CN110751662B - Image segmentation method and system for quantum-behaved particle swarm optimization fuzzy C-means - Google Patents
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Abstract
The invention discloses an image segmentation method and system for a fuzzy C mean value optimized by quantum-behaved particle swarm, comprising the following steps: an acquisition step: acquiring an image to be processed, and converting the image to be processed into a Zhongzhi image; an image preprocessing step: denoising the intermediate intelligent image, and then performing image enhancement operation on a denoised result; and (3) information entropy calculation: calculating the element information entropy of the image set I for the result after image enhancement; an image segmentation step: if the ratio of the information entropies of the adjacent elements is smaller than a set threshold, carrying out segmentation on the mesology image by using a fuzzy C-means algorithm optimized by quantum-behaved particle swarm to obtain an image segmentation result; otherwise, returning to the image preprocessing step.
Description
Technical Field
The disclosure relates to the technical field of image segmentation, in particular to an image segmentation method and system for optimizing a fuzzy C-means by quantum-behaved particle swarm.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
Images carry vivid and rich information and play an extremely important role in the age of multimedia information. The image may directly mimic or actually describe the objective presence of the thing. Image segmentation is an important pre-process for image recognition and computer vision. Without a correct segmentation, no correct recognition is possible. This is a key step from image processing to image analysis. Image segmentation has an increasing demand in military, remote sensing, meteorology, communications, traffic, and medical image applications.
In the course of implementing the present disclosure, the inventors found that the following technical problems exist in the prior art:
generally, image segmentation decomposes a complete image into several regions with the same or different features and extracts objects of interest and technical processes from these regions. Various difficulties are encountered when computers automatically process partitions. Segmentation errors often occur, for example, due to uneven lighting, the effects of noise, the presence of unclear portions in the image, and shadows. Therefore, image segmentation is a technique that needs further investigation.
Disclosure of Invention
In order to solve the defects of the prior art, the disclosure provides an image segmentation method and system for optimizing a fuzzy C mean value by using quantum behavior particle swarm;
in a first aspect, the present disclosure provides an image segmentation method for a fuzzy C-means by quantum-behaved particle swarm optimization;
the image segmentation method for the fuzzy C mean value by quantum-behaved particle swarm optimization comprises the following steps:
an acquisition step: acquiring an image to be processed, and converting the image to be processed into a Zhongzhi image;
an image preprocessing step: denoising the intermediate intelligent image, and then performing image enhancement operation on a denoised result;
and (3) information entropy calculation: calculating the element information entropy of the image set I according to the result of image enhancement;
an image segmentation step: if the ratio of the information entropies of the adjacent elements is smaller than a set threshold, carrying out segmentation on the mesology image by using a fuzzy C-means algorithm optimized by quantum-behaved particle swarm to obtain an image segmentation result; otherwise, returning to the image preprocessing step.
In a second aspect, the disclosure further provides an image segmentation system for quantum-behaved particle swarm optimization fuzzy C-means;
the image segmentation system of the fuzzy C mean value of quantum behavior particle swarm optimization comprises:
an acquisition module configured to: acquiring an image to be processed, and converting the image to be processed into a Zhongzhi image;
an image pre-processing module configured to: denoising the intermediate intelligent image, and then performing image enhancement operation on a denoised result;
an information entropy calculation module configured to: calculating the element information entropy of the image set I for the result after image enhancement;
an image segmentation module configured to: if the ratio of the information entropies of the adjacent elements is smaller than a set threshold, carrying out segmentation on the mesology image by using a fuzzy C-means algorithm optimized by quantum-behaved particle swarm to obtain an image segmentation result; otherwise, returning to the image preprocessing module.
In a third aspect, the present disclosure also provides an electronic device, including a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the steps of the method of the first aspect are completed.
In a fourth aspect, the present disclosure also provides a computer-readable storage medium for storing computer instructions which, when executed by a processor, perform the steps of the method of the first aspect.
