An Improved Cuckoo Search Algorithm for Multithreshold Image Segmentation

Hindawi
Security and Communication Networks
Volume 2021, Article ID 6036410, 10 pages
https://doi.org/10.1155/2021/6036410

Research Article
An Improved Cuckoo Search Algorithm for Multithreshold
Image Segmentation

 Wentan Jiao,1 Wenqing Chen ,1 and Jing Zhang2
 1
 Luoyang Institute of Science and Technology, Henan, Luoyang 471023, China
 2
 Luoyang Center of Quality and Metrology Inspection, Henan, Luoyang 471023, China

 Correspondence should be addressed to Wenqing Chen; hncwq197001@163.com

 Received 15 June 2021; Revised 14 July 2021; Accepted 22 July 2021; Published 3 August 2021

 Academic Editor: Chi-Hua Chen

 Copyright © 2021 Wentan Jiao et al. This is an open access article distributed under the Creative Commons Attribution License,
 which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 Image segmentation is an important part of image processing. For the disadvantages of image segmentation under multiple
 thresholds such as long time and poor quality, an improved cuckoo search (ICS) is proposed for multithreshold image seg-
 mentation strategy. Firstly, the image segmentation model based on the maximum entropy threshold is described, and secondly,
 the cuckoo algorithm is improved by using chaotic initialization population to improve the diversity of solutions, optimizing the
 step size factor to improve the possibility of obtaining the optimal solution, and using probability to reduce the complexity of the
 algorithm; finally, the maximum entropy threshold function in image segmentation is used as the individual fitness function of the
 cuckoo search algorithm for solving. The simulation experiments show that the algorithm has a good segmentation effect under
 four different thresholding conditions.

1. Introduction single pixel, and it is obvious that image analysis and un-
 derstanding based on multiple segmentation targets is better
With the rapid `development and application of computers, than image analysis and understanding based on isolated
handheld terminals, networks, and multimedia technologies, pixel points with no semantic meaning.
multimedia data have gained more and more widespread In recent years, a large amount of related literature has
applications, especially the explosive growth of image and shown that intelligent methods have significant advantages
video data [1]. Especially in SnapChat, Instagram, Facebook, over traditional methods in image segmentation [3, 4].
Telnet, and other image-related portals, the number of Therefore, in this paper, we choose the cuckoo search (CS)
images has reached hundreds of millions, and the trend is [5] for image segmentation, which is one of the relatively
multiplying [2]. Therefore, the study of these data images has new metaheuristics to efficiently solve the optimization
a relatively important value. In the field of image engi- problem by simulating the parasitic brood (Brood Parasit-
neering, image processing, image analysis, and image un- ism) of some species of cuckoo, which uses the Levy flight
derstanding are classified according to the data source to be search mechanism to improve the performance of the al-
processed and the level of semantic abstraction of the image. gorithm. The performance of the algorithm is improved by
The result of image processing directly affects the success or using the Levy flight search mechanism. Due to this unique
failure of image depth understanding and analysis. Image optimization mechanism, CS can provide good global search
segmentation is one of the most important tools in graphics functionality, which makes it popular in various engineering
processing methods, which has a wide range of applications problems. In this work, we will try to explore the application
in target recognition, feature detection, image annotation, of CS to image segmentation optimization problems under
and semantic search. It is the process of dividing a digital multiple thresholds. Specifically, the image segmentation
image into multiple regions or multiple targets, which can scheme is mapped into the model of CS algorithm, and then
provide more accurate and meaningful information than a the near-optimal solution is obtained using CS algorithm.

