CN106485244B - Sampling method and device - Google Patents

Sampling method and device Download PDF

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CN106485244B
CN106485244B CN201610891236.8A CN201610891236A CN106485244B CN 106485244 B CN106485244 B CN 106485244B CN 201610891236 A CN201610891236 A CN 201610891236A CN 106485244 B CN106485244 B CN 106485244B
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sampling
sampled
region
sampling method
shape
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CN106485244A (en
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赵轲俊
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Shanghai United Imaging Healthcare Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/255—Detecting or recognising potential candidate objects based on visual cues, e.g. shapes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N5/00—Radiation therapy
    • A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
    • A61N5/103—Treatment planning systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N5/00—Radiation therapy
    • A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
    • A61N5/1048—Monitoring, verifying, controlling systems and methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/22—Image preprocessing by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N5/00—Radiation therapy
    • A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
    • A61N2005/1092—Details
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10081—Computed x-ray tomography [CT]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10104—Positron emission tomography [PET]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30096—Tumor; Lesion

Abstract

The invention provides a sampling method which comprises the steps of identifying the shape of a region to be sampled, selecting a sampling method according to the shape, and sampling the region to be sampled by using the sampling method. The sampling method provided by the invention selects the adaptive sampling method by distinguishing the organ shape, so that the optimized dose distribution is more uniform.

Description

Sampling method and device
Technical Field
The invention relates to the field of radiotherapy, in particular to a method and a device for sampling images.
Background
Radiotherapy is a method of treating malignant tumors by using radiation such as alpha, beta, and gamma rays generated by radioisotopes and x-rays, electron beams, proton beams, and other particle beams generated by various x-ray treatment machines or accelerators.
Due to the high beam energy, normal cells are affected while tumor cells are killed. In order to minimize damage to normal tissues, radiation treatment plans need to be developed. In the radiation therapy plan, a doctor needs to give a prescription and a treatment plan, a physicist outlines Target areas such as organ and Tumor positions, total Tumor Volume (GTV), Clinical Target Volume (CTV) and Planned Treatment Volume (PTV) according to the doctor prescription, creates a radiation field (Beam) and a sub-field (Beam segment) according to the treatment plan, checks Dose-Volume distribution (Dose-Volume Histogram, DVH), performs optimization calculation until the prescription Target is satisfied if the prescription Target is not satisfied, finally approves a radiotherapy plan (Approve), and saves the whole radiotherapy plan data for treatment.
When a radiotherapy plan is made, the acquired image of the region of interest generally has higher resolution and more pixel points, so that the image of the region of interest is sampled before dose optimization, and a sampling point is used for representing the whole region of interest, so that the selection of the sampling point directly influences the result of the dose optimization.
For example, uneven sampling of a target organ may result in uneven dose distribution of the organ after optimization, or for an organ at risk, the dose in some regions may be too sparse relative to other regions to result in high dose in the region. The uniformity of sampling therefore has an important role in the optimization of the radiation treatment plan.
The uniformity of sampling is based on the shape of the organ. For example, for a square organ, if the sampling points are distributed in a grid-like manner, the sampling distribution is uniform. However, for a ring-shaped organ, if the sampling points are still distributed in a grid point manner, the area with large radian shows the phenomenon that the sampling points are obviously sparse.
Disclosure of Invention
In order to solve the technical problems, the invention provides a sampling method and a sampling device, which select an adaptive sampling method according to the characteristics of a region to be sampled, thereby ensuring the uniformity of sampling and being beneficial to improving the uniformity of optimized dose distribution.
According to an embodiment of the present invention, there is provided a sampling method including: identifying the shape of a region to be sampled, selecting a sampling method according to the shape, and sampling the region to be sampled by using the sampling method.
Optionally, the shape of the region to be sampled is obtained through the role information of the region to be sampled.
Optionally, the shape of the region to be sampled is judged by a pattern recognition algorithm.
Optionally, the pattern recognition algorithm is a pattern recognition algorithm.
Optionally, selecting a sampling method according to the shape includes: if the shape is square, selecting a sampling method of equidistant grid points; and if the shape is annular, a multilayer sampling method is selected.
Optionally, the multi-layer sampling method includes: and distinguishing the inner ring and the outer ring of the area to be sampled, calculating the lengths of the inner ring and the outer ring, segmenting the inner ring and the outer ring, determining the number of sampling layers, and determining sampling points.