Compared with the prior art, the beneficial effect of this disclosure is:
the invention provides a middle-intelligence image segmentation method based on a quantum-behavior particle swarm optimization fuzzy C-means algorithm. The fuzzy control algorithm is optimized through the simple effectiveness of the fuzzy control algorithm and the global optimization capability of the QPSO. The method is feasible and has good anti-noise capability through experimental verification, the overall segmentation effect of the method is better than that of FCM, the segmentation boundary is clearer, and the optimal result is selected without multiple experiments. The algorithm effectively solves the problems that the FCM has strong dependency on the initial value and is easy to fall into local optimization. The algorithm has strong global search capability and good image segmentation effect. Although good segmentation results can be accurately obtained, the single time of program operation is not obviously shortened.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
FIG. 1 is a flow chart of a method according to a first embodiment of the present disclosure;
fig. 2 (a) is a images (240 × 320) of a vessel according to a first embodiment of the present disclosure;
fig. 2 (b) is an effect diagram of a mesology image with an alpha mean operation and an image enhancement operation according to a first embodiment of the disclosure;
fig. 2 (C) is a graph of the fuzzy C-means segmentation effect based on fig. 2 (b) according to the first embodiment of the present disclosure;
fig. 2 (d) is a graph of the effect of the improved particle swarm optimization fuzzy C-means segmentation based on fig. 2 (b) in the first embodiment of the disclosure.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiment provides an image segmentation method for a fuzzy C mean value by quantum-behaved particle swarm optimization;
as shown in fig. 1, the image segmentation method for fuzzy C-means by quantum-behaved particle swarm optimization includes:
an acquisition step: acquiring an image to be processed, and converting the image to be processed into a Zhongzhi image;
an image preprocessing step: denoising the intermediate intelligent image, and then performing image enhancement operation on a denoised result;
and (3) information entropy calculation: calculating the element information entropy of the image set I for the result after image enhancement;
an image segmentation step: if the ratio of the information entropies of the adjacent elements is smaller than a set threshold, carrying out segmentation on the mesology image by using a fuzzy C-means algorithm optimized by quantum-behaved particle swarm to obtain an image segmentation result; otherwise, returning to the image preprocessing step.
As one or more embodiments, the fuzzy C-means algorithm optimized by quantum-behaved particle swarm is used for segmenting the mesology image to obtain an image segmentation result; the method comprises the following specific steps:
s41: the method comprises the steps of firstly, obtaining an initial clustering class number C, an ambiguity parameter m, a particle swarm size N and a maximum iteration number MaxIt; the number of cluster centers is the dimension of each particle;
s42: carrying out initialization coding on the N cluster centers to form N first-generation particles; the number of the clustering centers is equivalent to the dimension of the particles; pbest for each particle is its current location, and gbest is the best location for all particles in the current population;
s43: calculating each clustering center C (k) and a center vector U (k) of the membership degree;
s44: calculating the fitness of each particle; and if the fitness of the particle is better than the fitness of the current optimal position of the particle, updating the optimal position of the single particle. If the fitness of the current global optimal position is better than the fitness of the optimal positions in all the particles, updating the global optimal position;
s45: updating the location of each particle to generate a new population of particles;
s46: stopping iteration if the current iteration times reach the maximum times set previously; the best solution is found in the last generation, otherwise S43 is repeated.
Further, the step S45, updating the position of each particle with equations (30) - (33) to generate a new particle group:
X i,j (t+1)＝p i,j (t)±α·|C j (t)-X i,j (t)|·ln[1/u i,j (t)]；u i,j (t)～U(0,1) (31)
the parameter C in equation (31), called the mean optimum position, can be denoted as mbest, is the center point of the individual optimum positions of all particles:
wherein p is i,j (t) is the potential well (local attraction point) of the jth dimension of the ith particle at the tth iteration, whose location is actually at the individual optimal location pbest j (t) and the population-optimal position gbest (t) are in the hyper-rectangle of vertices and vary with pbest and gbest. Phi is a unit of j (t) and u i,j (t) are all t iterations, j dimension is [0,1]Random numbers uniformly distributed, X i,j (t + 1) is the position of the ith particle in the jth dimension, C, at the tth iteration j (t) is a vector in C (t), α is the contraction-expansion coefficient of QPSO, and the value of α is determined by equation (33):
α＝(α 1 -α 2 )*(MaxIt-t)/MaxIt+α 2 (33)
wherein alpha is 1 And alpha 2 The initial value and the final value of the parameter α, respectively, t is the current iteration number, maxIt is the maximum number of iterations allowed, by varying the value of α, from 1.0 at the beginning of the search to 0.5 at the end of the search.
As one or more embodiments, the wisdom images include wisdom subset image T, wisdom subset image I, and wisdom subset image F:
the middle wisdom subset image T is expressed as the reality expression of the original image;
the image I of the Chinese wisdom set is expressed as uncertainty expression of the original image;
the wisdom subset image F is represented as a non-reality representation of the original image.
As one or more embodiments, the converting the image to be processed into a mesology image; the method comprises the following specific steps:
calculating the Zhongzhi subset image T according to the pixel value area mean value of the original image;
calculating the image I of the Zhongzhi subset according to the absolute value of the difference between the pixel value of the original image and the average value of the area pixels;
and calculating the wisdom subset image F according to the wisdom subset image T.