2 Security and Communication Networks

Based on this, we propose an advanced method called ICS and accurately selects the optimal segmentation threshold
(Improved CS for Vehicle Scheduling), which aims to fur- to achieve image segmentation; literature [15] introduced
ther improve the best solution search capability of CS. We an improved image threshold segmentation method of bee
provide a detailed implementation of ICS and evaluate the colony algorithm; simulation experiment results show that
performance through simulation experiments. We sum- the proposed algorithm is efficient and robust. Literature
marize the contributions of this work as follows: (1) to [16] used the gray wolf algorithm to obtain the best seg-
improve the effectiveness of multithreshold image seg- mentation threshold of the image and obtain higher quality
mentation, we use the ICS algorithm to solve problem; (2) segmentation results. At the same time, literature [17]
we propose a new method called ICS, which improves the introduced the whale algorithm to image multithreshold
convergence speed and accuracy of the ICS-based method by segmentation. Literature [18] proposed image multi-
combining advanced optimization strategies to improve threshold segmentation based on harmony algorithm and
image segmentation; (3) we present the detailed design and electromagnetic optimization algorithm. Literature [19]
implementation of ICS and compare it with modern met- gave the application of social spider optimization and
aheuristic algorithms such as CS, ACO, and PSO. The ex- pollination algorithm in multithreshold image segmenta-
perimental results show that the ICS algorithm can provide tion. Experiments show that the algorithm can effectively
better performance in terms of image segmentation with avoid local optima as the number of thresholds increases,
multiple thresholds. and the segmentation stability is better.
 Section 2 describes the results of related researches, The single-element heuristic algorithm is easy to fall into
Section 3 introduces the basic concepts of image segmen- the problem of local optimization in the later stage. The use
tation, Section 4 describes the application of the improved of two intelligent algorithms of fusion is a research hotspot
cuckoo algorithm in image segmentation, Section 5 de- in the field of image segmentation. For example, literature
scribes the process of simulation experiments, and Section 6 [20] uses the combination of cuckoo algorithm and adaptive
concludes the paper. differential evolution algorithm to perform multithreshold
 segmentation of images. The experimental results show that
2. Related Research the fusion algorithm has better segmentation results and
 requires fewer parameters, which has better results; litera-
As the application of image segmentation technology be- ture [21] proposed an image segmentation method based on
comes more and more extensive, scholars at home and simulated annealing and genetic algorithm. Simulation
abroad have put forward a large number of related research experiments showed that the fusion algorithm was in seg-
results. The image segmentation is studied from different mentation accuracy. Literature [22] suggested mixing the
angles. Image segmentation based on metaheuristic algo- wind-driven optimization algorithm with the cuckoo al-
rithm is a segmentation method that has been used more in gorithm and using the fused algorithm to search for the best
recent years. Firstly, one or more optimization objective threshold under Kapur entropy. Compared with the two
functions are determined, and then these objective functions algorithms before mixing, the segmentation accuracy is
are maximized or minimized through metaheuristic algo- higher. Literature [23] proposed an image segmentation
rithm. At the same time as the best, the image can also algorithm based on PSO and FCM. Simulation experiments
complete the best segmentation. Scholars have used a large show that the fusion algorithm has better results in image
number of intelligent optimization methods for image segmentation.
segmentation, such as Improved Moth-Flame Optimization
Algorithm [6], Symbiotic Organisms Search [7], Dragonfly 3. Multithreshold Image Segmentation
algorithm [8], Particle Swarm Optimization- Artificial Bee
Colony [9], Bat Algorithm [10], Fruit Fly Optimization Image segmentation is to divide an image segmentation into
Algorithm [11], etc. The use of these intelligent algorithms several different areas. The color, shape, and information
improves the effect of image segmentation, which is the similarity of the image in the same area are relatively high. At
direction of this paper. the same time, the similarity between different regions is
 The selection of the optimal threshold in the seg- very low, and the purpose of image segmentation is to select
mentation algorithm is the main optimization direction of the region of interest from the image for subsequent image
the metaheuristic algorithm. It searches for the optimal processing algorithm application. Think of an image as a set
value of the objective function under a certain criterion in R, and each segmented A region is a nonempty subset
an iterative manner to obtain the optimal segmentation R1 R2 , . . . Rn . These subsets satisfy the following conditions.
threshold. Literature [12] proposed a multithreshold image Condition 1: ∪ ni�1 Ri � R, which means that the sum of pixels
segmentation method based on moth algorithm, which in n subregions includes all pixels of the original image and
combined moth algorithm with maximum entropy to all pixels in the same subregion are connected; condition 2:
achieve image segmentation quickly and accurately; liter- for the i th nonempty subset and the j th nonempty subset,
ature [13] proposed an image segmentation method based when i ≠ j, Ri ∩ Rj � ∅, it shows that any pixel in the image
on improved hybrid particle swarm optimization (PSO). cannot belong to different subregions at the same time;
Simulation experiments show that the algorithm can ef- condition 3: for i � 1, 2 . . . n, P(Ri ) � True; this means that
fectively improve the segmentation effect; literature [14] the pixel values in the same subregion have a high degree of
applied the firefly algorithm to color image segmentation similarity in some features; condition 4: for nonempty subset

