Abstract
X-ray Computed Tomography (CT), is a medical imaging technique used in non-invasive exploration by obtaining cross-sectional information of objects. This technique is based on scanning an object at different angles to obtain projections used later to reconstruct the object. Computed Tomography has wide applications in medical science and industry as non-destructive testing. Iterative reconstruction algorithms are very attractive due to their ability of reducing radiation dose while being of high image quality. To achieve high image quality when using these algorithms, relaxation factor plays a very important role in image reconstruction. The present work focuses on relaxation factor estimation using a heuristic algorithm: the ”cuckoo search optimization” for the reconstruction of CT images. We used our heuristic-based relaxation factor estimation with Gaussian noisy data: our method showed high robustness in preserving image features (edges and contours) and obtained improved image quality with a reduced number of projections views.
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