Process for determining a tooth color

Abstract

A process for determining a tooth color of a filling or another restoration is provided which in particular is produced using composite materials, wherein at least one tooth is scanned or is visually detected. The process is characterized in that natural teeth are spectrally measured in advance, that the color values measured within a color space are classified into several, in particular four, categories which each form a color cloud extending in a three-dimensional fashion within the color space, which is done with the help of a structuring algorithm, in particular a Nearest Neighbour Algorithm, or a symmetry recognition algorithm, and that the tooth color of the filling is determined by means of evaluating to which category the tooth scanned has the smallest color distance, or to which color cloud the tooth scanned belongs.

Claims

1. A process for determining a tooth color of a filling or restoration comprising electronically recording a color value of at least one tooth, wherein natural teeth are spectrally measured in advance or in a first step to determine color values of the natural teeth, wherein the measured color values of the natural teeth are classified into several categories within a color space, wherein each of the several categories form a three-dimensional color cloud within the color space, which is performed with a structure-recognizing algorithm, and wherein the tooth color of the filling or restoration is determined by evaluating to which of the several categories the recorded color value has the smallest color distance, or to which color cloud the recorded color value belongs.

2. The process according to claim 1, wherein the filling or restoration is produced using composite materials, wherein the at least one tooth is are scanned to determine the color value, and wherein the several categories comprise four categories.

3. The process according to claim 1, wherein the determined tooth color is used for the filling or the restoration.

4. The process according to claim 1, wherein the natural teeth are selected from a multitude of at least 20 different existing natural teeth, independently of the restoration or filling to be color-matched.

5. The process according to claim 1, wherein based on the spectral measurement, one point within the color space is determined for each of the natural teeth, and wherein, based on the points determined for each of the natural teeth measured, a convex envelope is determined within the color space, to which the color value points of all of the natural teeth measured belong.

6. The process according to claim 5, wherein a structure, a grid or a mesh, made up of uniformly distributed points is placed over the color space, and, based on initial categories, evaluating which initial category is closest to some O point or initial point, category boundaries between individual categories are iteratively determined as surfaces extending in a three-dimensional fashion.

7. The process according to claim 1, wherein the classification into categories is performed based on the largest number of neighboring points, wherein each category corresponds to one natural tooth.

8. The process according to claim 7, wherein a Euclidian distance is used as the measurement of the color distance.

9. The process according to claim 1, wherein the algorithm is a Nearest Neighbour Algorithm or one symmetry-recognition algorithm.

10. The process according to claim 1, wherein, for determining the categories, random initial categories are first selected, wherein entropy of categorisation is calculated as a measurement of quality over the entire color space, and wherein, with the help of the Nearest Neighbour Algorithm comprising a K Nearest Neighbour Algorithm, and pre-determining the number of categories, optimum category boundaries are iteratively determined.

11. The process according to claim 10, wherein the number of categories is first determined to be a specific value by way of trial, and wherein a maximum permissible color distance value is determined, and wherein the number of categories is increased if the color distance value is exceeded in the worst case.

12. The process according to claim 11, wherein the specific value comprises 4, and wherein the maximum permissible color distance value comprises E=5.8.

13. The process according to claim 11, wherein the specific value comprises 4, wherein a second maximum permissible color distance value is determined, and wherein the number of categories is reduced if the color distance value is undercut in a worst case.

14. The process according to claim 13, wherein the second maximum permissible color distance value comprises E=3.

15. A process for determining a tooth color of a filling, an inlay or an onlay which is produced using composite materials comprising scanning with a scanning device or manually visually detecting at least one tooth, spectrally measuring with a spectrophotometer, in advance, natural teeth to provide color values within a color space classified into several categories which each form a color space extending in a three-dimensional fashion within the color space, which is done with a structure-recognizing algorithm, and determining, manually or by comparison to a scan, the tooth color of the filling, inlay or onlay by evaluating to which center of the color cloud of one of the categories the tooth has the slightest color distance, wherein the color value centers were determined in advance.

16. The process according to claim 15, wherein the several categories comprise four categories.

17. The process according to claim 15, wherein four color clouds are formed corresponding to the categories, and wherein the centers of each of the four color clouds are situated at the following position: TABLE-US-00005 L a b 1. 75.8 2.7 17.2 2. 74.2 3.8 22.7 3. 69.9 5.8 26.2 4. 70.0 4.3 19.5, wherein there may be uncertainty areas with the following color distances extending around the centres, within which the centres may also be situated:
Delta L=+/−2,delta a=+/−1,delta b=+/−2.

18. An apparatus comprising at least one processor, a memory operatively coupled to the at least one processor and storing computer readable instructions, that when executed cause the apparatus to: receive measured color values from a plurality of natural teeth, classify the color values of the plurality of measured teeth into several categories within a color space, wherein each of the several categories form a three-dimensional color cloud within the color space using a structure-recognizing algorithm, wherein the several categories provide a set of color values for a set of tooth coloring materials.

