Dental detection image optimization processing method and dental handpiece

Abstract

The present invention discloses a dental probe image optimization processing method and a dental handpiece. The present invention relates to the field of dental probe technology and solves the problem of insufficient optimization of the overall accuracy of a selected tooth area. The present invention performs correlation confirmation on different grayscale points in a dental probe image, and then performs correlation confirmation on grayscale areas based on the CT values ​​associated with the different grayscale points. Then, based on the grayscale range of the confirmed grayscale area, the median of the corresponding grayscale range is locked. Then, based on several groups of medians confirmed in the image, the corresponding median sequence is determined. Then, based on a set preset range, the median sequence is numerically stretched and adjusted, and the CT value of each different grayscale area is proportionally adjusted. Based on the specific proportional adjustment results, the dental probe image achieves a better display effect, makes the light and dark areas in the image more obvious, performs preliminary optimization, and achieves a better optimization processing effect.

Description

Technical Field

[0001] The present invention relates to the technical field of dental detection, in particular to an optimization processing method for dental detection images and a dental mobile phone. Background Art

[0002] Dental exploratory imaging plays a vital role in dental diagnosis and treatment; it typically includes X-rays, CT scans, intraoral photographs, and other formats. These images provide detailed information about the teeth, gums, jaws, and other areas, helping dentists accurately assess a patient's oral health.

[0003] The application with publication number CN117392004A discloses a method for sharpening dental DR images, including the following steps: S1, pre-processing the original dental DR image image_1 to obtain image image_3, and using the mask method to perform overall sharpening on image image_3 to obtain image image_5; S2, extracting edge position information from image image_3, retaining strong edge position information, and eliminating weak edge position information to obtain image image_10; S3, using image image_10 to etch image image_5 to obtain image image_11; using the total variation method to smooth image image_11 to obtain image image_12; S4, performing a closing operation on image image_12 to close the etched edges therein to obtain image image_13; S5, superimposing image image_5 on image_13 to obtain image image_14, and using image_14 as the final output; this invention suppresses image noise and maintains the transparency of the image while sharpening the image.

[0004] The optimization processing process for the relevant features of dental images generally extracts and displays the tooth area of ​​the dental image based on the image features of the corresponding dental images, which facilitates medical staff to confirm the features of abnormal areas and related identification. However, the original identification method, in actual processing, does not fully confirm the dental feature areas and is mixed with other dark areas, resulting in the selected feature areas not being strong and unable to fully optimize the overall accuracy of the selected tooth areas. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the present invention provides a method for optimizing the processing of dental detection images and a dental handpiece, which solves the problem that the overall accuracy of the selected tooth area cannot be fully optimized.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for optimizing dental detection images, comprising the following steps:

[0007] Step 1: Based on the dental probe image determined by the CBCT machine, different grayscale regions within the dental probe image are identified, and based on the identified groups of grayscale regions, median values ​​are determined to generate a median data column belonging to the dental probe image. The specific sub-steps are as follows:

[0008] S11. Based on the CT values ​​associated with different points in the dental detection image, it is calibrated as HD i , where i represents different points, and the determined groups of CT values ​​HD i Sort by numerical value: Determine the CT value HD from smallest to largest i sequence;

[0009] S12, from the determined HD i Lock the minimum HD in the sequence imin , and then determine the selection range based on the set associated range value Y1: [HD imin , HD imin +Y1]、(HD imin +Y1, HD imin +2Y1]、……、(HD imin +nY1,HD imin +mY1], where n=m-1, and (HD imin +mY1)>HD imax , where Y1 is a preset value. Based on the determined multiple selected ranges, the CT values ​​belonging to the corresponding selected ranges are classified to determine the CT values ​​in the same range. Then, the areas associated with the CT values ​​in the same range are marked as the same characteristic grayscale areas. Based on the multiple groups of CT values ​​associated with the same characteristic grayscale areas, the grayscale range is determined, and then the median of the grayscale range is determined.

