Marching cubes: A high resolution 3D surface construction algorithm
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
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Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Level Set Evolution without Re-Initialization: A New Variational Formulation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Theoretical and Computational Framework for Isometry Invariant Recognition of Point Cloud Data
Foundations of Computational Mathematics
Probabilistic fingerprints for shapes
SGP '06 Proceedings of the fourth Eurographics symposium on Geometry processing
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
A concise and provably informative multi-scale signature based on heat diffusion
SGP '09 Proceedings of the Symposium on Geometry Processing
The bag of words approach for retrieval and categorization of 3D objects
The Visual Computer: International Journal of Computer Graphics - Special Issue on 3D Object Retrieval 2009
FIMH'11 Proceedings of the 6th international conference on Functional imaging and modeling of the heart
Vertex-Based Diffusion for 3-D Mesh Denoising
IEEE Transactions on Image Processing
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The complex morphological structure of the left ventricular endocardial surface and its relation to the severity of arterial stenosis has not yet been thoroughly investigated due to the limitations of conventional imaging techniques. By exploiting the recent developments in Multirow-Detector Computed Tomography (MDCT) scanner technology, the complex endocardial surface morphology of the left ventricle is studied and the cardiac segments affected by coronary arterial stenosis localized via analysis of Computed Tomography (CT) image data obtained from a 320-MDCT scanner. The non-rigid endocardial surface data is analyzed using an isometry-invariant Bag-of-Words (BOW) feature-based approach. The clinical significance of the analysis in identifying, localizing and quantifying the incidence and extent of coronary artery disease is investigated. Specifically, the association between the incidence and extent of coronary artery disease and the alterations in the endocardial surface morphology is studied. The results of the proposed approach on 15 normal data sets, and 12 abnormal data sets exhibiting coronary artery disease with varying levels of severity are presented. Based on the characterization of the endocardial surface morphology using the Bag-of-Words features, a neural network-based classifier is implemented to test the effectiveness of the proposed morphological analysis approach. Experiments performed on a strict leave-one-out basis are shown to exhibit a distinct pattern in terms of classification accuracy within the cardiac segments where the incidence of coronary arterial stenosis is localized.