Volume 20, Issue 4 (3-2024)
JSDP 2024, 20(4): 35-44 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Namiranian F, Latif A. A New Approach for Digital Image Segmentation with Genetic Algorithm and Random Forest. JSDP 2024; 20 (4) : 3
URL: http://jsdp.rcisp.ac.ir/article-1-1339-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-1339-en.html
Yazd University
Abstract: (395 Views)
In this study, a new method for image segmentation by genetic algorithms and random forest is resented. The main objective of image segmentation is to distinguish different components within an image, achieved by labeling pixels based on shared characteristics. In this novel approach, these distinguishing features are derived through the application of image filters (Gabor filters). The random forest algorithm is then employed as a classifier to perform image segmentation according to extracted features from these filters. The image filters utilized come with various hyperparameters, and tuning of these parameters significantly enhances the algorithm's performance.
The proposed methodology distinguishes itself by employing a genetic algorithm to fine-tune the hyperparameters of Gabor filters. In this context, the hyperparameters are treated as genes within the chromosome of the genetic algorithm. The success of this optimization is evaluated using f1-score, a metric derived from the random forest algorithm's execution in image segmentation. This step ensures that the selected hyperparameters contribute to optimal segmentation results. The achievement of this research lies not only in the implementation of this novel approach but also in surpassing the performance of other investigated methods through the enhancement of the f1-score in image segmentation.
Key to the success of the proposed method is the careful consideration of hyperparameters and their role in defining the characteristics crucial for accurate image segmentation. The use of genetic algorithms not only automates this parameter tuning process but also ensures that the algorithm adapts and evolves to find the most suitable values for the hyperparameters of Gabor filters. As a result, the research contributes to the broader field of image segmentation by providing a robust and effective methodology, demonstrating superior performance compared to alternative methods.
In conclusion, this study introduces an approach to image segmentation, leveraging the synergies between genetic algorithms, random forest, and image filters. The research not only emphasizes the importance of hyperparameter tuning but also showcases the effectiveness of the proposed methodology through the optimization of Gabor filter parameters. The overall impact of this work is evident in the improved f1-score achieved in image segmentation, establishing it as a noteworthy advancement in the field.
The proposed methodology distinguishes itself by employing a genetic algorithm to fine-tune the hyperparameters of Gabor filters. In this context, the hyperparameters are treated as genes within the chromosome of the genetic algorithm. The success of this optimization is evaluated using f1-score, a metric derived from the random forest algorithm's execution in image segmentation. This step ensures that the selected hyperparameters contribute to optimal segmentation results. The achievement of this research lies not only in the implementation of this novel approach but also in surpassing the performance of other investigated methods through the enhancement of the f1-score in image segmentation.
Key to the success of the proposed method is the careful consideration of hyperparameters and their role in defining the characteristics crucial for accurate image segmentation. The use of genetic algorithms not only automates this parameter tuning process but also ensures that the algorithm adapts and evolves to find the most suitable values for the hyperparameters of Gabor filters. As a result, the research contributes to the broader field of image segmentation by providing a robust and effective methodology, demonstrating superior performance compared to alternative methods.
In conclusion, this study introduces an approach to image segmentation, leveraging the synergies between genetic algorithms, random forest, and image filters. The research not only emphasizes the importance of hyperparameter tuning but also showcases the effectiveness of the proposed methodology through the optimization of Gabor filter parameters. The overall impact of this work is evident in the improved f1-score achieved in image segmentation, establishing it as a noteworthy advancement in the field.
