Image processing method and apparatus, and transmission scanning system

Abstract

The present disclosure relates to the technical field of radiation imaging. Provided are an image processing method and apparatus, and a transmission scanning system. The image processing method comprises: performing sampling processing on a transmission image, so as to obtain an image to be processed, wherein the transmission image is obtained by using a multi-column detector to detect the transmission intensity of an object during transmission scanning; determining a region to be identified in said image; using a candidate depth to perform reconstruction processing on said region, so as to obtain a reconstruction result for said region; determining the edge sharpness of the reconstruction result; and when the edge sharpness is greater than a sharpness threshold value, using the candidate depth as the depth of said region.

Description

Image processing method, device and transmission scanning system

[0001] Cross-reference to related applications

[0002] This application is based on and claims priority to CN application No. 202410584982.7, filed on May 11, 2024, the disclosure of which is incorporated herein in its entirety. TECHNICAL FIELD

[0003] The present disclosure relates to the field of radiation imaging technology, and in particular to an image processing method, device and transmission scanning system. BACKGROUND

[0004] X-ray transmission examination technology can be used to examine an object by measuring the degree of radiation intensity attenuation of X-rays after passing through the object. The X-ray transmission examination technology has the advantages of strong penetration, short measurement time and high image resolution, and is widely used in cargo examination in customs, airports and other places.

[0005] The transmission image of an object contains the projection information of the object in one or more ray irradiation directions. Due to the information overlap in the ray irradiation direction, it is difficult to directly extract the depth information of the object from the transmission image, which brings inconvenience to the examination.

[0006] In related technologies, a scanning device including a sparse area array detector is used to perform transmission scanning on an object to obtain scanning data. Then, by performing reconstruction, filtering out defocused pixel values and other processing on the scanning data, the identification of the depth information of the object is realized. SUMMARY

[0007] The present disclosure provides an image processing method, device and transmission scanning system.

[0008] According to a first aspect of the present disclosure, an image processing method is provided, comprising: performing sampling processing on a transmission image to obtain a to-be-processed image, the transmission image being obtained by detecting the transmission intensity when performing transmission scanning on an object by using a plurality of columns of detectors; determining a to-be-identified region from the to-be-processed image; performing reconstruction processing on the to-be-identified region by using a candidate depth to obtain a reconstruction result of the to-be-identified region; determining an edge sharpness of the reconstruction result; and in a case where the edge sharpness is greater than a sharpness threshold, taking the candidate depth as the depth of the to-be-identified region.

[0009] In some embodiments, the transmission image comprises images acquired by the plurality of columns of detectors under each of a plurality of pulses, and the sampling the transmission image to obtain the to-be-processed image comprises: extracting K pixel points from each row of pixel points in the image acquired under each of the plurality of pulses to obtain a pixel point sequence corresponding to the image acquired under each of the plurality of pulses, K being an integer greater than or equal to 1 and less than N, N being the number of columns of crystal units of the plurality of columns of detectors; and splicing the pixel point sequences corresponding to the images acquired under each of the plurality of pulses to obtain the to-be-processed image.

[0010] In some embodiments, K is 1, and the extracting K pixel points from each row of pixel points in the image acquired under each of the plurality of pulses comprises: extracting a q-th pixel point from an i-th row in the image acquired under each of the plurality of pulses according to an element value q of an i-th element in a sampling sequence, i taking all integer values from 1 to M, M being the number of rows of crystal units of the plurality of columns of detectors, and q being an integer greater than or equal to 1 and less than or equal to N.

[0011] In some embodiments, the sampling sequence comprises a first sampling sequence, and element values in the first sampling sequence are determined based on a random function.

[0012] In some embodiments, the sampling sequence comprises a second sampling sequence, and an arrangement of pixel points extracted by the second sampling sequence conforms to a specified pattern.

[0013] In some embodiments, the determining the edge sharpness of the reconstruction result comprises: extracting edge pixel points of the reconstruction result by using an edge extraction algorithm; and determining the edge sharpness of the reconstruction result according to the edge pixel points.

[0014] In some embodiments, the determining the edge sharpness of the reconstruction result according to the edge pixel points comprises: calculating a number of edge pixel points contained in a specified neighborhood of each pixel point in the reconstruction result; calculating a number of pixel points whose number of edge pixel points is less than a quantity threshold; and determining the edge sharpness of the reconstruction result according to the number of pixel points whose number of edge pixel points is less than the quantity threshold and a total number of edge pixel points in the reconstruction result.

[0015] In some embodiments, the determining the edge sharpness of the reconstruction result according to the number of pixel points whose number of edge pixel points is less than the quantity threshold and the total number of edge pixel points in the reconstruction result comprises: taking a ratio of the number of pixel points whose number of edge pixel points is less than the quantity threshold to the total number of edge pixel points in the reconstruction result as the edge sharpness of the reconstruction result.

[0016] In some embodiments, the reconstructing the to-be-identified region according to the candidate depth comprises: determining a focusing ratio according to the distance from the multi-column detector to the ray source and the candidate depth; and performing focusing processing on a coordinate component of a pixel point in the to-be-identified region in a specified direction according to the focusing ratio, the specified direction being parallel to a scanning direction of the object.

[0017] In some embodiments, the determining the focusing ratio according to the distance from the multi-column detector to the ray source and the candidate depth comprises: calculating a difference between the distance from the multi-column detector to the ray source and the candidate depth; and taking a ratio of the difference to the distance from the multi-column detector to the ray source as the focusing ratio.

