Segmentation of Ovarian Ultrasound Images Using Single Template Cellular Neural Networks Trained with 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 behavior. In previous research we utilized multiple time variant template elements cellular neural networks for segmentation, which has many advantages compared to conventional search space approaches. In this paper a novel approach which utilizes the formalism of support vector machines (SVMs) that utilizes only single time invariant set of CNN template elements, is introduced. The main advantage of this approach is reduction of the number of CNNs templates and confirms to the conventional applications of CNN.