According to another embodiment of the present invention, a sampling method is provided, which includes dividing a region to be sampled into a plurality of sub-regions, identifying a shape of each sub-region, selecting a sampling method according to the shape, and sampling each sub-region using the sampling method.
Optionally, the plurality of sub-regions are annular and an inner region surrounded by the annular.
According to another embodiment of the present invention, a sampling method is provided, which includes calculating a volume of a region to be sampled, selecting a sampling method according to the volume, and sampling the region to be sampled using the sampling method.
According to another embodiment of the invention, a sampling method is provided, which includes calculating a volume of a region to be sampled, determining whether the volume is greater than a threshold, if not, sampling the region to be sampled by using a random algorithm, if so, identifying a shape of the region to be sampled, selecting a sampling method according to the shape, and sampling the region to be sampled by using the sampling method.
According to another embodiment of the present invention, a sampling apparatus is provided, which includes an identification unit configured to identify a shape of a region to be sampled, a selection unit configured to select a sampling method according to the shape, and a sampling unit configured to sample the region to be sampled by using the sampling method.
According to another embodiment of the present invention, a sampling apparatus is provided, which includes a calculation unit configured to calculate a volume of a region to be sampled, a selection unit configured to select a sampling method according to the volume, and a sampling unit configured to sample the region to be sampled by using the sampling method.
According to another embodiment of the present invention, a sampling apparatus is provided, which includes a calculating unit configured to calculate a volume of a region to be sampled, a determining unit configured to determine whether the volume is greater than a threshold, a random sampling unit configured to sample the region to be sampled by using a random algorithm, an identifying unit configured to identify a shape of the region to be sampled, a selecting unit configured to select a sampling method according to the shape, and a sampling unit configured to sample the region to be sampled by using the sampling method.
According to another embodiment of the present invention, a sampling method is provided, which includes sampling a region to be sampled, dividing the region to be sampled into a plurality of sub-regions, calculating a sampling density of each sub-region, and determining whether to continue sampling on the sub-region, if so, continuing sampling on the sub-region, and if not, ending the sampling.
Compared with the prior art, the method selects the sampling method adaptive to the shape and/or the volume of the organ according to the shape and/or the volume of the organ, so that the sampling distribution more suitable for the organ is obtained, and the optimized dose distribution in the organ is more uniform;
on the other hand, the area which is not sufficiently sampled is further sampled, which is beneficial to obtaining the sampling distribution which is more suitable for the organ, so that the optimized dose distribution is more uniform;
furthermore, the organs in the region of interest are sampled more uniformly, so that in the dose distribution obtained by the optimized radiotherapy plan, high dose is concentrated in the target area of the tumor, and the damage to organs at risk is reduced.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a flow chart of a sampling method in an embodiment of the invention;
FIG. 2 is a flow chart of a multi-layered sampling method in an embodiment of the invention;
FIG. 3 is a flow chart of a sampling method for an elongated region in accordance with an embodiment of the present invention;
FIG. 4 is a schematic illustration of sampling an elongated region in accordance with an embodiment of the present invention;
FIG. 5 is a flow chart of a sampling method in another embodiment of the present invention;
FIG. 6 is a flow chart of a sampling method suitable for small volume regions in another embodiment of the present invention;
FIG. 7 is a flow chart of a sampling method in a further embodiment of the present invention;
FIG. 8 is a schematic view of a sampling device in an embodiment of the present invention;
FIG. 9 is a schematic view of a sampling device in another embodiment of the present invention;
FIG. 10 is a schematic view of a sampling device in yet another embodiment of the present invention;
fig. 11 is a flow chart of a sampling method in a further embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those described herein, and thus the present invention is not limited to the specific embodiments disclosed below.
In radiation therapy, it is desirable to deliver as high a dose of radiation as possible to the target tumor volume, minimizing damage to organs at risk. Thus, in planning a radiation treatment, the region of interest including the tumor target and the organ at risk requires that the dose in the tumor target be as high as possible and the dose in the organ at risk be as low as possible.
Since the dose distribution is greatly affected by the uniformity of sampling, it is desirable to sample each region as uniformly as possible in order to better control the dose distribution within each region. While the shape correlation of the sampling with the region is high, for example, the effect may be different by sampling regions of different shapes with the same sampling method. Therefore, an embodiment of the present invention provides a sampling method, which selects an adaptive sampling method according to a shape of a region to be sampled for sampling.
