A Hierarchical Skull Point Cloud Registration Method

Abstract

Skull registration is one of the important steps in craniofacial reconstruction, and its registration accuracy and efficiency have an important impact on the reconstruction results. To solve the problem of low accuracy and efficiency of existing skull registration methods, a hierarchical skull point cloud registration method is proposed in this paper. The whole registration process is divided into a rough registration stage and a fine registration stage. Firstly, feature points are extracted from the pre-processed skull point cloud model, and a local coordinate reference system is established according to the feature points and their neighbor points. The improved spin image is used to construct the local feature descriptor. The feature matching is carried out according to the nearest neighbor algorithm, and the k-means algorithm is used to eliminate the mismatching points to achieve skull rough registration. Then, based on rough registration, we use an improved ICP algorithm to achieve fine registration of the skull. In this process, we use random sampling to reduce the search scale of points and add geometric feature constraints to further eliminate mismatched points. Finally, the whole registration algorithm is applied to the skull point cloud data to verify. The experimental results show that, compared with other methods, the registration effect and efficiency of the proposed method are superior to those of other methods. In order to verify the universality of the method, we also use a common data set for verification. Experiments show that the method is also very effective.

A Coarse-to-Fine Registration Approach for Point Cloud Data with Bipartite Graph Structure

“…With the rapid development of optical measurement technology, three-dimensional (3D) models of real-world objects with different views have been employed to collect data for many application scenarios [1][2][3][4][5]. As the fundamental technique in 3D data processes, the registration method is mainly responsible for determining the correspondence and transformation of multiple-view 3D data, which facilitates the implementation of many reverse engineering applications, such as multi-platform data alignment for airborne and terrestrial laser scanning [6], foot alignment for reconstruction in the footwear consumer field [7], remote sensing image registration in military and civilian fields [8], skull registration for craniofacial reconstruction in the medical field [9], cross-source point cloud registration in large-scale scanned data for street views outdoors [10], 3D face registration for face recognition [11], etc. During the past few decades, the high-performance registration method has become an increasingly important research topic.…”

“…The ISS key points described by 3DSC were used for rough alignment with RANSAC, and ICP conducted accurate registration. Wen et al [9] proposed a hierarchical coarse-to-fine alignment method for skull registration. The improved spin image and k-means algorithm rough stage were used in the rough stage.…”

“…where CP(x, y, z) denotes the coordinate values of points in the point cloud, i is the i-th index of points, label nor denotes the normalized value of points, label max is the maximum value of points, and label min is the minimum value of points. Through the normalization process, the effect of translation and scale can be eliminated, and ( 7) can be simplified to (9).…”

Computational Intelligence in Metric Analysis of the Skull in the Context of Maxillofacial Surgery

“…Data processing methods are based on the values obtained by standard measurement techniques including three- dimensional scanning technology [17], and their analysis by specific imaging methods [18], [19] based on panoramic Xray images [20], magnetic resonance data [21], and thermographic images. Skull registration [22] forms another important method in craniofacial reconstruction.…”

Facial bones segmentation from skull point clouds based on deviation angle

“…A human skull is an object that can be represented by point clouds [12]. The skull is a center of study in various fields, including biometrics [13], craniofacial surgery [14], detection of abnormalities [15], and craniofacial reconstruction [16].…”