Registration of medical images like CT-MR, MR-MR etc. are challenging area for researchers. This chapter introduces a new cluster based registration technique with help of the supervised optimized neural network. Features are extracted from different cluster of an image obtained from clustering algorithms. To overcome the drawback regarding convergence rate of neural network, an optimized neural network is proposed in this chapter. The weights are optimized to increase the convergence rate as well as to avoid stuck in local minima. Different clustering algorithms are explored to minimize the clustering error of an image and extract features from suitable one. The supervised learning method applied to train the neural network. During this training process an optimization algorithm named Genetic Algorithm (GA) is used to update the weights of a neural network. To demonstrate the effectiveness of the proposed method, investigation is carried out on MR T1, T2 data sets. The proposed method shows convincing results in comparison with other existing techniques.
AMO is a simple and efficient optimization algorithm which is inspired by animal migration behavior. However, as most optimization algorithms, it suffers from premature convergence and often falls into local optima. This paper presents an opposition-based AMO algorithm. It employs opposition-based learning for population initialization and evolution to enlarge the search space, accelerate convergence rate, and improve search ability. A set of well-known benchmark functions is employed for experimental verification, and the results show clearly that opposition-based learning can improve the performance of AMO.
Augmented Lagrangian optimization under fixed-point arithmetic
