CT three-dimensional reconstruction slice data compression method and system
By employing contour detection and pixel supplementation techniques in grayscale channel images during CT 3D reconstruction slice data processing, the problem of balancing compression efficiency and image quality was solved, achieving efficient and lossless data compression and image optimization.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-02-25
- Publication Date
- 2026-05-15
AI Technical Summary
Existing technologies struggle to balance compression efficiency and image quality in CT 3D reconstruction slice data processing, resulting in massive data volumes, increased transmission and storage pressure, and compromised image display clarity and security inspection accuracy.
By using contour detection, cropping, filtering, binarization, and minimum bounding rectangle processing based on grayscale channel images, the effective object contours are accurately extracted and pixel supplementation is performed, redundant data is removed, and effective information is retained.
It achieves efficient compression of slice data, reduces redundant information, improves the accuracy and stability of image processing, reduces transmission and storage pressure, and optimizes image display effects.
Smart Images
Figure CN122053758A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of slice data processing technology, and in particular to a method and system for compressing CT three-dimensional reconstruction slice data. Background Technology
[0002] In CT security screening equipment, 3D reconstruction is a core component. The generated 3D slice data contains three channels: grayscale, atomic number, and electron density, with each channel's image size being 512×512 pixels. Currently, existing technologies generally retain all pixel values from each channel without designing a dedicated compression scheme for this type of data, and there is a lack of supporting compression methods and systems.
[0003] The lack of a full-data saving mode and dedicated compression technology results in a massive amount of CT scan data, significantly increasing the pressure on data transmission and storage and reducing system operating efficiency. Meanwhile, redundant information from the conveyor belt and scanning noise mixed in the data will affect the clarity of the 3D image display and the accuracy of security inspection interpretation. Existing general compression technologies cannot adapt to its multi-channel, high-resolution characteristics, making it difficult to balance compression efficiency and image quality.
[0004] Furthermore, there is an urgent need for a compression method and system adapted to the characteristics of CT 3D reconstruction slice data. This system should achieve efficient data compression while ensuring that critical security information is not lost, thereby reducing transmission and storage pressure, improving system efficiency, and optimizing image display effects, thus providing technical support for accurate security checks. Summary of the Invention
[0005] Based on this, the purpose of the present invention is to provide a method and system for compressing CT three-dimensional reconstruction slice data, so as to solve the problem that it is difficult to balance compression efficiency and image quality in the process of processing slice data in the prior art.
[0006] The first aspect of the present invention proposes: A method for compressing CT three-dimensional reconstruction slice data, wherein the method includes: Based on the grayscale channel image of the slice, the reconstructed original slice data is used to perform contour detection calculations to detect the complete rectangle occupied by the real scanned object in the grayscale channel image. The target position and size of the complete rectangle are detected, and the grayscale channel image is cropped simultaneously according to the target position and size to generate the corresponding intermediate image; The intermediate image is sequentially filtered, binarized, and contour detection is performed to determine the contour rectangles corresponding to each valid object in the grayscale channel image. Calculate the minimum bounding rectangle corresponding to each of the outline rectangles, and simultaneously perform pixel supplementation processing on the intermediate image based on the minimum bounding rectangles to complete the compression processing of the slices.
[0007] The beneficial effects of this invention are as follows: This technical solution first locates the complete rectangle of the scanned object in the grayscale slice image and removes invalid background. Then, it accurately extracts the effective object contour through filtering, binarization, and contour detection. Combined with the minimum bounding rectangle for pixel supplementation, it not only greatly reduces redundant data to improve slice compression efficiency, but also preserves the complete shape and image details of the effective object through accurate contour detection and regularized pixel supplementation. This avoids problems such as image distortion and loss of effective information during compression. By working together from multiple aspects such as redundant data removal, effective information preservation, and pixel regularization optimization, it effectively solves the technical problem of difficulty in balancing compression efficiency and image quality in existing slice data processing, while improving the accuracy and stability of slice processing.