[0018] In some embodiments, the image processing method further comprises: in a case where the edge sharpness is less than or equal to the sharpness threshold, iteratively performing the candidate depth updating, the region reconstructing according to the updated candidate depth, and the edge sharpness determining until the edge sharpness is greater than the sharpness threshold.

[0019] In some embodiments, the image processing method further comprises: reconstructing the to-be-identified region according to a depth of the to-be-identified region to obtain a reconstructed image.

[0020] In some embodiments, the image processing method further comprises: displaying the reconstructed image and the depth information of the to-be-identified region.

[0021] According to a second aspect of the present disclosure, an image processing apparatus is provided, comprising: a memory; and a processor coupled to the memory, the processor being configured to execute an image processing method as previously described based on instructions stored in the memory.

[0022] According to a third aspect of the present disclosure, a transmission scanning system is provided, comprising: an image processing apparatus as previously described; a multi-column detector; and a ray source.

[0023] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, having stored thereon computer program instructions which, when executed by a processor, implement an image processing method as previously described.

[0024] According to a fifth aspect of the present disclosure, a computer program product is provided, having stored thereon computer program instructions which, when executed by a processor, implement an image processing method as previously described.

[0025] According to a sixth aspect of the present disclosure, a computer program is provided, comprising: instructions which, when executed by a processor, cause the processor to perform an image processing method as previously described.

[0026] Other features and advantages of the present disclosure will be apparent from the detailed description of the exemplary embodiments thereof, taken in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0027] The accompanying drawings, which form a part of the specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0028] The present disclosure can be understood more readily by reference to the following detailed description of the exemplary embodiments of the present disclosure and the attached drawings.

[0029] FIG. 1 is a schematic diagram of the principle of a transmission image containing depth information.

[0030] FIG. 2 is a schematic diagram of a flow of an image processing method according to some embodiments of the present disclosure.

[0031] FIG. 3 is a schematic diagram of the principle of image sampling according to some embodiments of the present disclosure.

[0032] FIG. 4 is a schematic diagram of the principle of image sampling according to some other embodiments of the present disclosure.

[0033] FIG. 5 is a schematic diagram of a flow of a region reconstruction process according to some embodiments of the present disclosure.

[0034] FIG. 6 is a schematic diagram of the principle of imaging based on a multi-column detector according to some embodiments of the present disclosure.

[0035] FIG. 7 is a schematic diagram of a flow of determining edge sharpness according to some embodiments of the present disclosure.

[0036] FIG. 8a is a schematic diagram of a neighborhood template according to some embodiments of the present disclosure.

[0037] FIG. 8b is a schematic diagram of a sharp edge image according to some embodiments of the present disclosure.

[0038] FIG. 8c is a statistical histogram of the number of edge pixel points in a neighborhood corresponding to a sharp edge image according to some embodiments of the present disclosure.

[0039] FIG. 8d is a schematic diagram of a non-sharp edge image according to some embodiments of the present disclosure.

[0040] FIG. 8e is a statistical histogram of the number of edge pixel points in a neighborhood corresponding to a non-sharp edge image according to some embodiments of the present disclosure.

[0041] FIG. 9 is a schematic diagram of a flow of an image processing method according to some other embodiments of the present disclosure.

[0042] FIG. 10a is a schematic diagram of a transmission image according to some embodiments of the present disclosure.

[0043] Figure 10b is a schematic diagram of a sampled image according to some embodiments of the present disclosure.

[0044] Figure 10c is a schematic diagram of an image processed according to a depth reconstruction according to some embodiments of the present disclosure.

[0045] Figure 10d is a schematic diagram of an image processed according to another depth reconstruction according to some embodiments of the present disclosure.

[0046] Figure 10e is a schematic diagram of an image with added depth information according to some embodiments of the present disclosure.

[0047] Figure 11 is a schematic diagram of the structure of an image processing apparatus according to some embodiments of the present disclosure.

[0048] Figure 12 is a schematic diagram of the structure of a transmission scanning system according to some embodiments of the present disclosure.

[0049] Figure 13 is a schematic diagram of the structure of a computer system according to some embodiments of the present disclosure. Detailed Implementation

[0050] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present disclosure.

[0051] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0052] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit this disclosure or its application or use.

[0053] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0054] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0055] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0056] To make the objectives, technical solutions, and advantages of this disclosure clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0057] The inventors of this disclosure have discovered the following shortcomings in the related technology: First, the related technology uses a sparse array detector, which requires significant modifications to the detector structure in the scanning system, and the sparse array detector is difficult to manufacture, install, and fix; Second, the related technology has not conducted further research on how to accurately extract the depth information of the object based on image processing algorithms.

[0058] In view of this, this disclosure proposes an image processing method that can obtain the depth information of an object without adjusting the conventional detector structure, which helps to improve the efficiency and recognition effect of object inspection.

[0059] Figure 1 is a schematic diagram illustrating the principle of transmission images containing depth information. As shown in Figure 1, a transmission image of an object is obtained by scanning using multiple rows of detectors. These multiple rows of detectors, also known as detector matrices or multi-row detectors, contain at least two rows of detector crystals (or two rows of crystal units). The transmission image can be, for example, a DR (Digital Radiography) image.

[0060] As shown in Figure 1, assume that multiple detectors are placed vertically, with the column direction parallel to the detector centerline 102 and the row direction parallel to the line indicated by the arrow. The central ray emitted by the ray source 101 passes through points A0 and B0 at different depths on the object and is received by the same crystal D0 on the detector centerline 102. In this case, the projection information of the object detected by the detector in different depth directions overlaps, making it difficult to detect the depth information of the object from this overlapping information.

