Skip to main content
main-content

Tipp

Weitere Artikel dieser Ausgabe durch Wischen aufrufen

01.05.2019 | Ausgabe 3/2019

Automatic Control and Computer Sciences 3/2019

Improving Sparse Compressed Sensing Medical CT Image Reconstruction

Zeitschrift:
Automatic Control and Computer Sciences > Ausgabe 3/2019
Autoren:
Jingyu Zhang, Jianfu Teng, Yu Bai

Abstract

For the incomplete scanning data, the traditional algorithms cannot guarantee that the medical CT reconstruction image meets the diagnostic requirements. In this case, a medical CT image reconstruction method with the limited angle projection is proposed. According to the theory of compressed sensing, medical CT images with a sparse representation can be reconstructed from the incomplete scanning data and provide reliable information for the diagnosis. The sparse representation of CT images is performed by the sparse patch-ordering wavelet-tree transform, and the digital features of sparse coefficients are used as regularization terms to ensure the validity of the solution. Meantime, the weighting term is added into the fidelity item to reduce the influence of noise on the reconstruction results. The extended Lagrange method is used to solve the constrained objective function iteratively so as to realize the reconstruction of low dose medical CT images. Simulation results demonstrate that the reconstructed image can not only satisfy the completeness condition of projection data, but also can reconstruct the high quality image and effectively improve the mean square error and the structural similarity index.

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Literatur
Über diesen Artikel

Weitere Artikel der Ausgabe 3/2019

Automatic Control and Computer Sciences 3/2019 Zur Ausgabe