Security and Communication Networks 3

i ≠ j, P(Ri ∪ Rj ) � False. This means that pixels in different the image needs to be segmented into n + 1 regions, the
subregions should have different characteristics in some objective function of maximum entropy is as follows:
respect.
 Threshold segmentation algorithm is a simple and ef- J t1 , t2 , . . . tn � H0 + H1 + · · · + Hn
ficient method in image segmentation, which has the ad-
 t1 −1 t −1
vantages of small computation, simple implementation, and pi pi 2
 pi
stable performance. It is to divide the image pixel points into �− ln + 
 w t w0 t1 k�0 w0 t1 , t2 
 k�0 0 1
several classes based on several suitable thresholds found in
the whole image, and to ensure that each set of pixel points L−1
divided is consistent from the perspective of gray level. pi pi
 · ln + ··· + 
Generally speaking, image segmentation is mainly divided w0 t1 , t2 k�t
 w0 tn , L − 1 
 n
into single-threshold and multithreshold, and this paper will
study image segmentation under multithreshold based on pi
 · ln .
the maximum entropy value method. w0 tn , L − 1 
 The maximum entropy thresholding method [24] is an (8)
image segmentation algorithm proposed by Kapur et al. in
1985, which finds the optimal threshold to maximize the In equation (8), pi is used to denote the probability of
amount of information contained in the background and occurrence of gray value i in the image, and w is used to
target regions of an image by analyzing the entropy of the denote the probability of occurrence of gray values in a class.
image grayscale histogram. For an image with grayscale When the value of J([t1 , t2 , . . . tn ]) reaches the maximum
range [0, L − 1], k represents any gray value, let the seg- value of [t∗1 , t∗2 , . . . t∗n ], then this is the optimal segmentation
mentation threshold be t, the pixel points with grayscale threshold. The multithreshold segmentation algorithm has
level below t are taken as the target region (T) as in ex- better segmentation accuracy than the single-threshold
pression (2), and the pixel points with grayscale level higher segmentation algorithm, but the traditional multithreshold
than t above are taken as the background region (B) as in segmentation algorithm increases the time complexity with
expression (3), which is described as follows: the increase of the number of segmentation thresholds, and
 L−1 sometimes even exponentially, which can cause the image
 Pk � 1, (1) segmentation process time complexity to be too large or
 k�0 even the algorithm to be difficult to achieve; particularly, the
 segmentation effect of the segmentation time and seg-
 P0 P1 P mentation quality effect indicators are difficult to ensure.
 T: , , . . . , t, (2)
 Pn Pn Pn The image segmentation problem can be converted into a
 multi-dimensional function problem, which is essentially a
 Pt+1 Pt+2 P multi-objective optimization problem, and a large number
 B: , , . . . , L−1 , (3)
 1 − P n 1 − Pn 1 − Pn of experiments have proved that the cuckoo algorithm has a
 strong global convergence ability and is better than other
 t group intelligent optimization algorithms in solving the
 P n � Pi . (4) optimal value of the function of the local explosive; par-
 k�0 ticularly, the use of Levy behavior makes the individual jump
 The entropies associated with the probability densities of out of the local optimal solution, so this paper will use the
these two regions are equations (5) and (6), respectively. bionic cuckoo algorithm for image segmentation to convert
 the problem of maximum entropy threshold objective
 t
 pk pk function to segment the optimal threshold of an image into
 H(T) � − ln , (5)
 i�0 pn pn the problem of finding the optimal value of the fitness
 function.
 L−1
 pk pk
 H(B) � − ln , (6) 4. Research on Improved Cuckoo Search in
 i�t+1
 1 − pn 1 − pn
 Multithreshold Image Segmentation
 φ(t) � H(T) + H(B). (7) British scholars Xin-She Yang and Sua Deb proposed
 Cuckoo search in 2009 based on the unique nest parasitic
 In equation (7), φ(t) is denoted as the sum of H(T) and
 behavior of cuckoos and the unique Levy behavior of birds.
H(B). When φ(t) achieves the maximum value, t is the
 It is a metaheuristic algorithm based on nature. The content
optimal threshold.
 of the algorithm is described as follows:
 The maximum entropy thresholding method is applied
to multithreshold segmentation, and the multithreshold (1) Randomly generate N bird nests, and the location is
problem to be solved can be viewed as a problem optimi- shown as X0 � (x01 , x02 , . . . x0N ), by choosing an op-
zation in N dimensions, let the range of grayscale values of timal bird’s nest location and keeping this location
the image to be segmented be {0, 1, 2, . . . , L − 1}, and since for the next generation.