Fig. 1 is a flow chart of a sampling method in an embodiment of the invention. Referring to fig. 1, the sampling method provided in this embodiment includes:
step S102, identifying the shape of the area to be sampled.
In the present embodiment, a CT image is taken as an example for description, but the scope of the present invention is not limited thereto. In other embodiments, the images may be MR images, PET images, or other single mode or multi-mode fused images.
And (3) delineating each organ in the region of interest on the CT image to obtain a plurality of regions to be sampled. The physicist can identify the shapes of the different regions obtained by drawing, can give roles corresponding to the different regions, and the roles can be the shapes corresponding to the regions. In this case, the shape of the region to be sampled can be recognized by acquiring the character of the region.
For example, the physicist typically adds a ring-shaped auxiliary organ (called ring) to the treatment planning volume PTV during the delineation. The high dose applied to the treatment planning volume PTV is prevented from leaking to the surrounding normal tissue by limiting the dose radiated to the ancillary organ ring, thus serving as a protection. The auxiliary organ ring is an expanded portion obtained by expanding the treatment plan volume PTV, and therefore the auxiliary organ ring is annular, and a physicist can set the role of the auxiliary organ to ring, so that the shape of the auxiliary organ is identified as annular only by the role information in this step.
Of course, the physicist may not identify the shape of each region to be sampled, in which case the shape of the region to be sampled may be determined by a pattern recognition algorithm, such as a pattern recognition algorithm.
For example, for a target organ or organ at risk of type C, the organ may be identified by a pattern recognition algorithm.
Whether the organ is elongated and whether the organ is significantly curved may be determined, for example, by calculating the change of the organ contour, for example, by taking a series of points on the organ contour, and if the included angle between the tangents at two adjacent points exceeds a preset first threshold, the organ is considered to be significantly curved; or connecting two adjacent points, and if the included angle between the two adjacent connecting lines exceeds a preset first threshold value, determining that the organ is bent obviously. If the organ is elongated but not significantly curved, determining that the organ is elongated in shape; if the organ is elongated and significantly curved, the organ is judged to be C-shaped. The pattern recognition algorithm is used to recognize the shape as the prior art, and will not be described in detail herein. Any other scheme for recognizing the shape of the figure that can be applied to the present embodiment is within the scope of the present invention. The preset first threshold is set for the doctor based on experience.
And step S104, selecting a sampling method according to the shape.
The shape of the region to be sampled is obtained in step S102, and in this step, a sampling method suitable for the shape is selected based on the recognized shape.
In this embodiment, different sampling methods may be classified and stored in advance, and may be called by the computer as needed. That is, when the region to be sampled is identified as a square, the computer calls a sampling method of equidistant grid points, when the region to be sampled is identified as a ring shape, a C shape, or an elongated shape, the computer calls a sampling method adapted to the ring shape, the C shape, or the elongated shape, of course, when the region to be sampled is identified as another shape, the sampling method adapted to the region to be sampled is called, and the shape, the number, and the number of corresponding sampling methods of the region to be sampled are not limited in the present invention.
And S106, sampling the area to be sampled by using the sampling method.
And sampling the region to be sampled by using the sampling method selected in the step S104.
If the area to be sampled is identified as square, it is preferably sampled using a sampling method with equidistant grid points. In the sampling process of each layer of CT image, firstly limiting the number and sampling intervals of sampling points, then calculating the number of grids along the X-axis direction by taking the sampling intervals as equidistant intervals according to a rectangular coordinate system in the CT image, and calculating the number of grids along the Y-axis direction by taking the sampling intervals as equidistant intervals so as to establish equidistant grids; and then, judging whether each candidate sampling point on the equal distance grid is in the area to be sampled, if the point belongs to the area to be sampled, selecting the point as the sampling point, and if not, not selecting the point as the sampling point.
If the total number of samples exceeds the defined number of samples, the sampling interval is scaled and re-sampled until the requirements are met.
And if the area to be sampled is identified as the ring shape, sampling by adopting a sampling method adaptive to the ring shape.
In this embodiment, a new sampling method, that is, a multi-layer sampling method, is provided, which is applicable to an annular region to be sampled, and as shown in fig. 2, the specific steps are as follows:
step S202, distinguishing an inner ring and an outer ring of an annular region to be sampled, calculating the lengths of the inner ring and the outer ring, and recording the length of the inner ring as LI。
And step S204, segmenting the inner ring and the outer ring.
