CN115423834A - Image entropy threshold segmentation method, device, electronic equipment and storage medium - Google Patents

Abstract

The application provides an entropy threshold segmentation method and device for an image, an electronic device and a storage medium, wherein the method comprises the following steps: determining the posterior probability of each pixel belonging to the target class and the background class under the segmentation of different thresholds according to the target class probability and the background class probability of the image to be segmented under each threshold by using Bayes posterior estimation; calculating the Tsallis-BE entropy value of the whole image pixel of the image to BE segmented under each threshold value, wherein the Tsallis-BE entropy is formed by combining posterior probability and Tsalis entropy; and finding out the maximum entropy value from all Tsallis-BE entropy values, and determining the threshold corresponding to the maximum entropy value as the optimal threshold. According to the scheme, the time consumption of an algorithm is reduced while high-precision segmentation is performed, and the performance of the entropy threshold method is improved.

Description

Image entropy threshold segmentation method, device, electronic equipment and storage medium

technical field

The invention belongs to the technical field of optimal scheduling, and in particular relates to an image entropy threshold segmentation method, device, electronic equipment and storage medium.

Background technique

Image segmentation is one of the main processes in medical image analysis. It is useful in many applications in the medical field, including quantifying lesions, surgical simulation, assisting surgical decision-making, aiding in the diagnosis of multiple sclerosis, and more. Thresholding is a popular image segmentation technique, especially in the field of medical image processing. The main challenge of image thresholding is to determine the optimal threshold according to the intensity distribution of objects and background in the image, and divide the pixels of the whole image into two categories: object and background through this threshold. According to the imaging principle of MRI images, different tissues will show different intensities and distribution rules in the image, so the entropy threshold method can maximize or minimize a cost function constructed based on grayscale to find the best segmentation. threshold.

In the prior art, it is proposed to use classical Tsallis entropy to segment images, which proves the superiority of Tsallis entropy for image segmentation, especially medical image segmentation. And the existing technology extends the Tsallis entropy to two dimensions. In addition to the gray level of the pixel, the correlation between the pixel and its adjacent pixels is also considered, which further improves the accuracy of the Tsallis entropy threshold segmentation.

However, it is difficult for the above existing technologies to balance the accuracy and efficiency of segmentation. In order to improve the accuracy, the two-dimensional entropy threshold method needs to search for the optimal threshold in two-dimensional space, which is easy to fall into local optimum and takes a long time.

Contents of the invention

The purpose of the embodiments of this specification is to provide an image entropy threshold segmentation method, device, electronic equipment and storage medium.

In order to solve the above technical problems, the embodiments of the present application are implemented in the following ways:

In a first aspect, the present application provides a method for entropy threshold segmentation of an image, the method comprising:

Using Bayesian posterior estimation, according to the target class probability and background class probability of the image to be segmented under each threshold, determine the posterior probability of each pixel belonging to the target class and background class under the segmentation of different thresholds;

Calculate the value of the Tsallis-BE entropy of the full picture pixel of the image to be segmented under each threshold, where the Tsallis-BE entropy is composed of the combination of the posterior probability and the Tsaliis entropy;

Find the maximum entropy value from all Tsallis-BE entropy values, and determine the threshold corresponding to the maximum entropy value as the optimal threshold.

In one embodiment, the method also includes:

Segment the image to be segmented into a target area and a background area according to different thresholds;

Calculate the number of target pixels in the target area segmented under each threshold and the number of background pixels in the background area;

According to the number of target pixels and the number of background pixels under different thresholds, determine the target class probability and background class probability of the image to be segmented under each threshold.

In one of the embodiments, according to the number of target pixels and the number of background pixels under different thresholds, the target class probability and the background class probability of the image to be segmented under each threshold are determined, including:

p _o,t (i)=P[f(s)=i|s∈S _o,t ]

p _b,t (i)=P[f(s)=i|s∈S _b,t ]

S＝S _o,t ∪S _b,t

Among them, p _o,t (i) is the target class probability of the target pixel value i at the threshold t, p _b,t (i) is the background class probability of the background pixel value i at the threshold t; S is the target class to be segmented The set of all pixels of the image; S _o,t is the target pixel set at the threshold t; S _b,t is the background pixel set at the threshold t.

In one of the embodiments, assuming that the minimum value and the maximum value of the gray value of the pixel in the image to be segmented are t_min and t_max respectively, the interval traversed by the threshold value is set to [t_min+2, t_max+2].

