Optimization processing method of dental detection image and dental handpiece

Abstract

The invention discloses an optimization processing method of a dental detection image and a dental handpiece, relates to the technical field of dental detection, and solves the problem that the overall accuracy of a tooth area selected by the dental detection image cannot be fully optimized. The method comprises the following steps: acquiring CT values associated with different gray point positions, performing correlation confirmation on a gray area based on the CT values associated with the different gray point positions, locking the median of the corresponding gray range for the confirmed gray range of the gray area, determining the corresponding median sequence based on a plurality of groups of median confirmed in the image, and determining the corresponding median sequence based on the set preset range. According to the method, the median sequence is subjected to numerical stretching adjustment, the CT value of each different gray scale region is subjected to equal ratio adjustment, the dental detection image achieves a better display effect based on a specific equal ratio adjustment result, the bright and dark regions in the image are more obvious, preliminary optimization is carried out, and a better optimization processing effect is achieved.

Description

Technical Field

[0001] The 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; dental exploratory imaging usually includes X-rays, CT scans, intraoral photos, etc. These images can provide detailed information about teeth, gums, jaws and other parts, helping dentists accurately judge the patient's oral health status.

[0003] The application with publication number CN117392004A discloses a method for sharpening dental DR images, comprising 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 edge 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; while sharpening the image, the invention suppresses image noise and maintains the transparency of the image.

[0004] The process of optimizing the relevant features of dental images is generally based on the image features of the corresponding dental images. The tooth areas of the dental images are extracted and displayed to facilitate 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 failing to fully optimize the overall accuracy of the selected tooth areas. Summary of the invention

[0005] In view of the shortcomings of the prior art, 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 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, relevant confirmation of the median is performed to generate a median data column belonging to the dental detection image. The specific sub-steps are:

[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 values: Determine the CT value HD from small to large 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 several selected ranges, the CT values ​​belonging to the corresponding selected ranges are classified, the CT values ​​of the same range are determined, and then the areas associated with the CT values ​​of the same range are marked as the same characteristic grayscale areas, and the grayscale range is determined based on several groups of CT values ​​associated with the same characteristic grayscale areas, 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 associated in the median data column is numerically changed, and the grayscale area associated with the median is feature adjusted according to the associated change ratio. The specific sub-steps are:

[0012] S21, based on the minimum value and the maximum value 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 the preset CT value range of 0-4000, the median data column is numerically stretched and adjusted, so that Z1 is stretched to 0 and Z2 is stretched to 4000, and based on the characteristic median Tz of the median range in the median data column, Tz is adjusted to 2500, and then according to the numerical range F1 of Z1-Tz and the numerical range F2 of 0-2500, the adjustment ratio is locked: (F2÷F1)=Bq, and 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 different medians in the median data column, GBo=[(Zo-Z1)×Bq]+0, and Zo is the related median before the characteristic median Tz, and GBo is the changed value after the numerical change of Zo;

[0014] The subsequent median of the characteristic median Tz is processed: GBo=4000-[(Z2-Uo)×Bq]. There is another calculation method: GBo=2500+[(Z2-Uo)×Bq], where Uo is the related median after the characteristic 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 according to the change ratio between the median and the change value, perform geometric changes on the features of different points in the grayscale region, so that the features of the grayscale region are changed;

[0016] 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. 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 ratio Zb in the corresponding historical completed images, and then select the minimum value and the maximum value from the groups of area ratio Zb to lock the corresponding area ratio interval, and mark the area ratio interval as the selection standard of the image feature;

[0019] Step 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 the confirmed groups of 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. The specific sub-steps are:

[0020] S41, selecting the center point of the dental detection image, and locking the associated display brightness L of the center point, then confirming the associated display brightness of related points around the center point and calibrating them as Lp, where p represents different points in the dental detection image, performing variance processing on the selected associated display brightness Lp and L in turn, and confirming their corresponding variance values ​​F in turn:

[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 they are calculated and processed in sequence to lock the same display brightness area. If other relevant points around the center point all meet F≥Y2, other relevant points are selected and processed in the same way as the center point to perform relevant confirmation of the same display brightness area;

