High-Speed Point Cloud Matching Algorithm for Medical Volume Images Using 3D Voronoi Diagram

Several respiratory diseases, such as COPD and asthma, requires periodical checkups and past data comparison. While this kind of analysis is usually done by a medical expert, it depends greatly on the medical expertise and the image quality. Image registration, a technique which compares images volumes automatically using predefined computational algorithms, is a great tool to assist on diagnosis and disease surveillance. Most studies analyze the registration on 3D CT images slice-by-slice. However, by segmenting a 3D point clouds from the 3D CT volumes, it is possible to analyze the data in different and more accurate ways. This paper proposes a high speed algorithm improvement that calculates the rigid registration between two point clouds, adapting the Iterative Closest Point (ICP) algorithm to use 3D Voronoi diagrams for point correspondence determination, reducing the processing time greatly. A benchmark performance test is done with a point-by-point variation of the algorithm, showing that the proposed algorithm yield the same results with a considerable processing time reduction.