Article number: 3
Keywords: Image Segmentation, Genetic Algorithm, RImage Segmentation, Genetic Algorithm, Random Forest Algorithm, Gabor Filter, Feature Extractionandom Forest Algorithm
Type of Study: Research |
Subject:
Paper
Received: 2022/09/4 | Accepted: 2023/12/11 | Published: 2024/04/25 | ePublished: 2024/04/25
Received: 2022/09/4 | Accepted: 2023/12/11 | Published: 2024/04/25 | ePublished: 2024/04/25
References
1. [1] C. Tan, S. Ying, L. Gongfa, T. Bo, X. Shuang and Z. Fei, "Image Segmentation Technology based on Genetic Algorithm, " in International Conference on Digital Signal Processing, 2019. [DOI:10.1145/3316551.3318229]
2. [2] T. Wang, Y. Yao, Y. Chen, M. Zhang, F. Tao and Hichem, "Auto-Sorting System Toward Smart Factory based on Deep Learning for Image Segmentation, " IEEE Sensors Journal, vol. 18, no. 20, pp. 8493 - 8501, 2018.
3. [3] A. Chaudhry, M. Hassan and A. Khan, "Robust Segmentation and Intelligent Decision System for Cerebrovascular Disease, " Medical & Biological Engineering & Computing, vol. 54, p. 1903-1920, 2016. [DOI:10.1007/s11517-016-1481-1] [PMID]
4. [4] C. Yuan, Z. Liu and Zhang, "Aerial Images-Based Forest Fire Detection for Firefighting Using Optical Remote Sensing Techniques and Unmanned Aerial Vehicles, " Journal of Intelligent & Robotic Systems, vol. 88, pp. 635-654, 2017. [DOI:10.1007/s10846-016-0464-7]
5. [5] T. Maruyama, H. Norioa, S. Shingoa and Wakaya, "Comparison of Medical Image Classification Accuracy among Three Machine Learning Methods, " Journal of X-Ray Science and Technology, vol. 26, pp. 885-893, 2018. [DOI:10.3233/XST-18386] [PMID]
6. [6] K. ShouvikChakraborty, "SuFMoFPA: A Superpixel and Meta-heuristic based Fuzzy Image Segmentation Approach to Explicate COVID-19 Radiological Images, " Expert Systems with Applications, vol. 167, 2021. [DOI:10.1016/j.eswa.2020.114142] [PMID] []
7. [7] S. Minaee, Y. Boykov, F. Porikli, A. Plaza, N. Kehtarnavaz and D. Terzopoulos, "Image Segmentation using Deep Learning: A Survey, " IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 7, pp. 3523 - 3542, 2022.
8. [8] C. Han, T. Ma, J. Huyan, X. Huang and Y. Zhang, "CrackW-Net: A Novel Pavement Crack Image Segmentation Convolutional Neural Network, " IEEE Transactions on Intelligent Transportation Systems, pp. 1-10, 2021.
9. [9] B. S. Sathish, P. Ganesan, L. Joseph, K. Palani and R. Murugesan, "A Two-Level Approach to Color Space-Based Image Segmentation using Genetic Algorithm and Feed-Forward Neural Network, " Advances in Artificial Intelligence and Data Engineering, pp.67-78, 2020. [DOI:10.1007/978-981-15-3514-7_6]
10. [10] L. Xiao, H. Ouyang, Ch. Fan, T. Umer, R.C. Poonia,Sh. Wan," Gesture Image Segmentation with Otsu's Method based on Noise Adaptive Angle Threshold", Multimedia Tools and Applications,pp35619-35640,2020 [DOI:10.1007/s11042-019-08544-7]
11. [11] G. Xu, X. Li, B. Lei, K. Lv, " Unsupervised Color Image Segmentation with Color-alone Feature using Region Growing Pulse Coupled Neural Network", Neurocomputing, vol.306, pp.1-16,2018 [DOI:10.1016/j.neucom.2018.04.010]
12. [12] PB. Chanda and SK. Sarkar, "Study on Efficient DRLSE-Oriented Edge-Based Medical Image Segmentation of Cardiac Images, " Emerging Technologies in Data Mining and Information Security, vol. 164, pp. 823-831, 2021. [DOI:10.1007/978-981-15-9774-9_75]
