A Neuro-Fuzzy System for Automatic Multi-Level Image Segmentation using KFCM and Exponential Entropy

An auto adaptive neuro-fuzzy segmentation and edge detection architecture is presented. This system consists of a multilayer perceptron (MLP)-like network that performs image segmentation by adaptive thresholding of the input image using labels automatically pre-selected by kernel based fuzzy clustering technique. The proposed architecture is feed forward, but unlike the conventional MLP the learning is unsupervised. The output status of the network is described as a fuzzy set. Fuzzy entropy is used as a measure of the error of the segmentation system as well as a criterion for determining potential edge pixels. Exponential entropy was employed to overcome the drawbacks of using conventional logarithmic entropy. The proposed system is capable to perform automatic multilevel segmentation of images, based solely on information contained by the image itself. No a priory assumptions whatsoever are made about the image (type, features, contents, stochastic model, etc.). Such an “universal” algorithm is most useful for applications that are supposed to work with different (and possibly initially unknown) types of images. The proposed system can be readily employed, “as is,” or as a basic building block by a more sophisticated and/or applicationspecific image segmentation algorithm. By monitoring the fuzzy entropy relaxation process, the system is able to detect edge pixels