As one or more embodiments, the converting the image to be processed into a mesology image; the method comprises the following specific steps:
P NS ＝{T,I,F} (1)
F(i,j)＝1-T(i,j) (6)
wherein, P NS Is a pixel point of the image in the NS domain;
t (i, j) is the value of the point (i, j) of the Zhongzhi subset image T;
i (I, j) is the value of the point (I, j) of the Zhongzhi subset image I;
f (i, j) is the value of the point (i, j) of the Zhongzhi subset image F;
g (m, n) is the value of the point (m, n) of the Zhongzhi subset image T;
δ (i, j) is the mean value of pixel points g (i, j) and g (i, j) in the region w × w
The absolute value of the difference.
As one or more embodiments, the denoising processing is performed on the mesopic image; the method comprises the following specific steps:
wherein the content of the first and second substances,
the pixels of the image in the NS domain pass through a set of point alpha-means,
the alpha-means subset of the image T of the wisdom subset,
is an alpha-means subset of the image I of the wisdom subset,
is an alpha-mean value subset of the image F of the middle wisdom subset, T is a set of actual values of the original image,
for the alpha-mean set to be performed, I is the set of the uncertain values of the original image, alpha takes a value of 0.85, w W is the size of the limited region, (I, j) is the pixel point of the original image, (m, n) is the leading region pixel information point in the w W region, T (m, n) is the value of the point (m, n) of the image T of the Zhongzhi subset,
a gray-scale average intensity value of alpha-means is performed for the image T of the wisdom subset,
an alpha-mean set is needed, F (m, n) is the value of the point (m, n) of the image F of the Zhongzhi subset,
the alpha-mean gray-level mean intensity values are performed for the wisdom subset image F.
For the average intensity value of the image I of the wisdom subset,
is T after alpha mean operation is carried out on the pixel point (i, j) α (i, j) and
the absolute value of the difference between the two values,
and
is that
Minimum and maximum values of.
It should be understood that the purpose of the denoising step is to reduce noise points and high-uncertainty pixel points in the image, so that the distribution of image pixel information is more reasonable and beneficial to the subsequent processing of the image.
As one or more embodiments, the performing an image enhancement operation on the denoised result; the method comprises the following specific steps:
wherein:
for the set of realisms of the wisdom set subjected to the beta enhancement operation,
is as follows
The authenticity subset of the noon set after beta enhancement operation,
is a subset of uncertainty values representing the medium intelligence set, beta is a value of 0.85,
is composed of
The true value of the beta-enhancement operation is performed,
is the value of the set T after the mean operation,
for the non-authentic set of the wisdom set subjected to the beta enhancement operation,
is as follows
The non-reality subset of the noon set is subjected to beta enhancement operation,
is composed of
A non-true value of the beta boost operation is performed,
the pixel points are non-real pixel points after the mean value operation,
is that
δ' (i, j) is the value of the pixel (i, j) after image enhancement
And mean value
Absolute value of the difference, δ' min Value after image enhancement operation for pixel point (i, j)
And the mean value
Minimum value of absolute value of the difference, δ' max Value after image enhancement operation for pixel point (i, j)
And mean value
The maximum value of the absolute value of the difference,
as a subset
The average intensity in the region w x w,
as a subset
And (5) performing beta enhancement operation on the authenticity set in the w x w region. Delta' (i, j) is the pixel (i, j) after the image enhancement operation
And with
The absolute value of the difference.
It will be appreciated that the purpose of the enhancement processing step is to enhance the visual effect of the image to enlarge the feature differences between different objects in the image, such as objects and backgrounds, to improve the recognition of the image.
As one or more embodiments, calculating the element information entropy of the image set I as a result of the image enhancement; the method comprises the following specific steps:
wherein, en I Is the entropy of the Mega subset image I, p I (i) Is the probability of element I in the wisdom subset image I.
In order to overcome the limitation of a general image segmentation method and improve the expression and processing capacity of image uncertainty information, an extension fuzzy theory of an interval value fuzzy set, an intuitive fuzzy set and an interval value intuitive fuzzy set is provided. The fuzzy theory uses a membership function to describe the degree of the elements belonging to a certain class, so that the ambiguity and the uncertainty in the segmentation process can be well expressed and processed. The theory of central intelligence is a new extended fuzzy theory, which summarizes the classical fuzzy theory and the related extended fuzzy theory.
The relation between a real thing, theory, proposition, concept or entity 'A' and its opposite 'Anti-A', its negative 'Non-A' and 'Netu-A' (neither 'A' nor 'Anti-A') is studied in middle wisdom. The middle wisdom is the basis for the middle wisdom logic, the middle wisdom probability, the middle wisdom aggregate and the middle wisdom statistics.
The basic idea of neutronic physics is that any view has a true value of t%, an uncertainty of i%, and a false value of f%.
The fuzzy segmentation technology generated based on the fuzzy theory can effectively describe the fuzziness of the image, and is very suitable for processing the uncertainty problem of accurate segmentation in the medical image. With the continuous improvement of the fuzzy theory, the application of the fuzzy theory in image segmentation is increasingly active, and the fuzzy theory becomes a research hotspot.
