
Multilevel thresholding has important research value in image segmentation and can effectively solve region analysis problems of complex images. In this paper, Otsu and Kapur's entropy are adopted among thresholding segmentation methods. They are used as the objective functions. When the number of threshold increases, the time complexity increases exponentially. In order to overcome this drawback, a modified ant lion optimizer algorithm based on opposition-based learning (MALO) is proposed to determine the optimum threshold values by the maximization of the objective functions. By introducing the opposition-based learning strategy, the search accuracy and convergence performance are increased. In addition to IEEE CEC 2017 benchmark functions validation, 11 state-of-the-art algorithms are selected for comparison. A series of experiments are conducted to evaluate the segmentation performance of the algorithm. The evaluation metrics include: fitness value, peak signal-to-noise ratio, structural similarity index, feature similarity index, and computational time. The experimental data are analyzed and discussed in details. The experimental results significantly demonstrate that the proposed method is superior over others, which can be considered as a powerful and efficient thresholding technique.
Citation: Shikai Wang, Kangjian Sun, Wanying Zhang, Heming Jia. Multilevel thresholding using a modified ant lion optimizer with opposition-based learning for color image segmentation[J]. Mathematical Biosciences and Engineering, 2021, 18(4): 3092-3143. doi: 10.3934/mbe.2021155
[1] | Shifan Xie, Yik Roy Hwang, Revin Thomas . Hyperacute management of ischemic strokes, a British perspective. AIMS Medical Science, 2023, 10(2): 107-117. doi: 10.3934/medsci.2023009 |
[2] | Brandon M. Jones, E. Murat Tuzcu, Amar Krishnaswamy, Samir R. Kapadia . Incidence and Prevention of Strokes in TAVI. AIMS Medical Science, 2015, 2(1): 51-64. doi: 10.3934/medsci.2015.1.51 |
[3] | Saleh Hadi Alharbi, Fahad A. Alateeq, Khalil Ibrahim Alshammari, Hussain Gadelkarim Ahmed . IBS common features among Northern Saudi population according to Rome IV criteria. AIMS Medical Science, 2019, 6(2): 148-157. doi: 10.3934/medsci.2019.2.148 |
[4] | Abdulah Saeed, Alhanouf AlQahtani, Abdullah AlShafea, Abdrahman Bin Saeed, Meteb Albraik . The impact of telemedicine on cardiac patient outcomes: A study in Saudi Arabian hospitals. AIMS Medical Science, 2024, 11(4): 439-451. doi: 10.3934/medsci.2024030 |
[5] | Lin Kooi Ong, Frederick Rohan Walker, Michael Nilsson . Is Stroke a Neurodegenerative Condition? A Critical Review of Secondary Neurodegeneration and Amyloid-beta Accumulation after Stroke. AIMS Medical Science, 2017, 4(1): 1-16. doi: 10.3934/medsci.2017.1.1 |
[6] | Mujeeb Ur Rehman Parrey, Hanaa El-Sayed Bayomy, Fawaz Salah M Alanazi, Asseel Farhan K Alanazi, Abdullah Hamoud M Alanazi, Abdulelah Raka A Alanazi . Diabetic retinopathy: knowledge, attitudes, and practices among diabetic patients. AIMS Medical Science, 2025, 12(2): 210-222. doi: 10.3934/medsci.2025013 |
[7] | Waled Amen Mohammed Ahmed, Ziad Mohammad Yousef Alostaz, Ghassan Abd AL- Lateef Sammouri . Effect of Self-Directed Learning on Knowledge Acquisition of Undergraduate Nursing Students in Albaha University, Saudi Arabia. AIMS Medical Science, 2016, 3(3): 237-247. doi: 10.3934/medsci.2016.3.237 |
[8] | Ahlam Al-Zahrani, Shorooq Al-Marwani . The effectiveness of an educational session about folic acid on pregnant women's knowledge in Yanbu City, Kingdom of Saudi Arabia. AIMS Medical Science, 2022, 9(3): 394-413. doi: 10.3934/medsci.2022019 |
[9] | Waled Amen Mohammed Ahmed . Anxiety and Related Symptoms among Critical Care Nurses in Albaha, Kingdom of Saudi Arabia. AIMS Medical Science, 2015, 2(4): 303-309. doi: 10.3934/medsci.2015.4.303 |
[10] | Ayema Haque, Areeba Minhaj, Areeba Ahmed, Owais Khan, Palvisha Qasim, Hasan Fareed, Fatima Nazir, Ayesha Asghar, Kashif Ali, Sobia Mansoor . A meta-analysis to estimate the incidence of thromboembolism in hospitalized COVID-19 patients. AIMS Medical Science, 2020, 7(4): 301-310. doi: 10.3934/medsci.2020020 |
Multilevel thresholding has important research value in image segmentation and can effectively solve region analysis problems of complex images. In this paper, Otsu and Kapur's entropy are adopted among thresholding segmentation methods. They are used as the objective functions. When the number of threshold increases, the time complexity increases exponentially. In order to overcome this drawback, a modified ant lion optimizer algorithm based on opposition-based learning (MALO) is proposed to determine the optimum threshold values by the maximization of the objective functions. By introducing the opposition-based learning strategy, the search accuracy and convergence performance are increased. In addition to IEEE CEC 2017 benchmark functions validation, 11 state-of-the-art algorithms are selected for comparison. A series of experiments are conducted to evaluate the segmentation performance of the algorithm. The evaluation metrics include: fitness value, peak signal-to-noise ratio, structural similarity index, feature similarity index, and computational time. The experimental data are analyzed and discussed in details. The experimental results significantly demonstrate that the proposed method is superior over others, which can be considered as a powerful and efficient thresholding technique.
