Relaxation factor optimization based on cuckoo search algorithm for limited – Angle CT. (September 2021)

• Record Type: Journal Article
• Authors: Gomeri, Amina; Laouchedi, Makhlouf; Oulebsir-Boumghar, Fatima
• Is Part Of: Biomedical signal processing and control. Volume 70(2021)
• Publication Date: 2021-09
• Subjects: Computed tomography -- Medical imaging -- Algebraic reconstruction method -- Relaxation factor -- Cuckoo search optimization; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.103067
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Simultaneous Algebraic Reconstruction Technique (SART) in CT imaging is dependent on relaxation factor. Relaxation factor estimation using Cuckoo Search Optimization (CSO) heuristic algorithm (CSO-SART). Relaxation factor estimated value varied with noise standard deviation. Better performance using PSNR (Peak Signal to Noise Ratio) compared with randomly chosen factor, with well-preserved image quality in reconstructed images. CSO-SART could be used as a preprocessing step to set the relaxation factor. 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.