Gabor wavelet and unsupervised Fuzzy C-means clustering for edge detection of medical images

It is well known that the Gabor wavelet transform (GWT) provides directional information for the analysis of an image. In this paper, we proposed an approach based on the GWT by combining unsupervised Fuzzy c-means (FCM) clustering which provides plays an important role in recognition as a classifier. After enhancing the edge of the input image using GWT, the binary image showing the edge is obtained using FCM clustering and morphological skeletonization. When compared to the Canny method and other conventional method, the proposed method has showed a better performance in terms of detection accuracy for noisy medical images.