Assuming a defined step size of B, the inner circle is divided into N parts of equal length B, wherein
Figure BDA0001129544770000061
The outer ring is likewise divided into equal-length N portions. The sectional nodes of the inner ring and the outer ring are respectively marked as
I＝{i1,i2,…,iNAnd O ═ O1,o2,…,oN}
Step S206, determining the number of sampling layers.
Assuming that the upper limit of the number of sampling points is M, the number of layers for sampling the annular region to be sampled is M
Figure BDA0001129544770000062
In step S208, a sampling point is determined.
Connecting the segmented nodes of the inner and outer rings in sequence, i.e. i1And o1Connection of i2And o2Connection of iNAnd oNConnecting; then i isjAnd ojEach of the connecting lines is divided into C +1 segments, where j is 1,2, …, N, and the segmentation point of the connecting line is the sampling point. According to the sampling method, N x C sampling points which are uniformly distributed along the outline of the annular region to be sampled can be obtained, and the distribution of multilayer sampling is formed.
As in the upsampling method, i isjAnd ojIs divided into C +1 segments because there is no pairSampling the inner ring and outer ring profiles of the annular region to be sampled, and if the inner ring and outer ring profiles of the annular region to be sampled need to be sampled, sampling ijAnd ojEach connecting line is divided into C-1 sections, and obviously, technical personnel can carry out equivalent deformation according to actual needs and the connecting lines are all within the protection scope of the invention.
For a target region organ or a jeopardy organ obtained by sketching by a physicist, a C-shaped shape can also appear, a region of the shape can be sampled by adopting the multilayer sampling method to obtain a series of sampling points uniformly distributed along a contour line, wherein when the number of sampling layers is determined, if the upper limit of the number of the sampling points is M, the inner ring and the outer ring of the C-shaped region to be sampled are divided into N parts with equal length, the number of the sampling layers of the C-shaped region to be sampled is equal to N according to whether edge points of the inner ring and the outer ring are sampled or not
Figure BDA0001129544770000072
Or
Figure BDA0001129544770000071
In addition to the multi-layer sampling method described above, the C-shaped region to be sampled may also be sampled in accordance with the sampling method for elongated regions described below.
Referring to fig. 3, the method specifically includes the following steps:
step S302, every two adjacent points on the contour of the region to be sampled are used as a set of data points.
The resulting contour of the region to be sampled is composed of a series of data points, with every two adjacent data points as a group, as in fig. 4 (p1, p 2).
Step S304, connecting one group of data points and establishing a grid in the range of the segment length, so that the grid covers the region to be sampled of the segment length.
As shown in fig. 4, with p1 as the origin, the connection line between p1 and p2 as the x-axis, and the vertical direction of the connection line between p1 and p2 as the y-axis, a coordinate system is established, and a grid is established within the length range of the line segment p1p2, so that the grid can cover the part of the region to be sampled on the line segment p1p 2. In this step, the technician can set the size of the grid according to the limit on the number of sample points. Of course, the grid in fig. 4 is merely an example and does not limit the scope of the present invention.
Step S306, determining whether the grid point is in the region to be sampled.
And judging whether each grid point in the grid is in the area to be sampled, if so, reserving the grid point as a sampling point, otherwise, not taking the grid point as the sampling point.
Step S308, whether all the grid points are determined is completed, if yes, the process proceeds to step S310, and if not, the grid points are determined continuously.
Step S310, determining whether the sampling of the region to be sampled is completed, if yes, ending the process, otherwise, proceeding to step S304 to continue processing the next set of data points.
And when all the data points are traversed, completing sampling of the area to be sampled. Of course, in this case, a case of oversampling may occur. At this time, it may be determined whether the traversal of the data point along one side of the elongated region to be sampled is completed, if so, the sampling is completed, and if not, the process proceeds to step S304. The determination of whether the data points along one side of the elongated region to be sampled are traversed can be implemented by the prior art, and will not be described herein again.
Of course, the sampling method in fig. 3 is not only suitable for elongated regions to be sampled, but also for any other shape of regions to be sampled. Therefore, in the sampling method of fig. 1, it is preferable to sample a square region by using a sampling method using equidistant grid points, to sample an annular region by using the sampling method of fig. 2, and to sample a region having another shape by using the sampling method of fig. 3.