Stroke from cerebrovascular illness is one of the leading causes of death and disability in adults worldwide, particularly in industrialized countries [1].
The majority of stroke survivors suffer from physical and mental problems. This causes social and economic difficulties, and it is regarded as a major source of morbidity and the second leading cause of mortality worldwide, behind coronary heart disease and cancer [2],[3].
According to current epidemiological data, 16.9 million people have a stroke each year, giving a global incidence of 258/100,000 people per year and accounting for 11.8% of total deaths worldwide [3],[4].
As seen by studies conducted in Saudi Arabia, the hospital-based crude annual incidence rate of stroke is 15.1 per 100,000 people in Jizan [5], 29.8 per 100,000 people in the Eastern province [6], 43.8 per 100,000 people in Riyadh [7], and 57.64 per 100,000 people in Aseer [8] (Figure 1).
Taif region is located in western Saudi Arabia and covers an area of 321 km2. Located at an elevation of 1,879 m (6,165 ft) on the slopes of the Hejaz Mountains, which are part of the Sarawat Mountains.
Recent data on the incidence of first-time strokes in Saudi Arabia in general, and in the western region in particular, are limited.
The purpose of this study is to identify risk factors for CVA and discuss the first-time stroke incidence in the Taif region of western Saudi Arabia and raise awareness about modifiable risk factors.
A cross-sectional study was conducted between February 2020 to June 2021 at 2 governmental hospitals: Al-Hada Military Hospital, King Faisal Hospital in Taif city. It was approved by the ethics and research committee IRB is HAP-02-T-067. The data were collected based on the hospital's archive system. Data was collected from Al-Hada hospital's database. And as for King Faisal hospital it was collected manually from the hospital archives. The used code for the nervous system diseases is G00–G99. Specifically, G45.9 for TIA and G46.4 for cerebral infarction.
Data included age that meets our criteria (over 18 years old), sex, residence, occupation, history of hypertension, diabetes, cardiac diseases, smoking, previous history of stroke or transient ischemic attacks confirmed by Computed Tomography (CT) scan or Magnetic Resonance Imaging (MRI).
History of medication especially anticoagulants, contraceptive pills if female in childbearing period. Data was coded, tabulated and analyzed using SPSS version 25. Qualitative data was expressed as numbers and percentages, and Chi-squared test (χ2) was applied to test the relationship between variables. (“Risk factors for CVA and raise awareness about modifiable risk factors.”) Quantitative data was expressed as mean and standard deviation (Mean ± SD), the suitable statistical test was applied to assess the relationship between variables according to data normality.
This study aimed to assess the first-time incidence of stroke cerebrovascular accident in Taif, Saudi Arabia. The study included 404 patients, which had 40.6% females and 59.4% males. The mean age of the CVA patients was found to be 64.0 ± 14.9 years. The age distribution showed that 71.5% were above 55 years, 18.1% were 45–55 years, 8.4% were 35–45 years, and 2% were less than 35 years old (Table 1).
Frequency | Percent | ||
Age | < 35 years | 8 | 2.0 |
35–45 years | 34 | 8.4 | |
45–55 years | 73 | 18.1 | |
> 55 years | 289 | 71.5 | |
Gender | Female | 164 | 40.6 |
Male | 240 | 59.4 | |
Marital status | Single | 16 | 4.0 |
Married | 388 | 96.0 |
The analysis showed that the most common type of CVA was ischemic stroke (78.5%), whereas 11.9% had a transient ischemic attack (TIA), and 7.2% had hemorrhagic stroke (Figure 2).
When we evaluated the relationship of age of the patients with the type of stroke, it was found that ischemic shock was comparatively more frequent in subjects in the age group of 45–55 years and >55 years, whereas TIA was comparatively higher reported in subjects aged <35 years (p = 0.024). Gender and marital status didn't show any statistically significant association with the type of stroke (p > 0.05) (Table 2).
Cerebrovascular accident (CVA) |
Total | Chisquare value | P value * | |||||
Hemorrhagic | Ischemic | TIA | Others | |||||
Age | < 35 years | 0 | 4 | 3 | 1 | 8 | 19.203 | 0.024 |
0.0% | 50.0% | 37.5% | 12.5% | 2.0% | ||||
35–45 years | 2 | 22 | 9 | 1 | 34 | |||
5.9% | 64.7% | 26.5% | 2.9% | 8.4% | ||||
45–55 years | 3 | 61 | 7 | 2 | 73 | |||
4.1% | 83.6% | 9.6% | 2.7% | 18.1% | ||||
> 55 years | 24 | 230 | 29 | 6 | 289 | |||
8.3% | 79.6% | 10.0% | 2.1% | 71.5% | ||||
Gender | Female | 13 | 122 | 24 | 5 | 164 | 2.928 | 0.403 |
7.9% | 74.4% | 14.6% | 3.0% | 40.6% | ||||
Male | 16 | 195 | 24 | 5 | 240 | |||
6.7% | 81.3% | 10.0% | 2.1% | 59.4% | ||||
Marital status | Single | 1 | 10 | 5 | 0 | 16 | 6.234 | 0.101 |
6.3% | 62.5% | 31.3% | 0.0% | 4.0% | ||||
Married | 28 | 307 | 43 | 10 | 388 | |||
7.2% | 79.1% | 11.1% | 2.6% | 96.0% |
The assessment of the location of stroke showed that 23.8% of the strokes were in the basal ganglia, 9.4% were at the temporal lobe, and 8.9% at the frontal lobe in both sides (Figure 3).
