13. A fuzzy entropy-based multilevel image thresholding using neural network optimization algorithm
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Diksha Thakur
Abstract
In the analysis and preprocessing of images, image segmentation is a very important step. Due to their simplicity, robustness, reduced convergence times, and accuracy, standard multilevel thresholding techniques for bilevel thresholds are efficient. However, a number of computational expenditures are needed, and efficiency is broken down as extensive research is used to decide the optimum thresholds, resulting in the implementation of evolutionary algorithms and swarm intelligence (SI) to achieve the optimum thresholds. Object segmentation’s primary objective is to distinguish the foreground from the background. By optimizing Shannon or fuzzy entropy based on the neural network optimization algorithm, this chapter provided a multilevel image border for object segmentation. The suggested algorithm is evaluated on standard image sets using Firefly algorithm (FA), Differential Evolution (DE), and particle swarm optimization, and the results are compared with entropy approaches for Shannon or fuzzy. The suggested approach shows better efficiency in objective factor than state-of-the-art approaches, structural similarity index, Peak signal to noise ratio (PSNR), and standard derivation.
Abstract
In the analysis and preprocessing of images, image segmentation is a very important step. Due to their simplicity, robustness, reduced convergence times, and accuracy, standard multilevel thresholding techniques for bilevel thresholds are efficient. However, a number of computational expenditures are needed, and efficiency is broken down as extensive research is used to decide the optimum thresholds, resulting in the implementation of evolutionary algorithms and swarm intelligence (SI) to achieve the optimum thresholds. Object segmentation’s primary objective is to distinguish the foreground from the background. By optimizing Shannon or fuzzy entropy based on the neural network optimization algorithm, this chapter provided a multilevel image border for object segmentation. The suggested algorithm is evaluated on standard image sets using Firefly algorithm (FA), Differential Evolution (DE), and particle swarm optimization, and the results are compared with entropy approaches for Shannon or fuzzy. The suggested approach shows better efficiency in objective factor than state-of-the-art approaches, structural similarity index, Peak signal to noise ratio (PSNR), and standard derivation.
Chapters in this book
- Frontmatter I
- Preface VII
- Contents XI
- List of contributors XIII
- 1. A review of bone tissue engineering for the application of artificial intelligence in cellular adhesion prediction 1
- 2. Implementation and classification of machine learning algorithms in healthcare informatics: approaches, challenges, and future scope 21
- 3. Cardiac arrhythmia recognition using Stockwell transform and ABC-optimized twin SVM 35
- 4. Computational intelligence approach to address the language barrier in healthcare 53
- 5. Recent advancement of machine learning and deep learning in the field of healthcare system 77
- 6. Predicting psychological disorders using machine learning 99
- 7. Automatic analysis of cardiovascular diseases using EMD and support vector machines 131
- 8. Machine learning approach for exploring computational intelligence 153
- 9. Classification of various image fusion algorithms and their performance evaluation metrics 179
- 10. Recommender system in healthcare: an overview 199
- 11. Dense CNN approach for medical diagnosis 217
- 12. Impact of sentiment analysis tools to improve patients’ life in critical diseases 239
- 13. A fuzzy entropy-based multilevel image thresholding using neural network optimization algorithm 253
- 14. Machine learning in healthcare 277
- 15. Computational health informatics using evolutionary-based feature selection 309
- Index 329
Chapters in this book
- Frontmatter I
- Preface VII
- Contents XI
- List of contributors XIII
- 1. A review of bone tissue engineering for the application of artificial intelligence in cellular adhesion prediction 1
- 2. Implementation and classification of machine learning algorithms in healthcare informatics: approaches, challenges, and future scope 21
- 3. Cardiac arrhythmia recognition using Stockwell transform and ABC-optimized twin SVM 35
- 4. Computational intelligence approach to address the language barrier in healthcare 53
- 5. Recent advancement of machine learning and deep learning in the field of healthcare system 77
- 6. Predicting psychological disorders using machine learning 99
- 7. Automatic analysis of cardiovascular diseases using EMD and support vector machines 131
- 8. Machine learning approach for exploring computational intelligence 153
- 9. Classification of various image fusion algorithms and their performance evaluation metrics 179
- 10. Recommender system in healthcare: an overview 199
- 11. Dense CNN approach for medical diagnosis 217
- 12. Impact of sentiment analysis tools to improve patients’ life in critical diseases 239
- 13. A fuzzy entropy-based multilevel image thresholding using neural network optimization algorithm 253
- 14. Machine learning in healthcare 277
- 15. Computational health informatics using evolutionary-based feature selection 309
- Index 329