4 Security and Communication Networks

 (2) The position update is performed through the Levy Table 1: Population initialization.
 flight in the algorithm, and the next iteration posi- Set the population size N, dimension D, and the maximum chaotic iteration step pace
 tion in each current bird nest is obtained, and K
 for i � 1 to N do
 compared with the previous generation bird nest for j � 1 to D do
 position, the position of the better individual of the for k � 1 to K do
 two is used as the current position. xk,j � xk−1,j + rand(0, 1)
 endfor
 (3) Compare the random number r ∈ [0, 1] with the xi,j � xmax ,j − xk,j × (xmax ,j + xmin ,j )/2
 probability of host bird discovering alien eggs Pa . endfor
 endfor
 When r > Pa , then randomly change the position of
 the bird’s nest; otherwise, it will not change. Com-
 pare the position of the previous generation of bird’s large, the parameter α can be set larger, which can effectively
 nest, and select a group of bird’s nest positions with improve the search efficiency. On the contrary, when the
 better positions Xs � (xs1 , xs2 , . . . xsN ). search space is small, the value of parameter α can be selected
 (4) When the algorithm obtains the bird’s nest position relatively small. Therefore, the value of α may affect the
 to meet the accuracy requirements or terminate the possibility of obtaining the optimal solution of the algorithm
 iteration condition, the bird’s nest is the global to a certain extent. In this paper, the value of α and the
 optimal solution; otherwise, continue to (3) number of iterations are comprehensively considered, and
 execution. the ratio of the current number of iterations to the total
 number of iterations is used to assign the value of α to the
 In ICS, each egg in the nest represents a solution, so the value ofα, which can ensure that the value of α is selected to
ICS solution is expressed as follows: an appropriate value, as shown in
 xs+1 � xsi + α ⊗ L(λ). (9) s
 i a � a0 + × eΔfbest . (11)
 smax
 In the formula, xs+1 i means the location of the i bird nest
in the s generation; ⊗ is point-to-point multiplication, α is In the formula, smax is the maximum number of itera-
pace length, and L(λ) is random search path. After the tions, s is the number of current iterations, and Δfbest is the
update of location, use the random number r to compare absolute difference between the optimal value of individual
with Pa . If r > Pa , change xs+1
 i . Otherwise, keep it unchanged.
 fitness in the previous iteration and the optimal value of
 Like other metaheuristic algorithms, ICS also has the fitness in this iteration. a0 is 0.5. In the initial stage of al-
problem of falling into local optimality and insufficient gorithm, the value of α is small, which can ensure the local
global search capabilities. Therefore, in order to better use in better results in a smaller search space. As the number of
image segmentation, this article optimizes the three aspects iterations increases, the value of α gradually increases, and
of population initialization, step factor, and probability. the search scope expands.