In one of the embodiments, using Bayesian posterior estimation, according to the target class probability and background class probability of the image to be segmented under each threshold, the posterior probability of each pixel belonging to the target class and background class under different thresholds is determined. probabilities, including:

Suppose γ(t) represents the prior probability that the pixel belongs to the target at the threshold t, then 1-γ(t) represents the prior probability that the pixel belongs to the background at the threshold t, and p _o,t (i) is the prior probability at the threshold t The target class probability of the target pixel value i, p _b,t (i) is the background class probability of the background pixel value i at the threshold t;

The posterior probability of a pixel with pixel value i belonging to the target class and the background class can be expressed by Bayesian rule as:

Among them, p _t (i) can be calculated by the following formula:

p _t (i)=γ(t)p _o,t (i)+(1-γ(t))p _b,t (i).

In one of the embodiments, the expressions of the Tsallis-BE entropy of the target class and the background class are respectively:

Among them, L is the highest pixel gray value in the image to be segmented.

In one of the embodiments, the maximum entropy value t _opt is found from all Tsallis-BE entropy values, including:

In a second aspect, the present application provides an image entropy threshold segmentation device, which includes:

The probability determination module is used to use the Bayesian posterior estimation, according to the target class probability and the background class probability of the image to be segmented under each threshold, to determine the posteriori of each pixel belonging to the target class and the background class under the segmentation of different thresholds probability;

The entropy value determination module is used to calculate the value of the Tsallis-BE entropy of the full image pixel of the image to be segmented under each threshold, wherein the Tsallis-BE entropy is composed of a combination of posterior probability and Tsaliis entropy;

The search module is used to find out the maximum entropy value from all Tsallis-BE entropy values, and determine the threshold corresponding to the maximum entropy value as the optimal threshold.

In a third aspect, the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and operable on the processor. When the processor executes the program, the entropy threshold segmentation method of an image as in the first aspect is implemented. .

In a fourth aspect, the present application provides a readable storage medium on which a computer program is stored, and when the program is executed by a processor, the entropy threshold segmentation method of an image as in the first aspect is implemented.

It can be seen from the technical solution provided by the above embodiments of this specification that this solution: uses Bayesian posterior probability to give the uncertainty of each pixel belonging to the target and background, and improves the shortcoming of insufficient utilization of Tsallis entropy for the information of the image itself , while avoiding the problem of dimension increase caused by the introduction of more image feature information, it is still only necessary to solve the optimal threshold in one-dimensional space, achieving high-precision segmentation while reducing the time-consuming algorithm and improving the entropy threshold performance of the method.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only These are some embodiments described in this specification. Those skilled in the art can also obtain other drawings based on these drawings without any creative effort.

Fig. 1 is a schematic flow chart of the entropy threshold segmentation method of an image provided by the present application;

Fig. 2 is the original image of the image to be segmented and the segmented result figure provided by the application;

FIG. 3 is a schematic structural diagram of an image entropy threshold segmentation device provided by the present application;

FIG. 4 is a schematic structural diagram of an electronic device provided by the present application.

detailed description

In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described The embodiments are only some of the embodiments in this specification, not all of them. Based on the embodiments in this specification, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the protection scope of this specification.

In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

It will be obvious to those skilled in the art that various modifications and changes can be made to the specific embodiments described in the present application without departing from the scope or spirit of the present application. Other embodiments will be apparent to those skilled in the art from the description of this application. The specification and examples in this application are exemplary only.

As used herein, "comprising", "comprising", "having", "comprising" and so on are all open terms, meaning including but not limited to.

It is difficult for the existing technology to balance the accuracy and efficiency of segmentation. In order to improve the accuracy, the two-dimensional entropy threshold method needs to search for the optimal threshold in two-dimensional space, which is easy to fall into local optimum and takes a long time.

Based on the above defects, this application proposes an image entropy threshold segmentation method, which introduces the gray probability distribution based on Bayesian posterior estimation into the structure of Tsallis, so that this method can achieve high accuracy only by searching in one-dimensional space. Degree and efficient division.

The image entropy threshold segmentation method proposed in this application is aimed at the imaging characteristics and specific gray scale distribution of brain MRI images, and can effectively segment white matter (white matter, WM), gray matter (gray matter, GM) and Entropy threshold segmentation method for different tissues such as cerebrospinal fluid (CSF).