[0010] S13, based on different median values ​​determined for different grayscale regions, sorting the median values ​​from small to large to determine a median data column for the image;

[0011] Step 2: Based on the determined median data column and the set CT value range, the median values ​​associated with the median data column are numerically changed, and the grayscale region associated with the median value is feature-adjusted according to the associated change ratio. The specific sub-steps are as follows:

[0012] S21. Based on the minimum and maximum values ​​of the median data column, determine the associated median range [Z1, Z2], where Z1 is the minimum value and Z2 is the maximum value;

[0013] S22. Based on a preset CT value range of 0-4000, perform a numerical stretching adjustment on the median data column, stretching Z1 to 0 and Z2 to 4000. Based on the characteristic median Tz of the median range in the median data column, adjust Tz to 2500. Then, based on the numerical range F1 of Z1-Tz and the numerical range F2 of 0-2500, lock the adjustment ratio: (F2÷F1)=Bq. According to this adjustment ratio Bq, perform numerical changes on several medians before the characteristic median Tz, and lock the changed value: GBo, where o represents different medians in the median data column, GBo=[(Zo-Z1)×Bq]+0, where Zo is the related median before the characteristic median Tz, and GBo is the changed value after the numerical change of Zo;

[0014] For the median value after the feature median Tz, the following calculation method is used: GBo = 4000 - [(Z2 - Uo) × Bq]. Another calculation method is GBo = 2500 + [(Z2 - Uo) × Bq], where Uo is the related median value after the feature median Tz, and GBo is the changed value after the numerical change of Uo.

[0015] S23. Based on the change value associated with the median in the median data column, determine the grayscale region to which the median belongs, and perform geometric changes on the features of different points within the grayscale region according to the change ratio between the median and the change value, so as to change the features of the grayscale region;

[0016] Step 3: Identify the area ratio of the tooth area that has completed feature calibration from the historical completed data. Based on the identified groups of ratio parameters, confirm the parameter range to determine the selection criteria of the image features. The specific method is as follows:

[0017] S31. Based on the historical completed data, lock the historical completed image with the feature calibration, lock the tooth area with the feature calibration and the total image area from the historical completed image, and based on the area parameter M1 of the tooth area and the area parameter M2 of the total image area, lock the area ratio of the tooth area: Zb = M1 ÷ M2;

[0018] S32. For the extracted groups of historical completed images, determine the relevant area ratios Zb in the corresponding historical completed images, and then select the minimum and maximum values ​​from the groups of area ratios Zb to lock the corresponding area ratio interval, and mark the area ratio interval as the selection criterion for image features;

[0019] Step 4: For the dental probe image after feature adjustment, the display brightness partitions within the dental probe image are associated and confirmed. Then, for the confirmed groups of display brightness partitions, the relevant area ratios of the display brightness partitions are identified. Then, based on the determined selection criteria, the tooth area within the dental probe image is locked. The specific sub-steps are as follows:

[0020] S41. Select the center point of the dental probe image and lock the associated display brightness L of the center point. Then, confirm the associated display brightness of related points around the center point and calibrate them as Lp, where p represents different points in the dental probe image. Perform variance processing on the selected associated display brightness Lp and L in sequence, and confirm their corresponding variance values ​​F in sequence:

[0021] If F < Y2, then select the surrounding related points for variance processing, and so on, until F ≥ Y2, remove the related points added this time and remove them, and divide the areas associated with the several groups of related points confirmed before this time and the center point into the same display brightness area, where Y2 is the preset value;

[0022] If F≥Y2, the relevant points selected this time are not selected, and other relevant points around are selected and processed in sequence to lock the same display brightness area. If other relevant points around the center point all meet F≥Y2, then other relevant points are selected and processed in the same way as the center point to perform correlation confirmation on the same display brightness area;

[0023] S42: After the current same display brightness region is confirmed, randomly select other points in the dental probe image as center points and perform the same confirmation of the same display brightness region in the same manner until all the same display brightness regions in the dental probe image are confirmed. Points that have already been confirmed to be within the same display brightness region will no longer be selected.

[0024] S43, based on the confirmed groups of regions with the same display brightness, the area of ​​the regions with the same display brightness is calibrated as J k , where k represents different display brightness areas, and confirms the area Mz of the dental detection image, using Z k =J k ÷Mz determines the area ratio Z corresponding to the same display brightness area k ;

[0025] S44, based on the determined selection criteria, based on different area ratio values ​​Z of the same display brightness areas k, randomly combine the area ratio values ​​of several areas with the same display brightness until the total area ratio of the combination belongs to this selection standard and the difference with the maximum value of the selection standard is the smallest, the difference = the maximum value of the selection standard - the area ratio value, mark the several randomly combined areas with the same display brightness as the tooth area, and display the marked tooth area on the display screen.

