Computational and Mathematical Methods in Medicine
Volume 2019 (2019), Article ID 4909846, 16 pages
https://doi.org/10.1155/2019/4909846

Research Article

Lung Cancer Detection Using Image Segmentation by means of Various Evolutionary Algorithms

K. Senthil Kumar,¹ K. Venkatalakshmi,² and K. Karthikeyan³

¹Assistant Professor, Department of Electrical and Electronics Engineering, University College of Engineering, Arni, India
²Assistant Professor, Department of Electronics and Communication Engineering, University College of Engineering Tindivanam, Tindivanam, India
³Teaching Fellow, Department of Electronics and Communication Engineering, University College of Engineering, Arni, India

Correspondence should be addressed to K. Senthil Kumar; kslksenthil@gmail.com

Received 30 July 2018; Revised 12 November 2018; Accepted 15 November 2018; Published 8 January 2019

Academic Editor: Maria E. Fantacci

Copyright © 2019 K. Senthil Kumar 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.

Abstract.

The objective of this paper is to explore an expedient image segmentation algorithm for medical images to curtail the physicians’ interpretation of computer tomography (CT) scan images. Modern medical imaging modalities generate large images that are extremely grim to analyze manually. The consequences of segmentation algorithms rely on the exactitude and convergence time. At this moment, there is a compelling necessity to explore and implement new evolutionary algorithms to solve the problems associated with medical image segmentation. Lung cancer is the frequently diagnosed cancer across the world among men. Early detection of lung cancer navigates towards apposite treatment to save human lives. CT is one of the modest medical imaging methods to diagnose the lung cancer. In the present study, the performance of five optimization algorithms, namely, k-means clustering, k-median clustering, particle swarm optimization, inertia-weighted particle swarm optimization, and guaranteed convergence particle swarm optimization (GCPSO), to extract the tumor from the lung image has been implemented and analyzed. The performance of median, adaptive median, and average filters in the preprocessing stage was compared, and it was proved that the adaptive median filter is most suitable for medical CT images. Furthermore, the image contrast is enhanced by using adaptive histogram equalization. The preprocessed image with improved quality is subject to four algorithms. The practical results are verified for 20 sample images of the lung using MATLAB, and it was observed that the GCPSO has the highest accuracy of 95.89%.

1. Introduction

Lung cancer, also known as lung carcinoma, is a malignant tumor characterized by uncontrolled growth of the cell in tissues of the lung. It is mandatory to treat this to avoid spreading its growth by metastasis to other parts of the body. Most cancers that start in the lung are carcinomas. The two main types are small-cell lung carcinoma and non-small-cell lung carcinoma [1]. Long-period tobacco smoking is the primary factor for 85% of lung cancers [2]. About 10–15% of cases occur in people who have never smoked but due to air pollution, secondhand smoking, asbestos, and radon gas. Computer tomography (CT) and radiographs are the conventional methods to detect the presence of lung cancer. The diagnosis is confirmed by biopsy which is usually performed by bronchoscopy or CT scan. The cause of cancer-related death among men is mainly due to lung cancer. Hence, it is essential to determine a new robust method to diagnose the lung cancer at an earlier stage [3]. For the present study, 20 lung image samples and four algorithms have been taken for analysis. It was proved that the combination of adaptive median filter, adaptive histogram equalization, and guaranteed convergence particle swarm optimization- (GCPSO-) based algorithm has more accurate results among others.

2. Methods

In medical image segmentation, the accuracy is foremost important, as it deals with human lives. It is highly crucial to eradicate the incidence of noise content and to improve the image quality before an examination [4]. This part of work is known as preprocessing. In the preprocessing stage, noise removal and contrast enhancement are two primary steps. In the present study, the performance results of median, adaptive median, and average filters to isolate the presence of speckle noise have been compared. The coding for the same has been implemented using MATLAB. Furthermore, the image quality and visual appearance are improved by adaptive histogram equalization. The second stage of work is segmentation. This stage consists of applying five methods, namely, k-means, k-median, particle swarm optimization (PSO), inertia-weighted particle swarm optimization (IWPSO), and GCPSO. The tumor portion was extracted from the segmented results of the above-said five methods and compared with manual extraction. The results show that the GCPSO-based segmentation has more accuracy than the others. Figure 1 depicts the process of operation for the present study.

