A hybrid optimization strategy for registering images with large local deformations and intensity variations

Abstract

Purpose To develop a method for intra-patient registration of pre-and post-contrast abdominal MR images with large local deformations and large intensity variations. Method A hybrid method is proposed to deal with this problem. It consists of two coupled techniques: (1) descriptor matching (DM) at the original resolution using a discrete optimization strategy to avoid getting trapped in a local minimum; (2) continuous optimization to refine the registration outcome based on autocorrelation of local image structure (ALOST). Our method-called DM-ALOST-has become insensitive to the local uptake of contrast agent by exploiting the mean phase and the phase congruency extracted from the multi-scale monogenic signal. The method was extensively tested on abdominal MR data of 30 patients with Crohn's disease. Results DM-ALOST produced significantly larger mean Dice coefficients than two state-of-the-art methods ( p < 0.05). Conclusion Both qualitative and quantitative tests demonstrated improved registration using the proposed method compared to the state-of-the-art. The DM-ALOST method facilitates measurement of corresponding features from different abdominal MR images, which can aid to assess certain diseases, particularly Crohn's disease.

Multi-Scale Feature Fusion using Parallel-Attention Block for COVID-19 Chest X-ray Diagnosis

“…The local phase-based image enhancement method extracts the phase signatures from the image and are intensity invariant (Alessandrini et al (2012); Li et al (2018)). Therefore, the enhancement results and subsequent image analysis methods are not affected by the intensity variations due to patient characteristics or machine acquisition settings.…”