12 March 2014 Detection of tooth fractures in CBCT images using attention index estimation
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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.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Andre Souza, Andre Souza, Alexandre Falcão, Alexandre Falcão, Lawrence Ray, Lawrence Ray, } "Detection of tooth fractures in CBCT images using attention index estimation", Proc. SPIE 9036, Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling, 90361R (12 March 2014); doi: 10.1117/12.2041708; https://doi.org/10.1117/12.2041708
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