The present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments.

Referring to FIG. 1 , it shows a schematic flowchart of an entropy threshold segmentation method applicable to an image provided by an embodiment of the present application. The main task of the image entropy threshold segmentation method is to determine an optimal threshold t to distinguish target pixels from background pixels.

As shown in Figure 1, an entropy threshold segmentation method of an image may include:

S110. Using Bayesian posterior estimation, according to the target class probability and the background class probability of the image to be segmented under each threshold, determine the posterior probability of each pixel belonging to the target class and the background class under segmentation with different thresholds.

Specifically, the image to be segmented may be an MRI image. Exemplarily, the image to be segmented is an MRI slice image of a typical human brain. In this example, white matter (WM) and gray matter (GM) are regarded as target regions, while cerebrospinal fluid (CSF) are considered as background regions.

Wherein, the target class probability and the background class probability of the image to be segmented under each threshold can be determined in the following manner.

In one embodiment, the method also includes:

Segment the image to be segmented into a target area and a background area according to different thresholds;

Calculate the number of target pixels in the target area segmented under each threshold and the number of background pixels in the background area;

According to the number of target pixels and the number of background pixels under different thresholds, determine the target class probability and background class probability of the image to be segmented under each threshold, including:

p _o,t (i)=P[f(s)=i|s∈S _o,t ]

p _b,t (i)=P[f(s)=i|s∈S _b,t ]

S＝S _o,t ∪S _b,t

Among them, p _o,t (i) is the target class probability (or called the probability density function of the target pixel) of the target pixel value i at the threshold t, p _b,t (i) is the background pixel value at the threshold t is the background class probability of i (or called the probability density function of background pixels); S is the set of all pixels of the image to be segmented; S _o,t is the target pixel set when the threshold t; S _b,t is the threshold t background pixel collection.

和

其中，m_o(t)、m_b(t)、

分别是目标像素和背景像素的平均值和标准差，则p_o,t(i)和p_b,t(i)分别可以表示为：Assuming that the distribution of the target and the background is a Gaussian distribution, use

with

Among them, m _o (t), m _b (t),

are the mean and standard deviation of the target pixel and the background pixel respectively, then p _o,t (i) and p _b,t (i) can be expressed as:

Specifically, the MRI image is segmented into the target area and the background area according to different thresholds, the number of pixels in the target area and the number of pixels in the background area are calculated under each threshold, and the probability of the target class and the background class is obtained, which is used to measure each Pixels belong to different categories of uncertainty.

It can be understood that the grayscale range of the image is 0-255, and in order to prevent the boundary overflow problem, the area is reduced by 2 as the search area for determining the optimal threshold.

In one embodiment, assuming that the minimum value and the maximum value of the pixel gray value in the image to be segmented are t_min and t_max respectively, the interval traversed by the threshold value is set to [t_min+2, t_max+2].

Determine the target class probability and background class probability of the image to be segmented under each threshold by the above method, and then use Bayesian posterior estimation to determine the probability distribution of each pixel belonging to the target class and background class under different thresholds.

Specifically, assuming that γ(t) represents the prior probability that the pixel belongs to the target at the threshold t, then 1-γ(t) represents the prior probability that the pixel belongs to the background at the threshold t. Among them, γ(t) is estimated by the following formula:

Therefore, the posterior probability of a pixel with pixel value i belonging to the target class and the background class can be expressed by Bayesian rule as:

Among them, p _t (i) can be calculated by the following formula:

p _t (i)=γ(t)p _o,t (i)+(1-γ(t))p _b,t (i)

S120. Calculate the value of the Tsallis-BE entropy of the pixels in the entire image of the image to be segmented at each threshold, wherein the Tsallis-BE entropy is formed by combining the posterior probability and the Tsaliis entropy.

Specifically, at present, the Tsaliis entropy of the target class and the background class can be expressed as:

Among them, q is the entropy index that characterizes the degree of non-additivity, p _i is the probability of pixel i; where, given the threshold t, the image is divided into background area and target area, where the grayscale intervals are {0,1,… ,t} and {t+1,...,L-1},

After the combination of posterior probability and Tsaliis entropy, the expressions of Tsallis-BE entropy of target class and background class are respectively:

Among them, L is the highest pixel gray value in the image to be segmented, which means that the gray value of all pixels in the whole image is between 0-L.

