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Introduction and Acknowledgements

Extensions: BoneTextureExtesion
Author: Jean-Baptise Vimort, Kitware Inc.
Contributors: Beatriz Paniagua (Kitware Inc), Lucia Cevidanes (University of Michigan - School of Dentistry), Erika Benavides (University of Michigan - School of Dentistry), Antônio Carlos de Oliveira Ruellas (University of Michigan - School of Dentistry)
Contact: Jean-Baptiste Vimort, <email></email>
Acknowledgments: This work was supported by the National Institute of Health (NIH) National Institute for Dental and Craniofacial Research (NIDCR) grant R21DE025306 (Textural Biomarkers of Arthritis for the Subchondral Bone in the Temporomandibular Joint), NIDCR grant R01DE024450 (Quantification of 3D bony Changes in Temporomandibular Joint Osteoarthritis) and National Institute of Biomedical Imaging and Bioengineering NIBIB) grant R01EB021391 (Shape Analysis Toolbox for Medical Image Computing Projects).
License: Apache License, Version 2.0

Module Description

This module can be used in order to compute bone morphometry feature maps of an input image. The computation of the bone morphometry features is based on a binary segmentation of the whole image and it based on the itk remote module: itkBoneMorphometry itkBoneMorphometry.
The following Bone Morphompetry Features are computed:

  • percent bone volume [BVTV]
  • trabecular thickness [TbTh]
  • trabecular separation [TbSp]
  • trabecular number [TbN]
  • Bone Surface to Bone Volume ratio [BSBV]

Use Cases

  • Inputs:
    • Input volume [index: 0] : Input Volume
    • Output volume [index: 1] : Output diffusion-weighted volume where the 8 feature maps will be stored
    • Input mask [-s --inputMask] (None) : A mask defining the region over which texture features will be calculated
    • Threshold [-i --inputMask] (0) : The threshold value that will be used for the binary segmentation of the image
    • Neighborhood radius [-n --neighborhoodRadius] (4) : The size of the neighborhood radius

Additional Information

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Information for Developers

The source code is available on github