Figure 1: Process flow diagram of the projected method.

2.1. Median and Adaptive Median Filters

The median filter removes the noise and retains the sharpness of the image. Accordance to the name, each pixel is replaced by the median value from the neighborhood pixels. A 3 × 3 window is used in this filter [5]. This is one of the best filters among conventional filters which remove the speckle noise. The steps followed to construct the median filter are given in Algorithm 1.

(1)
Assume the input matrix “A” which has M rows and N columns.

(2)
Construct a matrix with M + 2 rows and N + 2 columns by appending zeros to sides of the input matrix

(3)
Take a mask of size 3 × 3.

(4)
Place the mask on the first element, i.e., element on the first row and first column of matrix “A”.

(5)
Select all the elements listed by the mask and sort them in ascending order.

(6)
Take the median value (center element) from the sorted array and replace the element A(1, 1) by the median value

(7)
Slide the mask to the next element.

(8)
Repeat the steps from 4 to 7 until all the elements of matrix “A” are replaced by their corresponding median value.

Algorithm 1: Median filter.

Spatial processing to preserve the edge detail and to eliminate nonimpulsive noise by the adaptive median filter plays a vital role. The small structure in the image and edges are retained by the adaptive median filter. In the adaptive median filter, the window size varies with respect to each pixel.

2.2. Average Filter

This is a simple filter which removes the spatial noise from a digital image. The presence of spatial noise is mainly due to the data acquisition process. The neighborhood mean value is measured for each and every pixel and is replaced by the corresponding mean value. This process is repeated for every pixel in the image [5]. All the pixels in the digital image are modified by sliding the operator over the entire range of pixels. The steps followed for the average filter are given in Algorithm 2.

(1)
Assume the input matrix “A” which has M rows and N columns.

(2)
Construct a matrix with M + 2 rows and N + 2 columns by appending zeros to sides of the input matrix.

(3)
Take a mask of size 3 × 3.

(4)
Place the mask on the first element, i.e., element on the first row and first column of matrix “A”.

(5)
Select all the elements listed by the mask and find the average

(6)
Take the mean value from the sorted array and replace the element A(1, 1) by the median value.

(7)
Slide the mask to the next element.

(8)
Repeat the steps from 4 to 7 until all the elements of matrix “A” are replaced by their corresponding median value.

Algorithm 2: Median filter.

2.3. Histogram Equalization

Image enhancement is the technique which is used to improve the image quality. For better understanding and analysis, it is mandatory to enhance the contrast of medical images. The conventional method used for this operation is histogram equalization. A minor adjustment on the intensity of image pixels is done in this method. Each pixel is mapped to intensity proportional to its rank in the surrounding pixels. The steps followed for histogram equalization are given in Algorithm 3 [6].

(1)
Obtain the histogram for the input image and find the probability mass function.

(2)
Find the cumulative distributive function; from that, find the CDF according to gray levels.

(3)
Find the new gray levels by using the following equation:

CDFNew = CDF (number of gray levels − 1).

(4)
Map the new gray levels into a total number of pixels and plot the modified histogram.

Algorithm 3: Histogram equalization.

2.4. k-Means Clustering Algorithm

The simplest and conventional method in cluster analysis is the k-means clustering algorithm. This algorithm segregates the given dataset into two or more clusters [7]. The accuracy of this method completely depends on the selection of the cluster center. It is mandatory to select the optimum cluster center to get a better result. The Euclidean distance is the general measure to segregate the dataset [8]. Pixels are assigned to an individual cluster based on the Euclidean distance. The objective function used in this algorithm is

where are the pixels, are the cluster centers, is the Euclidean distance between and , is the number of data points for the cluster, and is the number of cluster centers [9]. The steps followed for k-means clustering are given in Algorithm 4.

(1)
Select the cluster centers. Let them be “C.”

(2)
Calculate the Euclidean distance.

(3)
Take each and every pixel and assign them into the appropriate cluster if the Euclidean distance is minimum between the cluster and pixel.

(4)
Once the segregation is completed for all the pixels, recalculate the new cluster center using the following formula:

(5)
Repeat the steps from 2 to 4 for some number of iterations or until a certain condition is encountered.