13. [13] F. Jiang, Q. Gu, H. Hao, N.Li," Feature Extraction and Grain Segmentation of Sandstone Images Based on Convolutional Neural Networks", International Conference on Pattern Recognition (ICPR), pp. 2636-2641, 2018. [DOI:10.1109/ICPR.2018.8545649]
14. [14] Y. Songa, B. Heb, P. Liuc and T. Yand, "Side Scan Sonar Image Segmentation and Synthesis based on Extreme Learning Machine, " Applied Acoustics, vol. 146, pp. 56-65, 2019. [DOI:10.1016/j.apacoust.2018.10.031]
15. [15] K. Roopa, G. Neena and K. Narender, "Image Segmentation using Improved Genetic Algorithm, " International Journal of Engineering and Advanced Technology, vol. 9, no. 1, pp. 1784-1792, 2019. [DOI:10.35940/ijeat.F9063.109119]
16. [16] S. K. Saha, S. Pradhan and S.V. Barai, "Use of Machine Learning based Technique to X-ray Microtomographic Images of Concrete for Phase Segmentation at Meso-scale," Construction and Building Materials, vol. 249, 2020. [DOI:10.1016/j.conbuildmat.2020.118744]
17. [17] V.R. Patil, T.H. Jaware," Random Forest and Gabor Filter Bank Based Segmentation Approach for Infant Brain MRI", Applied Information Processing Systems, vol.1354, pp. 265-272, 2022. [DOI:10.1007/978-981-16-2008-9_25]
18. [18] P. Asadi, L.E. Beckingham," Intelligent framework for mineral segmentation and fluid-accessible surface area analysis in scanning electron microscopy", Applied Geochemistry, vol.143, 2022. [DOI:10.1016/j.apgeochem.2022.105387]
19. [19] A. Fauzi, L.E. Lubis, "Optimization of retinal blood vessel segmentation based on Gabor filters and particle swarm optimization", Indonesian Journal of Electrical Engineering and Computer Science, vol.29, no. 3, pp. 1590-1596, 2023. [DOI:10.11591/ijeecs.v29.i3.pp1590-1596]
20. [20] Z.Shahidizandi, A.Latif, "Developing a modern method in circle detection in digital images by using genetic algorithm", Journal of Machine Vision and Image Processing,vol. 8, no. 1, pp.35-44,2021
21. [21] El-Diraby, S. M. Piryonesi and E. Tamer, "Data Analytics in Asset Management: Cost-Effective Prediction of the Pavement Condition Index, " Journal of Infrastructure Systems, vol. 26, no. 1, 2020. [DOI:10.1061/(ASCE)IS.1943-555X.0000512]
22. [22] V. Rodriguez-Galiano, M. Sanchez-Castillo, M. Chica-Olmo and M. Chica-Riv, "Machine Learning Predictive Models for Mineral Prospectivity: An Evaluation of Neural networks, Random forest, Regression Trees and Support Vector Machines, " Ore Geology Reviews, vol. 71, pp. 804-818, 2015. [DOI:10.1016/j.oregeorev.2015.01.001]
23. [23] T. Vijayan, M.Sangeetha, A. Kumaravel, B. Karthik "Gabor Filter and Machine learning Based Diabetic Retinopathy Analysis and Detection," Microprocessors and Microsystems, 2020. [DOI:10.1016/j.micpro.2020.103353]
24. [24] Y. Xua, W. Yuxin, Y. Jie, C. Qian and W. Xueding, "Medical Breast Ultrasound Image Segmentation by Machine Learning, " Ultrasonics, vol. 91, pp. 1-9, 2019. [DOI:10.1016/j.ultras.2018.07.006] [PMID]
25. [25] "https://drive.google.com/file/d/1HWtBaSa-LTyAMgf2uaz1T9o1sTWDBajU/view", visited on 2 july 2022
26. [26] A.Karimi,L S. Hoseini. "An Optimal Algorithm for Dividing Microscopic Images of Blood for the Diagnosis of Acute Pulmonary Lymphoblastic Cell Using the FCM Algorithm and Genetic Optimization." JSDP,pp.45-54,2018 [DOI:10.29252/jsdp.15.2.45]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