[0026] Preferably, a dental handpiece comprises a dental handpiece, a collar, a display screen, a latch and a slot;

[0027] The ring is mounted on the dental handpiece, and its display screen is used to display the image of the dental handpiece working on the teeth or alveolar bone. The pin is set on the display screen, driving the display screen to rotate around the axis of the dental handpiece, and the slot is for the pin to be inserted. When rotating, the probe inside the dental handpiece can rotate in conjunction with it in the oral cavity and produce a detection image. The display screen is a ball head structure and can rotate in any direction around the connection point relative to the pin.

[0028] The system further includes an optimization processing module, which determines the spatial position of the tooth region inside the oral cavity based on the determined tooth region, and then enhances the tooth region at the same spatial position based on the detection image generated by the probe when the probe rotates, and fuses the tooth region at the same spatial point with the detection image to complete the specific process of image enhancement. During the specific fusion, different pixel values ​​at the same point are assigned different weight factors to obtain a merged pixel value, and the enhanced related images are displayed on a display screen;

[0029] The display screen has a ball head structure and can rotate in any direction around the connection relative to the pin.

[0030] The present invention provides a method for optimizing the processing of dental probe images and a dental handpiece. Compared with the prior art, it has the following advantages:

[0031] The present invention associates and confirms different grayscale points in a dental detection image, then performs correlation confirmation of grayscale areas based on the CT values ​​associated with the different grayscale points, then locks the median of the corresponding grayscale range based on the grayscale range of the confirmed grayscale area, then determines the corresponding median sequence based on several groups of medians confirmed in the image, then performs numerical stretching adjustment on the median sequence based on a set preset range, and performs geometric adjustment on the CT value of each different grayscale area. Based on the specific geometric adjustment results, the dental detection image achieves a better display effect, makes the light and dark areas in the image more obvious, performs preliminary optimization, and achieves a better optimization processing effect.

[0032] For the relevant images after optimization processing, based on the specific light and dark features in this image and the selection criteria confirmed from the past historical completed data, the corresponding tooth area is directly extracted from the image, and the extracted relevant tooth area is displayed with features, so as to achieve better specific confirmation of the tooth area, effectively lock the corresponding tooth area, make the confirmed area standard more accurate, and achieve better tooth feature confirmation effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 Schematic diagram of the process of the present invention;

[0034] Figure 2 This is a plan view of a dental handpiece according to the present invention;

[0035] Figure 3 A schematic diagram of a process flow diagram adapted for the features of the present invention;

[0036] Figure 4 Schematic diagram of the tooth region determination process of the present invention. DETAILED DESCRIPTION

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0038] First embodiment

[0039] See also Figure 1 , the present application provides a method for optimizing processing of dental detection images, comprising the following steps:

[0040] Step 1: Based on the dental detection image determined by the CBCT machine, different grayscale areas in the dental detection image are confirmed, and based on the confirmed groups of grayscale areas, the median is confirmed to generate a median data column belonging to the dental detection image. Specifically, because different points in the corresponding image have different CT values, the corresponding image can be divided into regions based on the specific numerical display of the specific CT value. Based on the numerical analysis clustering method, the regions associated with the multiple CT values ​​are associated and confirmed, and the grayscale areas with the same characteristics are locked. Their CT values ​​can be directly confirmed from the corresponding image. The specific sub-steps for generating the median data column are as follows:

[0041] S11. Based on the CT values ​​associated with different points in the dental detection image, it is calibrated as HD i , where i represents different points, and the determined groups of CT values ​​HDi Sort by numerical value: Determine the CT value HD from smallest to largest i sequence;

[0042] S12, from the determined HD i Lock the minimum HD in the sequence imin , and then determine the selection range based on the set associated range value Y1: [HD imin , HD imin +Y1]、(HD imin +Y1, HD imin +2Y1]、……、(HD imin +nY1,HD imin +mY1], where n=m-1, and (HD imin +mY1)>HD imax , where Y1 is a preset value, and its specific value is determined by the operator based on experience. Based on the determined several selection ranges, the CT values ​​belonging to the corresponding selection ranges are classified to determine the CT values ​​in the same range. Then, the areas associated with the CT values ​​in the same range are marked as the same characteristic grayscale areas. Based on the several groups of CT values ​​associated with the same characteristic grayscale areas, the grayscale range is determined, and then the median of the grayscale range is determined. For example, the grayscale range determined by the grayscale area is proposed to be [1, 9], and its corresponding median is 5;