The majority of episodes are ischemic, which represent 78.5% and the hemorrhagic is 7.2%. The most common site for hemorrhagic stroke was basal ganglia (17.2%) and occipital lobe (13.8%) in both sides. For ischemic stroke, it was basal ganglia (26.5%), and other sites (23.7%), and TIA occurred more frequently on other parts of the brain (68.8%), in the right occipital lobe, frontal lobe and basal ganglia. Which showed a statistically significant association (p < 0.001) (Table 3).
The most commonly reported chief impairment in stroke patients was slurred speech (23%) followed by dizziness (13.6%), weakness in the left side (10.9%), and weakness in the right side (Figure 4).
About 46% (n = 186) patients had multiple chronic diseases and 5.4% (n = 22) had no relevant medical history. It was found that 62.6% and 60.4% had hypertension and diabetes mellitus, respectively. Ischemic heart disease was reported in 9.4%, and chronic renal disease (CKD) was seen in 4.5% of the stroke patients (Figure 5).
A multivariate logistic regression showed that age >55 years TIA OR = 1.74 (1.15–2.61) and dyslipidemia OR = 1.89 (1.25–3.58) are independent risk factors for TIA. Whereas for ischemic stroke, hypertension showed an increased risk OR = 1.43 (0.97–2.71).
Cerebrovascular accident (CVA) |
Chisquare value | P value * | ||||||
Hemorrhagic | Ischemic | TIA | Others | |||||
Site of stroke | Basal ganglia | N | 5 | 84 | 7 | 0 | 77.736 | < 0.001 |
% | 17.2% | 26.5% | 14.6% | 0.0% | ||||
Frontal lobe | N | 4 | 30 | 2 | 0 | |||
% | 13.8% | 9.5% | 4.2% | 0.0% | ||||
Occipital lobe | N | 4 | 14 | 1 | 0 | |||
% | 13.8% | 4.4% | 2.1% | 0.0% | ||||
Parietal lobe | N | 1 | 28 | 0 | 0 | |||
% | 3.4% | 8.8% | 0.0% | 0.0% | ||||
Temporal lobe | N | 4 | 31 | 3 | 0 | |||
% | 13.8% | 9.8% | 6.3% | 0.0% | ||||
Thalamus | N | 4 | 14 | 0 | 0 | |||
% | 13.8% | 4.4% | 0.0% | 0.0% | ||||
Carotid | N | 0 | 11 | 1 | 0 | |||
% | 0.0% | 3.5% | 2.1% | 0.0% | ||||
Cerebellar | N | 0 | 30 | 1 | 1 | |||
% | 0.0% | 9.5% | 2.1% | 10.0% | ||||
Others | N | 7 | 75 | 33 | 9 | |||
% | 24.1% | 23.7% | 68.8% | 90.0% |
Stroke is a significant public health problem, identification and treating high-risk individuals is the key to minimizing its magnitude. The prevalence of stroke and the economic burden on the aging population are rising [10]. Epidemiologic studies on stroke help researchers, physicians, and public health policymakers to critically analyze the risk factors and develop effective prevention and control strategies.
According to the 2020 census report, the Kingdom of Saudi Arabia (KSA) has a population of over 35 million, of which nearly 14% are above the age of 50 years [11]. According to Saudi Arabia General Authority for Statistics, in 2019 Taif city population was 682,959 [12]. The Kingdom has witnessed a drastic increase in life expectancy compared to other countries, which has risen from 69 years in 1990 to 75 years in 2020 [13]. According to the reports of the World Health Organization, stroke was the second leading cause of death in KSA in 2020 [14]. In our study, the most common type of stroke was ischemic stroke (78.5%), followed by hemorrhagic stroke (7.2%). According to the American Heart Association, ischemic stroke accounts for the majority of stroke cases (87%), followed by intracerebral hemorrhage (10%) and subarachnoid hemorrhage (3%) [15].
A study from KSA reported that the mean age of the first stroke was 63 years [7].
Age is a non-modifiable risk factor for stroke, especially ischemic stroke, and incidence doubles every ten years after the age of 55 [16]. However, recent evidence shows that the incidence of stroke is also rising in the younger population [17], which reflects the increased diagnostic tastings and greater sensitivity for its detection among people with minor symptoms [18]. With aging, many structural and functional changes in cerebral vasculature may lead to microvascular injury, and also the prevalence of other risk factors such as hypertension, diabetes, coronary diseases, peripheral artery disease, and atrial fibrillation increases with age [19],[20]. It was reported that the incidence of hemorrhagic stroke increases after the age of 45 years [21]. The gender differences in stroke are not well established; however, females at younger ages are likely to have a higher risk than males, though the risk is higher for males at older ages [22]. The higher risk in younger females could be due to pregnancy and hormone-related changes. It is also postulated that stroke occurs more in females than males due to the longer lifespan of females compared to males [17]. In our study, there were no significant gender differences seen between different types of stroke.
