Segmentation of Ovarian Ultrasound Images Using Cellular Neural Networks Trained by Support Vector Machines

Various applications of cellular neural networks (CNNs) on complex image processing tasks raise questions about an appropriate selection of template elements that determine the CNN's behaviour. There are two possibilities: either to resort to the existing and published templates suitable for the problem under consideration or to construct the templates by one of well-known training methods, such as genetic algorithms, simulated annealing, etc. In this paper, a novel approach which utilizes the formalism of support vector machines (SVMs) is introduced. We found the CNN template optimisation done by this machine learning technique superior to other training methods. The learning time reduced from several hours to less than a minute. Testing our novel approach on ultrasound ovarian images, the obtained segmentation results and recognition rates for ovarian follicles were significantly better than with comparable solutions.