[0043] S13. Based on the different median values ​​determined for different grayscale regions, the median values ​​are sorted in ascending order to determine the median data column of the image. Specifically, the set range value Y1 is generally very small, so the related CT values ​​belonging to the same selected range also have strong similar characteristics. Therefore, analysis and confirmation are performed in sequence based on the set value Y1, thereby ensuring that the determined grayscale regions are relatively clustered in terms of values, and achieving a better grayscale region division effect. In this way, the related regions with different CT value characteristics in the image can be effectively confirmed and divided one by one. Based on the corresponding CT value range of each determined grayscale region, the corresponding median value can be selected from the existing CT value range to reflect the range value of the grayscale region;

[0044] Step 2: Combine Figure 3 Based on the determined median data column and the set CT value range, the median value associated with the median data column is numerically changed. According to the associated change ratio, the grayscale area associated with the median is feature-adjusted. Specifically, in order to achieve a better display effect for the associated image, it is necessary to adjust the features associated with the image, so that the corresponding grayscale area can achieve a better image display effect. The specific sub-steps of feature adjustment are:

[0045] S21. Based on the minimum and maximum values ​​of the median data column, determine the associated median range [Z1, Z2], where Z1 is the minimum value and Z2 is the maximum value;

[0046] S22. Based on a preset CT value range of (-1000) to 4000, perform a numerical stretching adjustment on the median data column, so that Z1 is stretched to -1000 and Z2 is stretched to 4000. Based on the characteristic median Tz of the median range in the median data column (that is, the median of this median range), adjust Tz to 1500 (this 1500 is the median of (-1000) to 4000). Then, based on the numerical range F1 of Z1-Tz and the numerical range F2 of 0-2500, lock the adjustment ratio: (F2÷F1)=Bq. According to the adjustment ratio Bq, perform numerical changes on several medians before the characteristic median Tz, and lock the changed value: GBo, where o represents different medians in the median data column, GBo=[(Zo-Z1)×Bq]+(-1000), Zo is the related median before the characteristic median Tz, and GBo is the changed value after the numerical change of Zo;

[0047] Process the subsequent median value of the characteristic median Tz: GBo = 4000 - [(Z2 - Uo) × Bq] (it can also be: GBo = 1500 + [(Z2 - Uo) × Bq]), where Uo is the related median value after the characteristic median Tz, and GBo is the changed value after the numerical change of Uo;

[0048] For example: the proposed median data column is 20, 25, 30, 35, and 40, and the determined median range is [Z1, Z2] = [20, 40]. The middle value of the value range [20, 40] is 30, which corresponds to 1500. 30 is adjusted to 1500. Then, after the numerical confirmation, the corresponding adjustment ratio Bq is: 2500÷10=250. Then, after the corresponding numerical stretching processing, 25 changes to 250. Similarly, after the value of 40 changes to 4000, its corresponding original median 35 changes to: 2750. It can be understood that a group of data columns with a value range of [20, 40] is stretched to a data column from (-1000) to 4000. After the relevant values ​​within it are specifically stretched, the internally associated medians can be confirmed one by one, and the front and back division judgments are made based on the determined median values ​​to lock them one by one, achieving a better processing method.

[0049] S23. Based on the change value associated with the median in the median data column, the grayscale area to which this median belongs is confirmed. According to the change ratio between this median and the change value, the features of different points in this grayscale area are geometrically changed to change the features of the grayscale area. For example, the corresponding median in this median data column is proposed to be 30, which becomes 1500 after geometric change. It is proposed that there is a grayscale point in this grayscale area with a CT value of 29, then the feature of this grayscale point is changed to 1250. Similarly, the other grayscale points in this grayscale area adopt the same change method to perform related changes in grayscale values, so as to complete the overall feature adjustment of the entire grayscale area. After the CT values ​​of several different grayscale areas in this image are relatedly changed, the brightness and darkness adjustment of the image will be more obvious, which can achieve a better image display effect for the entire image.