19. The apparatus according to claim 18, further comprising a connection to a spectrophotometer.

20. A computer implemented method comprising computer program code which is stored on a machine-readable medium, the machine-readable medium comprising computer instructions executable by a processor to perform a method for determining color values of dental materials comprising; with a spectrophotometer, spectrally measuring a plurality of natural teeth to determine color values of the natural teeth, with the processor, classifying the measured color values of the natural teeth into several categories within a color space, wherein each of the several categories form a three-dimensional color cloud within the color space, and wherein the several categories provide a set of color values for a set of dental materials or tooth coloring materials.

21. The computer implemented method according to claim 20, wherein the forming of the three-dimensional color cloud is done with a structure-recognizing algorithm.

22. The computer implemented method according to claim 20, further comprising determining which dental material or tooth coloring material most closely matches a patient's tooth for which a dental restoration is being made.

23. A set of dental materials comprising a set of different colored dental materials having color values determined using the method of spectrally measuring a plurality of natural teeth to determine color values of the natural teeth, classifying the measured color values of the natural teeth into several categories within a color space, wherein each of the several categories form a three-dimensional color cloud within the color space, and wherein the several categories provide a set of color values for the set of different colored dental materials.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) Further advantages, details, and features result from the subsequent description of an exemplary embodiment of the invention with the help of the drawings, wherein:

(2) FIG. 1a shows a two-dimensional depiction of the color values of the natural teeth measured, within a three-dimensional color space, as well as the envelope around the points;

(3) FIG. 1b shows a two-dimensional depiction of the color values of the natural teeth measured, within a three-dimensional color space, as well as the envelope around the points;

(4) FIG. 1c shows a two-dimensional depiction of the color values of the natural teeth measured, within a three-dimensional color space, as well as the envelope around the points;

(5) FIG. 1d shows a two-dimensional depiction of the color values of the natural teeth measured, within a three-dimensional color space, as well as the envelope around the points;

(6) FIG. 2 shows a schematic depiction of the uniformly distributed auxiliary points within the envelope according to FIGS. 1a to 1d and outside said;

(7) FIG. 3 shows the result of the iteration for determining the four color clouds adjacent to one another for determining the tooth color; and

(8) FIG. 4 shows a depiction of the color space measured belonging to step 1 of the exemplary calculation;

(9) FIG. 5 shows a depiction of selected points of the color space belonging to step 2 of the exemplary calculation;

(10) FIG. 6 shows a depiction of selected points of the color space belonging to step 3 of the exemplary calculation;

(11) FIG. 7 shows a depiction of selected points of the color space belonging to step 4 of the exemplary calculation;

(12) FIG. 8 shows a depiction of selected points of the color space belonging to step 4a of the exemplary calculation;

(13) FIG. 9a shows a depiction of selected points of the color space belonging to step 5 of the exemplary calculation;

(14) FIG. 9b shows a depiction of selected points of the color space belonging to step 6 of the exemplary calculation;

(15) FIG. 9c shows a depiction of selected points of the color space belonging to step 7 of the exemplary calculation;

(16) FIG. 9d shows a depiction of selected points of the color space belonging to step 8 of the exemplary calculation;

(17) FIG. 9e shows a depiction of selected points of the color space belonging to step 9 of the exemplary calculation;

(18) FIG. 9f shows a depiction of selected points of the color space belonging to step 10 of the exemplary calculation;

(19) FIG. 9g shows a depiction of selected points of the color space belonging to step 11 of the exemplary calculation; and

(20) FIG. 10 shows a depiction of selected points of the color space belonging to step 50,000 of the exemplary calculation.

DETAILED DESCRIPTION

(21) From FIG. 1a, a cloud 10 of points 12 inside a Lab color space 14 is visible. The two-dimensional distribution is plotted over L and b in FIG. 1a. In addition, an enveloping envelope 16 is provided which extends along each of the outermost points 12 and within which thus all points 12 extend.

(22) In FIGS. 1b, 1c, and 1d, points 12 and envelope 16 are also depicted to be two dimensional; in FIG. 1b, in a three-dimensional perspective with b in the X direction, a in the Z direction, and L in the Y direction.

(23) In FIG. 1c, the same cloud of points is plotted in dimension L over a, and in FIG. 1d in dimension b over a.

(24) It can be taken from the distribution that with values L<=68, b=20 through 25, and a=4 through 6, there are only a few points, but there still are points after all, whereas, for example at these L values and B values, no points could be measured at a<=3 or a=>7. On the other hand, tooth color C4 is situated approximately at a=2, b=24, and L=62, i.e. outside the values actually measured, within the random sample taken out of 368 natural teeth.

