Share Email Print

Proceedings Paper

Detection of tooth fractures in CBCT images using attention index estimation
Author(s): Andre Souza; Alexandre Falcão; Lawrence Ray
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

The attention index (𝜑) is a number from zero to one that indicates a possible fracture is detected inside a selected tooth. The higher the 𝜑 number, the greater the likelihood for needed attention in the visual examination. The method developed for the 𝜑 estimation extracts a connected component with image properties that are similar to those of a typical tooth fracture. That is, in cone-beam computed tomography (CBCT) images, a fracture appears as a dark canyon inside the tooth. In order to start the visual examination process, the method provides a plane across the geometric center of the suspicious fracture component, which maximizes the number of pixels from that component inside the plane. During visual examination, the user (doctor) can change plane orientations and locations, by manipulating the mouse toward different graphical elements that represent the plane on a 3-D rendition of the tooth, while the corresponding image of the plane is shown at its side. The visual examination aims at confirming or disproving the fracture-detection event. We have designed and implemented these algorithms using the image-foresting transform methodology.

Paper Details

Date Published: 12 March 2014
PDF: 10 pages
Proc. SPIE 9036, Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling, 90361R (12 March 2014); doi: 10.1117/12.2041708
Show Author Affiliations
Andre Souza, Carestream Health, Inc. (United States)
Alexandre Falcão, Univ. Estadual de Campinas (Brazil)
Lawrence Ray, Carestream Health, Inc. (United States)

Published in SPIE Proceedings Vol. 9036:
Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling
Ziv R. Yaniv; David R. Holmes, Editor(s)

© SPIE. Terms of Use
Back to Top