Another most important modifiable risk factor for stroke is hypertension, and our study showed a strong association with ischemic stroke. A previous meta-analysis of 147 clinical trials reported that a decrease of systolic blood pressure of 10 mm Hg and diastolic 5 mm Hg were associated with a 40% reduction in the incidence of stroke risk [23]. Our findings imply that hypertension has a major effect on stroke risk and are consistent with many studies [20],[24]. Diabetes is considered an independent risk factor incidence of stroke, and studies show a 2-fold increase in diabetic patients compared to nondiabetic patients [25],[26]. The risk is also found to be higher in pre-diabetic patients [27]. Hyperlipidemia is a crucial risk factor for stroke, and evidence shows a reduction in cholesterol level significantly reduces the incidence of ischemic stroke [28],[29]. Increased total cholesterol is found to show an increased risk of ischemic stroke incidence, whereas decreased risk is seen with elevated HDL cholesterol levels [30],[31]. Sedentary behavior and physical inactivity is a risk factor for many morbidities including stroke. Diet studies are complex and have several limitations, such as recall bias and sampling errors, but some specific diets such as high salt and potassium intake are found to have an increased association with stroke [32],[33]. Alcohol consumption and smoking are also found to have a direct effect on the incidence of ischemic and hemorrhagic stroke [34],[35].
The current study findings showed that ischemic stroke and hemorrhagic stroke were more seen in the basal ganglia, whereas TIA was found to be more in other parts of the brain. Evidence shows that the middle cerebral artery (MCA), which supplies a large area of basal ganglia and lateral surface of the brain, is the commonly involved artery for ischemic stroke [36]. Hemorrhagic stroke occurs as a result of bleeding into the brain by rupture of blood vessels, which may be subdivided into intracerebral hemorrhage (ICH) and subarachnoid hemorrhage (SAH). The former type is bleeding into the brain parenchyma, whereas for SAH, it is bleeding into the subarachnoid space [37]. In ischemic stroke, the occlusion of arteries impedes perfusion of oxygenated blood to brain parenchyma causing cerebral edema and necrosis of parenchyma. Understanding anatomic variation of the site of lesion is an important consideration for vascular surgery, and this will help vascular surgeons to decide the best surgical approach.
Our study poses certain limitations. First, this is a hospital bases study and may have been subjected to referral bias. Second, we used convenience sampling, which might not have reflected the actual prevalence of different types of stroke in the reference population. Third, there may have many variables that were not matched or controlled, resulting in confounding bias. Fourth, the clinical way of diagnosis rather than imaging methods might have distorted the accuracy and reliability of the data. Finally, due to the short recruitment period, our sample size was comparatively small, and this might lead to poor identification of certain risk factors.
Our study found relations between the risk factors and the different types of strokes, and we found that the incidence of first CVA in Taif was higher in age 18 to 55. And it was higher in males at about 59.4% meanwhile in females it was 40.6%. Significantly, we noticed that it was higher among married people. That indicates a strong relation between diabetes which represent 60.4%, hypertension was 62.6%. We suggest running campaigns that target people with these risk factors to reduce the possibility of CVA occurrence, one of the campaigns could be to increase the awareness of these risk factors by getting screened for early detection and control.
[1] |
N. M. Zaitoun, M. J. Aqel, Survey on Image Segmentation Techniques, Procedia Comput. Sci., 65 (2015), 797-806. doi: 10.1016/j.procs.2015.09.027
![]() |
[2] |
M. Sridevi, C. Mala, A Survey on Monochrome Image Segmentation Methods, Procedia Technol., 6 (2012), 548-555. doi: 10.1016/j.protcy.2012.10.066
