Neural network based edge detection for CBCT segmentation
2015 E-Health and Bioengineering Conference (EHB), 2015•ieeexplore.ieee.org
Edge detection is an important task in image processing, many times as part of the
segmentation process. When segmentation is performed in medical imaging, one of the
preferred tools is neural networks, because of their capabilities of adaptive learning and non-
linear mapping. We present in this paper the neural network tools used for edge detection
and we propose one that is able to perform edge detection in dental Cone Beam Computer
Tomography (CBCT) images, a necessary step for the teeth 3D reconstruction.
segmentation process. When segmentation is performed in medical imaging, one of the
preferred tools is neural networks, because of their capabilities of adaptive learning and non-
linear mapping. We present in this paper the neural network tools used for edge detection
and we propose one that is able to perform edge detection in dental Cone Beam Computer
Tomography (CBCT) images, a necessary step for the teeth 3D reconstruction.
Edge detection is an important task in image processing, many times as part of the segmentation process. When segmentation is performed in medical imaging, one of the preferred tools is neural networks, because of their capabilities of adaptive learning and non-linear mapping. We present in this paper the neural network tools used for edge detection and we propose one that is able to perform edge detection in dental Cone Beam Computer Tomography (CBCT) images, a necessary step for the teeth 3D reconstruction.
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