4.1. Population Initialization. The diversity of the population 4.3. Probability Optimization. In cuckoo search, probability
during initialization will affect the convergence speed and Pa is usually a fixed value. However, when the Pa ratio is
accuracy of the swarm intelligence algorithm, while the large, the convergence speed of algorithm will be accelerated,
cuckoo search uses a random method to initialize the but the optimal solution with high accuracy may not be
population, which cannot guarantee the diversity of the obtained. On the contrary, the time complexity of the al-
population solution. Chaos mapping has the characteristics gorithm will be significantly improved. Therefore, the
of randomness, ergodicity, and regularity and is widely used probability Pa is optimized as shown in the following
in the optimization of intelligent algorithms. In order to expression:
make better use of the solution space, this paper uses chaotic s
mapping to initialize the population. This paper adopts Pa � Pmin + Pmax − Pmin × . (12)
 smax
formula (10) chaotic mapping, so the specific steps are
shown in Table 1 In the formula, Pmin and Pmax , respectively, represent the
 xn+1 � xn + rand(0, 1). (10) minimum and maximum value of probability Pa , smax is the
 largest iteration number, and s is the number of iterations.
 Among them, xk−1,j represents the i individual in the j As the number of iterations increases, the value of Pa is
dimension of the k − 1 iteration, xk,j represents the i indi- constrained by the maximum and minimum probability.
vidual in the j dimension of the k iteration after the chaotic The convergence rate of algorithm is controlled and closer to
map is adopted, xi,j represents the i individual in the j the optimal solution. Thus, the performance of algorithm is
dimension, and xmax ,j and xmin ,j represent upper and lower improved.
bounds in j dimensional space, respectively.
 4.4. Algorithm Complexity. Time complexity refers to the
4.2. Text Layout. The setting of step factor α in ICS is related computational workload required for the execution of the
to the performance of algorithm. When the search space is algorithm, which mainly depends on the number of repeated

Security and Communication Networks 5

executions of the problem. In the cuckoo search algorithm, algorithm in this paper with the CS algorithm, and compare
the time complexity is mainly affected by the population size it with Ant Clony Optimization (ACO) and Particle Swarm
N, the number of iterations T, and the search dimension D. Optimization (PSO) in terms of image segmentation.
The algorithm optimization step shows that its time com- Matlab2012 is selected, the CPU is Core i7, the memory is
plexity is O(N ∗ T ∗ D). Since the ICS algorithm is improved 4GDDR3, and the operating system is running on a
from the CS algorithm, on the basis of the CS algorithm time Windows 7 host.
complexity, more optimization steps can be known. The
newly introduced strategy of judging individual attributes
and dynamically adjusting the search equation increases the 5.1. Performance Comparison. In this paper, four test
number of calculations of O(N ∗ T ∗ D), and dynamically function algorithms are selected for comparison; the four
adjusting the convergence factor increases the number of test functions are
calculations of O(T). In general, the time complexity of the n