[0061] Another ray emitted by the radiation source 101 forms an angle α with the central ray. This ray passes through point A1 on the object and is detected by the crystal D on the detector. A Received. Simultaneously, another ray from ray source 101 passes through point B1 on the object and is detected by crystal D on the detector. C Reception. This shows that, due to the different angles of the rays emitted by the X-ray source, points A1 and B1 at different depths on the object are received by crystals at different positions on the detector (i.e., crystals with different horizontal displacements from the detector's center line 102), and not by the same crystal D. B The received data will be transmitted through the multi-array detectors (specifically, detectors with horizontal displacement) to contain depth information of the object. The following will explain how to extract the depth information of the object from the transmitted image, in conjunction with an embodiment.

[0062] Figure 2 is a schematic flowchart of an image processing method according to some embodiments of the present disclosure. As shown in Figure 2, the image processing method includes steps S201 to S205.

[0063] In step S201, the transmission image is sampled to obtain a to-be-processed image.

[0064] In some embodiments, the image processing method is performed by an image processing device described below.

[0065] The transmission image is obtained by detecting transmission intensity of an object when a plurality of detectors are used to perform transmission scanning on the object. For example, a DR (Digital Radiography) scanning device including a radiation source and a plurality of detectors is used to perform transmission scanning on the object to obtain a transmission image of the object.

[0066] In some embodiments, the transmission image includes images acquired by the plurality of detectors under each of a plurality of pulses. In these embodiments, the transmission image is sampled according to the following manner: from each row of pixels in the image acquired under each pulse, K pixels are extracted to obtain a sequence of pixels corresponding to the image acquired under each pulse; and the sequences of pixels corresponding to each of the plurality of pulses are spliced to obtain the to-be-processed image. K is an integer greater than or equal to 1 and less than N, and N is the number of columns of crystal cells of the plurality of detectors.

[0067] In some examples, K is 1. In these examples, one pixel is extracted from each row of pixels in the image according to the following manner: according to an element value q of an i-th element in the sampling sequence, the q-th pixel in the i-th row of the image acquired under each pulse is extracted. i takes all integers from 1 to M, M is the number of rows of crystal cells of the plurality of detectors, and q is an integer greater than or equal to 1 and less than or equal to N. Then, the extracted pixels are spliced in the extraction order to obtain the sequence of pixels corresponding to the image acquired under each pulse. Next, the sequences of pixels corresponding to the plurality of pulses are spliced to obtain the to-be-processed image.

[0068] For example, the multi-column detector is a crystal array of 10 columns and 5 rows, and the sampling sequence is (1, 3, 5, 7, 10). According to the element value 1 of the first element, the first pixel point in the first row of the image acquired under the first pulse is extracted, that is, the pixel point in the first row and the first column is extracted. According to the element value 3 of the second element, the third pixel point in the second row of the image acquired under the first pulse is extracted. According to the element value 5 of the third element, the fifth pixel point in the third row of the image acquired under the first pulse is extracted. According to the element value 7 of the fourth element, the seventh pixel point in the fourth row of the image acquired under the first pulse is extracted. According to the element value 10 of the fifth element, the tenth pixel point in the fifth row of the image acquired under the first pulse is extracted. Then, the five extracted pixel points are spliced to obtain the pixel point sequence corresponding to the image acquired under the first pulse.

[0069] In some examples, the sampling sequence includes a first sampling sequence, and the element values in the first sampling sequence are determined based on a random function. In these examples, the first sampling sequence is used for sampling processing of the images acquired under different pulses.

[0070] In some examples, as shown in FIG. 3, assuming that the multi-column detector is a crystal array of M rows and N columns, a sequence S of integers (i.e., the first sampling sequence) with values ranging from 1 to N is generated by using a random function, and the sequence S includes M elements, and each element has a random integer value ranging from 1 to N. For example, assuming that the multi-column detector is a crystal array of 4 rows and 5 columns, the sequence S includes 4 elements, and the values of the 4 elements can be 1, 2, 5, 3, and 5 respectively. The sequence S is used for sampling processing of the image 301 acquired under pulse 1 to obtain a pixel point sequence 1. The sequence S is used for sampling processing of the image 302 acquired under pulse 2 to obtain a pixel point sequence 2. Similarly, the images acquired under multiple pulses are all subjected to sampling processing to obtain multiple pixel point sequences. Then, the multiple pixel point sequences including the pixel point sequence 1 and the pixel point sequence 2 are spliced to obtain the image to be processed. It should be noted that the pixel point sequence 1 and the pixel point sequence 2 are composed of pixels with horizontal displacement relative to the center line 102 of the detector, and thus carry horizontal displacement information.

[0071] In some examples, the sampling sequence includes a second sampling sequence, and the arrangement of the pixel points extracted by the second sampling sequence conforms to a specified pattern. In these examples, the second sampling sequence is used for sampling processing of the images acquired under different pulses.

[0072] In some examples, the second sampling sequence is a sine sequence, an m-sequence, or another sequence. The sine sequence is a sampling sequence in which the arrangement of the extracted pixel points conforms to a sine pattern, as shown in FIG. 4. The m-sequence is a sampling sequence in which the arrangement of the extracted pixel points conforms to an m shape. In a specific implementation, after obtaining the original transmission image, different sampling sequences can be used to process the transmission image of an object with a different contour. For example, for a contour similar to a straight line or an oblique line, a random sequence or an m-sequence can be used to achieve a better processing effect.