Algorithm 4: k-Means clustering.

2.5. k-Median Clustering Algorithm

This is also a clustering algorithm slightly modified from the k-means algorithm. In centroid calculation instead of calculating the mean value, the median value is considered. This algorithm significantly reduces the error since there is no squared operation as in the calculation of the Euclidean distance. The clusters formed by this method are more compact. As an alternate, this approach uses the Lloyd-style iteration. The steps followed for k-median clustering are given in Algorithm 5 [10].

(1)
Select the random cluster centers. Let the number of cluster centers be “C.”

(2)
Calculate the Euclidean distance.

(3)
Take each and every pixel and assign them into the appropriate cluster if the Euclidean distance is minimum between the cluster and pixel.

(4)
Once the segregation is completed for all the pixels, recalculate the new cluster center using the median value instead of using a squared formula.

(5)
Repeat the steps from 2 to 4 for some number of iterations or until a certain condition is encountered.

Algorithm 5: k-Median clustering.

2.6. Particle Swarm Optimization

PSO is a metaheuristic algorithm used efficiently in medical image analysis [11]. It mimics the social behavior of the birds searching for food [12]. The fundamental idea of PSO is sharing and communicating the information. In this approach, each particle has initial position and velocity. Based on the fitness value, the velocity and position are updated. The relevant two equations in PSO to update the position and velocity are as follows [11, 12]:

where r₁ and r₂ are the random numbers and the acceleration coefficients c₁ and c₂ are two positive constants. The success of PSO relies on the fitness function. The following fitness function has been used for the present study:

where n is the number of clusters. The steps followed for the particle swarm optimization are shown in Algorithm 6.

(1)
Initialize the velocity and position of all the particles with random values.

(2)
Define a fitness function.

(3)
Find the fitness value for each particle.

(4)
Compare the fitness value with the best fitness. If the fitness values are better, then set the current value as new pbest.

(5)
Repeat steps from 3 to 5 for each particle.

(6)
Update the velocity using equation (1).

(7)
Upgrade the position.

(8)
Update gbest.

(9)
Repeat steps from 7 to 9 until certain conditions are encountered or for the predefined number of iterations.

Algorithm 6: Particle swarm optimization [11, 13].

2.7. Inertia-Weighted Particle Swarm Optimization

The exploration and exploitation in PSO are based on the inertia weight. The basic PSO, presented by Eberhart and Kennedy in 1995, has no inertia weight. In 1998, Shi and Eberhart introduced the concept of inertia weight by adding constant inertia weight. They stated that a significant inertia weight facilitates a global search, while a small inertia weight facilitates a local search [14]. This enhances the convergence rate and reduces the number of iterations. Inertia weight less than 1, in general, improves the results. The used method improves the convergence rate and saves the time taken and some iterations.

The resulting velocity update equation becomes

where is the inertia weight, with constant inertia weight = 0.7 and random inertia weight = 0.5 + rand()/2.

2.8. Guaranteed Convergence Particle Swarm Optimization

The GCPSO focuses on a new particle which deals with the current best position in the region. In this task, this particle is treated as a member of the swarm, and the velocity update equation for this new particle is given as follows [15]:

The search ability is increased by the social part. This will improve the random search in the area around the gbest position. The random vector and diameter of the search area are r and ρ(t), respectively. The range of the random vector lies between 0 and 1. The diameter of the search area can be updated using the following equation:

where the terms #successes and #failures are defined as the number of consecutive successes and failures, respectively. The threshold parameters sc and fc are determined empirically. Since it is hard to obtain a better value in only a few iterations in a high-dimensional search space, the recommended values are thus sc = 15 and fc = 5. On some benchmark tests, the GCPSO has shown an excellent performance of locating the minimal of a space after unimodal with only a small amount of particles. The steps to be followed for the GCPSO are shown in Algorithm 7.

Initialization

(1)Initialize the number of clusters and number of iterations.(2)Initialize , sc, fc, numSuccess = 0, and numFailures = 0.(3)Define a fitness function.

Clustering

(4)Find the fitness value for each particle.(5)Update the local best solution obtained so far.(6)Repeat steps 4 and 5 for the predefined number of iterations.(7)Update velocity and position of each particle for the current global best particle.

