Hyper heuristic particle swarm optimization method for medical images segmentation

Abstract

Purpose. Development of new methods for medical images segmentation, since modern methods of medical diagnostics are largely based on image processing MRI, CT scan etc. Materials and Methods. A hybrid particle swarm algorithm for medical image segmentation is proposed, which includes a modified and elite exponential particle swarm segmentation algorithm in combination with the k-means method. The time complexity of the developed algorithm is investigated on the basis of the drift analysis. It is shown that the developed algorithm for segmentation of MRI images has a polynomial time complexity. Results. To test the developed algorithms, images of the Ossirix test set and real medical images were used. When comparing the operating time of the proposed segmentation methods, it was found that the hyper-heuristic swarm method shows the operating time on average 2 times less than when using the hybrid ant method, and the segmentation results for good quality images and blurry images are comparable. Conclusions. Several modifications of swarm image segmentation algorithms have been proposed for various types of medical images, including contrasting, noisy and blurred images, which showed good results during testing and low time complexity.