The main basis of image segmentation is the similarity and discontinuity of the gray levels. Image segmentation algorithms can be divided into region-based segmentation algorithms and boundary-based segmentation algorithms and segmentation methods incorporating certain theoretical tools. Region-based image segmentation algorithms separate objects from the background in the image by selecting an appropriate threshold. The method is simple and intuitive, but only considers the gray information of the image, does not consider the spatial information of the image, and is very sensitive to noise and gray nonuniformity. Edge-based image segmentation algorithms complete the segmentation of regions by detecting edges between different homogeneous regions. The difficulty of the edge detection method is the contradiction between noise immunity and detection accuracy. At present, a few improved multi-scale edge detection methods have been proposed, but the effect is still not ideal. The segmentation method combined with a specific theoretical tool is to use a plurality of theoretical methods at present stage for image segmentation, such as an FCM method based on cluster analysis. The fuzzy C-means algorithm (FCM) based on the cluster analysis is more and more concerned by people due to the characteristics of simple design, wide solving range, easy realization of a computer and the like, and is widely applied in various fields. In addition, the conventional FCM algorithm does not consider spatial information during image segmentation, and is sensitive to noise and gray level non-uniformity.
The fuzzy C-means algorithm is widely applied due to simple realization; the fuzzy C-means algorithm is very sensitive to initial parameters and sometimes even to initial parameters. Manual intervention parameters are needed to approach the global optimal solution, and the segmentation speed is improved. But it is limited by the initial cluster center selection, is easy to fall into local optimum and is more sensitive to noise. The classical improvement of FCM has two main aspects: one is an improvement to the objective function in FCM; and the other is to introduce other intelligent algorithms in the iterative solution process. In recent years, many scholars have tried various methods to solve the problem that the fuzzy C-means algorithm falls into local extrema due to improper initial value selection. If the FCM is combined with the particle swarm optimization algorithm, the image segmentation quality is improved to a certain extent.
Particle Swarm Optimization (PSO) algorithms were proposed by Kennedy and Eberhar in 1995 in the United states. PSO calculation is simple and easy to realize, but the flight speed of the particles cannot be effectively controlled in the later evolution stage, so that the algorithm is easy to fly and jump the optimal solution, the convergence speed of the algorithm is further caused, and the accuracy is reduced. In view of the above disadvantages, sun Jun et al describe the motion state of particles by using the principle of Quantum inaccuracy measurement from the perspective of Quantum mechanics, and establish a new PSO algorithm model, namely a Quantum-behaved PSO (QPSO) algorithm model. The algorithm has the advantages of small number of parameters and better searching capability than the developed PSO algorithm.
In order to solve the problems of the FCM algorithm, a quantum-behaved particle swarm optimization (QPSO) algorithm is applied to fuzzy C-means clustering.
The FCM obtains the membership degree of each data sample point to all class centers by optimizing an objective function, so that the attribution of the sample points is determined to achieve the purpose of automatically classifying the data samples. Since the FCM algorithm essentially finds the optimal solution by the gradient descent method, there is a problem of falling into the local optimal solution. Aiming at the situations, the particle swarm algorithm capable of ensuring global convergence is introduced, namely the quantum behavior particle swarm algorithm forms a fuzzy clustering algorithm based on evolutionary computation.
(1) Image for middle intelligence
Due to the fact that the classical fuzzy segmentation algorithm is difficult to obtain good results when the problems of complexity and high uncertainty are processed. Therefore, the intelligent theory is introduced into the image to generate the intelligent image so as to enhance the expression capability of uncertain information in the image.
The image is expressed in the field of middle intelligence, and the basic method is to convert the original digital image and the relevant features of the image into a middle intelligence area by utilizing the middle intelligence theory so as to obtain the middle intelligence image. The intelligent image can better express and describe the fuzziness and uncertainty of the image, and not only can utilize gray information, but also can utilize edge and space information to solve the problem which cannot be solved by a fuzzy segmentation algorithm.
Definition 1 (mesopic images): given an image as P, assume the full domain of discourse is U, the full set of image pixels is W, and W is a non-empty subset of U. P NS The image has three membership sets T, I and F.
P NS ＝{T,I,F} (1)
The subset image T is expressed as an original image authenticity expression, the subset image I is expressed as an original image uncertainty expression, and the subset image F is expressed as an original image non-authenticity expression.
The characteristic information used by the intelligent image is the gray value of the image pixel point. A pixel P in the image is described as P (t, i, f), belonging to W for the element P (t, i, f) in the following way: t% true, I% uncertain, and F% false, where T ∈ T, I ∈ I, and F ∈ F. The pixel P (i, j) in the image domain is converted into an intellectual region.