(25) This virtually results in the claim that tooth color C4 does not appear among the natural teeth measured, and therefore is not relevant as a target tooth color.

(26) As can be seen, the color values each measured at the margins of envelope 16 are virtually corner points of the three-dimensional envelope. For instance, point 18 is taken as an example here. It is connected by schematic lines with neighboring points, which also extend on envelope 16. In this respect, envelope 16 is depicted as a polyhedron, although it is mathematically built by convex outer surfaces, wherein the envelope is occupied by points similar to point 18.

(27) From FIG. 2, it is visible how a three-dimensional raster or grid of auxiliary points 20 is arranged over the relevant color space. The auxiliary points extend both within and outside envelope 16, wherein in practice, those points extending outside are deleted or mathematically suppressed.

(28) With the help of a more structured process, in particular the K Nearest Neighbour Algorithm, after determining four random initial points, color clouds 22, 24, 26, and 28 are now formed, which are visible from FIG. 3, based on the auxiliary points 20, in which color clouds, all points 12 in accordance with FIG. 1 are situated.

(29) These four color clouds 22 to 28 correspond to the four categories. The geometrical center, 30, 32, 34, and 36, of each color cloud corresponds to the target tooth color. The result is that within the determined color space, i.e., within the cloud 10, there is no greater color distance between the respective neighboring center 30 to 36 and any tooth color than a predetermined threshold value, which, in the exemplary case was determined to be Delta E=5.8.

(30) Concrete Exemplary Calculation

(31) Step 1

(32) As is visible from FIG. 4, a large number of points are plotted in the three-dimensional Lab color space. These correspond to the tooth colors of the natural teeth measured. These define the tooth color space, cf. FIGS. 1a and 1 b.

(33) The blue areas around them form envelope 16 around these red points.

(34) Step 2

(35) From these red dots or points, 4 are randomly selected and are defined as the category values. The number of category values is k, i.e. here k=4.

(36) This is shown in FIG. 5.

(37) In the present case, these have the following Lab values:

(38) TABLE-US-00001 L a b Cat. 1 79.5 1.8 16.3 Cat. 2 70.9 3.6 22.6 Cat. 3 70.2 7.0 24.5 Cat. 4 66.9 4.1 24.1

(39) Step 3

(40) All points are assigned to a category. The assignment is carried out with the help of the color distance to the respective category values. This is why the algorithm is referred to as the “Nearest Neighbor” Algorithm.

(41) At the top of FIG. 6, the category probability p(j) is indicated, which is the probability that some points belongs to category j.

(42) At k=4, j may assume the values 1, 2, 3 and 4.

(43) Here: 21% of all points belong to category 1, 51% to category 2, 18% to category 3, and 9% to category 4.

(44) Value E corresponds to entropy. This is highest if all categories have an equal number of points (which is 25% in each). The maximum value of E/E_{max} is therefore 1.

(45) Here, we achieve 0.865.

(46) Step 4

(47) In the smallest and largest categories (here: the pink-colored and light blue points, see FIG. 7), new category values are determined. These are categories 2 and 4.

(48) For the pink category, all pink points are taken, and a new point is randomly determined to be the category value among them. The same happens for light blue.

(49) The category values of green and blue remain unchanged.

(50) TABLE-US-00002 L a b Cat. 1 79.5 1.8 16.3 Cat. 2 75.2 2.9 24.8 Cat. 3 70.2 7.0 24.5 Cat. 4 65.5 5.7 28.8

(51) Step 4a

(52) For all points, the assignment is again determined with the help of the color distance to the category values, according to FIG. 8.

(53) New probabilities result, and thus a new entropy, as is recorded on top in FIG. 8.

(54) Step 5

(55) Now, the entire process is repeated all over again and again, see first FIG. 9a. Categories 3 and 4 were those with the extreme values of probabilities here, such that these were determined anew.

(56) The Lab values are recorded on the upper left in FIG. 9a, and the probabilities p(j) and entropy E on the upper right.

(57) Step 6

(58) The next iteration step can be taken from FIG. 9b.

(59) Step 7

(60) The next iteration step can be taken from FIG. 9c.

(61) Step 8

(62) The next iteration step can be taken from FIG. 9d.

(63) Step 9

(64) The next iteration step can be taken from FIG. 9e.

(65) Step 10

(66) The next iteration step can be taken from FIG. 9f.

(67) Step 11

(68) The next iteration step can be taken from FIG. 9g. Etc.

(69) Step 50,000

(70) The 50,000th iteration step can be taken from FIG. 10.

(71) The following Lab values result:

(72) TABLE-US-00003 L a b Cat. 1 76.8 3.8 15.9 Cat. 2 70.3 5.8 25.8 Cat. 3 69.5 3.9 18.4 Cat. 4 72.4 2.6 24.4

(73) Step 50,001

(74) The average value of all points within the respective category is taken. These form the Lab values for the process in accordance with the invention.

