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Hybrid multiagent based adaptive genetic algorithm for limited view tomography using oppositional learning
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AI
DOI：10.1016/j.bspc.2022.103610.png)
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Abstract
En 中文
Computed tomography (CT) plays a crucial role in the field of medical diagnosis. The prime objective of limited view tomography is to estimate the object's internal structure with limited view projection data. Limited view computed tomography (CT) has the ability to reduce the X-ray radiation dose imposed on the patient. This manuscript presents Hybrid Multiagent based Adaptive Genetic Algorithm (HMAGA) for limited view tomography. HMAGA is a combination of multiagent-based genetic algorithms and simulated annealing with adaptive crossover and mutation rate. The proposed algorithm uses two methods for reducing the loss of diversity and increases the convergence rate, and these methods are oppositional learning and a new random population. Experimental results reveal that the proposed algorithm provides satisfactory results with low computation overhead. The present manuscript also outperforms other states of the art reconstruction algorithms.
Keywords:
Multiagent GA
Simulated annealing
Fitness function
Oppositional learning
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