In general, we represent the uncertainty of the data by standard deviation. Since the uncertainty of the target region and the background region is much smaller than the uncertainty of the edge, this section defines I according to the standard deviation and discontinuity of the image gray. The standard deviation represents the difference in the local area of the image and the discontinuity represents an abrupt change in the gray level. The standard deviation reflects the difference of the local area of the image. The background and target areas in the image are uniform, while the blurred edges are gradual from target to background, and then we describe T, I, F using the mean, standard deviation and range of pixel values areas.
In the process of image conversion, the pixel point (i, j) is limited in a small area, and the size of the area is w × w. The transformation process of the intelligent image is the acquisition process of the three sets T, I and F. The process is calculated by the following formula:
F(i,j)＝1-T(i,j) (6)
wherein:
is the local average of the image, and δ (i, j) is the pixel g (i, j) and its local average
The absolute value of the difference.
(2) Information entropy of Zhongzhi image
The information entropy of the image describes the aggregation characteristics of the gray level distribution of the image pixel points. In the image of the middle intelligence, the information entropy of the image is used for describing the aggregation characteristics of the gray level distribution of image pixel points.
Definition 2 (entropy of information of mesopic image) the entropy of information of a mesopic image is defined as the sum of the entropies of three sets T, I and F, used to evaluate the distribution of elements in a mesopic area:
En NS ＝En T +En I +En F (7)
wherein: en T ，En I ，En F Entropy of sets T, I and F, respectively, and probability of element i in three sets T, I and F, respectively. And En T And En F Is used to describe the distribution of elements in the mesology collection; en I To describe the distribution of elemental uncertainty.
According to the concept of mesology, the variation of elements in sets T and F can affect the uncertain distribution of set I. The larger the entropy of the set T and the smaller the entropy of the set I, the more uniform the gray level distribution of the image is, and the more reasonable the distribution of the gray level values is. In order to increase the entropy of the set T and reduce the entropy of the set I, a new operation, namely alpha-mean value operation, is proposed in the image denoising of the mesology, and the operation utilizes the values of the pixel points in the uncertainty set to judge and update the current pixel value in the field of the mesology.
When noise exists in an image, the image segmentation is easily interfered by the noise, the segmentation effect is poor, and the image analysis and understanding are influenced. In order to solve the problem, the method of alpha average filtering is adopted in the disclosure to eliminate image noise, so that subsequent image segmentation is facilitated.
Definition 3 (α -means operation): to P NS Carrying out alpha-mean calculation,
Is defined as:
wherein:
and
the mean values of T (i, j) and F (i, j) in the region w x w, respectively; delta α (i, j) is T after alpha mean operation is carried out on the pixel point (i, j) α (i, j) and
the absolute value of the difference between them.
After the alpha-mean operation, the entropy of the uncertain subset I decreases, and the distribution of elements in I becomes more uneven, and the non-uniformity reduces the uncertainty of the intelligent set PNS. Noise points and high-uncertainty pixel points in the image are reduced, and the distribution of image pixel information is more reasonable and beneficial to the subsequent processing of the image.
(4) Image enhancement operations
In the image segmentation process of the mesology, the image is converted into the mesology image, and alpha mean value operation is carried out, so that the image contour is blurred, and therefore, the problem is solved by utilizing image enhancement in the processing process. The main purpose of image enhancement is to improve the contrast of the image and highlight the information of interest. Image enhancement may improve image blur and image quality. It can further enhance the edge information of the object and weaken the non-edge information, thereby reducing the blurring of the image. Centering an intelligent image P by using the principle of fuzzy set pair set element enhancement operation NS The T image in (1) is obtained by carrying out beta-enhancement operation
The blurred image contour becomes clearer, and the enhancement formula is
Wherein: delta' (i, j) is the pixel (i, j) after the image enhancement operation
And
the absolute value of the difference.
(5) Fuzzy C-means algorithm
The key of image segmentation based on the FCM clustering algorithm lies in how to unify the mathematical form of an image with that of the FCM clustering algorithm. For this purpose, the image may be regarded as a sample set, each pixel in the image may be regarded as a clustering sample, the feature of the pixel may be regarded as a feature vector of the sample, and the pixel may be clustered in the feature space. Dividing pixels with the same or similar characteristics into a class as much as possible; then, the class of each pixel is marked, so that the image segmentation is completed.
Aiming at the noon subset, a new clustering method and process are defined
Namely the alpha mean and the noon subset after the image enhancement operation.
Considering the influence of uncertainty, we combine the two sets T and I into a new cluster value.
An improved fuzzy c-means algorithm for the middle intelligent set is provided. The objective function is defined as:
the FCM algorithm obtains fuzzy classification of the sample set by performing iterative optimization on the target function.
The algorithm is sensitive to the initial value and depends largely on the choice of initial cluster center. When the initial cluster center deviates significantly from the global optimal cluster center, the FCM tends to fall into a local minimum. The disadvantage is more pronounced when the number of clusters is larger.
