Multilevel Image Segmentation Techniques
Summary
Multilevel image segmentation divides a digital image into distinct regions by selecting multiple intensity thresholds. Classical approaches treat the image histogram as a probability distribution and identify threshold values that maximise criteria such as between-class variance or information entropy. As the number of thresholds increases, exhaustive search becomes computationally prohibitive, rendering segmentation an NP-hard problem. In response, researchers have developed a wide array of metaheuristic and hybrid algorithms—drawing on swarm intelligence, evolutionary strategies and optimisation heuristics—to locate optimal thresholds efficiently. Recent trends also integrate deep learning, coupling segmentation with convolutional neural networks to refine region delineation. These techniques have found global application in fields as diverse as medical diagnostics, where they isolate anatomical structures in X-ray and CT scans; remote sensing, for land-cover classification of satellite imagery; and industrial inspection, for surface defect detection. Advances continue to emphasise robust convergence, avoidance of local optima and scalability to high-dimensional histograms, thereby broadening practical utility in real-time and large-scale settings.
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Multilevel Image Segmentation Techniques publication trend
The graph below shows the total number of articles in multilevel image segmentation techniques across all publications each year (not limited to Nature Index journals).
Technical terms
Multilevel thresholding: A segmentation technique that partitions an image histogram into more than two intensity ranges, assigning pixels to classes based on multiple threshold values.
Entropy (Kapur/Tsallis): An information-theoretic measure used as an objective function to maximise the uncertainty between segmented regions.
Metaheuristic algorithm: A high-level problem-independent search strategy—such as genetic algorithms, particle swarm optimisation or nature-inspired methods—designed to find near-optimal solutions in complex search spaces.
Swarm intelligence: An optimisation paradigm inspired by collective behaviour in natural systems (e.g., ants, bees, coyotes) used to explore and exploit search spaces collaboratively.
Otsu’s method: A histogram-based thresholding criterion that maximises between-class variance for bi-level or multilevel segmentation.
References
- CNN-IKOA: convolutional neural network with improved Kepler optimization algorithm for image segmentation: experimental validation and numerical exploration. Journal of Big Data (2024).
- Modified Remora Optimization Algorithm for Global Optimization and Multilevel Thresholding Image Segmentation. Mathematics (2022).
- Fuzzy Multilevel Image Thresholding Based on Improved Coyote Optimization Algorithm. IEEE Access (2021).
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