Selection step

(8)Execute the selection operator.(9)If any local best position yi has changed, perform the clustering algorithm. Otherwise, end the algorithm.

Algorithm 7: GCPSO algorithm [15].

3. Performance Measures

Certain performance measures are used to evaluate the results obtained from medical image segmentation. The list of performance measures used to assess the filter operation is shown in Figure 2 [16]. Let If be the image after noise reduction and I₀ be the noisy image.

Figure 2: Performance measures of the filter.

Performance measures used for the evaluation of the results of the segmentation algorithm are given in Figure 3 [17].

Figure 3: Performance measures for the medical image segmentation.

4. Results and Discussion

The used methods are practically implemented using MATLAB coding, and the results were verified.

In the preprocessing stage, a comparison was done between the performance of median, adaptive median, and mean filters. The SSI and SMPI values are shown in Table 1 and Figures 4 and 5. From the results, it is evident that the adaptive median filter has accurate characteristics than the mean and median filters for medical image segmentation.

Table 1: SSI and SMPI values of input images.

Sample images
SSI
SMPI

Mean filter
Median filter
Adaptive median filter
Mean filter
Median filter
Adaptive median filter

Image 1
0.9621
0.8208
0.8086
0.9857
0.9788
0.9638

Image 2
0.9658
0.8232
0.8087
0.9895
0.959
0.9452

Image 3
0.9588
0.8209
0.8091
0.9883
0.9799
0.9696

Image 4
0.9671
0.8080
0.7937
0.9958
0.9836
0.9703

Image 5
0.9705
0.8220
0.8078
0.9851
0.9833
0.9706

Image 6
0.9708
0.8218
0.7900
0.9948
0.9775
0.9457

Image 7
0.9660
0.8202
0.8067
0.9979
0.9608
0.9464

Image 8
0.9640
0.8265
0.8154
0.9922
0.9622
0.9493

Image 9
0.9638
0.8272
0.8141
0.9990
0.9716
0.9576

Image 10
0.9644
0.8238
0.8112
0.9944
0.9804
0.9659

Image 11
0.9639
0.8231
0.8122
0.9765
0.9788
0.9643

Image 12
0.9642
0.8289
0.8152
1.0012
0.9826
0.9721

Image 13
0.9648
0.8239
0.8135
0.9920
0.9782
0.9674

Image 14
0.9564
0.8242
0.8098
0.9888
0.9767
0.9648

Image 15
0.9573
0.8208
0.8084
1.0005
0.9785
0.9636

Image 16
0.9631
0.8242
0.8095
0.9912
0.9755
0.9613

Image 17
0.9919
0.8239
0.8352
0.9722
0.9770
0.9882

Image 18
0.9912
0.7983
0.7857
1.0003
0.9808
0.9696

Image 19
0.9921
0.8020
0.7884
1.0037
0.9838
0.9706

Image 20
0.9939
0.8085
0.7690
0.9968
0.9741
0.9432

Figure 4: Comparative results of SSI values.

Figure 5: Comparative results of SMPI values.

The segmentation accuracy was measured using the true positive rate, true negative rate, false positive rate, and false negative rate by comparing the results from the algorithm with manual segmentation results. The practical results of the k-means clustering segmentation algorithm are shown in Table 2.

Table 2: Statistical results from the k-means algorithm.

Images
True positive rate
True negative rate
False positive rate
False negative rate
Accuracy

