cloud obtained in the step S7 is the curved surface generated based on the reconstruction of the poisson surface, the position of the tooth root is offset by a certain amount compared with the original tooth root position, and the offset position of each tooth is not uniform, the relative position of each tooth in the point cloud fusion result of the step S7 is inconsistent with the actual situation, and the diagnosis of oral diseases and the planning of orthodontic operation are easily misled. Aiming at the problem, the application registers the tooth root and the tooth crown point cloud fusion result with the tooth root of the CBCT again, thereby correcting the position of the tooth root and the tooth crown point cloud fusion result, avoiding the point cloud position deviation generated in the fusion process and improving the accuracy of the three-dimensional point cloud fusion result.
According to the application, the CBCT tooth reconstruction model is registered into the laser scanning tooth model, and then the crown model of the laser scanning tooth and the tooth root of the CBCT tooth reconstruction model are fused to obtain a complete tooth model, as shown in fig. 2, the method is a graph of registering and fusing effects of the tooth root and the crown, the problems of low accuracy and low efficiency of the fusion result of the laser scanning tooth model and the CBCT tooth reconstruction model are effectively solved, and the method has the advantages of high efficiency, accuracy, high robustness and the like, and the registering and fusing effects of the laser scanning tooth model and the CBCT tooth reconstruction model can be effectively improved.
As shown in fig. 3, a CBCT tooth point cloud processing flow chart in the present application is shown, for a CBCT tooth point cloud, the present application automatically divides the CBCT tooth point cloud into two parts, namely a BCT tooth crown point cloud and a tooth root point cloud, then registers the CBCT tooth crown point cloud to a laser mouth-scan tooth crown point cloud space based on PCA, ICP and interaction, the obtained registration parameters register the CBCT tooth root to the laser mouth-scan tooth crown point cloud space, and finally, the CBCT tooth root point cloud and the laser mouth-scan tooth crown point cloud are fused by using a poisson reconstruction algorithm. The method has the advantages of high efficiency, accuracy, high robustness and the like, and can effectively improve the registration and fusion effects of the two.
Practical application:
huang Mou when suffering from oral diseases, a three-dimensional tooth model is required to be obtained for diagnosis, firstly, an interactive point cloud processing software is utilized to extract a crown point cloud part in yellow laser mouth sweeping tooth point cloud; secondly, inputting Huang Mou CBCT tooth point clouds, extracting geometric features of the tooth crowns and the tooth root point clouds, automatically dividing the CBCT tooth point clouds into two parts of CBCT tooth crown point clouds and CBCT tooth root point clouds, optimizing the form of the CBCT tooth root point clouds by searching a maximum point cloud communication area, and removing discrete point clouds on the tooth root point clouds; secondly, extracting three-dimensional coordinate information of CBCT dental crown point cloud and laser mouth scanning point cloud, and calculating a transfer matrix and an offset value of the CBCT dental crown point cloud space transferred to the laser mouth scanning dental crown point cloud space based on a Principal Component Analysis (PCA) algorithm to realize coarse registration of the CBCT dental crown point cloud to the laser mouth scanning dental crown point cloud; secondly, based on an iterative closest point algorithm (ICP) algorithm, on the basis of a coarse registration result, fine registration from the coarse registration CBCT dental crown point cloud to the laser mouth scanning dental crown point cloud is realized, and a CBCT dental crown point cloud registration result with smaller error is obtained; secondly, in order to ensure that the error of registration is small enough, whether an interactive registration algorithm is performed is selected by Huang Mou by displaying a registration result, if yes, three pairs of registration points are selected by Huang Mou on a CBCT dental crown point cloud configuration result and a laser mouth dental crown point cloud respectively, and then transfer matrixes of the three pairs of configuration points are calculated by utilizing an ICP algorithm, so that the interactive registration of the CBCT dental crown point cloud is realized; thirdly, registering the CBCT dental root point cloud to a laser mouth-sweeping dental crown point cloud space based on registration parameters of the CBCT dental crown point cloud, and geometrically splicing the CBCT dental root point cloud and the laser mouth-sweeping dental crown point cloud based on a poisson reconstruction algorithm to generate a fusion result of the CBCT dental root point cloud and the laser mouth-sweeping dental crown point cloud; and finally, analyzing the connected domain of the point cloud fusion result, removing the discrete point cloud, and correcting the fusion result to obtain the three-dimensional tooth model.
The application also provides an electronic device, as shown in fig. 4, which is a schematic structural diagram of an electronic device in the application, and includes one or more processors 101 and a storage device 102; the number of processors 101 in the electronic device may be one or more, one processor 101 being taken as an example in fig. 4; the storage device 102 is used for storing one or more programs; the one or more programs are executed by the one or more processors 101 to cause the one or more processors 101 to implement a dental point cloud fusion method based on laser oroscan and CBCT reconstruction as in any of the embodiments of the present application. The electronic device may further include: an input device 103 and an output device 104. The processor 101, the storage device 102, the input device 103, and the output device 104 in the electronic device may be connected by a bus 105 or otherwise, for example, in fig. 4 by a bus 105.
