Medical image registration based on fuzzy c-means clustering segmentation approach using SURF

Abstract

An approach to medical image registration using Fuzzy c-Means (FCM) clustering segmentation and Speeded-Up Robust Feature (SURF) detector is presented. This approach uses FCM to obtain reference- and floating-segmented images. Volume control points of these segmented images determine the quality of image registration. Based on these volume control points, features are extracted from reference and floating images using SURF and then matched to perform image registration. The proposed registration algorithm using FCM and SURF is faster and robust against different image transformations like standard SIFT, other recent fuzzy and neural-based methods quantitatively. Simulations for FCM clustering using SURF based on a multi-resolution approach using the image of the same size but with different scales are also shown here.