![]() |
[3] |
A. K. M. Khairuzzaman, S. Chaudhury, Multilevel thresholding using grey wolf optimizer for image segmentation, Expert Syst. Appl., 86 (2017), 64-76. doi: 10.1016/j.eswa.2017.04.029
![]() |
[4] |
J. Tang, Y. Wang, C. Huang, H. Liu, N. Al-Nabhan, Image edge detection based on singular value feature vector and gradient operator, Math. Biosci. Eng., 17 (2020), 3721-3735. doi: 10.3934/mbe.2020209
![]() |
[5] | X. Song, Y. Wang, Q. Feng, Q. Wang, Improved graph cut model with features of superpixels and neighborhood patches for myocardium segmentation from ultrasound image, Infinite Study, 2019. |
[6] |
X. Lu, Z. You, M. Sun, J. Wu, Z. Zhang, Breast cancer mitotic cell detection using cascade convolutional neural network with U-Net, Math. Biosci. Eng., 18 (2021), 673-695. doi: 10.3934/mbe.2021036
![]() |
[7] |
H. Jia, K. Sun, W. Song, X. Peng, C. Lang, Y. Li, Multi-Strategy Emperor Penguin Optimizer for RGB Histogram-Based Color Satellite Image Segmentation Using Masi Entropy, IEEE Access, 7 (2019), 134448-134474. doi: 10.1109/ACCESS.2019.2942064
![]() |
[8] | S. Wang, H. Jia, X. Peng, Modified salp swarm algorithm based multilevel thresholding for color image segmentation, Math. Biosci. Eng., 17 (2019), 700-724. |
[9] |
A. Dirami, K. Hammouche, M. Diaf, P. Siarry, Fast multilevel thresholding for image segmentation through a multiphase level set method, Signal Process., 93 (2013), 139-153. doi: 10.1016/j.sigpro.2012.07.010
![]() |
[10] |
E. Hamuda, M. Glavin, E. Jones, A survey of image processing techniques for plant extraction and segmentation in the field, Comput. Electron. Agric., 125 (2016), 184-199. doi: 10.1016/j.compag.2016.04.024
![]() |
[11] |
S. Kotte, R. K. Pullakura, S. K. Injeti, Optimal multilevel thresholding selection for brain MRI image segmentation based on adaptive wind driven optimization, Measurement, 130 (2018), 340-361. doi: 10.1016/j.measurement.2018.08.007
![]() |
[12] |
N. Otsu, A threshold selection method from gray-level histograms, IEEE Trans. Syst. Man Cybern., 9 (1979), 62-66. doi: 10.1109/TSMC.1979.4310076
![]() |
[13] |
J. N. Kapur, P. Sahoo, A. K. C. Wong, A new method for gray-level picture thresholding using the entropy of the histogram, Comput. Vis. Graph Image Process., 29 (1985), 273-285. doi: 10.1016/0734-189X(85)90125-2
![]() |
[14] |
A. K.Bhandari, V. K. Singh, A. Kumar, G. K. Singh, Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur's entropy, Expert Syst. Appl., 41 (2014), 3538-3560. doi: 10.1016/j.eswa.2013.10.059
![]() |
[15] |
M. A. E. Aziz, A. A. Ewees, A. E. Hassanien, Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation, Expert Syst. Appl., 83 (2017), 242-256. doi: 10.1016/j.eswa.2017.04.023
![]() |
[16] | K. P. Baby Resma, M. S. Nair, Multilevel thresholding for image segmentation using Krill Herd Optimization algorithm, J. King Saud Univ. Comput. Inf. Sci., (2018), forthcoming. |
[17] | A. Ibrahim, A. Ahmed, S. Hussein, A. E. Hassanien, Fish image segmentation using Salp Swarm Algorithm, in The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018), Springer, (2018), 42-51. |
[18] |
S. Ouadfel, A. Taleb-Ahmed, Social spiders optimization and flower pollination algorithm for multilevel image thresholding: A performance study, Expert Syst. Appl., 55 (2016), 566-584. doi: 10.1016/j.eswa.2016.02.024
![]() |
[19] |
M. Díaz-Cortés, N. Ortega-Sánchez, S. Hinojosa, D. Oliva, E. Cuevas, R. Rojas, et al., A multi-level thresholding method for breast thermograms analysis using Dragonfly algorithm, Infrared Phys. Technol., 93 (2018), 346-361. doi: 10.1016/j.infrared.2018.08.007
![]() |
[20] |
S. C. Satapathy, N. Sri Madhava Raja, V. Rajinikanth, A. S. Ashour, N. Dey, Multi-level image thresholding using Otsu and chaotic bat algorithm, Neural Comput. Appl., 29 (2018), 1285-1307. doi: 10.1007/s00521-016-2645-5
![]() |
[21] |
M. Salvi, F. Molinari, Multi-tissue and multi-scale approach for nuclei segmentation in H & E stained images, BioMed. Eng. OnLine., 17 (2018), 89. doi: 10.1186/s12938-018-0518-0