The storage device 102 in the electronic device is used as a computer readable storage medium, and may be used to store one or more programs, which may be software programs, computer executable programs, and modules, such as program instructions/modules corresponding to the tooth point cloud fusion method based on laser mouth scanning and CBCT reconstruction provided in the embodiments of the present application. The processor 101 executes software programs, instructions and modules stored in the storage device 102 to perform various functional applications and data processing of the electronic device, namely, to implement the tooth point cloud fusion method based on laser mouth scan and CBCT reconstruction in the above method embodiment.
The storage device 102 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, at least one application program required for a function; the storage data area may store data created according to the use of the electronic device, etc. In addition, the storage 102 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some examples, the storage 102 may further include memory located remotely from the processor 101, which may be connected to the device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input means 103 may be used to receive entered numeric or character information and to generate key signal inputs related to user settings and function control of the electronic device. The output device 104 may include a display device such as a display screen.
And, when one or more programs included in the above-described electronic device are executed by the one or more processors 101, the programs perform the following operations:
s1: dividing a laser mouth-sweeping tooth model, and extracting laser mouth-sweeping crown point clouds;
s2: dividing a CBCT tooth reconstruction model, and extracting a CBCT tooth crown point cloud and a CBCT tooth root point cloud;
s3: performing point cloud optimization on the CBCT root point cloud obtained in the step S2;
s4: performing first registration on the CBCT dental crown point cloud in the S2 and the laser mouth scanning dental crown point cloud in the S1 based on PCA to obtain registration parameters of the CBCT dental crown point cloud and the PCA of the first registration;
s5: performing second registration on the CBCT crown point cloud after the first registration in the S4 and the laser mouth scanning crown point cloud in the S1 based on an ICP algorithm and a KD-tree algorithm to obtain registration parameters of the CBCT crown point cloud and ICP of the second registration;
s6: performing interactive registration on the CBCT dental crown point cloud subjected to the second registration in the step S5 and the laser mouth scanning dental crown point cloud in the step S1 to obtain a final registered CBCT dental crown point cloud and three-point registration parameters;
s7: based on PCA registration parameters, registering the CBCT single-tooth point cloud to a single-tooth laser mouth-sweeping crown point cloud, calculating the distance between each tooth center point cloud of the registered CBCT and the center coordinates of the laser mouth-sweeping crown point cloud to obtain a nearest neighbor CBCT single-tooth point cloud, and registering the nearest neighbor CBCT single-tooth point cloud to the laser mouth-sweeping single-crown point cloud by utilizing an ICP algorithm; searching the distance between the nearest neighbor CBCT single tooth point cloud and the nearest neighbor point cloud of the laser single tooth crown point cloud by using a Kd-tree algorithm, generating a CBCT single tooth root point cloud according to the distance, and fusing the CBCT tooth root point cloud and the laser mouth scanning tooth crown point cloud by using a poisson algorithm;
s8: and (3) registering the fusion result in the step (S7) with the laser mouth scanning crown point cloud in the step (S1) to obtain a complete tooth model after position correction.
Of course, those skilled in the art will appreciate that the program(s) may also perform the operations associated with the laser oroscan and CBCT reconstructed dental point cloud fusion method provided in any of the embodiments of the present application when the program(s) included in the electronic device described above are executed by the processor(s) 101.
It should be further noted that the present application also provides a computer readable storage medium, where a computer program is stored, where the computer program may be executed by a computer processor to implement the tooth point cloud fusion method based on laser mouth scan and CBCT reconstruction in the foregoing embodiment. The computer program may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
Since it will be readily apparent to those skilled in the art that any modifications, equivalent substitutions, improvements, etc. that are within the spirit and principles of the present application are intended to be included within the scope of the claims.