[0050] Second embodiment

[0051] During the specific implementation process of this embodiment, the following steps are also included:

[0052] Step 3: Identify the area ratio of the tooth region that has completed feature calibration from the historical completed data. Based on the identified groups of ratio parameters, confirm the parameter range to determine the selection criteria of the image features. The specific method of determining the selection criteria is as follows:

[0053] S31. Based on the historical completed data, lock the historical completed image with the feature calibration, lock the tooth area with the feature calibration and the total image area from the historical completed image, and based on the area parameter M1 of the tooth area and the area parameter M2 of the total image area, lock the area ratio of the tooth area: Zb = M1 ÷ M2;

[0054] S32. For the extracted groups of historical completed images, determine the relevant area ratios Zb in the corresponding historical completed images. Then, based on the groups of area ratios Zb, select the minimum and maximum values ​​to lock the corresponding area ratio interval, and calibrate the area ratio interval as the selection standard for image features. Specifically, the historical completed images in the historical completed data are all detected by the detection component related to the dental handpiece. Therefore, the features associated with the tooth areas in the corresponding area ratio range are relatively consistent. Based on the determined relatively consistent specific features, the corresponding relevant standards can be locked, thereby facilitating the subsequent specific confirmation of the corresponding tooth areas, thereby achieving a better confirmation effect and making the selected tooth features more obvious.

[0055] Step 4: Combine Figure 4, for the dental detection image after feature adjustment, the display brightness partitions in the dental detection image are associated and confirmed, and then for several groups of confirmed display brightness partitions, the relevant area ratios of the display brightness partitions are identified, and then based on the determined selection criteria, the tooth area in the dental detection image is locked. Specifically, after the CT value is adjusted, the display brightness in the corresponding image will also change accordingly, thereby making the relevant display brightness partitions more obvious, and based on the adjusted display brightness partition areas, it can achieve a better feature selection effect;

[0056] The specific sub-steps for locking the tooth area in the dental detection image are as follows:

[0057] S41. Select the center point of the dental probe image and lock the associated display brightness L of the center point. Then, confirm the associated display brightness of related points around the center point and calibrate them as Lp, where p represents different points in the dental probe image. Perform variance processing on the selected associated display brightness Lp and L in sequence, and confirm their corresponding variance values ​​F in sequence:

[0058] If F < Y2, then select the surrounding related points for variance processing, and so on, until F ≥ Y2, remove the related points added this time and remove them, and divide the several groups of related points confirmed before this time and the area associated with the center point into the same display brightness area (after the center point is confirmed, a group of surrounding points is preferentially selected for display brightness variance calculation. If it meets the standard, continue to select the surrounding points. If it does not meet the standard, remove this point and select other points, so that the same display brightness area can be effectively confirmed one by one). Y2 is a preset value, and its specific value is determined by the operator based on experience;

[0059] If F≥Y2, the relevant points selected this time are not selected, and other relevant points around are selected and calculated and processed in sequence to lock the same display brightness area. If other relevant points around the center point all meet F≥Y2 (this situation basically does not occur), then other relevant points are selected and processed in the same way as the center point to perform correlation confirmation on the same display brightness area;

[0060] S42: After the current same display brightness region is confirmed, randomly select other points in the dental probe image as center points and perform the same confirmation of the same display brightness region in the same manner until all the same display brightness regions in the dental probe image are confirmed. Points that have already been confirmed to be within the same display brightness region will no longer be selected.

[0061] S43, based on the confirmed groups of regions with the same display brightness, the area of ​​the regions with the same display brightness is calibrated as J k, where k represents different display brightness areas, and confirms the area Mz of the dental detection image, using Z k =J k ÷Mz determines the area ratio Z corresponding to the same display brightness area k ;

[0062] S44, based on the determined selection criteria, based on different area ratio values ​​Z of the same display brightness areas k , randomly combining the area ratio values ​​of several regions with the same display brightness until the total area ratio of the combined region falls within the selection standard and the difference with the maximum value of the selection standard is the smallest, the difference = the maximum value of the selection standard - the area ratio value, marking the several randomly combined regions with the same display brightness as the tooth area, and displaying the marked tooth area on the display screen;

[0063] By adopting this method of confirming the features of the tooth area, the tooth area can be specifically confirmed based on the specified relevant image features, and the corresponding tooth area can be effectively locked, making the confirmed area standard more accurate and achieving better tooth feature confirmation effect.

[0064] Third embodiment

[0065] A dental handpiece includes a dental handpiece 1, a collar 2, a display screen 3, a latch 4, and a slot 5. The collar 2 is mounted on the dental handpiece 1. The display screen 3 is used to display an image of the dental handpiece operating on teeth or alveolar bone. The latch 4 is disposed on the display screen to drive the display screen to rotate about the axis of the dental handpiece. The slot 5 is configured for insertion of the latch 4. The display screen 3 can rotate in any direction relative to the latch 4 about the connection, similar to a ball head structure. The dental handpiece 1 includes, but is not limited to, a dental pneumatic high-speed handpiece, a dental implant handpiece, an endodontic motor, and a dental scaler.

