Automatic body segmentation for accelerated rendering of digitally reconstructed radiograph images
Osama M. Dorgham, Mohammad Hashem Ryalat, Md. Abu Naser · Informatics in Medicine Unlocked · 2020
The rendering of digitally reconstructed radiograph (DRR) images involves creating a digital reconstruction of an image made by a three-dimensional (3D) imaging system, such as a computed tomography (CT) scan, to produce a new two-dimensional (2D) image that emulates a medical X-ray scan. Acceleration of DRR generation has been the focus of much research in recent years, especially for medical applications that are designed to work on different imaging systems such as 2D/3D medical image registration. In this study, we succeeded in reducing the DRR generation time by using an automatic body segmentation approach on the CT volume. The proposed automatic body segmentation methodology consists of several steps that are carried out sequentially, starting with the use of fractional order Darwinian particle swarm optimization to generate a binary mask for all CT volume slices and ending with the use of a watershed transform to obtain the parameters required to identify the human body or part thereof as a confirmed region of interest (ROI). In our experiment, nearly half of the CT volume was identified as a non-ROI by the proposed approach. The approach also achieved an acceleration in the DRR generation process because the time complexity rate is reduced from O(N3) to O(M3), where M≈12N. That is to say, the DRR generation process was accelerated from 9.3 s when using the original (512 × 512 × 350) CT volume to 4.9 s when using the automated segmented CT volume, which involved the applications of the traditional ray-casting projection technique. The proposed methodology was applied by using a computer with an Intel® Core™ i7-7500 U processor, 8 GB RAM and 2.70 GHz processing speed. We used the two most commonly employed measurements, i.e., the dice similarity coefficient (DSC) and the Tanimoto coefficient (TC) to evaluate the segmentation results for the tested datasets. The results were also compared with ground truth images drawn by a specialist at the King Hussein Cancer Center in Jordan. The comparison of the automated segmented volume and its ground truth showed that the proposed methodology obtained 99.50% accuracy based on the DSC and 99.03% based on TC.