ICS algorithm is slightly higher than that of the CS algo- min f1 (x) � x2i ,
 i�1
rithm. The space complexity is mainly affected by the
 n n 
population size and search dimension, so the space com- 
plexity of the two algorithms is O(N ∗ D). min f2 (x) � xi + xi ,
 i�1 i�1 (13)
 n
 2
4.5. Application of Improved Algorithm in Image Segmentation min f3 (x) � xi + 0.5 ,
 i�1
 Step 1: convert the image to be segmented into a
 grayscale image to obtain the grayscale distribution. min f4 (x) � max abs xi .
 According to the grayscale value of the image within [0, Run each algorithm 50 times under the conditions of 5,
 255], the position limit of the ICS is also set in this 10, and 50 dimensions, and compare the optimal value and
 interval. variance results as shown in Tables 2 to 5.
 Step 2: initialize a certain number of cuckoo individuals Tables 2–5 show the comparison results of the optimal
 in a specific solution space. The initialization process value and variance of CS and ICS algorithms. From the
 represents the population number, dimension, and table, it is found that the ICS algorithm has better results in
 number of iterations, and each individual represents a the four benchmark functions than the CS algorithm. This
 solution in the solution space. shows that the performance of the ICS algorithm, after
 Step 3: use the segmentation algorithm in formula (2) population initialization, text layout, and probability op-
 as the fitness value function of the ICS, and calculate the timization, is significantly improved so that the optimal
 fitness value of each particle according to the fitness value and variance of the algorithm have better results.
 function. Table 6 shows the comparative effects of CS and ICS al-
 Step 4: CS to different degrees according to the sections gorithms in different dimensions under the 4 benchmark
 population initialization, text layout, and probability functions. When the dimension is 5, the running time of
 optimization. ICS and CS in f1 and f3 is not much different. When the
 dimension is 10, the running time in f3 is not much
 Step 5: calculate the individual fitness value of ICS, and different. The ICS proposed in this article has obvious
 reserve the respective fitness function values through advantages in other dimensions. It shows that the opti-
 the selection strategy to save the current optimal fitness mized algorithm can indeed improve the performance of
 function value, and save the position of the maximum the algorithm.
 function value during each iteration.
 Step 6: when the individual does not meet the ter-
 mination condition, the next iteration needs to be 5.2. Image Segmentation Effect Comparison. This paper uses
 carried out according to Steps 3–5, and the optimal the GC value in literature [24] as the multithreshold image
 effect is obtained through repeated operations until segmentation quality evaluation standard, i.e., the evalu-
 the termination condition is met, and the result ob- ation of the GC value ranges between 0 and 1, and the larger
 tained is the optimal threshold for segmenting the the GC value, the better the segmentation quality. This
 image. paper chooses the segmentation quality and segmentation
 time as the standard hardware platform for comparison.
5. Simulation Experiment This paper selects the Berkeley Segmentation Data Set and
 Benchmarks 500 (BSDS500) data set to randomly select 3
In order to further illustrate the advantages of this algo- images as the segmentation object, set the segmentation
rithm in image segmentation, the simulation experiment is threshold to 4, and the segmentation effect with the
divided into two parts: the first part verifies the perfor- threshold value of 1–4 as shown in Figures 1(a) –3(e). Three
mance of this algorithm, and the second part verifies the pictures under the same threshold are represented by 1, 2,
effect of this algorithm in the index of image segmentation. and 3, respectively.
In order to better illustrate the advantages of the algorithm Figures 4– 7 show the comparison of the four algorithms
in this paper, in terms of performance, compare the for image segmentation quality GC values under different

6 Security and Communication Networks

 Table 2: Optimization f1 results of two algorithm pairs.
Dimension Algorithm Optimal value Variance
 CS 4.129e − 8 1.973e − 7
5
 ICS 2.237e − 10 2.673e − 12
 CS 1.346e − 11 2.809e − 7
10
 ICS 1.276e − 13 4.192e − 11
 CS 3.863e − 13 5.431e − 11
50
 ICS 1.524e − 16 8.315e − 14

 Table 3: Optimization f2 results of two algorithm pairs.
Dimension Algorithm Optimal value Variance
 CS 4.412e − 11 1.724e − 10
5
 ICS 1.913e − 12 2.713e − 11
 CS 1.612e − 12 3.091e − 7
10
 ICS 1.312e − 14 3.832e − 11
 CS 3.911e − 14 4.131e − 12
50
 ICS 2.243e − 16 6.352e − 15

 Table 4: Optimization f3 results of two algorithm pairs.
Dimension Algorithm Optimal value Variance
 CS 3.262e − 9 1.714e − 8
5
 ICS 1.613e − 15 2.313e − 9
 CS 1.527e − 11 3.101e − 9
10
 ICS 2.932e − 14 4.102e − 12
 CS 4.127e − 13 4.272e − 12
50
 ICS 3.141e − 16 7.124e − 14