Image 1
87.8783
89.1554
10.8446
12.1217
88.5937

Image 2
86.6527
89.5874
10.4126
13.3473
88.2682

Image 3
83.8975
87.3900
12.6100
16.1025
85.7501

Image 4
82.6502
85.7011
14.2989
17.3498
84.2186

Image 5
83.5680
84.2582
15.7418
16.4320
83.9216

Image 6
82.7250
82.4643
17.5654
17.2750
82.5795

Image 7
81.1893
79.0554
20.9446
18.8107
80.1519

Image 8
80.2543
77.7549
22.2451
19.7457
79.0656

Image 9
81.7874
78.4139
21.5861
18.2126
80.1606

Image 10
80.4304
77.4794
22.5206
19.5696
79.0378

Image 11
81.7725
78.0352
21.9648
18.2275
79.9755

Image 12
84.0795
78.8912
21.1088
15.9205
81.5238

Image 13
81.6145
78.7989
21.2011
18.3855
80.2806

Image 14
79.8951
78.6152
21.3848
20.1049
79.3023

Image 15
80.9012
78.4626
21.5374
19.0988
79.7600

Image 16
80.1249
78.1480
21.8520
18.8751
79.2121

Image 17
80.1220
78.1687
21.8318
19.8780
79.2229

Image 18
78.2509
83.5148
16.4852
21.7491
80.7020

Image 19
78.7041
83.7431
16.2569
21.2959
81.0816

Image 20
76.7118
84.3245
15.6755
23.2882
80.1831

The practical results of the k-median clustering segmentation algorithm are shown in Table 3.

Table 3: Statistical results from the k-median clustering segmentation algorithm.

Images
True positive rate
True negative rate
False positive rate
False negative rate
Accuracy

Image 1
87.9631
90.6864
9.3136
12.3069
89.3672

Image 2
86.5908
90.1719
9.8281
13.4092
88.5622

Image 3
83.3821
88.9051
11.0949
16.6179
86.2969

Image 4
82.0844
86.3637
13.6363
17.9156
84.2695

Image 5
83.2410
85.7769
14.2231
16.7590
84.5294

Image 6
82.5053
84.2412
15.7588
17.4947
83.3654

Image 7
81.1107
80.4281
19.5719
18.8893
80.7832

Image 8
80.1857
79.4033
20.5967
19.8143
79.8186

Image 9
82.1213
79.7647
20.2353
17.8787
80.9977

Image 10
80.6627
79.1577
20.8423
19.3373
79.9611

Image 11
82.0209
79.1588
20.8412
17.9791
80.6621

Image 12
84.3809
80.4121
19.5879
15.6191
82.4514

Image 13
82.0487
80.3496
19.6504
17.9513
81.2545

Image 14
80.4506
79.4375
20.5625
19.5494
79.9876

Image 15
81.3002
80.0536
19.9464
18.6998
80.7248

Image 16
80.1942
80.0503
19.9497
19.8058
80.1291

Image 17
80.2984
80.3756
19.6244
19.7016
80.3332

Image 18
78.6327
85.5226
14.4774
21.3673
81.7792

Image 19
78.9322
85.2163
14.7837
21.0678
81.8439

Image 20
77.2752
85.8000
14.2000
22.7248
81.0973

The practical results of the PSO-based segmentation algorithm are shown in Table 4.

Table 4: Statistical results from the PSO algorithm.

Images
True positive rate
True negative rate
False positive rate
False negative rate
Accuracy

Image 1
87.5413
90.1196
9.8804
12.4587
89.0624

Image 2
85.9612
85.9612
8.5479
8.5479
88.9689

Image 3
82.7919
89.8314
10.1686
17.2081
86.4850

Image 4
81.1271
88.7838
11.2162
18.8729
84.9967

Image 5
82.6343
87.3995
12.6005
17.3657
85.0299

Image 6
81.8996
85.2900
14.7100
18.1004
83.5581

Image 7
81.7281
80.0949
19.9051
18.2719
80.9438

Image 8
80.4182
80.0721
19.9279
19.5818
80.2571

Image 9
82.2573
81.1450
18.8550
17.7427
81.7340

Image 10
80.8521
80.3433
19.6567
19.1479
80.6182

Image 11
82.2198
80.9837
19.0163
17.7802
81.6421

Image 12
84.6322
81.5070
18.4930
15.3678
83.1347

Image 13
82.6283
81.1153
18.8847
17.3617
81.9351

Image 14
80.9338
80.9090
19.0910
19.0662
80.9226

Image 15
81.8790
81.1729
18.8271
18.1210
81.5586

Image 16
80.8120
81.4222
18.5778
19.1880
81.0836

Image 17
80.8582
81.8136
18.1864
19.1418
81.2824

Image 18
79.1387
85.0084
14.9916
20.8613
81.8114

Image 19
79.4655
85.1570
14.8430
20.5345
82.0954

Image 20
77.7826
85.3446
14.6554
22.2174
81.1744

The practical results of the IWPSO segmentation algorithm are shown in Table 5.