Claims (10)
1. A tooth point cloud fusion method based on laser mouth scanning and CBCT reconstruction is characterized in that: the method comprises the following steps:
s1: dividing a laser mouth-sweeping tooth model, and extracting laser mouth-sweeping crown point clouds;
s2: dividing a CBCT tooth reconstruction model, and extracting a CBCT tooth crown point cloud and a CBCT tooth root point cloud;
s3: performing point cloud optimization on the CBCT root point cloud obtained in the step S2;
s4: performing first registration on the CBCT dental crown point cloud in the S2 and the laser mouth scanning dental crown point cloud in the S1 based on PCA to obtain registration parameters of the CBCT dental crown point cloud and the PCA of the first registration;
s5: performing second registration on the CBCT crown point cloud after the first registration in the S4 and the laser mouth scanning crown point cloud in the S1 based on an ICP algorithm and a KD-tree algorithm to obtain registration parameters of the CBCT crown point cloud and ICP of the second registration;
s6: performing interactive registration on the CBCT dental crown point cloud subjected to the second registration in the step S5 and the laser mouth scanning dental crown point cloud in the step S1 to obtain a final registered CBCT dental crown point cloud and three-point registration parameters;
s7: based on PCA registration parameters, registering the CBCT single-tooth point cloud to a single-tooth laser mouth-sweeping crown point cloud, calculating the distance between each tooth center point cloud of the registered CBCT and the center coordinates of the laser mouth-sweeping crown point cloud to obtain a nearest neighbor CBCT single-tooth point cloud, and registering the nearest neighbor CBCT single-tooth point cloud to the laser mouth-sweeping single-crown point cloud by utilizing an ICP algorithm; searching the distance between the nearest neighbor CBCT single tooth point cloud and the nearest neighbor point cloud of the laser single tooth crown point cloud by using a Kd-tree algorithm, generating a CBCT single tooth root point cloud according to the distance, and fusing the CBCT tooth root point cloud and the laser mouth scanning tooth crown point cloud by using a poisson algorithm;
s8: and (3) registering the fusion result in the step (S7) with the laser mouth scanning crown point cloud in the step (S1) to obtain a complete tooth model after position correction.
2. The method of tooth point cloud fusion based on laser mouth scan and CBCT reconstruction according to claim 1, wherein the step S1 specifically includes: the laser mouth sweep tooth model comprises point cloud data of the dental crowns and gums of the upper and lower jaws, and the dental crown point cloud data of each tooth of the upper jaw and the lower jaw of the laser mouth sweep tooth model is extracted through a tooth point cloud segmentation algorithm.
3. The method of tooth point cloud fusion based on laser mouth scan and CBCT reconstruction of claim 1, wherein the step S2 specifically includes:
s21, counting the highest value and the lowest value of the three-dimensional coordinate value of the point cloud of the teeth on the Z axis for the maxillary teeth, classifying the point cloud data of the maxillary teeth into a crown and a tooth root by using a threshold value method, wherein the point cloud with the Z axis coordinate value smaller than or equal to the threshold value is the crown or the tooth root, and storing the point cloud as an STL file by using a Poisson curved surface reconstruction algorithm;
s22, counting the highest value and the lowest value of the three-dimensional coordinate values of the point cloud of the teeth on the Z axis for the mandibular teeth, classifying the point cloud data of the mandibular teeth into two parts of a crown and a root by using a threshold method, wherein the point cloud with the Z axis coordinate value being greater than or equal to the threshold value is the crown or the root, and storing the point cloud as an STL file by using a Poisson curved surface reconstruction algorithm.
4. The tooth point cloud fusion method based on laser mouth scan and CBCT reconstruction of claim 1, wherein the tooth point cloud fusion method is characterized by comprising the following steps: the step S3 specifically comprises the following steps: and analyzing the connection relation between the point clouds by using a traversal and search algorithm of the graph model to obtain a plurality of point cloud communication areas, and obtaining an optimized root point cloud result by counting the number of the point clouds of each communication area and reserving the point cloud communication area with the largest number of the point clouds.
5. The tooth point cloud fusion method based on laser mouth scan and CBCT reconstruction of claim 1, wherein the tooth point cloud fusion method is characterized by comprising the following steps: the step S4 specifically comprises the following steps:
s41, calculating CBCT dental crown point cloud V C ＝{[x 1 ，y 1 ，z 1 ]，...，[x m ，y m ，z m ]Mean v of } C ＝[x c ，y c ，z c ]Performing mean value removal processing on the point cloud data;
s42, calculating a covariance matrix through an SVD singular value decomposition methodThe characteristic values and the characteristic vectors of the (a) are sequenced from big to small, and the position of the characteristic vectors is adjusted according to the sequence to obtain a characteristic vector matrix P C ；
S43, scanning laser mouth with crown point cloud V by using PCA method L Calculating to obtain corresponding mean value v L ＝[x L ，y L ，z L ]And a feature vector matrix P L ；
S44, for CBCT dental crown point cloud V C Cloud V of laser mouth scanning dental crown point L The first registration is performed as follows:
v T ＝v L -v L R C
V T ＝V C R C +v T
wherein R is C For transfer matrix, v T For displacing the bias term, V T For the first registration to the CBCT dental crown point cloud of the laser mouth-scan dental crown model, the registration of the upper and lower jaw dental crowns is respectively carried out in the registration process.