[0073] For example, as shown in FIG. 4, assuming that the multi-column detector is an M-row and N-column crystal array, a second sampling sequence (including M elements) conforming to the specified pattern shown in FIG. 4 is used to sample the image 301 acquired under the pulse 1, to obtain a pixel point sequence 1. The second sampling sequence is used to sample the image 302 acquired under the pulse 2, to obtain a pixel point sequence 2. In this way, the images acquired under multiple pulses are all sampled to obtain multiple pixel point sequences. Then, the multiple pixel point sequences including the pixel point sequence 1 and the pixel point sequence 2 are spliced to obtain a to-be-processed image.

[0074] In some examples, the sampling sequence includes a first sampling sequence and a second sampling sequence. In these examples, the first sampling sequence is used to sample the images acquired under part of the multiple pulses, and the second sampling sequence is used to sample the images acquired under another part of the multiple pulses.

[0075] In the embodiments of the present disclosure, considering that the projection information of different rays passing through different depths of an object overlaps together, it is difficult to extract depth information. Therefore, the transmission image is sampled, so that the information collected by the crystal units at different positions in the detector can be highlighted, facilitating subsequent depth identification.

[0076] In step S202, a to-be-identified region is determined from the to-be-processed image.

[0077] In some embodiments, the to-be-processed image obtained through step S201 includes multiple objects. In these embodiments, the regions where the objects are located are segmented from the to-be-processed image as multiple to-be-identified regions. For example, according to the gray scale, geometric shape, spatial texture, and other characteristics of the image, the regions where different objects are located are segmented.

[0078] In some embodiments, the to-be-processed image includes multiple objects. In these embodiments, the region where the object of interest is located is segmented from the to-be-processed image as the to-be-identified region.

[0079] In some embodiments, the same object in the image to be processed is segmented into multiple regions to be recognized. For example, for a vehicle in the image, the vehicle is segmented into two regions to be recognized, i.e., a vehicle head and a vehicle tail.

[0080] In step S203, the region to be recognized is reconstructed using the candidate depth to obtain a reconstruction result of the region to be recognized.

[0081] In some embodiments, the region to be recognized is reconstructed using the candidate depth, which can also be referred to as focusing processing of the region to be recognized using the candidate depth.

[0082] In some embodiments, there are multiple regions to be recognized, and the same value range of the candidate depth is set for different regions to be recognized.

[0083] For example, the value range of the candidate depth is determined according to the distance between the ray source and the detector in the scanning system. Assuming that the distance between the ray source and the detector is 7 meters, the value range of the candidate depth corresponding to each region to be recognized is set to 1-6 meters. For example, the initial value of the candidate depth is set to 1 meter, the increase step of the candidate depth is set to 0.1 meter, and the maximum value of the candidate depth is set to 6 meters.

[0084] For example, the value range of the candidate depth is determined according to the size estimation information of the object. Assuming that the object is a container, the value range of the candidate depth corresponding to each region to be recognized is set to 1-3 meters. For example, the initial value of the candidate depth is set to 1 meter, the increase step of the candidate depth is set to 0.2 meter, and the maximum value of the candidate depth is set to 3 meters.

[0085] In some embodiments, there are multiple regions to be recognized, and different candidate depths are set for different regions to be recognized.

[0086] In some embodiments, in step S203, the region to be recognized is reconstructed according to the flowchart shown in FIG. 5.

[0087] In step S204, the edge definition of the reconstruction result is determined.

[0088] The edge definition is an index for characterizing whether the edge of the reconstructed image region is clear.

[0089] In some embodiments, the edge definition of the reconstruction result is determined according to the flowchart shown in FIG. 7. That is, the edge definition is determined by counting the features of the edge pixel points in the reconstructed image.

[0090] In step S205, in the case where the edge definition is greater than a definition threshold, the candidate depth is taken as the depth of the region to be recognized.

[0091] After the edge sharpness of the reconstruction result is determined through step S204, the edge sharpness is compared with the sharpness threshold. In the case where the edge sharpness is greater than the sharpness threshold, the candidate depth is taken as the depth of the region to be identified.

[0092] In some embodiments, the image processing method further comprises: in the case where the edge sharpness is less than or equal to the sharpness threshold, iteratively performing the candidate depth updating, the region reconstruction according to the updated candidate depth, and the edge sharpness determination step, until the edge sharpness is greater than the sharpness threshold.

[0093] For example, when the candidate depth is 1 meter, steps S203 and S204 are performed to obtain the edge sharpness. If the edge sharpness obtained according to the candidate depth of 1 meter is less than or equal to the sharpness threshold, the candidate depth is updated. For example, the candidate depth is increased from 1 meter to 1.1 meter, and 0.1 meter can be the step value. Then, steps S203 and S204 are performed for the candidate depth of 1.1 meter to obtain a new edge sharpness. If the edge sharpness obtained according to the candidate depth of 1.1 meter is greater than the sharpness threshold, 1.1 meter is taken as the depth of the region to be identified.

[0094] In the embodiments of the present disclosure, by sampling the transmission image, reconstructing the region to be identified based on the candidate depth, and determining the depth of the region to be identified based on the edge sharpness of the reconstructed region, the depth information of the object can be accurately and efficiently extracted without adjusting the detector structure, thereby helping to improve the efficiency and identification effect of object inspection.

[0095] FIG. 5 is a flowchart of a region reconstruction process according to some embodiments of the present disclosure. As shown in FIG. 5, the region reconstruction process includes steps S501 and S502.

