3D registration of the point cloud data using ICP algorithm in medical image analysis

In this article, we are combining an advanced implementation of the popular ICP algorithm using the transformation of 3D invariant properties based on 3D scale-invariant feature transform to register 3D free-form closed surfaces (3D model of the human skull). Unlike point and surface registers, our method based on the ICP algorithm better captures the bulk nature of data such as bone thickness. The proposed ICP algorithm is divided into three main steps: the 3D function extraction, comparison of the Euclidean metric distance and gross alignment and ICP enhancement. The input to our system are biomedical data (CT, MRI). Our proposed method first performs image segmentation and then 3D reconstruction of the human skull. In addition, 3D models for the point clouds (PCs) are created. Finally, the differences between point clouds of 3D models are visualized. We compare the performance of three different algorithms with their accuracy and robustness. The proposed solution is fast and accurate compared to the state of art in the field of reconstruction and registration of medical imagery. We apply our algorithm to study skull deformities. Experiments on skull models demonstrate the efficiency and robustness of our algorithm.