Table 5: Statistical results from the IWPSO algorithm.

Images
True positive rate
True negative rate
False positive rate
False negative rate
Accuracy

Image 1
87.4649
90.3272
9.6728
12.5351
88.9813

Image 2
86.1950
89.8126
10.1874
13.8050
88.1810

Image 3
82.9347
88.9622
11.0378
17.0653
86.1018

Image 4
81.4285
87.2013
12.7987
18.5715
84.3584

Image 5
82.9023
86.3940
13.6060
17.0977
84.6631

Image 6
82.1065
84.5145
15.4855
17.8935
83.2876

Image 7
82.1361
79.1744
20.8256
17.8639
80.7064

Image 8
80.3274
80.3855
19.6145
19.6726
80.3544

Image 9
82.4185
80.3924
19.6076
17.5815
81.4631

Image 10
81.0769
79.5965
20.4035
18.9231
80.3943

Image 11
82.2198
79.7401
20.2599
17.4299
81.2411

Image 12
84.6322
81.1390
18.8610
15.2172
83.0334

Image 13
82.6283
80.7231
17.3596
17.3596
81.8905

Image 14
80.9338
80.8231
19.1769
19.0328
80.9025

Image 15
81.8790
81.5899
18.4101
18.2694
81.6669

Image 16
80.8120
81.4173
18.5827
19.1734
81.0896

Image 17
80.8582
81.0677
18.9323
19.0166
81.0209

Image 18
79.1387
84.9622
15.0378
20.8310
81.8081

Image 19
79.4655
84.8219
15.1781
20.3936
82.0213

Image 20
77.7684
85.4281
14.5719
22.2316
81.2033

The practical results of the GCPSO segmentation algorithm are shown in Table 6.

Table 6: Statistical results from the GCPSO algorithm.

Images
True positive rate
True negative rate
False positive rate
False negative rate
Accuracy

Image 1
91.6158
99.9999
0.0001
8.3842
95.8079

Image 2
90.9563
99.9999
0.0001
9.0437
95.4782

Image 3
88.8404
99.9999
0.0001
11.1592
94.4204

Image 4
87.2946
99.9999
0.0001
12.7054
93.6473

Image 5
87.3583
99.9999
0.0001
12.6417
93.6792

Image 6
86.1567
99.9999
0.0001
13.8433
93.0784

Image 7
83.4867
99.9999
0.0001
16.5133
91.7434

Image 8
83.1082
99.9999
0.0001
16.8918
91.5541

Image 9
84.2907
99.9999
0.0001
15.7093
92.1453

Image 10
83.1917
99.9999
0.0001
16.8083
91.5958

Image 11
84.2122
99.9999
0.0001
15.7878
92.1061

Image 12
85.7977
99.9999
0.0001
14.2023
92.8988

Image 13
84.6397
99.9999
0.0001
15.3603
92.3198

Image 14
83.7442
99.9999
0.0001
16.2558
91.8721

Image 15
84.3299
99.9999
0.0001
15.6701
92.1649

Image 16
83.8867
99.9999
0.0001
16.1133
91.9433

Image 17
83.9061
99.9999
0.0001
16.0939
91.9531

Image 18
84.6836
99.9999
0.0001
15.3164
92.3418

Image 19
84.9324
99.9999
0.0001
15.0676
92.4662

Image 20
83.9867
99.9999
0.0001
16.0124
91.9938

The graphical view of the comparison of the true positive rate, true negative rate, false positive rate, and false negative rate for the algorithms used is shown in Figures 6–9. It is proved that the true positive and true negative rates are high and false positive and false negative rates are low for the GCPSO algorithm.

Figure 6: Comparative results of the true positive rate value.

Figure 7: Comparative results of the true negative rate value.

Figure 8: Comparative results of the false positive rate value.

Figure 9: Comparative results of the false negative rate value.

The comparative evaluation based on the accuracy of the segmentation is shown in Table 7 and Figure 10. The results indicate that the GCPSO-based technique has the highest average value of accuracy than the other methods.

Table 7: Statistical comparative result of accuracy.