According to the expressions of the Tsallis-BE entropy of the target class and the background class, the value of the Tsallis-BE entropy of the whole pixel of the image to be segmented under each threshold is calculated respectively.

S130. Find the maximum entropy value from all Tsallis-BE entropy values, and determine the threshold corresponding to the maximum entropy value as the optimal threshold.

Specifically, the threshold corresponding to the maximum entropy value is found, which is considered to be the optimal threshold for dividing the entire image into the target area and the background area.

Optionally, find the maximum entropy value t _opt from all Tsallis-BE entropy values, including:

As shown in Figure 2, the left side is the original image of the image to be segmented, and the right side is the image of the segmentation result after the optimal threshold is calculated using the entropy threshold segmentation method of the image of the present application and segmented according to the optimal threshold.

In the embodiment of the present application, Bayesian posterior probability is used to replace the probability of each gray value in classical Tsallis entropy, and a new Tsallis-BE entropy for image segmentation is proposed. Using the form of one-dimensional entropy, the accuracy of threshold segmentation comparable to two-dimensional entropy is achieved, and compared with two-dimensional entropy, the time required for the segmentation algorithm is greatly shortened. In addition, the present application uses the form of Bayesian posterior probability and considers the class uncertainty of pixels. Compared with other one-dimensional entropy threshold methods, it deepens the utilization of information of the image itself.

The Tsallis-BE entropy structure proposed by this application is different from the classic Tsallis entropy which assumes that the target class and background class segmented by each threshold are independently distributed. The posterior probability is used to give the uncertainty that each pixel belongs to the target and the background, which improves the shortcoming of Tsallis entropy’s insufficient utilization of the information of the image itself, and at the same time avoids the problem of dimension increase caused by introducing more image feature information. , still only need to solve the optimal threshold in one-dimensional space, achieve high-precision segmentation, reduce the time consumption of the algorithm, and improve the performance of the entropy threshold method.

Experimental verification

A comparative experiment with classical methods (including method 1 and method 2) was done on two benchmark datasets of medical image segmentation, Brainweb and MRBS13. The quantitative results are shown in Table 1. We used three general evaluation indicators DSC (Dice Similarity Coefficient, Dice Similarity Coefficient), JC (Jaccard Similarity Coefficient, Jaccard Similarity Coefficient), Accuracy (accuracy) to evaluate the segmentation effect. for evaluation verification. It can be seen that thanks to the proposed new entropy structure, this application has achieved better results than Method 1 and Method 2 on the two benchmark datasets. Note that method one corresponds to the method recorded in the literature Albuquerque M P D, Esquef I A, Mello A R G, et al. Imagethresholding using Tsallis entropy[J]. N. A threshold selection method from gray-histogram [J]. The method described in IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1): 62-66.

Table 1 Quantitative results (%) on medical image segmentation datasets

method one Method Two this application DSC 93.56 95.27 99.67 JC 93.09 94.84 98.02 Accuracy 90.79 93.88 98.97

Referring to FIG. 3 , it shows a schematic structural diagram of an apparatus for entropy threshold segmentation of an image described according to an embodiment of the present application.

As shown in Figure 3, the image entropy threshold segmentation device 300 may include:

The probability determination module 310 is used for using Bayesian posterior estimation, according to the target class probability and the background class probability of the image to be segmented under each threshold, to determine the posterior probability of each pixel belonging to the target class and the background class under the segmentation of different thresholds. test probability;

The entropy value determination module 320 is used to calculate the value of the Tsallis-BE entropy of the full image pixel of the image to be segmented under each threshold, wherein the Tsallis-BE entropy is composed of a combination of posterior probability and Tsaliis entropy;

The search module 330 is configured to find the maximum entropy value from all Tsallis-BE entropy values, and determine the threshold corresponding to the maximum entropy value as the optimal threshold.

Optionally, the image entropy threshold segmentation device 300 is also used for:

Segment the image to be segmented into a target area and a background area according to different thresholds;

Calculate the number of target pixels in the target area segmented under each threshold and the number of background pixels in the background area;

According to the number of target pixels and the number of background pixels under different thresholds, determine the target class probability and background class probability of the image to be segmented under each threshold.

