  Computed tomography (CT) is an important imaging technique used in medical
analysis of the internal structure of the human body. Previously, image
segmentation methods were required after acquiring reconstructed CT images to
obtain segmented CT images which made it susceptible to errors from both
reconstruction and segmentation algorithms. However, this paper introduces a
new approach using an advanced quantum optimization algorithm called quadratic
unconstrained binary optimization (QUBO). This algorithm enables acquisition of
segmented CT images from X-ray projection data with minimized discrepancies
between experimentally obtained sinograms and quantized sinograms derived from
quantized segmented CT images using the Radon transform. This study utilized
D-Wave's hybrid solver system for verification on real-world X-ray data.