[0008] Furthermore, the step of cropping the grayscale channel image according to the target location and the target size to generate a corresponding intermediate image includes: Based on the target position and the target size, the actual position of the conveyor belt is detected in the complete rectangle, and the pixel thickness of the conveyor belt in the grayscale channel image is detected simultaneously. Based on the actual position of the conveyor belt, the row pixels in the complete rectangle are detected, and the row pixels and the pixel thickness are simultaneously cropped to generate the intermediate image.
[0009] Furthermore, the step of performing pixel supplementation processing on the intermediate image based on the minimum bounding rectangle to correspondingly complete the compression processing of the slice includes: Add the row pixels of the conveyor belt to the bottom of the minimum bounding rectangle to generate the target rectangle corresponding to the area where the actual scanned object is located; Based on the size of the target rectangle, each slice is uniformly cropped, and simultaneously, data with pixels of 0 in the atomic number channel and density channel images are removed from the cropped slice data to complete the slice compression process.
[0010] Furthermore, the step of completing the compression process of the slice also includes: When compressing data, k represents grayscale channel data, z represents atomic number channel data, r represents electron density channel data, i represents a pixel position in the grayscale channel, and N represents the number of effective pixels. The pixels of the grayscale channels of the slice are traversed sequentially. When k[i]>0, the values of z[i] and r[i] are retained and placed into the saved z channel queue and r channel queue in the order of the traversed pixels, and counted in the number of effective pixels N.
[0011] Furthermore, the step of completing the compression process of the slice also includes: When k[i]=0, the corresponding z[i] and r[i] values are discarded and not saved. For grayscale channel data, no deletion or modification is made, and it is saved as is.
[0012] Furthermore, the step of performing pixel supplementation processing on the intermediate image based on the minimum bounding rectangle includes: When restoring data, n represents the effective pixel position of the atomic number channel and the electron density channel, i represents a pixel position of the grayscale channel, k represents the grayscale channel data, z represents the compressed atomic number channel data, r represents the compressed electron density channel data, Z represents the restored atomic number channel data, and R represents the restored electron density channel data. The pixels of the grayscale channel of the slice are traversed in order. When k[i]>0, the value of the corresponding effective pixel n is found in order through the pixel position i of the grayscale channel, so that Z[i]=z[n] and R[i]=r[n].
[0013] Furthermore, the step of performing pixel supplementation processing on the intermediate image based on the minimum bounding rectangle also includes: When k[i]=0, make Z[i]=0 and R[i]=0.
[0014] The second aspect of the present invention proposes: A CT three-dimensional reconstruction slice data compression system, wherein the system comprises: The calculation module is used to perform contour detection calculation on the reconstructed original slice data based on the gray-scale channel image of the slice, so as to detect the complete rectangle occupied by the real scanned object in the gray-scale channel image. The detection module is used to detect the target position and target size of the complete rectangle, and simultaneously crop the grayscale channel image according to the target position and target size to generate the corresponding intermediate image; The processing module is used to sequentially filter, binarize, and perform contour detection calculations on the intermediate image to determine the contour rectangles corresponding to each valid object in the grayscale channel image. The compression module is used to calculate the minimum bounding rectangle corresponding to each of the outline rectangles, and simultaneously perform pixel supplementation processing on the intermediate image according to the minimum bounding rectangle to complete the compression processing of the slice.
[0015] Furthermore, the detection module is specifically used for: Based on the target position and the target size, the actual position of the conveyor belt is detected in the complete rectangle, and the pixel thickness of the conveyor belt in the grayscale channel image is detected simultaneously. Based on the actual position of the conveyor belt, the row pixels in the complete rectangle are detected, and the row pixels and the pixel thickness are simultaneously cropped to generate the intermediate image.
[0016] Furthermore, the compression module is specifically used for: Add the row pixels of the conveyor belt to the bottom of the minimum bounding rectangle to generate the target rectangle corresponding to the area where the actual scanned object is located; Based on the size of the target rectangle, each slice is uniformly cropped, and simultaneously, data with pixels of 0 in the atomic number channel and density channel images are removed from the cropped slice data to complete the slice compression process.