Optionally, the image entropy threshold segmentation device 300 is also used for:

p _o,t (i)=P[f(s)=i|s∈S _o,t ]

p _b,t (i)=P[f(s)=i|s∈S _b,t ]

S＝S _o,t ∪S _b,t

Among them, p _o,t (i) is the target class probability of the target pixel value i at the threshold t, p _b,t (i) is the background class probability of the background pixel value i at the threshold t; S is the target class to be segmented The set of all pixels of the image; S _o,t is the target pixel set at the threshold t; S _b,t is the background pixel set at the threshold t.

Optionally, assuming that the minimum value and maximum value of the gray value of the pixel in the image to be segmented are t_min and t_max respectively, the interval traversed by the threshold value is set to [t_min+2, t_max+2].

Optionally, the probability determination module 310 is also used for:

Suppose γ(t) represents the prior probability that the pixel belongs to the target at the threshold t, then 1-γ(t) represents the prior probability that the pixel belongs to the background at the threshold t, and p _o,t (i) is the prior probability at the threshold t The target class probability of the target pixel value i, p _b,t (i) is the background class probability of the background pixel value i at the threshold t;

The posterior probability of a pixel with pixel value i belonging to the target class and the background class can be expressed by Bayesian rule as:

Among them, p _t (i) can be calculated by the following formula:

p _t (i)=γ(t)p _o,t (i)+(1-γ(t))p _b,t (i).

Optionally, the expressions of the Tsallis-BE entropy of the target class and the background class are respectively:

Among them, L is the highest pixel gray value in the image to be segmented.

Optionally, the search module 330 is also used for:

An image entropy threshold segmentation device provided in this embodiment can execute the above-mentioned method embodiment, and its implementation principle and technical effect are similar, and will not be repeated here.

FIG. 4 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. As shown in FIG. 4 , a schematic structural diagram of an electronic device 400 suitable for implementing the embodiments of the present application is shown.

As shown in FIG. 4 , the electronic device 400 includes a central processing unit (CPU) 401, which can operate according to a program stored in a read-only memory (ROM) 402 or a program loaded from a storage section 408 into a random access memory (RAM) 403 Instead, various appropriate actions and processes are performed. In the RAM 403, various programs and data necessary for the operation of the device 400 are also stored. The CPU 401 , ROM 402 , and RAM 403 are connected to each other through a bus 404 . An input/output (I/O) interface 405 is also connected to bus 404 .

The following components are connected to the I/O interface 405: an input section 406 including a keyboard, a mouse, etc.; an output section 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker; a storage section 408 including a hard disk, etc. and a communication section 409 including a network interface card such as a LAN card, a modem, or the like. The communication section 409 performs communication processing via a network such as the Internet. Drive 410 is also connected to I/O interface 406 as needed. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is mounted on the drive 410 as necessary so that a computer program read therefrom is installed into the storage section 408 as necessary.

In particular, according to an embodiment of the present disclosure, the process described above with reference to FIG. 1 may be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product including a computer program tangibly embodied on a machine-readable medium, the computer program including program code for performing the above-mentioned entropy threshold segmentation method for an image. In such an embodiment, the computer program may be downloaded and installed from a network via communication portion 409 and/or installed from removable media 411 .

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, program segment, or part of code that includes one or more logical functions for implementing specified executable instructions. It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved. It should also be noted that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by a dedicated hardware-based system that performs the specified functions or operations , or may be implemented by a combination of dedicated hardware and computer instructions.

The units or modules involved in the embodiments described in the present application may be implemented by means of software or by means of hardware. The described units or modules may also be provided in a processor. The names of these units or modules do not constitute limitations on the units or modules themselves in some cases.

The systems, devices, modules, or units described in the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions. A typical implementing device is a computer. Specifically, the computer can be, for example, a personal computer, a notebook computer, a mobile phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any of these devices combination of devices.

As another aspect, the present application also provides a storage medium, which may be the storage medium contained in the aforementioned device in the above embodiment, or may be a storage medium that exists independently and is not assembled into the device. The storage medium stores one or more programs, and the aforementioned programs are used by one or more processors to execute the entropy threshold segmentation method for images described in this application.

Storage media includes permanent and non-permanent, removable and non-removable media. Information storage can be realized by any method or technology. Information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Flash memory or other memory technology, Compact Disc Read-Only Memory (CD-ROM), Digital Versatile Disc (DVD) or other optical storage, Magnetic tape cartridge, tape magnetic disk storage or other magnetic storage device or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media excludes transitory computer-readable media, such as modulated data signals and carrier waves.