[0066] It also includes an optimization processing module, which determines the spatial position of the tooth area inside the oral cavity based on the determined tooth area, and then based on the detection image generated by the probe when it rotates, performs image enhancement on the tooth area at the same spatial position based on the detection image, and fuses the tooth area at the same spatial point with the detection image to complete the specific process of image enhancement. During the specific fusion, different pixel values ​​at the same point are assigned different weight factors to obtain the merged pixel value, and the enhanced related images are displayed on the display screen. Since image fusion is relatively simple in the existing technology, it will not be described in detail here.

[0067] The pixel value of the corresponding point in the tooth area of ​​the same spatial point is proposed to be SSw, and the pixel value of this spatial point in the detection image is proposed to be XXw, where w represents the corresponding spatial point. HBw=SSw×C1+XXw×C2 is used to determine the pixel value HBw after the spatial point is merged, where C1 and C2 are both weight factors associated with the corresponding pixels, and their specific values ​​are proposed in advance by the operator.

[0068] Fourth embodiment

[0069] The specific implementation process of this embodiment includes all the implementation processes of the above three groups of embodiments.

[0070] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0071] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for optimizing dental detection images, characterized in that: The following steps are involved: Step 1: Based on the dental probe image determined by the CBCT machine, different grayscale regions within the dental probe image are identified, and based on the identified groups of grayscale regions, median values ​​are determined to generate a median data column belonging to the dental probe image. The specific sub-steps are as follows: S11. Based on the CT values ​​associated with different points in the dental detection image, it is calibrated as HD i , where i represents different points, and the determined groups of CT values ​​HD i Sort by numerical value: Determine the CT value HD from smallest to largest i sequence; S12, from the determined HD i Lock the minimum HD in the sequence imin , and then determine the selection range based on the set associated range value Y1: [HD imin , HD imin +Y1]、(HD imin +Y1, HD imin +2Y1]、……、(HD imin +nY1,HD imin +mY1], where n=m-1, and (HD imin +mY1)>HD imax , where Y1 is a preset value. Based on the determined multiple selected ranges, the CT values ​​belonging to the corresponding selected ranges are classified to determine the CT values ​​in the same range. Then, the areas associated with the CT values ​​in the same range are marked as the same characteristic grayscale areas. Based on the multiple groups of CT values ​​associated with the same characteristic grayscale areas, the grayscale range is determined, and then the median of the grayscale range is determined. S13, based on different median values ​​determined for different grayscale regions, sorting the median values ​​from small to large to determine a median data column for the image; Step 2: Based on the determined median data column and the set CT value range, the median values ​​associated with the median data column are numerically changed, and the grayscale region associated with the median value is feature-adjusted according to the associated change ratio. The specific sub-steps are as follows: S21. Based on the minimum and maximum values ​​of the median data column, determine the associated median range [Z1, Z2], where Z1 is the minimum value and Z2 is the maximum value; S22. Based on a preset CT value range of (-1000) to 4000, and according to the numerical range F1 of Z1-Tz and the numerical range F2 of (-1000) to 1500, an adjustment ratio is locked: (F2 ÷ F1) = Bq. According to the adjustment ratio Bq, a number of medians before the characteristic median Tz are numerically changed, and the changed value is locked: GBo, where o represents a different median in the median data column, GBo = [(Zo-Z1) × Bq] + (-1000), Zo is the related median before the characteristic median Tz, and GBo is the changed value after the numerical change of Zo. Then, a numerical stretching adjustment is performed on the median data column, such that Z1 is stretched to -1000 and Z2 is stretched to 4000. Based on the characteristic median Tz in the median range in the median data column, Tz is adjusted to 1500. Process the subsequent median value of the characteristic median Tz: GBo=4000-[(Z2-Uo)×Bq], where Uo is the related median value after the characteristic median Tz, and GBo is the changed value after the numerical change of Uo; S23. Based on the change value associated with the median in the median data column, determine the grayscale region to which the median belongs, and perform geometric changes on the features of different points within the grayscale region according to the change ratio between the median and the change value, so as to change the features of the grayscale region; Step 3: Identify the area ratio of the tooth region that has completed feature calibration from the historical completed data, and confirm the parameter range based on the identified groups of ratio parameters to determine the selection criteria of the image features; Step 4: For the dental probe image after feature adjustment, the display brightness zones within the dental probe image are associated and confirmed. Then, for the confirmed groups of display brightness zones, the relevant area ratios of the display brightness zones are identified. Then, based on the determined selection criteria, the tooth area within the dental probe image is locked.