[0017] Furthermore, the compression module is specifically used for: When compressing data, k represents grayscale channel data, z represents atomic number channel data, r represents electron density channel data, i represents a pixel position in the grayscale channel, and N represents the number of effective pixels. The pixels of the grayscale channels of the slice are traversed sequentially. When k[i]>0, the values of z[i] and r[i] are retained and placed into the saved z channel queue and r channel queue in the order of the traversed pixels, and counted in the number of effective pixels N.
[0018] Furthermore, the compression module is specifically used for: When k[i]=0, the corresponding z[i] and r[i] values are discarded and not saved. For grayscale channel data, no deletion or modification is made, and it is saved as is.
[0019] Furthermore, the compression module is specifically used for: When restoring data, n represents the effective pixel position of the atomic number channel and the electron density channel, i represents a pixel position of the grayscale channel, k represents the grayscale channel data, z represents the compressed atomic number channel data, r represents the compressed electron density channel data, Z represents the restored atomic number channel data, and R represents the restored electron density channel data. The pixels of the grayscale channel of the slice are traversed in order. When k[i]>0, the value of the corresponding effective pixel n is found in order through the pixel position i of the grayscale channel, so that Z[i]=z[n] and R[i]=r[n].
[0020] Furthermore, the compression module is specifically used for: When k[i]=0, make Z[i]=0 and R[i]=0.
[0021] The third aspect of the present invention proposes: A computer includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the CT three-dimensional reconstruction slice data compression method as described above.
[0022] The fourth aspect of the present invention proposes: A readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the CT three-dimensional reconstruction slice data compression method as described above.
[0023] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description
[0024] Figure 1 A flowchart of a CT three-dimensional reconstruction slice data compression method provided in the first embodiment of the present invention; Figure 2 This is a schematic diagram of image binarization processing in the CT three-dimensional reconstruction slice data compression method provided in the second embodiment of the present invention; Figure 3 This is a schematic diagram of the minimum bounding rectangle in the CT three-dimensional reconstruction slice data compression method provided in the second embodiment of the present invention; Figure 4 This is a schematic diagram of the target rectangle in the CT three-dimensional reconstruction slice data compression method provided in the second embodiment of the present invention; Figure 5 This is a structural block diagram of a CT three-dimensional reconstruction slice data compression system provided in the third embodiment of the present invention.
[0025] The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation
[0026] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
[0027] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.
[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.
[0029] Please see Figure 1 The figure shows a CT three-dimensional reconstruction slice data compression method provided in the first embodiment of the present invention. The CT three-dimensional reconstruction slice data compression method provided in this embodiment can accurately complete the identification and compression of data, thereby improving the data compression efficiency.
[0030] Specifically, this embodiment provides: A method for compressing CT three-dimensional reconstruction slice data, wherein the method includes: Step S10: Based on the grayscale channel image of the slice, perform contour detection calculation on the reconstructed original slice data to detect the complete rectangle occupied by the real scanned object in the grayscale channel image. Step S20: Detect the target position and target size of the complete rectangle, and simultaneously crop the grayscale channel image according to the target position and target size to generate the corresponding intermediate image; Step S30: The intermediate image is sequentially filtered, binarized, and contour detection calculated to determine the contour rectangles corresponding to each valid object in the grayscale channel image. Step S40: Calculate the minimum bounding rectangle corresponding to each of the outline rectangles, and simultaneously perform pixel supplementation processing on the intermediate image based on the minimum bounding rectangle to complete the compression processing of the slice.
[0031] Second Embodiment Furthermore, the step of cropping the grayscale channel image according to the target location and the target size to generate a corresponding intermediate image includes: Based on the target position and the target size, the actual position of the conveyor belt is detected in the complete rectangle, and the pixel thickness of the conveyor belt in the grayscale channel image is detected simultaneously. Based on the actual position of the conveyor belt, the row pixels in the complete rectangle are detected, and the row pixels and the pixel thickness are simultaneously cropped to generate the intermediate image.
[0032] Furthermore, the step of performing pixel supplementation processing on the intermediate image based on the minimum bounding rectangle to correspondingly complete the compression processing of the slice includes: Add the row pixels of the conveyor belt to the bottom of the minimum bounding rectangle to generate the target rectangle corresponding to the area where the actual scanned object is located; Based on the size of the target rectangle, each slice is uniformly cropped, and simultaneously, data with pixels of 0 in the atomic number channel and density channel images are removed from the cropped slice data to complete the slice compression process.