[0023] S42, after the current same display brightness area is confirmed, randomly select other points in the dental detection image as the center point to perform the same display brightness area confirmation in the same manner, until all the same display brightness areas in the dental detection image are confirmed, and the points that have been confirmed to be within the same display brightness area 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 areas of the same display brightness, and confirms the area Mz of this 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 proportion 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, and the difference = the maximum value of the selection standard - the area ratio value, and mark the several randomly combined areas with the same display brightness as tooth areas, and display the marked tooth areas 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 sleeve ring is mounted on the dental handpiece, and its display screen is used to display the working image of the dental handpiece on the teeth or alveolar bone, and the pin is arranged on the display screen to drive 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 the oral cavity and generate a detection image, and the display screen is a ball head structure, which can rotate in any direction around the connection relative to the pin;

[0028] It also 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 based on the detection image generated when the probe rotates, performs image enhancement on the tooth region at the same spatial position based on the detection image, and fuses the tooth region at the same spatial point with the detection image to complete the specific process of image enhancement, and 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;

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

[0030] The present invention provides an optimization processing method for dental detection images and a dental handpiece. Compared with the prior art, the present invention has the following beneficial effects:

[0031] The present invention performs correlation confirmation on different grayscale points in a dental detection image, and then performs correlation confirmation on grayscale areas based on CT values ​​associated with different grayscale points, and then locks the median of the corresponding grayscale range based on the grayscale range of the confirmed grayscale area, and then determines the corresponding median sequence based on several groups of medians confirmed in the image, and 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 result, the dental detection image can achieve a better display effect, and the light and dark areas in the image can be made more obvious, and preliminary optimization is performed to achieve a better optimization processing effect;

[0032] For the relevant images after optimization processing, based on the specific light and dark features in the 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 It is a schematic diagram of the process of the present invention;

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

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

[0036] Figure 4 FIG. 1 is a schematic diagram of a process for determining a tooth region according to the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work 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 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 several CT values ​​are confirmed to be associated, and the grayscale areas with the same characteristics are locked, and their CT values ​​can be directly confirmed from the corresponding image. The specific sub-steps for generating the median data column are:

[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 values: Determine the CT value HD from small to large 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 ​​of the same range, and then the areas associated with the CT values ​​of the same range are marked as the same characteristic grayscale areas. The grayscale range is determined based on several groups of CT values ​​associated with the same characteristic grayscale areas, and then the median of the grayscale range is determined. For example: Here, the grayscale range determined by the grayscale area is [1, 9], and its corresponding median is 5;

[0043] S13, based on the different median values ​​determined for different grayscale areas, the median values ​​are sorted in ascending order to determine the median data column of this 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, the set value Y1 is analyzed and confirmed in sequence to ensure that the determined grayscale areas are relatively clustered in value, and a better grayscale area division effect can be achieved. In this way, the related areas with different CT value characteristics in the image can be effectively confirmed and divided one by one. Based on each determined grayscale area, the corresponding grayscale area has a corresponding CT value range, and the corresponding median value can be selected from the existing CT value range to reflect the range value of the grayscale area.

[0044] Step 2: Combine Figure 3 Based on the determined median data column and the set CT value range, the median associated in the median data column is numerically changed, and the grayscale area associated with the median is feature adjusted according to the associated change ratio. Specifically, in order to achieve a better display effect for the associated image, it is necessary to make relevant adjustments to 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 value and the maximum value 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 the preset CT value range (-1000) to 4000, the median data column is numerically stretched and adjusted, 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), Tz is adjusted to 1500 (this 1500 is the median of (-1000) to 4000), and then according to the numerical range F1 of Z1-Tz and the numerical range F2 of 0-2500, the adjustment ratio is locked: (F2÷F1)=Bq, and according to the adjustment ratio Bq, a number of medians before the characteristic median Tz are numerically changed, and the change value is locked: GBo, where o represents different medians in the median data column, GBo=[(Zo-Z1)×Bq]+(-1000), Zo is the relevant median before the characteristic median Tz, and GBo is the change value after the numerical change of Zo;

