Proceedings ArticleDOI
Teeth labeling from CBCT data using the Circular Hough Transform
Ionel-Bujorel Pavaloiu,Andrei Vasilateanu,Nicolae Goga,Iuliana Marin,Andrei Ungar,Ion Pătraşcu +5 more
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TLDR
The Circular Hough Transform (CHT) is used to find the teeth positions, segmentation using intensity level to determine the mandible and deformable templates to finding the best fit position for the teeth on the mandibles.Abstract:
In this paper, we explore the possibilities to detect and label straightforward the teeth in Cone Beam Computed Tomography images, without performing complex segmentation procedures on teeth. We are using the Circular Hough Transform (CHT) to find the teeth positions, segmentation using intensity level to determine the mandible and deformable templates to find the best fit position for the teeth on the mandible. The dental arch is split into 16 regions and the teeth are labeled by their position in a given region.read more
Citations
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Design of AGV Low-Delay Visual Servo System
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Brain tumor's approximate correspondence and area with interior holes filled
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References
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Use of the Hough transformation to detect lines and curves in pictures
Richard O. Duda,Peter E. Hart +1 more
TL;DR: It is pointed out that the use of angle-radius rather than slope-intercept parameters simplifies the computation further, and how the method can be used for more general curve fitting.
Journal ArticleDOI
Generalizing the hough transform to detect arbitrary shapes
TL;DR: It is shown how the boundaries of an arbitrary non-analytic shape can be used to construct a mapping between image space and Hough transform space, which makes the generalized Houghtransform a kind of universal transform which can beused to find arbitrarily complex shapes.
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Statistical shape models for 3D medical image segmentation: a review.
TL;DR: Statistical shape models (SSMs) have by now been firmly established as a robust tool for segmentation of medical images as discussed by the authors, primarily made possible by breakthroughs in automatic detection of shape correspondences.
Book ChapterDOI
Automatic segmentation of jaw tissues in CT using active appearance models and semi-automatic landmarking
TL;DR: The contribution is a completely automated segmentation of tissues and a semi-automatic landmarking process necessary to create the AAM model, trained using 215 images and tested with a leave-4-out scheme.