[0096] In step S501, the focusing ratio is determined according to the distance of the plurality of columns of detectors to the ray source and the candidate depth.

[0097] In some embodiments, the region to be identified is one. The focusing ratio is calculated according to the candidate depth corresponding to the region to be identified.

[0098] In some embodiments, the region to be identified is multiple, and there is a corresponding candidate depth for each region to be identified. The candidate depths corresponding to different regions to be identified can be the same or different. In these embodiments, for each region to be identified, the corresponding focusing ratio is calculated using the candidate depth corresponding thereto.

[0099] In some embodiments, the focusing ratio is determined according to the following manner: calculating the difference between the distance of the plurality of columns of detectors to the ray source and the candidate depth; and taking the ratio of the difference to the distance of the plurality of columns of detectors to the ray source as the focusing ratio.

[0100] In step S502, according to the focusing ratio, the coordinate component of the pixel point in the to-be-identified region in the specified direction is subjected to focusing processing.

[0101] The specified direction is parallel to the scanning direction of the object. For example, as shown in FIG. 1, the scanning direction of the object is parallel to the direction of the straight line DO D A The straight line DO D A The straight line DO D

[0102] In some embodiments, the focusing processing is performed in the following manner: the coordinate component of the pixel point in the to-be-identified region in the specified direction is subjected to multiplication operation with the focusing ratio to obtain an offset; and the coordinate component of the pixel point in the to-be-identified region in the specified direction is subjected to translation operation according to the offset to obtain the coordinate component after the focusing processing.

[0103] In the embodiments of the present disclosure, the reconstruction processing of the to-be-identified region based on the candidate depth is realized through the above steps. In this way, it is convenient to subsequently determine the depth of the to-be-identified region according to the edge definition of the reconstruction result.

[0104] The reconstruction processing steps are further described below in combination with FIG. 6. As shown in FIG. 6, it is assumed that the scanning direction of the object 103 is parallel to the direction of the X axis, for example, the object 103 moves along the direction parallel to the X axis, or the detector and the ray source move along the direction parallel to the X axis. The multi-column detector includes multi-column crystal units, and the multi-column crystal units are distributed along the X axis direction on the detector plane 104. Here, it is assumed that the detector plane 104 is set to be perpendicular to the horizontal plane.

[0105] It is assumed that a certain column of crystal units has a certain horizontal displacement ΔD relative to the detector center line 102 (if the detector center line is taken as the vertical coordinate axis, it is equivalent to the horizontal coordinate of the pixel point corresponding to the column of crystal units in the image). For the crystal unit with the horizontal displacement ΔD, the A point on the plane Z away from the detector plane will appear in the image by a distance of Δx later than when the crystal unit is located on the center line 102. Therefore, the scanning line obtained by the crystal unit can be aligned with the center line by being moved forward by Δx. For another crystal unit, the A2 point on the plane Z away from the detector plane will appear in the image by a certain distance earlier than when the crystal unit is located on the center line 102, so the scanning line obtained by the crystal unit can be aligned with the center line by being moved backward by a certain distance. Further, when the image is focused, each pixel is inversely translated by a distance of Δx, thereby obtaining a focused image with depth information.

[0106] Further, assuming the distance from point A on object 103 to the detector is Z, and the distance from the ray source 101 to the detector is L, according to the similarity of triangles, the following relationship can be obtained:

[0107] From the above formula, when the horizontal displacement ΔD of the crystal unit from the center line of the detector is fixed, for the same projection ray emitted by the ray source, the imaging position offset Δx is different for object points at different depths.

[0108] In the focusing process, the translation distance Δx is calculated based on a specific depth position Z, and thus is only effective for objects near the depth Z. That is, for a given candidate depth, the pixel point coordinates in the image to be processed obtained by the sampling process can be aligned with the center line according to the focusing ratio (L-Z) / L, according to the above formula, to obtain the focusing image at the given candidate depth position, i.e., the reconstruction result.

[0109] FIG. 7 is a flowchart of determining edge sharpness according to some embodiments of the present disclosure. As shown in FIG. 7, the flow of determining edge sharpness includes steps S701 and S702.

[0110] In step S701, an edge extraction algorithm is used to extract edge pixel points of the reconstruction result.

[0111] In some embodiments, a gradient calculation operator such as a Roberts operator, a Prewitt operator, or a Sobel operator is used to extract edge pixel points of the reconstruction result.

[0112] In some embodiments, a Laplace operator, a Canny operator, or a sub-pixel edge detection algorithm based on a Facet model is used to extract edge pixel points of the reconstruction result.

[0113] In step S702, edge sharpness of the reconstruction result is determined according to the edge pixel points.

[0114] In some embodiments, the edge sharpness is determined in the following manner: the number of edge pixel points contained in a specified neighborhood of each pixel point in the reconstruction result is calculated; the number of pixel points whose number of edge pixel points is less than a quantity threshold is calculated; and the edge sharpness of the reconstruction result is determined according to the number of pixel points whose number of edge pixel points is less than the quantity threshold and the total number of edge pixel points in the reconstruction result. The quantity threshold can be 2, 3, or other values.

[0115] In some examples, the specified neighborhood is an eight-point neighborhood, a twelve-point neighborhood, or other style of neighborhood. For example, the number of edge pixel points in the neighborhood of each pixel point is counted using the neighborhood template shown in FIG. 8a.

[0116] In some examples, after counting the number of edge pixels less than the number threshold and the total number of edge pixels in the reconstruction result, the ratio of the number of edge pixels less than the number threshold to the total number of edge pixels in the reconstruction result is taken as the edge sharpness of the reconstruction result.