[0047] Process the subsequent median 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 after the characteristic median Tz, and GBo is the changed value after Uo is changed in value;

[0048] For example: the proposed median data column is 20, 25, 30, 35, 40, and the determined median range is [Z1, Z2] = [20, 40]. The middle value of the value range [20, 40] is 30, corresponding to 1500. 30 is adjusted to 1500. After the numerical value is confirmed, 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 as stretching a group of data columns with a value range of [20, 40] to a data column from (-1000) to 4000. After the relevant values ​​inside 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 middle values ​​to lock them one by one, so as to achieve 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 the median belongs is confirmed. According to the change ratio between the median and the change value, the features of different points in the grayscale area are geometrically changed to change the features of the grayscale area. For example, the corresponding median in the 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, other grayscale points in this grayscale area adopt the same change method to make relevant 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 changed, the brightness adjustment of the image will be more obvious, which can make the entire image achieve a better image display effect.

[0050] Second embodiment

[0051] In 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, and confirm the parameter range based on the identified groups of ratio parameters 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 ratio Zb in the corresponding historical completed images, and then select the minimum value and the maximum value from the groups of area ratio Zb to lock the corresponding area ratio interval, and mark the area ratio interval as the selection standard of the image feature. Specifically, the historical completed images existing in the historical completed data are all detected by the detection components related to the dental mobile phone, so the features associated with the tooth area located in the corresponding area ratio range are relatively consistent. Based on the determined relatively consistent specific features, the corresponding relevant standards can be locked, so as to facilitate the subsequent specific confirmation of the corresponding tooth area, thereby achieving a better determination effect and making the selected tooth features more obvious.

[0055] Step 4: Combination Figure 4, for the dental detection image after feature adjustment, the display brightness partition in the dental detection image is associated and confirmed, and then for the confirmed groups of 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, so that the relevant display brightness partitions will be more obvious, and based on the adjusted display brightness partition area, it can achieve a better feature selection effect;

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

[0057] S41, selecting the center point of the dental detection image, and locking the associated display brightness L of the center point, then confirming the associated display brightness of related points around the center point and calibrating them as Lp, where p represents different points in the dental detection image, performing variance processing on the selected associated display brightness Lp and L in turn, and confirming their corresponding variance values ​​F in turn:

[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 areas associated with the several groups of related points confirmed before this time and 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), where 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 they are 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), other relevant points are selected as the center point in the same processing method to perform relevant confirmation of the same display brightness area;

[0060] S42, after the current same display brightness area is confirmed, randomly select other points in the dental detection image as the center point to perform the same display brightness area confirmation in the same manner, until all the same display brightness areas in the dental detection image are confirmed, and the points that have been confirmed to be within the same display brightness area 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 areas of the same display brightness, and confirms the area Mz of this 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 proportion 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 the selection standard and the difference with the maximum value of the selection standard is the smallest, and 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 tooth areas, and display the marked tooth areas on the display screen;

[0063] By adopting this method of confirming the characteristics of the tooth area, the tooth area can be specifically confirmed based on the specified relevant image features, and its 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, comprising a dental handpiece 1, a collar 2, a display screen 3, a latch 4 and a slot 5, wherein the collar 2 is sleeved on the dental handpiece 1, the display screen 3 is used to display the working image of the dental handpiece on the teeth or alveolar bone, and the latch 4 is arranged on the display screen to drive the display screen to rotate around the axis of the dental handpiece, and the slot 5 is for the latch 4 to be inserted, and the display screen 3 can rotate in any direction around the connection relative to the latch 4, similar to a ball head structure, and the dental handpiece 1 includes but is not limited to a dental pneumatic high-speed handpiece, a dental implant handpiece, a root canal motor and a dental scaler, etc.;

[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, and in 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. Because image fusion is relatively common in the prior art, 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, and HBw=SSw×C1+XXw×C2 is used to determine the pixel value HBw after the spatial point is merged, where C1 and C2 are 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 rather than to limit it. 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 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, relevant confirmation of the median is performed to generate a median data column belonging to the dental detection image; Step 2: 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, and the grayscale area associated with the median value is feature adjusted according to the associated change ratio; 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, so as to determine the selection criteria of the image features; Step 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.

