A new fusion of whale optimizer algorithm with Kapur’s entropy for multi-threshold image segmentation: analysis and validations

The separation of an object from other objects or the background by selecting the optimal threshold values remains a challenge in the field of image segmentation. Threshold segmentation is one of the most popular image segmentation techniques. The traditional methods for finding the optimum threshol...

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Bibliographic Details
Published in:The Artificial intelligence review Vol. 55; no. 8; pp. 6389 - 6459
Main Authors: Abdel-Basset, Mohamed, Mohamed, Reda, Abouhawwash, Mohamed
Format: Journal Article
Language:English
Published: England Springer 01.12.2022
Springer Nature B.V
Springer Netherlands
Subjects:
Algorithms
Cetacea
Convergence
Entropy
Image processing
Image segmentation
Mathematical optimization
Medical imaging equipment
Minima
NMR
Nuclear magnetic resonance
Optimization
Optimization algorithms
Rankings
Segmentation
Signal to noise ratio
Thresholds
Whales & whaling
Kapur’s entropy
Image segmentation
Local Minima
Whale optimization algorithm
Linearly convergence
ISSN:0269-2821, 1573-7462
Online Access:Get full text
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Summary:The separation of an object from other objects or the background by selecting the optimal threshold values remains a challenge in the field of image segmentation. Threshold segmentation is one of the most popular image segmentation techniques. The traditional methods for finding the optimum threshold are computationally expensive, tedious, and may be inaccurate. Hence, this paper proposes an Improved Whale Optimization Algorithm (IWOA) based on Kapur's entropy for solving multi-threshold segmentation of the gray level image. Also, IWOA supports its performance using linearly convergence increasing and local minima avoidance technique (LCMA), and ranking-based updating method (RUM). LCMA technique accelerates the convergence speed of the solutions toward the optimal solution and tries to avoid the local minima problem that may fall within the optimization process. To do that, it updates randomly the positions of the worst solutions to be near to the best solution and at the same time randomly within the search space according to a certain probability to avoid stuck into local minima. Because of the randomization process used in LCMA for updating the solutions toward the best solutions, a huge number of the solutions around the best are skipped. Therefore, the RUM is used to replace the unbeneficial solution with a novel updating scheme to cover this problem. We compare IWOA with another seven algorithms using a set of well-known test images. We use several performance measures, such as fitness values, Peak Signal to Noise Ratio, Structured Similarity Index Metric, Standard Deviation, and CPU time.
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ISSN:0269-2821
1573-7462
DOI:10.1007/s10462-022-10157-w