 Table 5: Optimization f4 results of two algorithm pairs.
Dimension Algorithm Optimal value Variance
 CS 8.172e − 11 2.714e − 9
5
 ICS 1.872e − 12 2.823e − 11
 CS 1.915e − 12 3.751e − 10
10
 ICS 1.173e − 13 6.912e − 11
 CS 4.173e − 15 6.201e − 12
50
 ICS 2.271e − 18 7.412e − 14

 Table 6: Comparison of algorithm running time.
Algorithm Dimension f1 f2 f3 f4
 5 0.732 0.896 1.854 0.734
CS 10 1.915 1.877 5.225 1.815
 50 12.318 13.039 40.416 12.337
 5 0.631 0.822 1.852 0.936
ICS 10 1.372 1.610 5.172 1.516
 50 9.134 9.864 36.719 7.421

thresholds. When the threshold is 1, the GC value of the algorithms, but it is compared with the GC value of the ICS
algorithm in this paper is significantly better than the other algorithm. When the threshold is 3, the GC value of the
three comparison algorithms, especially compared to CS. In algorithm in this paper is still stable at about 0.8, and the GC
terms of the algorithm, the algorithm segmentation effect is values of the other three algorithms have increased to
obvious. When the threshold is 2, the GC value of the al- varying degrees. When the threshold is 4, the GC value of the
gorithm in this paper is slightly lower than when the algorithm in this paper is close to 0.9, and the GC values of
threshold is 1, but overall it is still better than the other three the other three algorithms have risen to varying degrees.

Security and Communication Networks 7

 (a) (b) (c) (d) (e)

 Figure 1: (a) Lean image, (b) threshold � 1, (c) threshold � 2, (d) threshold � 3, and (e) threshold � 4.

 (a) (b) (c) (d) (e)

 Figure 2: (a) Rice image, (b) threshold � 1, (c) threshold � 2, (d) threshold � 3, and (e) threshold � 4.

 (a) (b) (c) (d) (e)

 Figure 3: (a) Woman image, (b) threshold � 1, (c) threshold � 2, (d) threshold � 3, and (e) threshold � 4.

 1.0

 0.8

 0.6
 GC

 0.4

 0.2

 0.0
 1 2 3
 Image
 ACO PSO
 CS ICS
 Figure 4: Comparison of GC values of four algorithms with a threshold of 1.

8 Security and Communication Networks

 1.0
 1.0

 0.8
 0.8

 0.6 0.6
 GC

 GC
 0.4 0.4

 0.2 0.2

 0.0
 0.0
 1 2 3
 1 2 3
 Image
 Image
 ACO PSO
 ACO PSO
 CS ICS
 CS ICS
Figure 5: Comparison of GC values of four algorithms with a
 Figure 7: Comparison of GC values of four algorithms with a
threshold of 2.
 threshold of 4.

