Robust registration of medical images in the presence of spatially-varying noise

TL;DR

This paper introduces EMD-based hierarchical algorithms for robust medical image registration that effectively reduce spatially-varying noise, significantly improving accuracy over traditional intensity-based methods.

Contribution

The paper presents novel EMD-based multi-resolution algorithms that enhance registration robustness in noisy medical images, outperforming existing intensity-based approaches.

Findings

EMD effectively reduces bias field noise in medical images.

Proposed algorithms achieve lower error rates and higher convergence in brain MR registration.

Algorithms improve retina image registration under spatially-varying noise conditions.

Abstract

Spatially-varying intensity noise is a common source of distortion in medical images. Bias field noise is one example of such a distortion that is often present in the magnetic resonance (MR) images or other modalities such as retina images. In this paper, we first show that the bias field noise can be considerably reduced using Empirical Mode Decomposition (EMD) technique. EMD is a multi-resolution tool that decomposes a signal into several principle patterns and residual components. We show that the spatially-varying noise is highly expressed in the residual component of the EMD and could be filtered out. Then, we propose two hierarchical multi-resolution EMD-based algorithms for robust registration of images in the presence of spatially varying noise. One algorithm (LR-EMD) is based on registration of EMD feature-maps from both floating and reference images in various resolution levels. In the second algorithm (AFR-EMD), we first extract an average feature-map based on EMD from both floating and reference images. Then, we use a simple hierarchical multi-resolution algorithm to register the average feature-maps. For the brain MR images, both algorithms achieve lower error rate and higher convergence percentage compared to the intensity-based hierarchical registration. Specifically, using mutual information as the similarity measure, AFR-EMD achieves 42% lower error rate in intensity and 52% lower error rate in transformation compared to intensity-based hierarchical registration. For LR-EMD, the error rate is 32% lower for the intensity and 41% lower for the transformation. Furthermore, we demonstrate that our proposed algorithms improve the registration of retina images in the presence of spatially varying noise.