(6) Improved PSO-FCM
The FCM algorithm based on gradient descent is essentially a local search algorithm, is prone to fall into local minimum values, and is sensitive to initial values, i.e., different initial values may result in different clustering results. The QPSO algorithm has global searching capability and is not easy to sink into a local area, so the present disclosure provides a new intermediate intelligent image segmentation hybrid clustering algorithm (QPSO-FCM), which combines the QPSO algorithm and the FCM to be applied to the intermediate intelligent image for segmentation.
QPSO operates on the fitness of individuals (particles) using the concepts of "population" and "evolution". Assuming that m is the number of particles in a multi-target search space, the position of the first particle is represented as vector Xi = (Xi 1, xi2, … xiD), in each iteration, the particle updates itself by tracking the two best positions. One is the optimal solution found by the particle itself, called the single optimal position pi = (pi 1, pi2, … pid); the other is the optimal solution found by the whole particle swarm optimization at present, and is called as the global optimal position pg. After finding the two extreme values, the average optimal position (mbest) or C (t) is taken as the average of the optimal positions of all particles. Move to search for the best solution to the problem according to the following formula.
X i,j (t+1)＝p i,j (t)±α·|C j (t)-X i,j (t)|·ln[1/u i,j (t)] u i,j (t)～U(0,1) (31)
p i,j (t) is the individual optimum position of the ith particle at the tth iteration (1)<＝j<= D). α is the contraction-expansion coefficient of QPSO. It is an important parameter for QPSO convergence, and the value of α is determined by the following equation:
α＝(α 1 -α 2 )*(MaxIt-t)/MaxIt+α 2 (33)
in the formula, alpha 1 And alpha 2 The initial and final values of the parameter, respectively, t is the current iteration number, maxIt is the maximum number of iterations allowed, by varying value, from 1.0 at the beginning of the search to 0.5 at the end of the search.
In the application of the QPSO-FCM algorithm, the fitness function of each individual in the QPSO is defined as follows:
wherein, J m (U, C) is the fitness of the imageA function. k is the gray scale, k =0-C, and C is the maximum value of the gray scale.
The QPSO-based FCM algorithm randomly generates a next generation solution, and therefore, it is easy to search for a global optimum value. Moreover, each generation of solution has the dual advantages of self-promotion and learning to others; therefore, it has a fast convergence speed.
(7) Intelligent image segmentation based on QPSO-FCM algorithm
A new intermediate intelligent image segmentation algorithm-QPSO-FCM algorithm is provided, which comprises the following steps:
step 1: inputting an image;
step 2: transforming the image into the NS domain using equations (1) - (6);
and step 3: performing an alpha mean operation using equations (11) - (17);
and 4, step 4: performing an image enhancement operation using equations (18) - (24);
and 5: calculating the entropy of the indefinite subset I using equation (9);
and 6: if it is not
Entering step 7; otherwise, entering step 3;
and 7: applying the improved FCM algorithm to the set of nooses:
And 8: and obtaining an image segmentation result.
The QPSO-FCM segmentation algorithm is applied to the mesopic image subjected to denoising of various real images, and is compared with the mesopic denoising image segmentation method based on the fuzzy C mean value.
In the FCM image segmentation method and the QPSO-FCM image segmentation method, the clustering centers set by the invention are both 5, so that the clustering centers are compared with each other.
As can be seen from fig. 2 (a) -2 (d), the warship boundary is clearer in fig. 2 (d) compared with fig. 2 (c). Especially at the front of the ship, the boundaries of the section of fig. 2 (d) are more clear. Warship boundary ambiguity in fig. 2 (c).
From the experimental data of fig. 2 (a) -2 (d), it can be seen that the QPSO and FCM are applied to the mesology image more clearly than the FCM is applied to the mesology image.
The ultimate goal of image segmentation is to accurately acquire the target image in one run. Therefore, we need to constantly find new methods to test whether this method is good or bad. As can be seen from the data obtained in a large number of experiments, FCM is greatly affected by the initial value, so we can only test whether a global optimum can be found. In many experiments we can find the best performing image (the border is clearer). The QPSO-FCM method is applied to the intelligent image, the global optimum can be found through one-time operation, the segmentation boundary is clearer, the processing of the fuzzy area is greatly improved, and the global optimum is the same even if the experiment is repeated.
However, the running time of the program is not shortened significantly because the data of the method needs to be calculated repeatedly. According to the free lunch theorem, a good and accurate segmentation effect is obtained at the cost of spending more running time.