[0117] For example, assuming that the number threshold is 2, there are 50 edge pixels in the reconstructed region, and the number of edge pixels in the neighborhood of 20 pixels is less than 2, then the edge sharpness is 0.4.

[0118] For example, as shown in FIGS. 8b-8e, when the image edge is clear, the statistical histogram of the number of edge pixels in the neighborhood is concentrated in the region with fewer edge pixels; and when the image edge is not clear, the statistical histogram of the number of edge pixels in the neighborhood is not obviously concentrated. As can be seen, the edge sharpness determined in the above manner can accurately determine whether the image edge is clear.

[0119] In the embodiments of the present disclosure, by taking into account the number of edge pixels less than the number threshold in the neighborhood and the total number of edge pixels in the reconstruction result, the edge sharpness of the reconstructed region can be better represented, and the depth of the region to be recognized can be accurately determined accordingly.

[0120] FIG. 9 is a flowchart of an image processing method according to some other embodiments of the present disclosure. As shown in FIG. 9, the image processing method includes steps S901-S909.

[0121] In step S901, the transmission image is subjected to sampling processing to obtain a to-be-processed image.

[0122] In some embodiments, in the DR scanning mode, a scanning device including multiple columns of detectors is used to scan an object at an appropriate speed to obtain a transmission image of the object. For example, the obtained transmission image is as shown in FIG. 10a.

[0123] In some embodiments, the transmission image includes images acquired by the multiple columns of detectors under each of multiple pulses. In these embodiments, one pixel point is extracted from each row of pixel points in the image acquired under each pulse to obtain a sequence of pixel points corresponding to the image acquired under each pulse; and the sequences of pixel points corresponding to the images acquired under each of the multiple pulses are spliced to obtain the to-be-processed image. For example, the to-be-processed image obtained by sampling processing is as shown in FIG. 10b.

[0124] In step S902, a region to be recognized is determined from the to-be-processed image.

[0125] For example, each object (such as a bundle of wires, a long stick, etc. appearing in FIG. 10b) appearing in FIG. 10b is segmented out, and the area where each object is located is taken as a to-be-identified region.

[0126] In step S903, the to-be-identified region is reconstructed using the candidate depth to obtain a reconstruction result of the to-be-identified region.

[0127] In some embodiments, the to-be-identified region is reconstructed according to the following manner: a focusing ratio is determined according to the distance from the plurality of detectors to the ray source and the candidate depth; and a coordinate component of a pixel point in the to-be-identified region in a specified direction is focused according to the focusing ratio, the specified direction being parallel to the scanning direction of the object.

[0128] In step S904, the edge definition of the reconstruction result is determined.

[0129] In step S905, it is determined whether the edge definition is greater than a definition threshold.

[0130] In the case where the edge definition is greater than the definition threshold, step S906, step S908 and step S909 are executed; in the case where the edge definition is less than or equal to the definition threshold, step S907 is executed.

[0131] In step S906, the candidate depth is taken as the depth of the to-be-identified region. In step S907, the candidate depth is updated.

[0132] In some examples, the candidate depth is incremented by a set step length within a certain value range. For example, the first value of the candidate depth is 0.1 meters, and assuming that the set step length is 0.1 meters, the second value is 0.2 meters, and the third value is 0.3 meters.

[0133] After step S907, steps S903 to S905 are executed again, and then the subsequent processing steps are selected for execution according to the determination result of step S905.

[0134] In step S908, the to-be-identified region is reconstructed according to the depth of the to-be-identified region to obtain a reconstructed image.

[0135] In this step, when the to-be-identified region is a plurality of regions, each to-be-identified region is reconstructed according to the depth of each to-be-identified region.

[0136] For example, the depth of the to-be-identified region 1 is 3 meters, and the to-be-identified region 1 is reconstructed based on 3 meters. The depth of the to-be-identified region 2 is 3.6 meters, and the to-be-identified region 2 is reconstructed based on 3.6 meters. If the to-be-identified regions 1 and 2 are reconstructed based on 3 meters, the to-be-identified region 1 is clear, and the to-be-identified region 2 is blurred.

[0137] For example, the to-be-processed image in FIG. 10b is reconstructed based on a depth of 3800 mm, and the reconstructed result is shown in FIG. 10c. As shown in FIG. 10c, in the reconstructed image, the region where the object with a depth of 3800 mm is located (for example, the bundle of wires on the left side in FIG. 10c) is relatively clear, and the regions where the objects with other depths are located (for example, the two long rods on the right side in FIG. 10c) are relatively blurred.

[0138] For example, the to-be-processed image in FIG. 10b is reconstructed based on a depth of 4600 mm, and the reconstructed result is shown in FIG. 10d. As shown in FIG. 10d, in the reconstructed image, the image of the object with a depth of 4600 mm (for example, the two long rods on the right side in FIG. 10d) is clear, and the images of the objects with other depths (for example, the bundle of wires on the left side in FIG. 10d) are relatively blurred.

[0139] In some examples, the step S908 includes: determining a focusing ratio according to the distance from the plurality of columns of detectors to the ray source and the depth of the to-be-identified region; and performing focusing processing on the coordinate component of the pixel point in the to-be-identified region in a specified direction parallel to the scanning direction of the object according to the focusing ratio.

[0140] In some examples, the focusing ratio is determined according to the following manner: calculating the difference between the distance from the plurality of columns of detectors to the ray source and the depth; and taking the ratio of the difference to the distance from the plurality of columns of detectors to the ray source as the focusing ratio.

