Robust corner detection with fractional calculus for magnetic resonance imaging

Abstract

In medical image processing and analyzing, corners are one of the most important features, which makes point-based registration and diagnosis studies essential. However, existing corner detectors are impressible to noise. This work proposes an order-steerable and robust image corner detector, while a fractional gradient operator consisting of fractional-order forward and backward differentiation and integration is derived. The fractional gradient operator achieving a 90° phase shift as the traditional first derivative does provides a new idea to calculate the gradient in an image. Qualitative and quantitative comparison experiments with exiting detection methods based on integer-order derivative are performed on simulated and real medical slices. The comparison experiments indicate that the present corner detector in this paper allows very strong response for corner detection and provides a better ability of detecting and locating image corners and robustness against noise.