![]() |
[22] |
Y. Feng, H. Zhao, X. Li, X. Zhang, H. Li, A multi-scale 3D Otsu thresholding algorithm for medical image segmentation, Digital Signal Process., 60 (2017), 186-199. doi: 10.1016/j.dsp.2016.08.003
![]() |
[23] |
D. Zhao, L. Liu, F. Yu, A. A. Heidari, M. Wang, G. Liang, et al., Chaotic random spare ant colony optimization for multi-threshold image segmentation of 2D Kapur entropy, Knowl. Based Syst., 216 (2021), 106510. doi: 10.1016/j.knosys.2020.106510
![]() |
[24] |
D. Zhao, L. Liu, F. Yu, A. A. Heidari, M. Wang, D. Oliva, et al., Ant colony optimization with horizontal and vertical crossover search: Fundamental visions for multi-threshold image segmentation, Expert Syst. Appl., 167 (2021), 114122. doi: 10.1016/j.eswa.2020.114122
![]() |
[25] |
L. He, S. Huang, An efficient krill herd algorithm for color image multilevel thresholding segmentation problem, Appl. Soft Comput., 89 (2020), 106063. doi: 10.1016/j.asoc.2020.106063
![]() |
[26] |
I. Hilali-Jaghdam, A. B. Ishak, S. Abdel-Khalek, A. Jamal, Quantum and classical genetic algorithms for multilevel segmentation of medical images: A comparative study, Comput. Commun., 162 (2020), 83-93. doi: 10.1016/j.comcom.2020.08.010
![]() |
[27] |
B. Wu, J. Zhou, X. Ji, Y. Yin, X. Shen, An ameliorated teaching-learning-based optimization algorithm based study of image segmentation for multilevel thresholding using Kapur's entropy and Otsu's between class variance, Inf. Sci., 533 (2020), 72-107. doi: 10.1016/j.ins.2020.05.033
![]() |
[28] |
S. Mirjalili, The Ant Lion Optimizer, Adv. Eng. Software, 83 (2015), 80-98. doi: 10.1016/j.advengsoft.2015.01.010
![]() |
[29] |
M. J. Hadidian-Moghaddam, S. Arabi-Nowdeh, M. Bigdeli, D. Azizian, A multi-objective optimal sizing and siting of distributed generation using ant lion optimization technique, Ain Shams Eng. J., 9 (2018), 2101-2109. doi: 10.1016/j.asej.2017.03.001
![]() |
[30] |
M. Raju, L. C. Saikia, N. Sinha, Automatic generation control of a multi-area system using ant lion optimizer algorithm based PID plus second order derivative controller, Int. J. Electr. Power Energy Syst., 80 (2016), 52-63. doi: 10.1016/j.ijepes.2016.01.037
![]() |
[31] |
P. Saxena, A. Kothari, Ant Lion Optimization algorithm to control side lobe level and null depths in linear antenna arrays, Int. J. Electron. Commun., 70 (2016), 1339-1349. doi: 10.1016/j.aeue.2016.07.008
![]() |
[32] |
E. Umamaheswari, S. Ganesan, M. Abirami, S. Subramanian, Cost Effective Integrated Maintenance Scheduling in Power Systems using Ant Lion Optimizer, Energy Procedia, 117 (2017), 501-508. doi: 10.1016/j.egypro.2017.05.176
![]() |
[33] |
P. D. P. Reddy, V. C. V. Reddy, T. G. Manohar, Ant Lion optimization algorithm for optimal sizing of renewable energy resources for loss reduction in distribution systems, J. Electr. Syst. Inf. Technol., 5 (2018), 663-680. doi: 10.1016/j.jesit.2017.06.001
![]() |
[34] |
D. Oliva, S. Hinojosa, M. A. Elaziz, N. Ortega-Sánchez, Context based image segmentation using antlion optimization and sine cosine algorithm, Multimedia Tools Appl., 77 (2018), 25761-25797. doi: 10.1007/s11042-018-5815-x
![]() |
[35] | C. Jin, Z. Ye, L. Yan, Y. Cao, A. Zhang, L. Ma, et al., Image Segmentation Using Fuzzy C-means Optimized by Ant Lion Optimization, in 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), IEEE, (2019), 388-393. |
[36] |
X. Yue, H. Zhang, A Novel Industrial Image Contrast Enhancement Technique Based on an Improved Ant Lion Optimizer, Arab J. Sci. Eng., 46 (2021), 3235-3246. doi: 10.1007/s13369-020-05148-4
![]() |
[37] |
Z. Wu, D. Yu, X. Kang, Parameter identification of photovoltaic cell model based on improved ant lion optimizer, Energy Convers. Manage., 151 (2017), 107-115. doi: 10.1016/j.enconman.2017.08.088
![]() |
[38] |
K. R. Subhashini, J. K. Satapathy, Development of an Enhanced Ant Lion Optimization Algorithm and its Application in Antenna Array Synthesis, Appl. Soft Comput., 59 (2017), 153-173. doi: 10.1016/j.asoc.2017.05.007
![]() |
[39] |