[0033] Furthermore, the step of completing the compression process of the slice also includes: When compressing data, k represents grayscale channel data, z represents atomic number channel data, r represents electron density channel data, i represents a pixel position in the grayscale channel, and N represents the number of effective pixels. The pixels of the grayscale channels of the slice are traversed sequentially. When k[i]>0, the values of z[i] and r[i] are retained and placed into the saved z channel queue and r channel queue in the order of the traversed pixels, and counted in the number of effective pixels N.
[0034] Furthermore, the step of completing the compression process of the slice also includes: When k[i]=0, the corresponding z[i] and r[i] values are discarded and not saved. For grayscale channel data, no deletion or modification is made, and it is saved as is.
[0035] Furthermore, the step of performing pixel supplementation processing on the intermediate image based on the minimum bounding rectangle includes: When restoring data, n represents the effective pixel position of the atomic number channel and the electron density channel, i represents a pixel position of the grayscale channel, k represents the grayscale channel data, z represents the compressed atomic number channel data, r represents the compressed electron density channel data, Z represents the restored atomic number channel data, and R represents the restored electron density channel data. The pixels of the grayscale channel of the slice are traversed in order. When k[i]>0, the value of the corresponding effective pixel n is found in order through the pixel position i of the grayscale channel, so that Z[i]=z[n] and R[i]=r[n].
[0036] Furthermore, the step of performing pixel supplementation processing on the intermediate image based on the minimum bounding rectangle also includes: When k[i]=0, make Z[i]=0 and R[i]=0.
[0037] In addition, it should be noted in this embodiment that the CT three-dimensional reconstruction slice data compression method provided in this embodiment is specifically implemented by the following steps: The specific application of this technical solution is as follows: For each slice, the overall contour is identified, the contour rectangle containing the real and valid scanned object in the slice image is found, and this rectangle is recorded. Then, the image is cropped based on this rectangle (the cropping here refers to uniformly cropping all three channels of the image within each slice), removing redundant pixel data. Next, the grayscale image pixels within the cropped rectangle are traversed and scanned. If a grayscale pixel at a certain position is 0, then the values of the atomic number channel and density channel at that pixel must also be 0, and can be removed. In this way, the blank pixel data of the atomic number channel and density channel within the cropped rectangle is precisely removed.
[0038] When restoring image data, first restore the blank pixels of the atomic number channel and density channel within the cropping rectangle. That is, set the atomic number channel and density channel to 0 at the positions where the grayscale image pixel is 0, and restore the pixels at other positions to the originally recorded pixel values. Then restore the cropping rectangle to the original 512×512 size image, first generate a complete image data with all pixels set to 0, and then restore the pixels of the effective scanned object according to this cropping rectangle, while filling the pixels outside the rectangular area with 0. In this way, the effective restoration of the slice data is achieved.
[0039] The above outlines the principles and approaches for compressing and restoring sliced data. The specific implementation steps are as follows: Here, we use the grayscale channel image of the slice as the reference, perform contour detection calculation on the reconstructed original slice data, find the complete rectangle occupied by the real scanned object within the grayscale channel image of the slice, and record the position and size of this rectangle.
[0040] In stable CT slice data, the position of the conveyor belt in the image remains unchanged, and the pixel thickness of the conveyor belt on the slice image is also consistent. Therefore, we determine the row Rc and pixel thickness d of the conveyor belt in the image pixels through the image; we then perform the first cropping step on the image to obtain a grayscale image with the conveyor belt portion removed.
[0041] After cropping, the grayscale image with the conveyor belt portion removed is subjected to median filtering and Gaussian filtering to remove unwanted noise and artifacts, and then image binarization is performed, as follows: Figure 2 As shown.
[0042] Then, contour detection is performed on the binarized image to determine the contour rectangles of all valid objects within the slice. Next, the minimum bounding rectangle (boundRect) of these contour rectangles is calculated. Specifically, for example... Figure 3 As shown.