In another embodiment of the present invention, the same region to be sampled may be divided into a plurality of sub-regions, each of which may be sampled by a different sampling method, for example, for a non-annular region to be sampled, the region to be sampled may be divided into two parts, one part is an annular region that is obtained by retracting a certain distance along the contour toward the inside of the region, the other part is an inner region surrounded by an annular region, for the annular region, the multi-layer sampling method shown in fig. 2 may be used for sampling, and for the inner region, the sampling method of equidistant grid points may be used for sampling. Therefore, the problem that the edge of the area to be sampled is sampled sparsely can be avoided. Therefore, in the sampling method of fig. 1, it is preferable to sample a square region by using a sampling method with equidistant grid points, it is preferable to sample an annular region by using the sampling method of fig. 2, and for other shapes of regions, it is preferable to divide the region to be sampled into a plurality of sub-regions, and each sub-region can be sampled by selecting a suitable sampling method according to the shape.
Therefore, in the embodiment, the sampling method adaptive to the region to be sampled is selected according to the shape of the region to be sampled, so that the distribution of sampling points is uniform, and the optimized radiation treatment plan can obtain more uniform dose distribution; and sampling is carried out by using a sampling method adaptive to the shape of a region to be sampled, so that high dose is concentrated on a tumor target region, and the damage to organs at risk is reduced.
However, the sampling effect is influenced by not only the shape of the region to be sampled but also the size of the region to be sampled. If the volume of the area to be sampled is too small, this may result in poor sampling. Therefore, the invention also provides another sampling method, which selects an adaptive sampling method for sampling according to the volume size of the region to be sampled.
Fig. 5 is a flow chart of a sampling method in another embodiment of the present invention. Referring to fig. 5, the sampling method provided in this embodiment includes:
step S502, the volume of the region to be sampled is calculated.
Still taking the CT image as an example, the volume of the region to be sampled in the CT image is calculated.
Step S504, a sampling method is selected according to the volume.
If the volume calculated in step S502 is less than or equal to the preset second threshold, a sampling method suitable for sampling a small volume region is selected, otherwise, a sampling method suitable for sampling a large volume region is selected. The sampling method suitable for sampling in the small-volume area and the sampling method suitable for sampling in the large-volume area can be classified and stored in advance, and selection is performed according to the corresponding volume signals.
And step S506, sampling the area to be sampled by using the sampling method.
The small volume region can be sampled using a stochastic algorithm, as shown with reference to fig. 6, comprising the steps of:
step S602, an upper limit of the number of sampling points is obtained.
In step S604, a random point is generated in a range including all the regions to be sampled.
In the range including all the areas to be sampled, the random number generator is used to generate the x-axis coordinate value, the y-axis coordinate value and the z-axis coordinate value of the random point, and the random number generator can generate random values according to normal distribution, Gaussian distribution, uniform distribution, Poisson distribution or other distributions, and the random number generator is not limited in the invention.
Step S606, judging whether the random point is in the area to be sampled, if so, reserving the random point as a sampling point, otherwise, not taking the random point as the sampling point.
Step S608, determining whether the number of sampling points reaches the upper limit, if yes, ending the sampling, otherwise, proceeding to step S604.
For the region to be sampled whose volume is greater than the second threshold, the same sampling method may be used for sampling, for example, the sampling method in fig. 3, and an appropriate sampling method may also be selected according to the shape of the region to be sampled in the foregoing embodiment, as shown in fig. 7, including the following steps:
step S702, calculate the volume of the region to be sampled.
Step S704, determining whether the volume is greater than a threshold, if so, going to step S708, otherwise, going to step S706.
Step S706, sampling the area to be sampled by using a random algorithm.
In step S708, the shape of the region to be sampled is identified.
Step S710, selecting a sampling method according to the shape.
Step S712, sampling the region to be sampled by using the sampling method.
For details of the sampling method of the present embodiment, refer to the foregoing embodiments.
Correspondingly, the present invention also provides a sampling apparatus 800, including:
an identifying unit 801 for identifying the shape of the region to be sampled;
the identification unit 801 may identify the shape of the region to be sampled by acquiring the role information of the region to be sampled, or may determine the shape of the region to be sampled by using a pattern recognition algorithm, so as to identify the shape of the region to be sampled.
A selecting unit 802 for selecting a sampling method according to the shape;
the identification unit 801 sends a signal representing the shape of the region to be sampled to the selection unit 802, and the selection unit 302 receives the signal and calls a corresponding sampling method according to the signal.
