Ultrasound Images Registration Based on Optimal Feature Descriptor Using Speeded Up Robust Feature

Abstract

Image registration plays a significant role in the medical image processing field. This paper proposes a development on the accuracy and performance of the Speeded-Up Robust Surf (SURF) algorithm to create Extended Field of View (EFoV) Ultrasound (US) images through applying different matching measures. These measures include Euclidean distance, cityblock distance, variation, and correlation in the matching stage that was built in the SURF algorithm. The US image registration (fusion) was implemented depending on the control points obtained from the used matching measures. The matched points with higher frequency algorithm were proposed in this work to perform and enhance the EFoV for the US images, since the maximum accurate matching points would have been selected. The resulted fused images of these applied methods were evaluated subjectively and objectively. The objective assessment was conducted by calculating the execution time, peak signal to noise ratio (PSNR), and signal to noise ratio (SNR) of the registered images and the reference image which was fused manually by a physician. The results showed that the cityblock distance has the best result since it has the highest PSNR and SNR in addition to the lowest execution time.