Joint image reconstruction and segmentation: Comparison of two algorithms for few-view tomography

Компьютерная оптика

Alexander Konovalov¹  Sergey Kolchugin¹  Vitaly Vlasov¹ 

[1] Russian Federal Nuclear Center – Zababakhin Institute of Applied Physics, Chelyabinsk Region, Snezhinsk, 456770, Russia, 13 Vasiliev Str.;

关键词: few-view tomography;    image reconstruction and segmentation;    compressed sensing;    potts functional;    total variation;    shepp-logan phantom;    qr-code;    correlation coefficient;    deviation factor;   

DOI  :  10.18287/2412-6179-2019-43-6-1008-1020

来源: DOAJ

【 摘 要 】

Two algorithms of few-view tomography are compared, specifically, the iterative Potts minimization algorithm (IPMA) and the algebraic reconstruction technique with TV-regularization and adaptive segmentation (ART-TVS). Both aim to reconstruct piecewise-constant structures, use the compressed sensing theory, and combine image reconstruction and segmentation procedures. Using a numerical experiment, it is shown that either algorithm can exactly reconstruct the Shepp-Logan phantom from as small as 7 views with noise characteristic of the medical applications of X-ray tomography. However, if an object has a complicated high-frequency structure (QR-code), the minimal number of views required for its exact reconstruction increases to 17–21 for ART-TVS and to 32–34 for IPMA. The ART-TVS algorithm developed by the authors is shown to outperform IPMA in reconstruction accuracy and speed and in resistance to abnormally high noise as well. ART-TVS holds good potential for further improvement.

【 授权许可】

Unknown