[0043] Since the conveyor belt pixels were previously cropped at the bottom of the image, we now need to add back the conveyor belt thickness pixels. We add the conveyor belt thickness `d` rows of pixels to the bottom of the smallest bounding rectangle to generate the actual rectangle `Rect_i` (where `i` represents the slice number) of the scanned object area. That is, `Rect_i.bottom = boundRect.bottom + d`. Specifically, as shown... Figure 4 As shown.
[0044] To prevent errors in slice contour detection from cropping smaller, but actually needed, objects, we perform a uniform cropping of all slices generated from a full rotation of the CT spiral data. For example, a certain CT scanner generates 70 slices from a single full rotation of the slip ring. We stack the valid rectangles detected in step 1 from these 70 slices, calculate the minimum bounding rectangle valid_Rect encompassing all 70 valid rectangles, and then uniformly crop each channel image of the 70 slices using this minimum bounding rectangle. This minimum bounding rectangle valid_Rect is then recorded as the cropping rectangle in the slice data.
[0045] Then, remove data with a pixel value of 0 from the atomic number channel and density channel images of the cropped slice data.
[0046] In the CT security inspection equipment software system, after a series of calculations and processing, CT scan data generates a bunch of slice data files. Then, according to the principles and methods described below, invalid data pixels with a value of 0 in the two channels of the slice are removed, thereby reducing the number of bytes in the slice file.
[0047] As mentioned earlier, each slice in CT 3D reconstruction contains image data from three channels. These three channels represent the grayscale data (denoted by k), atomic number data (denoted by z), and electron density data (denoted by r) of the scanned object within that slice area. It's important to note that the image composed of these three channels has the same length and width, and each pixel corresponds one-to-one. That is, if a pixel in the grayscale image data is 0, it means that there is no matter at that location in the scanned object; it is only air. The corresponding atomic number and electron density channels at the same pixel location are also 0. Based on this uniform characteristic of the three-channel slice data, we can retain only the complete grayscale image channels, while discarding the corresponding grayscale pixels with 0 values for the other two channels to reduce redundancy. Only the valid pixel values greater than 0 are retained, and these valid pixel values are saved in positional order. When it is necessary to restore the complete image data of the atomic number channel and the electron density channel, we can use the previously saved complete grayscale channel data to search in pixel order. When the grayscale channel pixel is 0, the atomic number channel and the electron density channel at the same pixel will also be restored to 0. When the grayscale channel pixel is greater than 0, the atomic number channel and the electron density channel at that pixel will use the corresponding pixel value that was originally saved in order.
[0048] The following uses mathematical formulas to describe the data compression and recovery process: When compressing data, k represents grayscale channel data, z represents atomic number channel data, r represents electron density channel data, i represents a pixel position in the grayscale channel, and N represents the number of effective pixels. The pixels in the grayscale channels are traversed sequentially. When k[i] > 0, the values of z[i] and r[i] are retained and placed into the saved z-channel queue and r-channel queue in the order of the traversed pixels, and counted in the number of effective pixels N. When k[i] = 0, the corresponding z[i] and r[i] values are discarded and not saved. For grayscale channel data, no deletions or modifications are made; it is saved as is.
[0049] When restoring data, let n represent the effective pixel position of the atomic number channel and the electron density channel, i represent a pixel position in the grayscale channel, k represent the grayscale channel data, z represent the compressed atomic number channel data, r represent the compressed electron density channel data, Z represent the restored atomic number channel data, and R represent the restored electron density channel data. Similarly, the pixels of the grayscale channel are traversed sequentially. When k[i] > 0, the value of the corresponding effective pixel n can be found sequentially through a pixel position i in the grayscale channel, thus making Z[i] = z[n] and R[i] = r[n]. When k[i] = 0, Z[i] = 0 and R[i] = 0.
[0050] This method can significantly reduce the amount of pixel data in the atomic number and electron density channels of the slice data without loss, and can quickly restore the original complete channel data. In practice, most pixels in the slice data are invalid values of 0, but when deleting the invalid pixel values of 0 in two channels of the three-way data block, the data compression is approximately 50%.
