Efficient segmentation of degraded images by a neuro-fuzzy classifier

Segmentation of degraded images has always been a difficult problem to solve. Efficient object extraction from noisy images can be achieved by neuro-fuzzy clustering algorithms where noise pixels are identified during the clustering process and assigned low weights to avoid their degradation effect on prototype validity. We present here a new approach to noise reduction prior to segmentation by using a two step process namely AFLC (adaptive fuzzy leader clustering)-median. This new two step process has been specifically tailored to remove speckle noise. The first step is to use the AFLC that has been designed to follow leader clustering using a hybrid neuro-fuzzy model developed by integrating a modified ART-1 model with fuzzy-C-means (FCM). This integration provides a powerful yet fast method for recognizing embedded data structure. In speckled imagery, AFLC is used to isolate the speckle noise pixels by segmenting the image into several clusters controlled by a vigilance parameter. Once the speckles have been identified, a median filter is used centered on each speckle noise pixel. The resulting image after undergoing the AFLC-median process demonstrates reduction of speckle noise while retaining sharp edges for improved segmentation.