Skuller: A volumetric shape registration algorithm for modeling skull deformities

We present an algorithm for volumetric registration of 3D solid shapes. In comparison to previous work on image based registration, our technique achieves higher efficiency by leveraging a template tetrahedral mesh. In contrast to point- and surface-based registration techniques, our method better captures volumetric nature of the data, such as bone thickness. We apply our algorithm to study pathological skull deformities caused by a particular condition, i.e., craniosynostosis. The input to our system is a pair of volumetric 3D shapes: a tetrahedral mesh and a voxelized object represented by a set of voxel cells segmented from computed tomography (CT) scans. Our general framework first performs a global registration and then launches a novel elastic registration process that uses as much volumetric information as possible while deforming the generic template tetrahedral mesh of a healthy human skull towards the underlying geometry of the voxel cells. Both data are high-resolution and differ by large non-rigid deformations. Our fully-automatic solution is fast and accurate, as compared with the state of the arts from the reconstruction and medical image registration fields. We use the resulting registration to match the ground-truth surfaces extracted from the medical data as well as to quantify the severity of the anatomical deformity.


3B Izometrik Şekil Eşleme
Sahillioğlu, Yusuf (2010-06-01)
3B izometrik şekiller arasındaki eşleme problemini ele alıyoruz. Önerdiğimiz yöntem, verilen iki izometrik şekil arasındaki izometrik sapmayı enküçülten optimal eşlemeyi otomatik olarak bulabilmektedir.İzometri hatasını iki adımda eniyiliyoruz. İlk adımda, şekil yüzeylerinden örneklenmiş bir örnek 3B noktalar kesel ilginlik bilgisine dayanarak spektral uzaya aktarılır.İlk eşleme spektral uzayda tam iki kısımlı bir çizge eşleştirme yöntemi kullanarak izometri hatasının polinom zamanda enküçültülmesiyle eld...
3D Shape Correspondence by Isometry Driven Greedy Optimization
Sahillioğlu, Yusuf (null; 2010-06-01)
We present an automatic method that establishes 3D correspondence between isometric shapes. Our goal is to find an optimal correspondence between two given (nearly) isometric shapes, that minimizes the amount of deviation from isometry. We cast the problem as a complete surface correspondence problem. Our method first divides the given shapes to be matched into surface patches of equal area and then seeks for a mapping between the patch centers which we refer to as base vertices. Hence the correspondence is...
Shape Optimizations of Metallic Sheets Using a Multigrid Approach
Altinoklu, Askin; Karaova, Gokhan; Ergül, Özgür Salih (2017-09-27)
We present a novel multigrid approach for the shape optimizations of corrugated metallic sheets by using genetic algorithms (GAs) and the multilevel fast multipole algorithm (MLFMA). The overall mechanism is obtained by an efficient integration of GAs and MLFMA, while the optimizations are improved by applying multiple grids at different layers. We show that the multigrid approach provides more effective optimizations than the conventional no-grid optimizations that employ the discretization nodes directly....
A shape deformation algorithm for constrained multidimensional scaling
Sahillioğlu, Yusuf (2015-12-01)
We present a new Euclidean embedding technique based on volumetric shape registration. Extrinsic representation of the intrinsic geometry of a shape is preferable in various computer graphics applications as it poses only a small degrees of freedom to deal with during processing. A popular Euclidean embedding approach to achieve such a representation is multidimensional scaling (MDS), which, however, distorts the original geometric details drastically. Our method introduces a constraint on the original MDS ...
Detail-Preserving Mesh Unfolding for Nonrigid Shape Retrieval
Sahillioğlu, Yusuf (2016-06-01)
We present a shape deformation algorithm that unfolds any given 3D shape into a canonical pose that is invariant to nonrigid transformations. Unlike classical approaches, such as least-squares multidimensional scaling, we preserve the geometric details of the input shape in the resulting shape, which in turn leads to a content-based nonrigid shape retrieval application with higher accuracy. Our optimization framework, fed with a triangular or a tetrahedral mesh in 3D, tries to move each vertex as far away f...
Citation Formats
Y. Sahillioğlu, “Skuller: A volumetric shape registration algorithm for modeling skull deformities,” MEDICAL IMAGE ANALYSIS, pp. 15–27, 2015, Accessed: 00, 2020. [Online]. Available: