N-D Point Cloud Registration for Intensity Normalization on Magnetic Resonance Images

Abstract

Magnetic resonance imaging (MRI) is a non-invasive inspection method widely used in clinical environment. Therefore, this is one of the research hotspots in computer-aided medical diagnosis. However, due to the disparities in imaging protocols as well as the magnetic field strength, the variation of intensity in different MRI scanners results in performance reduction in automatic image analysis and diagnosis procedure. This paper aims at forming a non-rigid intensity transforming function to normalize the intensities of MRI images. The transforming function is obtained from an N-Dimensional (N-D) point cloud, which is formed of weighted sub-region intensity distribution. The proposed method consists of five parts, including pre-alignment, sub-region standard intensity estimation, weighted N-D point cloud generation, spline-based transforming function interpolation as well as final image normalization. This novel method could not only avoid the intensity distortion caused by inconsistent bright-dark relation between tissues in target images and reference images, but also reduce the dependence on the accuracy of multi-modality MRI image registration. The experiments were conducted on a database of 10 volunteers scanned with two different MRI scanners and three modalities. We show that the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) were both enhanced comparing with the histogram-matching method as well as the joint histogram registration method. It can be concluded that the intensities of MRI images acquired from different scanners could be normalized well using the proposed method so that multi-center/multi-machine correlation could be easily carried out in MRI image acquisition and analysis.