METHOD FOR RECORDING MULTIPLE THREE-DIMENSIONAL IMAGES OF A DENTAL OBJECT

• US 20140177931A1
• Filed: 08/01/2012
• Published: 06/26/2014
• Est. Priority Date: 08/01/2011
• Status: Active Grant

• Current Assignee: Sirona Dental Systems GmbH (Sirona Dental Systems Incorporated)
• Original Assignee: Sirona Dental Systems GmbH (Sirona Dental Systems Incorporated)
• Inventors: Thiel, Frank, Kocherscheidt, Gerrit, Adamson, Anders, Popilka, Bjrn
• Granted Patent: US 9,456,754 B2
• Time in Patent Office: Days
• US Class Current: 382/128
• CPC Class Codes: A61B 5/0064 Body surface scanning
• CPC Class Codes: A61B 5/0068 Confocal scanning
• CPC Class Codes: A61B 5/0088 for oral or dental tissue
• CPC Class Codes: A61C 9/0046 Data acquisition means or methods
• CPC Class Codes: A61C 9/006 projecting one or more stripes or patterns on the teeth
• CPC Class Codes: G06T 2207/10024 Color image
• CPC Class Codes: G06T 2207/10028 Range image; Depth image; 3D point clouds
• CPC Class Codes: G06T 2207/30036 Dental; Teeth
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/33 using feature-based methods

Abstract

The invention relates to a method for recording several three-dimensional recordings of a dental object, wherein each of the three-dimensional images comprises 3D measured data and color data of a measured surface of the object, wherein the individual images are combined to form an overall image using a computer-assisted recording algorithm, wherein first regions with hard tissue and second regions with soft tissue are identified in each of the images using a segmentation method that depends on the color data. In applying the recording algorithm, the first regions and the second regions are weighted with different weighting factors.

Claims

1. A method for recording multiple three-dimensional images of a dental object, wherein each of the three-dimensional images comprises 3D measured data and color data of a measured surface of the object, comprising combining the individual images to form an overall image using a computer-assisted recording algorithm, wherein

first regions with hard tissue and second regions with soft tissue are identified in each of the images using a segmentation method which depends on the color data, the first regions and the second regions being weighted with different weighting factors, the first regions and the second regions being weighted with a second variable weighting factor that is determined for each measured point of the measured surface and which decreases as the distance of the measured point from the most proximal first region with hard tissue increases.

2. The method according to claim 1, wherein the first regions depict tooth substance or a dental restoration, and the second regions depict gingiva or gingiva substitute.

3. The method according to claim 1, wherein a first area section of the first regions and the second area section of the second regions in each of the images are calculated based on the total area of the respective image, the first regions being weighted with a first weighting factor when the first area section of the first regions falls below a threshold value, and the second regions being weighted with a second weighting factor, that increases as the first area section decreases.

4. The method according to claim 3, wherein the threshold value is a value between 5% and 30%.

6. The method according to claim 1, wherein the second weighting factor has a value of 1.0 at the border to the first region and decreases to a minimum value of 0.0 as the distance from the most proximal first region increases.

7. The method according to claim 1, further comprising the step of identifying third regions with soft tissue that are not assigned either to the first regions or to the second regions, the third regions comprising light regions that occur due to glaring light or dark regions that occur due to inadequate illumination, wherein the third regions are not taken into account in the recording.

8. The method according to claim 7, wherein a blur filter is applied to the color data in each of the images before the segmentation into first regions and second regions.

9. The method according to claim 8, wherein each image is recorded using dental handpiece that generages the 3D measured and color data, wherein the 3D measured data and the color data are detected simultaneously or in a short interval of time from the same imaging direction.

10. The method according to claim 9, wherein the 3D measured data is generated using triangulation method and a strip projection method, and the color data is generated using a color camera.

11. The method according to claim 9, wherein the interval of time is between 0.2 ms and 100 ms.

12. The method according to claim 2, wherein a first area section of the first regions and the second area section of the second regions in each of the images are calculated based on the total area of the respective image, the first regions being weighted with a first weighting factor when the first area section of the first regions falls below a threshold value, and the second regions being weighted with a second weighting factor that increases as the first area section decreases.

13. The method according to claim 12, wherein the threshold value is a value between 5% and 30.

14. The method according to claim 2, wherein the second weighting factor has a value of 1.0 at the border to the first region and decreases to a minimum value of 0.0 as the distance from the most proximal first region increases.

15. The method according to claim 3, wherein the second weighting factor has a value of 1.0 at the border to the first region and decreases to a minimum value of 0.0 as the distance from the most proximal first region increases.

16. The method according to claim 4, wherein the second weighting factor has a value of 1.0 at the border to the first region and decreases to a minimum value of 0.0 as the distance from the most proximal first region increases.

17. The method according to claim 12, wherein the second weighting factor has a value of 1.0 at the border to the first region and decreases to a minimum value of 0.0 as the distance from the most proximal first region increases.

18. The method according to claim 13, wherein the second weighting factor has a value of 1.0 at the border to the first region and decreases to a minimum value of 0.0 as the distance from the most proximal first region increases.

5. (canceled)