Images
k-Means
k-Median
PSO
IWPSO
GCPSO

Image 1
88.5937
89.3672
89.0624
88.9813
95.8079

Image 2
88.2682
88.5622
88.9689
88.1810
95.4782

Image 3
85.7501
86.2969
86.4850
86.1018
94.4204

Image 4
84.2186
84.2695
84.9967
84.3584
93.6473

Image 5
83.9216
84.5294
85.0299
84.6631
93.6792

Image 6
82.5795
83.3654
83.5581
83.2876
93.0784

Image 7
80.1519
80.7832
80.9438
80.7064
91.7434

Image 8
79.0656
79.8186
80.2571
80.3544
91.5541

Image 9
80.1606
80.9977
81.7340
81.4631
92.1453

Image 10
79.0378
79.9611
80.6182
80.3943
91.5958

Image 11
79.9755
80.6621
81.6421
81.2411
92.1061

Image 12
81.5238
82.4514
83.1347
83.0334
92.8988

Image 13
80.2806
81.2545
81.9351
81.8905
92.3198

Image 14
79.3023
79.9876
80.9226
80.9025
91.8721

Image 15
79.7600
80.7248
81.5586
81.6669
92.1649

Image 16
79.2121
80.1291
81.0836
81.0896
91.9433

Image 17
79.2229
80.3332
81.2824
81.0209
91.9531

Image 18
80.7020
81.7792
81.8114
81.8081
92.3418

Image 19
81.0816
81.8439
82.0954
82.0213
92.4662

Image 20
80.1831
81.0973
81.1744
81.2033
91.9938

Figure 10: Comparative results of accuracy.

The resultant images after preprocessing are shown in Figures 11(a) and 11(b).

(a)

(b)

Figure 11: Resultant images after preprocessing.

The resultant images after segmentation using k-means clustering are shown in Figure 12.

Figure 12: Resultant images by k-means clustering.

The resultant images after segmentation using k-median clustering are shown in Figure 13.

Figure 13: Resultant images by k-median clustering.

The resultant images after segmentation using the PSO algorithm are shown in Figure 14.

Figure 14: Resultant images by the PSO algorithm.

The resultant images after segmentation using the IWPSO algorithm are shown in Figure 15.

Figure 15: Resultant images by IWPSO algorithm clustering.

The resultant images after segmentation using the GCPSO algorithm are shown in Figure 16.

Figure 16: Resultant images by GCPSO algorithm clustering.

In an earlier research, lung cancer detection was done using PSO, genetic optimization, and SVM algorithm with the Gabor filter and produced an accuracy of 89.5% [18]. The method to detect lung cancer by means of K-NN classification using the genetic algorithm produced a maximum accuracy of 90% [19]. The comparative results with respect to the above-said methods are shown in Table 8.

Table 8: Comparative analysis of accuracy of the projected method with various methods.

Various methods
Accuracy (%)

PSO, GA, and SVM algorithm [18]
89.50

K-NN classification using GA [19]
90.00

Projected GCPSO method
95.81

The graphical comparative analysis between the used and existing methods is shown in Figure 17.

Figure 17: Graphical view of accuracy.

5. Conclusion

In this study, various optimization algorithms have been evaluated to detect the tumor. Medical images often need preprocessing before being subjected to statistical analysis. The adaptive median filter has better results than median and mean filters because the speckle suppression index and speckle and mean preservation index values are lower for the adaptive median filter. Comparing the five algorithms, the accuracy of the tumor extraction is improved in GCPSO with the highest accuracy of 95.8079%, and it obtained above 90% of precision in all the 20 images. It is more accurate when compared to the previous method which had an accuracy of 90% in 4 out of 10 datasets only. In future studies, the use of more number of optimization algorithms will be included to improve the accuracy.

Data Availability

The CT images data used to support the findings of this study have been deposited in the LungCT-Diagnosis repository (doi.org/10.7937/K9/TCIA.2015.A6V7JIWX).

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

References

A. A. Brindha, S. Indirani, and A. Srinivasan, “Lung cancer detection using SVM algorithm and optimization techniques,” Journal of Chemical and Pharmaceutical Sciences, vol. 9, no. 4, 2016.