[0141] In some examples, the focusing processing on the coordinate component according to the focusing ratio includes: multiplying the coordinate component of the pixel point in the to-be-identified region in the specified direction by the focusing ratio to obtain an offset; and performing a translation operation on the coordinate component of the pixel point in the to-be-identified region in the specified direction according to the offset to obtain the coordinate component after the focusing processing. The image after the focusing processing (i.e., the reconstructed image) can be obtained by processing in the above manner.

[0142] In step S909, the reconstructed image and the depth information of the to-be-identified region are displayed.

[0143] In some examples, the depth information is associated with the reconstructed image, and the associated image is displayed. For example, the depth information is superimposed on the reconstructed image in the form of color identification. In addition, the depth information can be superimposed on the reconstructed image in the form of a prompt box, a mark bar, or the like. For example, as shown in FIG. 10e, Z1 represents a depth of 3.8 meters, Z2 represents a depth of 3 meters, and Z3 represents a depth of 4.6 meters. In this way, it is convenient for the inspector to better identify the objects in the image, greatly reducing the difficulty of object identification and improving the efficiency of object inspection.

[0144] In the embodiments of the present disclosure, through the above process, the depth information of the container, cargo, or the like can be extracted based on the main structure of the conventional X-ray transmission inspection system, the transmission image is obtained by using the multi-column detector to perform scanning at a proper scanning speed, and the depth identification of the container, cargo, or the like is realized. Based on the method of the embodiments of the present disclosure, the depth identification can be realized based on the transmission image without making too many changes to the hardware configuration of the system, the depth identification function can be easily embedded into the existing DR transmission scanning process, the difficulty of DR image viewing and opening box inspection is reduced, and the inspector can easily identify the object.

[0145] FIG. 11 is a structural schematic diagram of an image processing apparatus according to some embodiments of the present disclosure. As shown in FIG. 11, the image processing apparatus 110 includes a memory 111 and a processor 112 coupled to the memory 111. The memory 111 is configured to store instructions for executing an embodiment of the image processing method. The processor 112 is configured to execute the image processing method in any of some embodiments of the present disclosure based on the instructions stored in the memory 111.

[0146] FIG. 12 is a structural schematic diagram of a transmission scanning system according to some embodiments of the present disclosure. As shown in FIG. 12, the transmission scanning system includes the image processing apparatus 110, a ray source 101, and a detector 121.

[0147] The ray source 101 is configured to emit rays to perform transmission scanning on the object 103 based on the rays.

[0148] The detector 121 is configured to detect the transmission intensity when the object 103 is subjected to transmission scanning, so as to obtain a transmission image.

[0149] In some embodiments, the scanning speed of the object is limited within a set value range, so that the geometric shape of the object in the image corresponding to the single-column detector is basically normal, or the geometric shape of the object in the to-be-processed image obtained after the sampling processing is basically normal. In this way, it is helpful to improve the subsequent image processing effect, and further improve the efficiency of object inspection and the identification effect of the object.

[0150] The image processing apparatus 110 is configured to perform the image processing method as described above.

[0151] In the embodiments of the present disclosure, the depth information of the object can be obtained without adjusting the detector structure by using the transmission scanning system, which helps to improve the efficiency and recognition effect of the object inspection.

[0152] FIG. 13 is a structural schematic diagram of a computer system according to some embodiments of the present disclosure.

[0153] As shown in FIG. 13, the computer system 130 can be in the form of a general-purpose computing device. The computer system 130 includes a memory 131, a processor 132, and a bus 133 connecting different system components.

[0154] The memory 131 can include, for example, a system memory, a non-volatile storage medium, and the like. The system memory stores, for example, an operating system, application programs, a boot loader, and other programs. The system memory can include a volatile storage medium, such as a random access memory (RAM) and / or a cache memory. The non-volatile storage medium stores, for example, instructions of at least one image processing method being executed. The non-volatile storage medium includes, but is not limited to, a magnetic disk storage, an optical disk storage, a flash memory, and the like.

[0155] The processor 132 can be implemented in the form of a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor, and the like discrete hardware component. Accordingly, each module such as a sampling module, a determination module, a reconstruction module, and the like can be implemented by a central processing unit (CPU) running instructions of corresponding steps in the memory, or by a dedicated circuit performing corresponding steps.

[0156] The bus 133 can use any of a variety of bus structures. For example, the bus structure includes, but is not limited to, an industry standard architecture (ISA) bus, a micro channel architecture (MCA) bus, a peripheral component interconnect (PCI) bus.

[0157] The interfaces 134, 135, 136 of the computer system 130, the memory 131, and the processor 132 can be connected through the bus 133. The input / output interface 134 can provide a connection interface for display, mouse, keyboard, and the like input / output devices. The network interface 135 provides a connection interface for various networking devices. The storage interface 136 provides a connection interface for external storage devices such as floppy disks, U disks, SD cards, and the like.

[0158] The computer readable program instructions can also be loaded onto a computer, other programmable apparatus, or other device to cause a series of operations to be performed on the computer, other programmable apparatus, or other device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus implement the functions specified in the flowchart and / or block diagram block or blocks.

[0159] These computer readable program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable apparatus, create means for implementing the functions specified in the flowchart and / or block diagram block or blocks.

[0160] These computer readable program instructions can also be stored in a computer readable storage medium that can direct a computer, a programmable apparatus, or other device to function in a particular manner, such that the instructions stored in the computer readable storage medium produce an article of manufacture including instructions which implement the function specified in the flowchart and / or block diagram block or blocks.