(75) Step 50,002

(76) This example shows an iteration handling.

(77) In an advantageous embodiment, the algorithm is carried out several times, and the best result (the one with the highest entropy, or with an equal number of points within each category) is used as the best final result.

(78) Finally, there will result the values below:

(79) TABLE-US-00004 L a b Cat. 1 75.8 2.7 17.2 Cat. 2 74.2 3.8 22.7 Cat. 3 69.9 5.8 26.2 Cat. 4 70.0 4.3 19.5

(80) Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and/or interpretable on programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

(81) These computer programs (also known as programs, software, software applications or code) include machine instructions for a programmable processor, and can be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. Other programming paradigms can be used, e.g., functional programming, logical programming, or other programming. As used herein, the terms “machine-readable medium” “computer-readable medium” refers to any computer program product, apparatus and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term “machine-readable signal” refers to any signal used to provide machine instructions and/or data to programmable processor.

(82) To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., an LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

(83) The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (“LAN”), a wide area network (“WAN”), and the Internet.

(84) The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

(85) While this specification contains many specific implementation details, these should not be construed as limitations on the scope of any inventions or of what may be claimed, but rather as descriptions of features specific to particular implementations of particular inventions. Certain features that are described in this specification in the context of separate implementations can also be implemented in combination in a single implementation.

(86) Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.

(87) Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the implementations described above should not be understood as requiring such separation in all implementations, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

(88) Thus, particular implementations of the subject matter have been described. Other implementations are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In certain implementations, multitasking and parallel processing may be advantageous.

(89) In some embodiments, the system may include a camera, a processor, an electronic data storage unit, and a display. The camera can be a standard camera, an infrared dot-projection detector, flood illuminator camera, structured-light three-dimensional scanner, standard infrared detector, ultrasonic imaging device, Doppler detector, or any other suitable visualization system capable of capturing information related to a patient's dentition. The processor can be a single processor having one or more cores, or a plurality of processors connected by a bus, network, or other data link. The electronic data storage unit can be any form of non-transitory computer-readable storage medium suitable for storing the data produced by the system. The display can be any display suitable for displaying a digital color or grayscale image.

(90) In some embodiments, the camera, processor, electronic data storage unit, and digital display are components of a single device. The single device may be a smartphone, tablet, laptop computer, personal digital assistant, or other computing device.

(91) In some embodiments, the processor is in communication over a network, which could be wired or wireless, with an external processor used for performing one or more calculation steps and/or a network-attached electronic data storage unit.

(92) In some embodiments, the present disclosure makes use of cloud computing to perform one or more calculations steps remotely and/or remote storage to enable the storage of data remotely for collaborative or remote analysis.

(93) In some embodiments, the system comprises a plurality of graphical user interfaces to permit multiple users to view or analyze the same data.

(94) In some embodiments, the system operates by capturing information related to a patient's dentition using a camera, creating a model of the patient's dentition on a processor, fitting a model of a proposed post-alteration dentition to the patient's dentition on the processor, coloring the model of the proposed post-alteration dentition to match an expected real post-alteration coloration, and displaying the fitted model of the proposed post-alteration dentition in place of the patient's actual dentition on a display which otherwise shows the patient's actual facial features. The information related to a patient's dentition, the model of the patient's dentition, and the model of the proposed post-alteration dentition may be stored on an electronic data storage unit.

(95) In some embodiments, the operations are performed in real-time.

(96) In some embodiments, a user interface is configured such that a user may view the “before” dentition image and the “after” dentition image simultaneously either side-by-side or with a full or partial overlay.

(97) Where used herein, the term “non-transitory” is a limitation on the computer-readable storage medium itself—that is, it is tangible and not a signal—as opposed to a limitation on the persistence of data storage. A non-transitory computer-readable storage medium does not necessarily store information permanently. Random access memory (which may be volatile, non-volatile, dynamic, static, etc.), read-only memory, flash memory, memory caches, or any other tangible, computer-readable storage medium, whether synchronous or asynchronous, embodies it.

(98) “Computer” is not limited to physical desktop computers. Such computers may include virtual machines running on local or remote servers. The computing power may be increased by combining or bridging at least two or a plurality of such machines, if required.

(99) Also, any kind of mobile devices, such as notebooks, laptops, tablets and smartphone may be used as computers. Such computers usually comprise all or at least several of the components mentioned above.

(100) It is also possible to use a local computer having access to a local or remote database, and any of the above mentioned mobile devices having access to the same database, intended to share tasks between local and mobile devices.

(101) Although the invention is illustrated above, partly with reference to some preferred embodiments, it must be understood that numerous modifications and combinations of different features of the embodiments can be made. All of these modifications lie within the scope of the appended claims.