2. The method for optimizing dental detection images according to claim 1, characterized in that: In step 1, the specific sub-steps for generating the median data column are: 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 values: Determine the CT value HD from small to large 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 several selected ranges, the CT values ​​belonging to the corresponding selected ranges are classified, the CT values ​​of the same range are determined, and then the areas associated with the CT values ​​of the same range are marked as the same characteristic grayscale areas, and the grayscale range is determined based on several groups of CT values ​​associated with the same characteristic grayscale areas, and then the median of the grayscale range is determined; S13. Based on different median values ​​determined in different grayscale areas, the median values ​​are sorted from small to large to determine a median data column of the image.

3. The method for optimizing dental detection images according to claim 1, characterized in that: In step 2, the specific sub-steps of adjusting the features of the grayscale area associated with the median are: S21, based on the minimum value and the maximum value 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 the preset CT value range (-1000) to 4000, and according to the numerical range F1 of Z1-Tz and the numerical range F2 of -1000 to 1500, the adjustment ratio is locked: (F2÷F1)=Bq, and the numerical values ​​of several medians before the characteristic median Tz are changed according to the adjustment ratio Bq, and the changed value is locked: GBo, where o represents different medians in the median data column, GBo=[(Zo-Z1)×Bq]+(-1000), and Zo is the related median before the characteristic median Tz, and GBo is the changed value after the numerical change of Zo, and then the median data column is numerically stretched to adjust Z1 to -1000 and Z2 to 4000, and based on the characteristic median Tz of the median range in the median data column, Tz is adjusted to 1500; Process the subsequent median of the characteristic median Tz: GBo=4000-[(Z2-Uo)×Bq], where Uo is the relevant median 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, the grayscale area to which the median belongs is confirmed, and according to the change ratio between the median and the change value, the features of different points in the grayscale area are geometrically changed to change the features of the grayscale area.

4. The method for optimizing dental detection images according to claim 3, characterized in that: In the step S22, there is another calculation method for the formula GBo=4000-[(Z2-Uo)×Bq] for processing the subsequent median of the characteristic median Tz: GBo=1500+[(Z2-Uo)×Bq].

5. The method for optimizing dental detection images according to claim 1, characterized in that: 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 ​​therefrom based on the groups of area ratios Zb to lock the corresponding area ratio interval, and mark this area ratio interval as the selection standard for image features.

6. The method for optimizing dental detection images according to claim 1, characterized in that: In the step 4, the specific sub-steps of locking the tooth area in the dental detection image are: S41, selecting the center point of the dental detection image, and locking the associated display brightness L of the center point, then confirming the associated display brightness of related points around the center point and calibrating them as Lp, where p represents different points in the dental detection image, performing variance processing on the selected associated display brightness Lp and L in turn, and confirming their corresponding variance values ​​F in turn: 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; If F≥Y2, the relevant points selected this time are not selected, and other relevant points around are selected, and they are calculated and processed in sequence to lock the same display brightness area. If other relevant points around the center point all meet F≥Y2, other relevant points are selected and processed in the same way as the center point to perform relevant confirmation of the same display brightness area; S42, after the current same display brightness area is confirmed, randomly select other points in the dental detection image as the center point to perform the same display brightness area confirmation in the same manner, until all the same display brightness areas in the dental detection image are confirmed, and the points that have been confirmed to be within the same display brightness area 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 areas of the same display brightness, and confirms the area Mz of this 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 proportion values ​​Z of the regions with the same display brightness 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, and 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.

7. A dental handpiece, used for performing correlation processing on a tooth area generated by a dental detection image optimization processing method according to any one of claims 1 or 6, characterized in that: Includes dental handpieces, rings, display screens, latches, and slots; The sleeve ring is mounted on the dental handpiece, and its display screen is used to display the working image of the dental handpiece on the teeth or alveolar bone, and the pin is arranged on the display screen to drive 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 the oral cavity and generate a detection image, and the display screen is a ball head structure, which can rotate in any direction around the connection 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.

Images