[0161] The computer readable program instructions can also be loaded onto a computer, other programmable apparatus, or other device to cause a series of operations to be performed on the computer, other programmable apparatus, or other device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus implement the functions specified in the flowchart and / or block diagram block or blocks.

[0162] By means of the image processing method, device and transmission scanning system in the above embodiments, the depth information of the object can be obtained without adjusting the detector structure, which helps to improve the efficiency and recognition effect of the object inspection.

[0163] Thus far, the image processing method, device and transmission scanning system according to the present disclosure have been described in detail. In order to avoid obscuring the concept of the present disclosure, some details known in the art are not described. Those skilled in the art can fully understand how to implement the technical solutions disclosed herein according to the above description.

Claims

1. An image processing method, comprising: sampling a transmission image to obtain a to-be-processed image, the transmission image being obtained by detecting transmission intensity of an object in a transmission scan using a plurality of detector columns; determining a to-be-identified region from the to-be-processed image; reconstructing the to-be-identified region using a candidate depth to obtain a reconstruction result of the to-be-identified region; determining an edge sharpness of the reconstruction result; in a case where the edge sharpness is greater than a sharpness threshold, regarding the candidate depth as a depth of the to-be-identified region.

2. The image processing method of claim 1, wherein, The transmission image comprises images acquired by the plurality of detector columns under each of a plurality of pulses, and the sampling of the transmission image to obtain the to-be-processed image comprises: extracting K pixel points from each row of pixel points in the image acquired under each of the pulses to obtain a pixel point sequence corresponding to the image acquired under each of the pulses, K being an integer greater than or equal to 1 and less than N, N being a number of columns of crystal units of the plurality of detector columns; and splicing the pixel point sequences corresponding to the images acquired under each of the plurality of pulses to obtain the to-be-processed image.

3. The image processing method of claim 2, wherein, K is 1, and the extraction of the K pixel points from each row of pixel points in the image acquired under each of the pulses comprises: extracting a qth pixel point from an ith row in the image acquired under each of the pulses according to an element value q of an ith element in a sampling sequence, i taking all integer values from 1 to M, M being a number of rows of crystal units of the plurality of detector columns, and q being an integer greater than or equal to 1 and less than or equal to N.

4. The image processing method of claim 3, wherein, The sampling sequence comprises a first sampling sequence, and element values in the first sampling sequence are determined based on a random function.

5. The image processing method of claim 3, wherein, The sampling sequence comprises a second sampling sequence, and an arrangement of pixel points extracted by the second sampling sequence conforms to a specified pattern.

6. The image processing method according to any one of claims 1 to 5, wherein The determination of the edge sharpness of the reconstruction result comprises: extracting edge pixel points of the reconstruction result using an edge extraction algorithm; and determining the edge sharpness of the reconstruction result according to the edge pixel points.

7. The image processing method of claim 6, wherein, The determination of the edge sharpness of the reconstruction result according to the edge pixel points comprises: calculating a number of the edge pixel points contained in a specified neighborhood of each pixel point in the reconstruction result; calculating a number of pixel points whose number of edge pixel points is less than a number threshold; and determining the edge sharpness of the reconstruction result according to the number of pixel points whose number of edge pixel points is less than the number threshold and a total number of edge pixel points in the reconstruction result.

8. The image processing method of claim 7, wherein, The determination of the edge sharpness of the reconstruction result according to the number of pixel points whose number of edge pixel points is less than the number threshold and the total number of edge pixel points in the reconstruction result comprises: regarding a ratio of the number of pixel points whose number of edge pixel points is less than the number threshold to the total number of edge pixel points in the reconstruction result as the edge sharpness of the reconstruction result.

9. The image processing method according to any one of claims 1 to 8, wherein The reconstruction of the to-be-identified region using the candidate depth comprises: determining a focusing ratio according to a distance from the plurality of detector columns to a radiation source and the candidate depth. According to the focusing ratio, a coordinate component of a pixel in the to-be-identified region in a specified direction is focused, the specified direction being parallel to a scanning direction of the object.

10. The image processing method of claim 9, wherein, According to the distance from the multi-column detector to the ray source and the candidate depth, the focusing ratio is determined by: calculating a difference between the distance from the multi-column detector to the ray source and the candidate depth; taking a ratio of the difference to the distance from the multi-column detector to the ray source as the focusing ratio.

11. The image processing method of any one of claims 1-10, further comprising: in a case where the edge sharpness is less than or equal to the sharpness threshold, iteratively performing the candidate depth updating, the region reconstruction according to the updated candidate depth, and the edge sharpness determining until the edge sharpness is greater than the sharpness threshold.

12. The image processing method of any one of claims 1-10, further comprising: reconstructing the to-be-identified region according to a depth of the to-be-identified region to obtain a reconstructed image.

13. The image processing method of claim 12, further comprising: displaying the reconstructed image and the depth information of the to-be-identified region.

14. An image processing apparatus, comprising: a memory; and a processor coupled to the memory, the processor being configured to perform the image processing method of any one of claims 1-13 based on instructions stored in the memory.

15. A transmission scanning system, comprising: the image processing apparatus of claim 14; a multi-column detector; and a ray source.

16. A computer-readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the image processing method of any one of claims 1-13.

17. A computer program product having stored thereon computer program instructions which, when executed by a processor, implement the image processing method of any one of claims 1-13.

18. A computer program, comprising: instructions which, when executed by a processor, cause the processor to perform the image processing method of any one of claims 1-13.

Images