 1.0
 1.0
 0.9
 0.8
 0.8

 0.6 0.7
 GC

 0.6
 Time (MS)

 0.4 0.5
 0.4
 0.2 0.3
 0.2
 0.0
 0.1
 1 2 3
 Image 0.0
 1 2 3
 ACO PSO
 Image
 CS ICS
 ACO PSO
Figure 6: Comparison of GC values of four algorithms with a CS ICS
threshold of 3.
 Figure 8: Comparison of the segmentation time of the four al-
 gorithms with a threshold of 1.
From the four results, as the threshold continues to increase,
the GC values of the four algorithms show an overall upward
trend, but overall the ICS algorithm has certain advantages, four algorithms to segment under different thresholds.
mainly because the optimization and improvement mea- When the threshold is 1, the image segmentation time of the
sures of the ICS algorithm provide the segmentation effect algorithm in this paper is more obvious than other algo-
which is a great help. With the use of chaotic initialization rithms. When the threshold value is 2, the image segmen-
population to increase the diversity of the solution, the use of tation time of the four algorithms is improved to a certain
optimized step size factors to increase the possibility of the extent compared with the threshold value of 1, but the al-
algorithm to obtain the optimal solution, and the use of gorithm in this paper still has advantages. When the
probability to reduce the complexity of the algorithm, overall threshold value is 3, the image segmentation time of the four
the algorithm’s image segmentation under multiple algorithms increases, but the gap between each other
thresholds is improved effect. This shows that under the shrinks. When the threshold is 4, the average image seg-
condition of multiple thresholds the image segmentation of mentation time of the algorithm in this paper has reached
the algorithm in this paper has a good effect. The speed of 0.6, which is still the smallest image segmentation time
segmentation time is a very important indicator in image required by the other three algorithms. In particular,
segmentation. Figures 8 –11 show the time required for the compared with the CS algorithm, the image segmentation

Security and Communication Networks 9

 1.0 0.8
 0.9 0.7
 0.8
 0.6
 0.7
 0.6 0.5

 Time (MS)
 Time (MS)

 0.5 0.4
 0.4 0.3
 0.3
 0.2
 0.2
 0.1 0.1

 0.0 0.0
 1 2 3 1 2 3
 Image Image

 ACO PSO ACO PSO
 CS ICS CS ICS

Figure 9: Comparison of the segmentation time of the four al- Figure 11: Comparison of the segmentation time of the four al-
gorithms with a threshold of 2. gorithms with a threshold of 4.

 0.8 Table 7: Analysis of Wilcoxon statistical test results.
 Algorithm type R+ R− P value
 0.7
 ICS versus CS 364 254 0.0056
 0.6 ICS versus PSO 205 134 0.1839
 ICS versus ACO 200 123 0.2189
 0.5
 Time (MS)

 0.4
 As can be seen from the results shown in Table 7, in all
 cases, the R+ value provided by ICS is higher than R−.
 0.3 Therefore, we can conclude that ICS has the best optimi-
 zation performance.
 0.2

 0.1 6. Conclusions
 0.0 Aiming at the problem of poor image segmentation under
 1 2 3 multithreshold, this paper proposes an improved cuckoo
 Image
 search image segmentation processing algorithm. First, the
 ACO PSO image segmentation is described, and then the chaotic
 CS ICS initialization population, step factor, and probability se-
Figure 10: Comparison of the segmentation time of the four al- lection are optimized. Finally, the maximum entropy
gorithms with a threshold of 3. threshold function in image segmentation is solved as an
 improved cuckoo search individual fitness function. The
 experiment shows that using the optimized improved
time of the algorithm in this paper can be shortened by 40%, cuckoo search for image segmentation has achieved good
which shows that the advantages of population initialization, results. The ICS algorithm proposed in this paper has good
optimization of the step size factor, and use of probability application value in multithreshold image segmentation,
optimization have been significantly improved. especially in the medical field. It can segment patient images
 to assist doctors in observing patient images, and can also be
 used in the analysis of urban road traffic conditions and the
5.3. Analysis of Statistical Characteristics of Optimal Algo- application of the extracted target vehicle and so on.
rithm Based on Wilcoxon Test. Due to the importance of However, because this article only considers the two indi-
multi-question statistical analysis, this paper uses R+ and R− cators of segmentation quality and segmentation time, the
in the literature [25] to represent the maximum and min- lack of consideration of the accuracy of the image after
imum sum ranks, respectively. Table 7 also shows the sta- segmentation makes the algorithm of this paper have certain
tistical analysis results of ICS and the other three comparison limitations. This requires continued research in the next
algorithms through Wilcoxon test. The parameters of step, while continuously improving the segmentation quality
Wilcoxon test are α � 0.01 and 0.05. and reducing the split time.

10 Security and Communication Networks

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Conflicts of Interest using PCNN based on multi-feature grey wolf optimizer
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The authors declare that they have no conflicts of interest.
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