The second embodiment further provides an image segmentation system for quantum-behaved particle swarm optimization fuzzy C-means;
the image segmentation system of the fuzzy C mean value of quantum behavior particle swarm optimization comprises:
an acquisition module configured to: acquiring an image to be processed, and converting the image to be processed into a Zhongzhi image;
an image pre-processing module configured to: denoising the intermediate intelligent image, and then performing image enhancement operation on a denoised result;
an information entropy calculation module configured to: calculating the element information entropy of the image set I for the result after image enhancement;
an image segmentation module configured to: if the ratio of the information entropies of the adjacent elements is smaller than a set threshold, carrying out segmentation on the mesology image by using a fuzzy C-means algorithm optimized by quantum-behaved particle swarm to obtain an image segmentation result; otherwise, returning to the image preprocessing module.
In a third embodiment, the present embodiment further provides an electronic device, which includes a memory, a processor, and computer instructions stored in the memory and executed on the processor, where the computer instructions, when executed by the processor, implement the steps of the method in the first embodiment.
In a fourth embodiment, the present invention further provides a computer-readable storage medium for storing computer instructions, and when the computer instructions are executed by a processor, the computer instructions perform the steps of the method according to the first embodiment.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (4)
1. The image segmentation method for the fuzzy C mean value by quantum-behaved particle swarm optimization is characterized by comprising the following steps of:
an acquisition step: acquiring an image to be processed, and converting the image to be processed into a Zhongzhi image;
converting the image to be processed into a mesology image; the method comprises the following specific steps:
P NS ＝{T,I,F} (1)
F(i,j)＝1-T(i,j) (6)
wherein, P NS Is a pixel point of the image in the NS domain;
t (i, j) is the value of the point (i, j) of the Zhongzhi subset image T;
i (I, j) is the value of the point (I, j) of the image I of the Zhongzhi subset;
f (i, j) is the value of the point (i, j) of the Zhongzhi subset image F;
g (m, n) is the value of the point (m, n) of the Zhongzhi subset image T;
δ (i, j) is the mean value of pixel points g (i, j) and g (i, j) in the region w × w
The absolute value of the difference;
an image preprocessing step: denoising the intermediate intelligent image, and then performing image enhancement operation on a denoised result;
denoising the mesology image; the method comprises the following specific steps:
wherein, the first and the second end of the pipe are connected with each other,
the pixels of the image in the NS domain pass through a set of point alpha-means,
is the alpha-mean of the image T of the wisdom subset,
is an alpha-means subset of the image I of the wisdom subset,
for the noon-child set image FT is the set of real values of the original image,
for the alpha-mean set to be performed, I is the set of the uncertain values of the original image, alpha takes a value of 0.85, w W is the size of the limited region, (I, j) is the pixel point of the original image, (m, n) is the leading region pixel information point in the w W region, T (m, n) is the value of the point (m, n) of the image T of the Zhongzhi subset,
a gray-scale average intensity value of alpha-means is performed for the image T of the wisdom subset,
alpha-mean value set is needed, F (m, n) is the value of the point (m, n) of the image F of the mesogen-junction set,
carrying out alpha-mean gray level average intensity value on the Zhongzhi subset image F;
for the average intensity value of the image I of the wisdom subset,
is T after alpha mean operation of pixel point (i, j) α (i, j) and
the absolute value of the difference between the two values,
and
is that
Minimum and maximum values of;
performing image enhancement operation on the denoised result; the method comprises the following specific steps:
wherein:
for the set of realisms of the wisdom set subjected to the beta enhancement operation,
is that when
The authenticity subset of the noon set after beta enhancement operation,
is a subset of uncertainty values representing the medium intelligence set, beta is a value of 0.85,
is composed of
The true value of the beta-enhancement operation is performed,
is the value of the set T after the mean operation,
for the non-authentic set of the wisdom set subjected to the beta enhancement operation,
is as follows
The non-reality subset of the noon set is subjected to beta enhancement operation,
is composed of
A non-true value of the beta boost operation is performed,
the non-real pixel points after the mean value operation,
is that
Of uncertainty of pixel point subset, δ' min Value after image enhancement operation for pixel point (i, j)
And mean value
Minimum value of absolute value of the difference, δ' max Value of pixel point (i, j) after image enhancement operation
And mean value
The maximum value of the absolute value of the difference between,
as a subset
The average intensity in the region w x w,
as a subset
Performing beta enhancement operation on the authenticity set in the w x w region; delta' (i, j) is the pixel (i, j) after the image enhancement operation
And
the absolute value of the difference;
and (3) information entropy calculation: calculating the element information entropy of the image set I for the result after image enhancement;
calculating the element information entropy of the image set I for the result after image enhancement; the method comprises the following specific steps:
wherein, en I Is the entropy, p, of the image of the Zhongzhi subset I I (i) Is the probability of element I in the wisdom subset image I;
