DOI: https://doi.org/10.20535/ibb.2019.3.1.154897

Method of Threshold CT Image Segmentation of Skeletal Bones

Anastasiia Kozei, Nikolay Nikolov, Oleksandr Haluzynskyi, Svitlana Burburska

Abstract


Background. Segmentation of images in the existing application software does not adequately separate the background area qualitatively, the allocation of anatomical structures, in particular skeletal bones, is partial with a significant number of artifacts, which are complicating further 3D modeling.

Objective. The aim of the paper is development of the technique for automatized CT images segmentation of skeletal bones.

Methods. The CT images of bone were segmented based on the developed algorithm, which included: threshold segmentation; morphological transformations of the unbound domains connections, the distance between them does not exceed the set value; the filling of areas with zero values, which are separated by pixels with values 1; comparison of segmentation results for neighboring sections. Testing techniques for segmentation multi-cut CT image of the patient with heterotopic ossification of the hip joints were analyzed. Segmentation results were compared with the images processed by specialists. The criteria for quality of segmentation were errors of the first and second kind: true-positive, true-negative, false-negative, false-positive voxels that were marked.

Results. The developed algorithm for automatized segmentation of skeletal bones according to CT data shows 22% more qualitative results of research objects selection compared to usual threshold method; segmentation error was less than 8%. Calculated values of specificity were 99.9%, accuracy – 99.8%, sensitivity – 92.5% and for threshold method – 99.9%, 99.3%, and 70.7% respectively.

Conclusions. The obtained results significantly reduce the time of CT images processing by a specialist in the area of radiation diagnostics and 3D printing of biological tissues and their models. Future prospects for the proposed methodology development are: its integration into specialized software tools with a user interface with a wide range of tools; improvement of machine code, reducing of computer time calculations; improvement of the segmentation algorithm, reducing of the segmentation artifacts.

Keywords


Computed tomography; Image processing; Threshold segmentation; Morphological operations; 3D modeling

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