6. The tooth point cloud fusion method based on laser mouth scan and CBCT reconstruction of claim 1, wherein the tooth point cloud fusion method is characterized by comprising the following steps: the step S5 specifically includes:
s51, registering the CBCT dental crown point cloud registered for the first time onto the laser mouth-scan dental crown point cloud by utilizing an ICP algorithm to obtain a point cloud transfer matrix M 1 CBCT dental crown point cloud V registered with ICP algorithm for the first time C1 ；
S52, constructing laser mouth-sweeping dental crown point cloud V by utilizing KD-tree algorithm L Extracting CBCT dental crown point cloud V from nearest neighbor search model of (1) C1 Dental crown V is swept with laser to well L Is used for filtering CBCT dental crown point cloud V C1 Intermediate redundancy and false crown point cloud to obtain CBCT crown point cloud
S53, utilizing ICP algorithm to enable CBCT dental crown point cloudRegistered to laser oral scan crown V L Obtaining a point cloud transfer matrix M 2 And CBCT dental crown point cloud V after secondary registration C2 。
7. The tooth point cloud fusion method based on laser mouth scan and CBCT reconstruction of claim 1, wherein the tooth point cloud fusion method is characterized by comprising the following steps: the step S6 specifically includes:
s61, adopting interactive three-point registration correction registration results to respectively register CBCT dental crown point cloud V in the second time C2 And a laser portDental crown point cloud V L Respectively selecting three groups of corresponding point clouds
V s ＝{[x s1 ，y s1 ，z s1 ]，[x s2 ，y s2 ，z t2 ]，[x s3 ，y s3 ，z s3 ]And
V t ＝{[x t1 ，y t1 ，z t1 ]，[x t2 ，y t2 ，z t2 ]，[x t3 ，y t3 ，z t3 ]}；
wherein V is s Is CBCT dental crown point cloud V C2 Three key point coordinates of V t Dental crown point cloud V for laser mouth sweeping L Is defined by the three key point coordinates;
s62, calculating V by utilizing ICP algorithm s Mapping to V t Is a transfer matrix M of (2) 3 ；
S63, based on a transfer matrix M 3 CBCT dental crown point cloud V with second registration C2 Laser mouth scan crown point cloud V registered into S1 L Obtaining CBCT dental crown point cloud and laser dental crown point cloud V L Results V of three registrations of (2) C3 。
8. The tooth point cloud fusion method based on laser mouth scan and CBCT reconstruction of claim 1, wherein the tooth point cloud fusion method is characterized by comprising the following steps: the step S7 specifically includes:
s71, based on PCA registration parameters, CBCT single tooth point cloud { V } 1 ，V 2 ，...，V n Registration to single tooth laser mouth scan crown point cloud { U } 1 ，U 2 ，...，U m Obtaining a registered CBCT single tooth point cloud { Vp } 1 ，Vp 2 ，...，Vp n }；
S72, scanning single-tooth crown point cloud U for laser mouth i First, the point cloud center coordinates [ x ] of the model are calculated i ，y i ，z i ]Then calculate the coordinate value and CBCT single tooth point cloud { Vp 1 ，Vp 2 ，...，Vp n The distance between the central point cloud coordinates of each tooth is used for obtaining the space position and the single tooth crown point cloud U of the laser mouth sweep i Nearest neighbor CBCT single tooth pointCloud Vp j ；
S73, utilizing ICP algorithm to enable CBCT single tooth point cloud Vp j Registering to laser mouth scanning single tooth crown point cloud U i Construction of laser mouth scanning single-tooth crown point cloud U by using Kd-tree algorithm i Searching a search model of CBCT single tooth point cloud Vp j Each point cloud of the laser single-crown point cloud U i Is considered to be a CBCT single tooth point cloud Vp if the distance is greater than 0.5 j Finally, reserving all the root point clouds to generate CBCT single root point cloud Vr j ；
S74, performing CBCT (Console computed tomography) on single-root point cloud Vr j Point cloud and laser mouth scanning single dental crown model U i Combining the point clouds of the laser dental plaque and the CBCT dental root point cloud to obtain a fusion point cloud Uc of the laser dental plaque and the CBCT dental root point cloud by reconstructing a curved surface of the fusion point cloud by using the Poisson surface i ；
S75, counting point cloud communication areas of the fusion result, reserving a maximum communication area, filtering discrete point cloud data, and obtaining optimized fusion point cloud
S76, utilizing ICP algorithm to fuse the resultsRegistering to laser mouth scan single crown model U i Obtaining the registered fusion point cloud Up i And saved as a ". Stl" file.
9. An electronic device, comprising:
one or more processors;
a storage means for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the laser oroscan and CBCT reconstruction-based dental point cloud fusion method of any of claims 1 to 8.
10. A computer readable storage medium storing a computer program, characterized in that the computer program is executed by a computer processor to implement computer readable instructions of the method of any one of claims 1 to 8.
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