Medical image thresholding using WQPSO and maximum entropy
Anusuya S. Venkatesan, Latha Parthiban · 2012
Image thresholding is an important method of image segmentation to find the objects of interest. Maximum entropy is an image thresholding method that exploits entropy of the distribution in gray level of the image. The performance of this method can be improved by using swarm intelligence techniques such as Particle Swarm Optimization (PSO) and Quantum PSO (QPSO). QPSO has attracted the research community due to its simplicity, easy implementation and fast convergence. The convergence of QPSO is faster than PSO and global convergence is guaranteed. In this paper, we propose a new combination of mean updated QPSO referred to as weighted QPSO with maximum entropy to find optimal threshold for magnetic resonance images (MRI). The performance of this method outperforms other existing methods in literature in terms of convergence speed and accuracy.