[0051] Please see Figure 5 The third embodiment of the present invention provides: A CT three-dimensional reconstruction slice data compression system, wherein the system comprises: The calculation module is used to perform contour detection calculation on the reconstructed original slice data based on the gray-scale channel image of the slice, so as to detect the complete rectangle occupied by the real scanned object in the gray-scale channel image. The detection module is used to detect the target position and target size of the complete rectangle, and simultaneously crop the grayscale channel image according to the target position and target size to generate the corresponding intermediate image; The processing module is used to sequentially filter, binarize, and perform contour detection calculations on the intermediate image to determine the contour rectangles corresponding to each valid object in the grayscale channel image. The compression module is used to calculate the minimum bounding rectangle corresponding to each of the outline rectangles, and simultaneously perform pixel supplementation processing on the intermediate image according to the minimum bounding rectangle to complete the compression processing of the slice.
[0052] Furthermore, the detection module is specifically used for: Based on the target position and the target size, the actual position of the conveyor belt is detected in the complete rectangle, and the pixel thickness of the conveyor belt in the grayscale channel image is detected simultaneously. Based on the actual position of the conveyor belt, the row pixels in the complete rectangle are detected, and the row pixels and the pixel thickness are simultaneously cropped to generate the intermediate image.
[0053] Furthermore, the compression module is specifically used for: Add the row pixels of the conveyor belt to the bottom of the minimum bounding rectangle to generate the target rectangle corresponding to the area where the actual scanned object is located; Based on the size of the target rectangle, each slice is uniformly cropped, and simultaneously, data with pixels of 0 in the atomic number channel and density channel images are removed from the cropped slice data to complete the slice compression process.
[0054] Furthermore, the compression module is specifically used for: When compressing data, k represents grayscale channel data, z represents atomic number channel data, r represents electron density channel data, i represents a pixel position in the grayscale channel, and N represents the number of effective pixels. The pixels of the grayscale channels of the slice are traversed sequentially. When k[i]>0, the values of z[i] and r[i] are retained and placed into the saved z channel queue and r channel queue in the order of the traversed pixels, and counted in the number of effective pixels N.
[0055] Furthermore, the compression module is specifically used for: When k[i]=0, the corresponding z[i] and r[i] values are discarded and not saved. For grayscale channel data, no deletion or modification is made, and it is saved as is.
[0056] Furthermore, the compression module is specifically used for: When restoring data, n represents the effective pixel position of the atomic number channel and the electron density channel, i represents a pixel position of the grayscale channel, k represents the grayscale channel data, z represents the compressed atomic number channel data, r represents the compressed electron density channel data, Z represents the restored atomic number channel data, and R represents the restored electron density channel data. The pixels of the grayscale channel of the slice are traversed in order. When k[i]>0, the value of the corresponding effective pixel n is found in order through the pixel position i of the grayscale channel, so that Z[i]=z[n] and R[i]=r[n].
[0057] Furthermore, the compression module is specifically used for: When k[i]=0, make Z[i]=0 and R[i]=0.
[0058] The fourth embodiment of the present invention provides a computer, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the CT three-dimensional reconstruction slice data compression method as described above.
[0059] The fifth embodiment of the present invention provides a readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the CT three-dimensional reconstruction slice data compression method as described above.
[0060] In summary, the CT three-dimensional reconstruction slice data compression method and system provided in the above embodiments of the present invention can accurately and effectively complete the data compression process, thereby improving the compression efficiency.
[0061] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can reside in the same processor; or the above modules can be located in different processors in any combination.
[0062] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.
[0063] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.
[0064] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.
[0065] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.
[0066] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.
Claims
1. A method for compressing CT three-dimensional reconstruction slice data, characterized in that, The method includes: Based on the grayscale channel image of the slice, the reconstructed original slice data is used to perform contour detection calculations to detect the complete rectangle occupied by the real scanned object in the grayscale channel image. The target position and size of the complete rectangle are detected, and the grayscale channel image is cropped simultaneously according to the target position and size to generate the corresponding intermediate image; The intermediate image is sequentially filtered, binarized, and contour detection is performed to determine the contour rectangles corresponding to each valid object in the grayscale channel image. Calculate the minimum bounding rectangle corresponding to each of the outline rectangles, and simultaneously perform pixel supplementation processing on the intermediate image based on the minimum bounding rectangles to complete the compression processing of the slices.