M. Kurkure and A. Thakare, “Introducing automated system for lung cancer detection using Evolutionary Approach,” International Journal of Engineering and Computer Science, vol. 5, no. 5, pp. 16736–16739, 2016.

B. Rani, A. K. Goel, and R. Kaur, “A modified approach for lung cancer detection using bacterial forging optimization algorithm,” International Journal of Scientific Research Engineering and Technology, vol. 5, no. 1, 2016.

N. Panpaliya, N. Tadas, S. Bobade, R. Aglawe, and A. Gudadhe, “A survey on early detection and prediction of lung cancer,” International Journal of Computer Science and Mobile Computing, vol. 4, no. 1, pp. 175–184, 2015.

G. Gupta, “Algorithm for image processing using improved median filter and comparison of mean, median and improved median filter,” International Journal of Soft Computing and Engineering, vol. 1, no. 5, 2011.

“Tutorial point with digital image processing,” http://www.tutorialspoint.com/dip/histogram_equalization.htm.

K. Venkatalakshmi and S. M. Shalinie, “Classification of multispectral images using support vector machines based on PSO and K-means clustering,” in Proceedings of IEEE International Conference on Intelligent Sensing and Information Processing, pp. 127–133, Melbourne, VIC, Australia, December 2005.

K. Venkatalakshmi and S. M. Shalinie, “Multispectral image classification using modified k-means clustering,” International Journal on Neural and Mass-Parallel Computing and Information Systems, vol. 17, no. 2, pp. 113–120, 2007.

K. Venkatalakshmi, P. Anisha Praisy, R. Maragathavalli, and S. Mercy Shalinie, “Multispectral image clustering using enhanced genetic k-means algorithm,” Information Technology Journal, vol. 6, no. 4, pp. 554–560, 2007.

P. I. Dalatu, “Time complexity of k-means and k-medians clustering algorithms in outliers detection,” Global Journal of Pure and Applied Mathematics, vol. 12, no. 5, pp. 4405–4418, 2016.

K. Senthilkumar, K. Venkatalakshmi, K. Karthikeyan, and P. Kathirkamasundari, “An efficient method for segmenting digital image using a hybrid model of particle swarm optimization and artificial bee colony algorithm,” International Journal of Applied Engineering Research, vol. 10, pp. 444–449, 2015.

K. Venkatalakshmi and S. Mercyshalinie, “Classification of multispectral images using support vector machines based on PSO and k-means clustering,” in Proceedings of IEEE International Conference on Intelligent Sensing and Information Processing, pp. 127–133, Bangalore, India, December 2005.

K. Venkatalakshmi, P. Anisha Praisy, R. Maragathavalli, and S. Mercyshalinie, “A customized particle swarm optimization for classification of multispectral imagery based on feature fusion,” International Arab Journal of Information Technology, vol. 5, no. 4, pp. 327–333, 2008.

J. C. Bansal and P. K. Singh, “Inertia weight strategies in particle swarm optimization,” in Proceedings of IEEE World Congress on Nature and Biologically Inspired Computing, pp. 640–647, Salamanca, Spain, October 2011.

P. K. Patel, V. Sharma, and K. Gupta, “Guaranteed convergence particle swarm optimization using Personal Best,” International Journal of Computer Applications, vol. 73, no. 7, pp. 6–10, 2013.

X. Wang, L. Ge, and X. Li, “Evaluation of filters for EnvisatAsar speckle suppression in pasture area,” ISPRS Annals of the Photogrammetry. Remote Sensing and Spatial Information Sciences, vol. 1–7, pp. 341–346, 2012.

H. Nagaveena, D. Devaraj, and S. C. Prasanna Kumar, “Vessels segmentation in diabetic retinopathy by adaptive median thresholding,” International Journal of Science and Technology, vol. 1, no. 1, pp. 17–22, 2013.

A. Asuntha, N. Singh, and A. Srinivasan, “PSO, genetic optimization and SVM algorithm used for lung cancer detection,” Journal of Chemical and Pharmaceutical Research, vol. 8, no. 6, pp. 351–359, 2016.

P. Bhuvaneswari and A. Brintha Therese, “Detection of cancer in lung with K-NN classification using genetic algorithm,” International Conference on Nanomaterials and Technologies, vol. 10, pp. 433–440, 2014.