A sampling unit 803, configured to sample the region to be sampled by using the sampling method.
The sampling unit 803 samples the region to be sampled by using the sampling method selected by the selection unit 802.
In another embodiment, another sampling apparatus 900 is provided, including
A calculation unit 901 for calculating the volume of the region to be sampled,
a selection unit 902 for selecting a sampling method in dependence of the volume,
the volume calculated by the calculation unit 901 may be compared with a threshold value, and the selection unit 902 may select an appropriate sampling method according to the comparison result.
And the sampling unit 903 is used for sampling the region to be sampled by using the sampling method.
In another embodiment, another sampling device 1000 is provided, comprising
A calculation unit 1001 for calculating the volume of the region to be sampled.
The determining unit 1002 is configured to determine whether the volume is greater than a threshold.
A random sampling unit 1003, configured to sample the region to be sampled by using a random algorithm.
An identifying unit 1004 for identifying a shape of the region to be sampled;
a selecting unit 1005 for selecting a sampling method according to the shape;
a sampling unit 1006, configured to sample the region to be sampled by using the sampling method.
For specific details, reference may be made to embodiments of the sampling method described above.
In the foregoing embodiments, a sampling method or apparatus for sampling according to the shape or volume size of a region to be sampled is described, and in the following embodiments, the present invention also proposes another sampling method.
Referring to fig. 11, a sampling method includes:
step S1102, samples a region to be sampled.
In the step, any sampling method is adopted to sample the area to be sampled, the sampling method can be set in advance, and in the step, only the sampling method needs to be directly called to sample the area to be sampled for one time.
In step S1104, the region to be sampled is divided into a plurality of sub-regions.
In this step, the region to be sampled is divided into a plurality of sub-regions, and the dividing method is not limited in this embodiment. Preferably, the region to be sampled is divided into a plurality of regularly shaped sub-regions, such as squares, circles, rings, etc.
In step S1106, the sampling density in each sub-region is calculated.
In this step, the area of each sub-region and the number of sampling points are calculated, so as to obtain the number of sampling points in the unit area of each sub-region, that is, the sampling density of each sub-region.
In step S1108, it is determined whether to continue sampling.
In this step, the sampling density of each sub-region is compared with a threshold, if the sampling density is less than or equal to the threshold, the sampling density is too small, and the sampling needs to be continued, and step S1110 is entered, if the sampling density is greater than the threshold, the sampling density meets the requirement, and the sampling does not need to be continued.
Step S1110, continue sampling the sub-region.
In this step, sub-regions with too low a sampling density may be continuously sampled by a single sampling method, for example, by the sampling method in fig. 3, or sub-regions may be sampled according to the shape or volume size of each sub-region.
The technical details of the foregoing embodiments may be incorporated into this embodiment and will not be described herein.
Although the present invention has been described with reference to the present specific embodiments, it will be appreciated by those skilled in the art that the above embodiments are merely illustrative of the present invention, and various equivalent changes and substitutions may be made without departing from the spirit of the invention, and therefore, it is intended that all changes and modifications to the above embodiments within the spirit and scope of the present invention be covered by the appended claims.

Claims (5)

1. A method of sampling, comprising:
the shape of the region to be sampled is identified,
a sampling method is selected according to the shape,
sampling the area to be sampled by using the sampling method,
wherein the sampling method comprises a multi-layer sampling method,
the multilayer sampling method comprises the following steps:
distinguishing an inner ring and an outer ring of the region to be sampled, calculating the lengths of the inner ring and the outer ring,
the inner ring and the outer ring are respectively equally divided according to the lengths of the inner ring and the outer ring to form segments with the same number,
determining the number of sampling layers according to the upper limit of the number of sampling points and the number of the segments,
and determining sampling points by utilizing the subsection and the number of the sampling layers.
2. The sampling method of claim 1, wherein the shape of the region to be sampled is acquired through character information of the region to be sampled.
3. The sampling method of claim 1, wherein the shape of the region to be sampled is determined by a pattern recognition algorithm.
4. A sampling method according to claim 3, wherein the pattern recognition algorithm is a pattern recognition algorithm.
5. The sampling method of claim 1, wherein selecting a sampling method according to the shape comprises:
and if the shape is square, selecting a sampling method of equidistant grid points.
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