Alignment of multimodal rigid cardiac angiography images with an improved particle swarm algorithm

Abstract

In this paper, we conduct a preliminary study on the current development status in medical image alignment and build up a basic framework for image alignment. The feature space, search space, similarity measure, and search strategy of cardiac angiography images are calculated and studied. The DGVF model is utilized to process the traditional snake model for optimization search and is combined with B-splines to construct the B-spline DGVF model. Optimize the traditional MsFCM algorithm by using the PSO algorithm and propose an MsFCM-PSO image segmentation method. It is applied together with the B-spline DGVF model to segment the vascular lumen in cardiac angiography ultrasound images. Finally, the model of this paper is analyzed in terms of segmentation performance, alignment stability, and evaluation of alignment results. The mean values of Dice, IoU, and HD of this paper’s MsFCM-PSO model in image segmentation of cardiac vessels are 94.27%, 92.60%, and 1.06, respectively (all optimal performances). In the ablation experiments, the MsFCMPSO model in this paper shows an increase of 6.02% and 5.47% in the mean values of Dice and IoU compared to the benchmark model. The stability calibration percentage of this paper’s MsFCM-PSO algorithm is 31.13% when the Gaussian factor is 0.5, which is significantly better than other algorithms. The algorithm in this paper is better than other methods in terms of alignment stability and alignment results.