Title: Medical image registration based on fuzzy c-means clustering segmentation approach using SURF
Authors: Sunanda Gupta; S.K. Chakarvarti; Zaheeruddin
Addresses: Department of Electronics Engineering, Faculty of Engineering and Technology, Manav Rachna International University, Faridabad 121006, Haryana, India ' Research and Development, Manav Rachna International University, Faridabad 121006, Haryana, India ' Department of Electrical Engineering, Faculty of Engineering and Technology, Jamia Millia Islamia, New Delhi 110025, India
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.
Keywords: SURF; speeded-up robust feature; FCM clustering; fuzzy c-means; medical images; image registration; blob detector; feature descriptor; control points; integral image; volume control points; feature extraction; floating images; simulation.
International Journal of Biomedical Engineering and Technology, 2016 Vol.20 No.1, pp.33 - 50
Received: 11 Mar 2015
Accepted: 13 Jul 2015
Published online: 11 Jan 2016 *