Construction of an Algorithm for Three-Dimensional Bone Segmentation from Images Obtained by Computational Tomography

This paper proposes a tool that extracts data from computational tomography (CT) scans of long bones, applies filters to allow a distinction between cortical and cancellous tissue, and converts the tissues into a three-dimensional (3D) model that can be used to generate finite element meshes. In order to identify the best segmentation technique for the problem under study, cortical, cancellous and medulla tissue segmentation was tested based on image histogram information, simple Hounsfield scale (HU) information, HU scale information with morphological operator filters, and active contour methods (active contour, random walker segmentation and findContours). These segmentations were evaluated qualitatively through a visual comparison and quantitatively through the calculation of the Dice Coefficient (DICE) and Mean-Squared Error (MSE) parameters. The developed algorithm presents a Dice higher than 0.95 and a MSE lower than 0.01 for cortical tissue segmentation, which allows it to be used as a bone characterization method.