Skeleton Graph Matching vs. Maximum Weight Cliques aorta registration techniques

Czajkowska, Joanna; Feinen, Christian; Grzegorzek, Marcin; Raspe, Matthias; Wickenhöfer, Ralph

doi:10.1016/j.compmedimag.2015.05.001

Cited by 9 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The point coordinates within the ToF-camera coordinate system and amplitude image are merged in the feature space. Such a two-element feature vector is then subjected to a Weighted Fuzzy C-Means (WFCM) [ 4 , 52 ] clustering procedure leading to segmentation results shown in Fig 4 .…”

Section: Methodsmentioning

confidence: 99%

Time-Of-Flight Camera, Optical Tracker and Computed Tomography in Pairwise Data Registration

et al. 2016

Self Cite

View full text Add to dashboard Cite

PurposeA growing number of medical applications, including minimal invasive surgery, depends on multi-modal or multi-sensors data processing. Fast and accurate 3D scene analysis, comprising data registration, seems to be crucial for the development of computer aided diagnosis and therapy. The advancement of surface tracking system based on optical trackers already plays an important role in surgical procedures planning. However, new modalities, like the time-of-flight (ToF) sensors, widely explored in non-medical fields are powerful and have the potential to become a part of computer aided surgery set-up. Connection of different acquisition systems promises to provide a valuable support for operating room procedures. Therefore, the detailed analysis of the accuracy of such multi-sensors positioning systems is needed.MethodsWe present the system combining pre-operative CT series with intra-operative ToF-sensor and optical tracker point clouds. The methodology contains: optical sensor set-up and the ToF-camera calibration procedures, data pre-processing algorithms, and registration technique. The data pre-processing yields a surface, in case of CT, and point clouds for ToF-sensor and marker-driven optical tracker representation of an object of interest. An applied registration technique is based on Iterative Closest Point algorithm.ResultsThe experiments validate the registration of each pair of modalities/sensors involving phantoms of four various human organs in terms of Hausdorff distance and mean absolute distance metrics. The best surface alignment was obtained for CT and optical tracker combination, whereas the worst for experiments involving ToF-camera.ConclusionThe obtained accuracies encourage to further develop the multi-sensors systems. The presented substantive discussion concerning the system limitations and possible improvements mainly related to the depth information produced by the ToF-sensor is useful for computer aided surgery developers.

show abstract

Section: Methodsmentioning

confidence: 99%

Time-Of-Flight Camera, Optical Tracker and Computed Tomography in Pairwise Data Registration

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…Various graph comparison techniques have been proposed, either focusing on the specific nature of the graphs, or addressing specific applicationrelated issues [46]. For example, 3D shapes may be converted to skeletons via a thinning procedure [47]. It is also possible to use a mapping function directly on a manifold, in order to generate a Reeb graph [48].…”

Section: Computer Visionmentioning

confidence: 99%

Deformable Protein Shape Classification Based on Deep Learning, and the Fractional Fokker–Planck and Kähler–Dirac Equations

Paquet

Viktor

Madi³

et al. 2023

IEEE Trans. Pattern Anal. Mach. Intell.

View full text Add to dashboard Cite

The classification of deformable protein shapes, based solely on their macromolecular surfaces, is a challenging problem in protein-protein interaction prediction and protein design. Shape classification is made difficult by the fact that proteins are dynamic, flexible entities with high geometrical complexity. In this paper, we introduce a novel description for such deformable shapes. This description is based on the bifractional Fokker-Planck and Dirac-K ähler equations. These equations analyse and probe protein shapes in terms of a scalar, vectorial and non-commuting quaternionic field, allowing for a more comprehensive description of the protein shapes. An underlying non-Markovian L évy random walk establishes geometrical relationships between distant regions while recalling previous analyses. Classification is performed with a multiobjective deep hierarchical pyramidal neural network, thus performing a multilevel analysis of the description. Our approach is applied to the SHREC'19 dataset for deformable protein shapes classification and to the SHREC'16 dataset for deformable partial shapes classification, demonstrating the effectiveness and generality of our approach.

show abstract

“…The method compares two algorithms applied for 3-D registration that does a segmentation of the aorta and internal organ using graph approaches. The analysis was carried out on real CTA data of the patients having abdominal aortic aneurysm [26]. C. Leng et al proposes a sparse based matrix factorization method called the graph regularized non-negative matrix factorization.…”

Section: Rigid Registrationmentioning

confidence: 99%

A Review on Registration of Medical Images Using Graph Theoretic Approaches

Akshaya

Menon

2018

IJEECS

View full text Add to dashboard Cite

<p>Image registration is very essential for image analysis especially in the medical field. The registered image helps us to find a lot of things like the presence of a tumor, aneurysm, and many more. Image registration is a process of aligning two images that were captured during different conditions that makes analyzing of images possible. There are various methods in which images can be registered. This paper mainly discusses the methods that use the graph approach to register the medical images. The main focus is on rigid and non-rigid registration techniques and gives a brief description of the methods present.</p>

show abstract

Skeleton Graph Matching vs. Maximum Weight Cliques aorta registration techniques

Cited by 9 publications

References 14 publications

Time-Of-Flight Camera, Optical Tracker and Computed Tomography in Pairwise Data Registration

Time-Of-Flight Camera, Optical Tracker and Computed Tomography in Pairwise Data Registration

Deformable Protein Shape Classification Based on Deep Learning, and the Fractional Fokker–Planck and Kähler–Dirac Equations

A Review on Registration of Medical Images Using Graph Theoretic Approaches

Contact Info

Product

Resources

About