S. K. Majhi, S. Biswal, Optimal cluster analysis using hybrid K-Means and Ant Lion Optimizer, Karbala Int. J. Mod. Sci., 4 (2018), 347-360. doi: 10.1016/j.kijoms.2018.09.001
![]() |
[40] |
R. Sarkhel, N. Das, A. K. Saha, M. Nasipuri, An improved Harmony Search Algorithm embedded with a novel piecewise opposition based learning algorithm, Eng. Appl. Artif. Intell., 67 (2018), 317-330. doi: 10.1016/j.engappai.2017.09.020
![]() |
[41] |
A. A. Ewees, M. A. Elaziz, E. H. Houssein, Improved grasshopper optimization algorithm using opposition-based learning, Expert Syst. Appl., 112 (2018), 156-172. doi: 10.1016/j.eswa.2018.06.023
![]() |
[42] |
M. A. Ahandani, Opposition-based learning in the shuffled bidirectional differential evolution algorithm, Swarm Evol. Comput., 26 (2016), 64-85. doi: 10.1016/j.swevo.2015.08.002
![]() |
[43] | N. H. Awad, M. Z. Ali, P. N. Suganthan, Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood for solving CEC2017 benchmark problems, in 2017 IEEE Congress on Evolutionary Computation (CEC), IEEE, (2017), 372-379. |
[44] |
R. Roy, S. Laha, Optimization of Stego image retaining secret information using genetic algorithm with 8-connected PSNR, Procedia Comput. Sci., 60 (2015), 468-477. doi: 10.1016/j.procs.2015.08.168
![]() |
[45] |
A. Tanchenko, Visual-PSNR measure of image quality, J. Visual Commun. Image Represent., 25 (2014), 874-878. doi: 10.1016/j.jvcir.2014.01.008
![]() |
[46] |
Z. Wang, A. C. Bovik, H. R. Sheikh, E. P. Simoncelli, Image quality assessment: from error visibility to structural similarity, IEEE Trans. Image Process., 13 (2004), 600-612. doi: 10.1109/TIP.2003.819861
![]() |
[47] |
V. Bruni, D. Vitulano, An entropy based approach for SSIM speed up, Signal Process., 135 (2017), 198-209. doi: 10.1016/j.sigpro.2017.01.007
![]() |
[48] |
C. Li, A. C. Bovik, Content-partitioned structural similarity index for image quality assessment, Signal Process. Image Commun., 25 (2010), 517-526. doi: 10.1016/j.image.2010.03.004
![]() |
[49] |
L. Zhang, L. Zhang, X. Mou, D. Zhang, FSIM: A feature similarity index for image quality assessment, IEEE Trans. Image Process., 20 (2011), 2378-2386. doi: 10.1109/TIP.2011.2109730
![]() |
[50] |
J. John, M. S. Nair, P. R. A. Kumar, M. Wilscy, A novel approach for detection and delineation of cell nuclei using feature similarity index measure, Biocybern. Biomed. Eng., 36 (2016), 76-88. doi: 10.1016/j.bbe.2015.11.002
![]() |
[51] |
S. K. Dinkar, K. Deep, Opposition based Laplacian Ant Lion Optimizer, J. Comput. Sci., 23 (2017), 71-90. doi: 10.1016/j.jocs.2017.10.007
![]() |
[52] |
M. Wang, X. Zhao, A. A. Heidari, H. Chen, Evaluation of constraint in photovoltaic models by exploiting an enhanced ant lion optimizer, Sol. Energy, 211 (2020), 503-521. doi: 10.1016/j.solener.2020.09.080
![]() |
[53] | The Berkeley Segmentation Dataset and Benchmark. Available from: https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/. |
[54] |
H. Jia, X. Peng, W. Song, C. Lang, Z. Xing, K. Sun, Multiverse Optimization Algorithm Based on Lévy Flight Improvement for Multithreshold Color Image Segmentation, IEEE Access, 7 (2019), 32805-32844. doi: 10.1109/ACCESS.2019.2903345
![]() |
[55] |
A. K. M. Khairuzzaman, S. Chaudhury, Masi entropy based multilevel thresholding for image segmentation, Multimed. Tools Appl., 78 (2019), 33573-33591. doi: 10.1007/s11042-019-08117-8
![]() |
[56] |
A. K. Bhandari, A. Kumar, G. K. Singh, Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur's, Otsu and Tsallis functions, Expert Syst. Appl., 42 (2015), 1573-1601. doi: 10.1016/j.eswa.2014.09.049
![]() |
[57] |
S. Kotte, P. R. Kumar, S. K. Injeti, An efficient approach for optimal multilevel thresholding selection for gray scale images based on improved differential search algorithm, Ain Shams Eng. J., 9 (2018), 1043-1067. doi: 10.1016/j.asej.2016.06.007
![]() |
[58] |
V. K. Bohat, K. V. Arya, A new heuristic for multilevel thresholding of images, Expert Syst. Appl., 117 (2019), 176-203. doi: 10.1016/j.eswa.2018.08.045
![]() |
[59] |
A. K. Bhandari, A novel beta differential evolution algorithm-based fast multilevel thresholding for color image segmentation, Neural Comput. Appl., 32 (2020), 4583-4613. doi: 10.1007/s00521-018-3771-z
![]() |
[60] |
D. H. Wolpert, W. G. Macready, No free lunch theorems for optimization, IEEE Trans. Evol. Comput., 1 (1997), 67-82. doi: 10.1109/4235.585893
![]() |
Frequency | Percent | ||
Age | < 35 years | 8 | 2.0 |
35–45 years | 34 | 8.4 | |
45–55 years | 73 | 18.1 | |
> 55 years | 289 | 71.5 | |
Gender | Female | 164 | 40.6 |
Male | 240 | 59.4 | |
Marital status | Single | 16 | 4.0 |
Married | 388 | 96.0 |
Cerebrovascular accident (CVA) |
Total | Chisquare value | P value * | |||||
Hemorrhagic | Ischemic | TIA | Others | |||||
Age | < 35 years | 0 | 4 | 3 | 1 | 8 | 19.203 | 0.024 |
0.0% | 50.0% | 37.5% | 12.5% | 2.0% | ||||
35–45 years | 2 | 22 | 9 | 1 | 34 | |||
5.9% | 64.7% | 26.5% | 2.9% | 8.4% | ||||
45–55 years | 3 | 61 | 7 | 2 | 73 | |||
4.1% | 83.6% | 9.6% | 2.7% | 18.1% | ||||
> 55 years | 24 | 230 | 29 | 6 | 289 | |||
8.3% | 79.6% | 10.0% | 2.1% | 71.5% | ||||
Gender | Female | 13 | 122 | 24 | 5 | 164 | 2.928 | 0.403 |
7.9% | 74.4% | 14.6% | 3.0% | 40.6% | ||||
Male | 16 | 195 | 24 | 5 | 240 | |||
6.7% | 81.3% | 10.0% | 2.1% | 59.4% | ||||
Marital status | Single | 1 | 10 | 5 | 0 | 16 | 6.234 | 0.101 |
6.3% | 62.5% | 31.3% | 0.0% | 4.0% | ||||
Married | 28 | 307 | 43 | 10 | 388 | |||
7.2% | 79.1% | 11.1% | 2.6% | 96.0% |
Cerebrovascular accident (CVA) |
Chisquare value | P value * | ||||||
Hemorrhagic | Ischemic | TIA | Others | |||||
Site of stroke | Basal ganglia | N | 5 | 84 | 7 | 0 | 77.736 | < 0.001 |
% | 17.2% | 26.5% | 14.6% | 0.0% | ||||
Frontal lobe | N | 4 | 30 | 2 | 0 | |||
% | 13.8% | 9.5% | 4.2% | 0.0% | ||||
Occipital lobe | N | 4 | 14 | 1 | 0 | |||
% | 13.8% | 4.4% | 2.1% | 0.0% | ||||
Parietal lobe | N | 1 | 28 | 0 | 0 | |||
% | 3.4% | 8.8% | 0.0% | 0.0% | ||||
Temporal lobe | N | 4 | 31 | 3 | 0 | |||
% | 13.8% | 9.8% | 6.3% | 0.0% | ||||
Thalamus | N | 4 | 14 | 0 | 0 | |||
% | 13.8% | 4.4% | 0.0% | 0.0% | ||||
Carotid | N | 0 | 11 | 1 | 0 | |||
% | 0.0% | 3.5% | 2.1% | 0.0% | ||||
Cerebellar | N | 0 | 30 | 1 | 1 | |||
% | 0.0% | 9.5% | 2.1% | 10.0% | ||||
Others | N | 7 | 75 | 33 | 9 | |||
% | 24.1% | 23.7% | 68.8% | 90.0% |
Frequency | Percent | ||
Age | < 35 years | 8 | 2.0 |
35–45 years | 34 | 8.4 | |
45–55 years | 73 | 18.1 | |
> 55 years | 289 | 71.5 | |
Gender | Female | 164 | 40.6 |
Male | 240 | 59.4 | |
Marital status | Single | 16 | 4.0 |
Married | 388 | 96.0 |
Cerebrovascular accident (CVA) |
Total | Chisquare value | P value * | |||||
Hemorrhagic | Ischemic | TIA | Others | |||||
Age | < 35 years | 0 | 4 | 3 | 1 | 8 | 19.203 | 0.024 |
0.0% | 50.0% | 37.5% | 12.5% | 2.0% | ||||
35–45 years | 2 | 22 | 9 | 1 | 34 | |||
5.9% | 64.7% | 26.5% | 2.9% | 8.4% | ||||
45–55 years | 3 | 61 | 7 | 2 | 73 | |||
4.1% | 83.6% | 9.6% | 2.7% | 18.1% | ||||
> 55 years | 24 | 230 | 29 | 6 | 289 | |||
8.3% | 79.6% | 10.0% | 2.1% | 71.5% | ||||
Gender | Female | 13 | 122 | 24 | 5 | 164 | 2.928 | 0.403 |
7.9% | 74.4% | 14.6% | 3.0% | 40.6% | ||||
Male | 16 | 195 | 24 | 5 | 240 | |||
6.7% | 81.3% | 10.0% | 2.1% | 59.4% | ||||
Marital status | Single | 1 | 10 | 5 | 0 | 16 | 6.234 | 0.101 |
6.3% | 62.5% | 31.3% | 0.0% | 4.0% | ||||
Married | 28 | 307 | 43 | 10 | 388 | |||
7.2% | 79.1% | 11.1% | 2.6% | 96.0% |
Cerebrovascular accident (CVA) |
Chisquare value | P value * | ||||||
Hemorrhagic | Ischemic | TIA | Others | |||||
Site of stroke | Basal ganglia | N | 5 | 84 | 7 | 0 | 77.736 | < 0.001 |
% | 17.2% | 26.5% | 14.6% | 0.0% | ||||
Frontal lobe | N | 4 | 30 | 2 | 0 | |||
% | 13.8% | 9.5% | 4.2% | 0.0% | ||||
Occipital lobe | N | 4 | 14 | 1 | 0 | |||
% | 13.8% | 4.4% | 2.1% | 0.0% | ||||
Parietal lobe | N | 1 | 28 | 0 | 0 | |||
% | 3.4% | 8.8% | 0.0% | 0.0% | ||||
Temporal lobe | N | 4 | 31 | 3 | 0 | |||
% | 13.8% | 9.8% | 6.3% | 0.0% | ||||
Thalamus | N | 4 | 14 | 0 | 0 | |||
% | 13.8% | 4.4% | 0.0% | 0.0% | ||||
Carotid | N | 0 | 11 | 1 | 0 | |||
% | 0.0% | 3.5% | 2.1% | 0.0% | ||||
Cerebellar | N | 0 | 30 | 1 | 1 | |||
% | 0.0% | 9.5% | 2.1% | 10.0% | ||||
Others | N | 7 | 75 | 33 | 9 | |||
% | 24.1% | 23.7% | 68.8% | 90.0% |