2. The CT three-dimensional reconstruction slice data compression method according to claim 1, characterized in that, The step of cropping the grayscale channel image according to the target location and the target size to generate a corresponding intermediate image includes: Based on the target position and the target size, the actual position of the conveyor belt is detected in the complete rectangle, and the pixel thickness of the conveyor belt in the grayscale channel image is detected simultaneously. Based on the actual position of the conveyor belt, the row pixels in the complete rectangle are detected, and the row pixels and the pixel thickness are simultaneously cropped to generate the intermediate image.
3. The CT three-dimensional reconstruction slice data compression method according to claim 2, characterized in that, The step of performing pixel supplementation processing on the intermediate image based on the minimum bounding rectangle to correspondingly complete the compression processing of the slice includes: Add the row pixels of the conveyor belt to the bottom of the minimum bounding rectangle to generate the target rectangle corresponding to the area where the actual scanned object is located; Based on the size of the target rectangle, each slice is uniformly cropped, and simultaneously, data with pixels of 0 in the atomic number channel and density channel images are removed from the cropped slice data to complete the slice compression process.
4. The CT three-dimensional reconstruction slice data compression method according to claim 1, characterized in that, The step of completing the compression process of the slice further includes: When compressing data, k represents grayscale channel data, z represents atomic number channel data, r represents electron density channel data, i represents a pixel position in the grayscale channel, and N represents the number of effective pixels. The pixels of the grayscale channels of the slice are traversed sequentially. When k[i]>0, the values of z[i] and r[i] are retained and placed into the saved z channel queue and r channel queue in the order of the traversed pixels, and counted in the number of effective pixels N.
5. The CT three-dimensional reconstruction slice data compression method according to claim 4, characterized in that, The step of completing the compression process of the slice further includes: When k[i]=0, the corresponding z[i] and r[i] values are discarded and not saved. For grayscale channel data, no deletion or modification is made, and it is saved as is.
6. The CT three-dimensional reconstruction slice data compression method according to claim 1, characterized in that, The step of performing pixel supplementation processing on the intermediate image based on the minimum bounding rectangle includes: When restoring data, n represents the effective pixel position of the atomic number channel and the electron density channel, i represents a pixel position of the grayscale channel, k represents the grayscale channel data, z represents the compressed atomic number channel data, r represents the compressed electron density channel data, Z represents the restored atomic number channel data, and R represents the restored electron density channel data. The pixels of the grayscale channel of the slice are traversed in order. When k[i]>0, the value of the corresponding effective pixel n is found in order through the pixel position i of the grayscale channel, so that Z[i]=z[n] and R[i]=r[n].
7. The CT three-dimensional reconstruction slice data compression method according to claim 6, characterized in that, The step of performing pixel supplementation processing on the intermediate image based on the minimum bounding rectangle further includes: When k[i]=0, make Z[i]=0 and R[i]=0.
8. A CT three-dimensional reconstruction slice data compression system, characterized in that, The system includes: The calculation module is used to perform contour detection calculation on the reconstructed original slice data based on the gray-scale channel image of the slice, so as to detect the complete rectangle occupied by the real scanned object in the gray-scale channel image. The detection module is used to detect the target position and target size of the complete rectangle, and simultaneously crop the grayscale channel image according to the target position and target size to generate the corresponding intermediate image; The processing module is used to sequentially filter, binarize, and perform contour detection calculations on the intermediate image to determine the contour rectangles corresponding to each valid object in the grayscale channel image. The compression module is used to calculate the minimum bounding rectangle corresponding to each of the outline rectangles, and simultaneously perform pixel supplementation processing on the intermediate image according to the minimum bounding rectangle to complete the compression processing of the slice.
9. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the CT three-dimensional reconstruction slice data compression method as described in any one of claims 1 to 7.
10. A readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the CT three-dimensional reconstruction slice data compression method as described in any one of claims 1 to 7.