an image segmentation step: if the ratio of the information entropies of the adjacent elements is smaller than a set threshold, carrying out segmentation on the mesology image by using a fuzzy C-means algorithm optimized by quantum-behaved particle swarm to obtain an image segmentation result; otherwise, returning to the image preprocessing step;
the objective function is defined as:
the fuzzy C-means algorithm optimized by quantum-behaved particle swarm is used for segmenting the mesology image to obtain an image segmentation result; the method comprises the following specific steps:
s41: the method comprises the steps of firstly, obtaining an initial clustering class number C, an ambiguity parameter m, a particle swarm size N and a maximum iteration number MaxIt; the number of cluster centers is the dimension of each particle;
s42: carrying out initialization coding on the N cluster centers to form N first-generation particles; the number of the clustering centers is equivalent to the dimension of the particles; pbest for each particle is its current location, and gbest is the best location for all particles in the current population;
s43: calculating each clustering center C (k) and a center vector U (k) of the membership degree;
s44: calculating the fitness of each particle; if the fitness of the particle is better than the fitness of the current optimal position of the particle, updating the optimal position of a single particle; if the fitness of the current global optimal position is better than the fitness of the optimal positions in all the particles, updating the global optimal position;
s45: updating the location of each particle to generate a new population of particles;
the step S45, updating the position of each particle with equations (30) - (33) to generate a new particle population:
X i,j (t+1)＝p i,j (t)±α·|C j (t)-X i,j (t)|·ln[1/u i,j (t)]；u i,j (t)～U(0,1) (31)
wherein p is i,j (t) is the potential well in the jth dimension of the ith particle at the tth iteration, whose location is actually at the individual optimal location pbest j (t) and the group optimal position gbest (t) are in the hyper-rectangle of the vertex and vary with the variation of pbest and gbest; phi is a j (t) and u i,j (t) are all t iterations, j dimension is [0,1]Random numbers uniformly distributed, X i,j (t + 1) is the position of the ith particle in the jth dimension, C, at the tth iteration j (t) is a vector in C (t), α is the contraction-expansion coefficient of QPSO, and α has a value defined byEquation (33) determines:
α＝(α 1 -α 2 )*(MaxIt-t)/MaxIt+α 2 (33)
wherein alpha is 1 And alpha 2 Respectively an initial value and a final value of the parameter alpha, t is the current iteration time, maxIt is the maximum time allowed to iterate, and the value of alpha is changed from 1.0 at the beginning of searching to 0.5 at the end of searching;
the fitness function for each individual is defined as follows:
wherein, J m (U, C) is the fitness function of the image; k is the gray scale, k =0-C, C is the maximum value of the gray scale;
s46: stopping iteration if the current iteration times reach the maximum times set previously; the best solution is found in the last generation, otherwise S43 is repeated.
2. The image segmentation system of the fuzzy C-means with the quantum-behaved particle swarm optimization, which adopts the image segmentation method of the fuzzy C-means with the quantum-behaved particle swarm optimization as claimed in claim 1, is characterized by comprising:
an acquisition module configured to: acquiring an image to be processed, and converting the image to be processed into a Zhongzhi image;
an image pre-processing module configured to: denoising the intermediate intelligent image, and then performing image enhancement operation on a denoised result;
an information entropy calculation module configured to: calculating the element information entropy of the image set I for the result after image enhancement;
an image segmentation module configured to: if the ratio of the information entropies of the adjacent elements is smaller than a set threshold, carrying out segmentation on the mesology image by using a fuzzy C-means algorithm optimized by quantum-behaved particle swarm to obtain an image segmentation result; otherwise, returning to the image preprocessing module;
the fuzzy C-means algorithm optimized by quantum-behaved particle swarm is used for segmenting the mesology image to obtain an image segmentation result; the method comprises the following specific steps:
s41: the method comprises the steps of firstly, obtaining an initial clustering class number C, an ambiguity parameter m, a particle swarm size N and a maximum iteration number MaxIt; the number of cluster centers is the dimension of each particle;
s42: carrying out initialization coding on the N cluster centers to form N first-generation particles; the number of the clustering centers is equivalent to the dimension of the particles; pbest for each particle is its current location, and gbest is the best location for all particles in the current population;
s43: calculating each clustering center C (k) and a center vector U (k) of the membership degree;
s44: calculating the fitness of each particle; if the fitness of the particle is better than the fitness of the current optimal position of the particle, updating the optimal position of a single particle; if the fitness of the current global optimal position is better than the fitness of the optimal positions in all the particles, updating the global optimal position;
s45: updating the location of each particle to generate a new population of particles;
s46: stopping iteration if the current iteration times reach the maximum times set previously; the best solution is found in the last generation, otherwise S43 is repeated.
3. An electronic device comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, wherein the computer instructions, when executed by the processor, perform the steps of the method of claim 1.
4. A computer-readable storage medium storing computer instructions which, when executed by a processor, perform the steps of the method of claim 1.
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