Sparse-induced similarity measure: Mono-modal image registration via sparse-induced similarity measure

Abstract

Similarity measure is an important key in image registration. Most traditional intensity-based similarity measures (e.g. sum-of-squared-differences, correlation coefficient, mutual information and correlation ratio) assume a stationary image and pixel-by-pixel independence. These similarity measures ignore the correlation among pixel intensities; hence, a perfect image registration cannot be achieved especially in the presence of spatially varying intensity distortions and outlier objects that appear in one image but not in the other. It is supposed here that non-stationary intensity distortion (such as bias field) has a sparse representation in the transformation domain. Based on this assumption, a novel similarity measure is proposed here based on sparse representation in a mono-modal setting. The zero norm (ℓ0) in the transform domain is introduced as a new similarity measure in the presence of non-stationary intensity distortion. The present study defines a sparsity similarity measure that indicates the complexity of the residual image between two registered images in the transform domain such as discrete cosine transform or wavelet. It is attempted here to analytically and statistically illustrate that the proposed similarity measure has important properties such as metric properties in vector space, from correntropy perspective. This measure produces accurate registration results on both artificial and real-world problems which are examined in the present study.