2. The method for optimizing dental detection images according to claim 1, wherein: In step S22, another calculation method for processing the subsequent median of the characteristic median Tz is GBo=4000-[(Z2-Uo)×Bq]: GBo=1500+[(Z2-Uo)×Bq].

3. The method for optimizing dental detection images according to claim 1, wherein: In step 3, the specific method of determining the selection criteria is: S31. Based on the historical completed data, lock the historical completed image with the feature calibration, lock the tooth area with the feature calibration and the total image area from the historical completed image, and based on the area parameter M1 of the tooth area and the area parameter M2 of the total image area, lock the area ratio of the tooth area: Zb = M1 ÷ M2; S32. For the extracted groups of historical completed images, determine the relevant area ratio Zb in the corresponding historical completed images, and then select the minimum and maximum values ​​from the groups of area ratio Zb to lock the corresponding area ratio interval, and calibrate this area ratio interval as the selection standard for image features.

4. The method for optimizing dental detection images according to claim 1, wherein: In step 4, the specific sub-steps of locking the tooth area in the dental detection image are: S41. Select the center point of the dental probe image and lock the associated display brightness L of the center point. Then, confirm the associated display brightness of related points around the center point and calibrate them as Lp, where p represents different points in the dental probe image. Perform variance processing on the selected associated display brightness Lp and L in sequence, and confirm their corresponding variance values ​​F in sequence: If F < Y2, then select the surrounding related points for variance processing, and so on, until F ≥ Y2, then remove the related points added this time, and divide the areas associated with the several groups of related points confirmed before this time and the center point into the same display brightness area, where Y2 is the preset value; If F≥Y2, the relevant points selected this time are not selected, and other relevant points around are selected and processed in sequence to lock the same display brightness area. If other relevant points around the center point all meet F≥Y2, then other relevant points are selected and processed in the same way as the center point to perform correlation confirmation on the same display brightness area; S42: After the current same display brightness region is confirmed, randomly select other points in the dental probe image as center points and perform the same confirmation of the same display brightness region in the same manner until all the same display brightness regions in the dental probe image are confirmed. Points that have already been confirmed to be within the same display brightness region will no longer be selected. S43, based on the confirmed groups of regions with the same display brightness, the area of ​​the regions with the same display brightness is calibrated as J k , where k represents different display brightness areas, and confirms the area Mz of the dental detection image, using Z k =J k ÷Mz determines the area ratio Z corresponding to the same display brightness area k ; S44, based on the determined selection criteria and based on different area ratio values ​​Z of the same display brightness area k , randomly combine the area ratio values ​​of several areas with the same display brightness until the total area ratio of the combination belongs to this selection standard and the difference with the maximum value of the selection standard is the smallest, the difference = the maximum value of the selection standard - the area ratio value, and mark the randomly combined areas with the same display brightness as the tooth area.

5. A dental handpiece for performing correlation processing on a tooth area generated by the method for optimizing dental detection images according to any one of claims 1 or 4, characterized in that: Includes dental handpiece, ring, display, latch and slot; The ring is mounted on the dental handpiece, and its display screen is used to display the image of the dental handpiece working on the teeth or alveolar bone. The pin is set on the display screen, driving the display screen to rotate around the axis of the dental handpiece, and the slot is for the pin to be inserted. When rotating, the probe inside the dental handpiece can rotate in conjunction with it in the oral cavity and produce a detection image. The display screen is a ball head structure and can rotate in any direction around the connection point relative to the pin. It also includes an optimization processing module, which determines the spatial position of the tooth area inside the oral cavity based on the determined tooth area, and then based on the detection image generated by the probe when it rotates, performs image enhancement on the tooth area at the same spatial position based on the detection image, and fuses the tooth area at the same spatial point with the detection image to complete the specific process of image enhancement. During the specific fusion, different pixel values ​​at the same point are assigned different weight factors to obtain the merged pixel value, and the enhanced related images are displayed on the display screen.

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