Blur robust extremal region-based interest points for medical image registration

Abstract

Various studies on interest point (IP) detection have concluded that maximally stable extremal region (MSER)-based IPs outperform others on repeatability, localization accuracy, robustness, efficiency and covariance to global and local image distortions. Since medical images lack sharp detail, corner IPs are not a suitable choice for them. Instead, MSERs which offer region-based IPs are useful. However, sensitivity of MSERs to image blur and scale makes them less useful practically. In this context, through this paper, following contributions are made—(1) It is proposed to study MSER-based IPs in Intensity Scale Space instead of conventional Scale Space to better understand and mitigate the problem of IP clutter. (2) By modulating the connectivity of previously proposed ER-based IPs (inspired from visual saliency approach), blur and scale sensitivity of region-based IPs is shown to reduce significantly. The newly developed IPs are called ‘blur robust extremal region (BRER)’ IPs. (3) Owing to the global nature of evaluation parameters (like repeatability) for IP detection, the problem of incorrect judgment is highlighted. As a solution to it, three new evaluation parameters called ‘Uniformity Index,’ ‘10 % core distance’ and ‘Informativeness’ are proposed. These indices capture the idea of uniform distribution of IPs over the entire image, IP clutter and the redundancy of registered IP pairs, respectively. Experiments on database of medical images of different modalities and various organs/diseases suggest that proposed BRER IPs are robust to blur and scale. Also, proposed indices of evaluation offer better judgment of quality of image registration.