It should be noted that the term "comprises", "comprises" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus comprising a set of elements includes not only those elements, but also includes none other elements specifically listed, or also include elements inherent in the process, method, commodity, or apparatus. Without further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

Each embodiment in this specification is described in a progressive manner, the same and similar parts of each embodiment can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and for relevant parts, refer to part of the description of the method embodiment.

Claims (10)

1. an entropy threshold segmentation method of image, is characterized in that, described method comprises: Using Bayesian posterior estimation, according to the target class probability and background class probability of the image to be segmented under each threshold, determine the posterior probability of each pixel belonging to the target class and background class under the segmentation of different thresholds; Calculate the value of the Tsallis-BE entropy of the full picture pixel of the image to be segmented under each threshold, wherein the Tsallis-BE entropy is composed of the combination of the posterior probability and Tsaliis entropy; Find the maximum entropy value from all the Tsallis-BE entropy values, and determine the threshold corresponding to the maximum entropy value as the optimal threshold. 2. The method according to claim 1, characterized in that the method further comprises: Segmenting the image to be segmented into a target area and a background area according to different thresholds; calculating the number of target pixels in the target area and the number of background pixels in the background area segmented under each threshold; According to the number of target pixels and the number of background pixels under different thresholds, determine the target class probability and the background class probability of the image to be segmented at each threshold. 3. The method according to claim 2, wherein, according to the target pixel quantity and the background pixel quantity under different thresholds, determine the target class probability and the background class probability of the image to be segmented under each threshold ,include: p _o,t (i)=P[f(s)=i|s∈S _o,t ] p _b,t (i)=P[f(s)=i|s∈S _b,t ] S＝S _o,t ∪S _b,t Among them, p _o,t (i) is the target class probability of the target pixel value i at the threshold t, p _b,t (i) is the background class probability of the background pixel value i at the threshold t; S is the The set of all pixels of the image to be segmented; S _o,t is the target pixel set when the threshold is t; S _b,t is the background pixel set when the threshold is t. 4. The method according to claim 2 or 3, wherein, assuming that the minimum and maximum values of pixel gray values in the image to be segmented are respectively t_min and t_max, the interval traversed by the threshold is set to [t_min+2,t_max+2]. 5. method according to claim 1, it is characterized in that, described utilization Bayesian posterior estimation, according to target class probability and background class probability of image to be segmented under each threshold, under the segmentation that determines different thresholds, every The posterior probability of pixels belonging to the target class and the background class, including: Suppose γ(t) represents the prior probability that the pixel belongs to the target at the threshold t, then 1-γ(t) represents the prior probability that the pixel belongs to the background at the threshold t, and p _o,t (i) is the prior probability at the threshold t The target class probability of the target pixel value i, p _b,t (i) is the background class probability of the background pixel value i at the threshold t; The posterior probability of a pixel with pixel value i belonging to the target class and the background class can be expressed by Bayesian rule as:

Among them, p _t (i) can be calculated by the following formula: p _t (i)=γ(t)p _o,t (i)+(1-γ(t))p _b,t (i). 6. method according to claim 5, is characterized in that, the expression of the Tsallis-BE entropy of target class and background class is respectively:

Among them, L is the highest pixel gray value in the image to be segmented. 7. method according to claim 6, is characterized in that, described finds out maximum entropy value t _opt from the value of all described Tsallis-BE entropy, comprises:

8. An entropy threshold segmentation device for an image, characterized in that the device comprises: The probability determination module is used to use Bayesian posterior estimation, according to the target class probability and background class probability of the image to be segmented under each threshold, to determine the posteriori of each pixel belonging to the target class and background class under the segmentation of different thresholds probability; An entropy value determination module is used to calculate the value of the Tsallis-BE entropy of the full image pixel of the image to be segmented under each threshold, wherein the Tsallis-BE entropy is composed of the combination of the posterior probability and Tsaliis entropy; A search module, configured to find the maximum entropy value from all the Tsallis-BE entropy values, and determine the threshold corresponding to the maximum entropy value as the optimal threshold. 9. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and operable on the processor, wherein the processor implements any of claims 1-7 when executing the program. A method for entropy threshold segmentation of an image. 10. A readable storage medium on which a computer program is stored, wherein when the program is executed by a processor, the entropy threshold segmentation method for an image according to any one of claims 1-7 is implemented.

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