WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394

Browse

Filters

20162

Settings

Subject: search.filters.subject.Total Variation

Search Results

Now showing 1 - 2 of 2
  • Article
    Citation - WoS: 14
    Citation - Scopus: 13
    Iterative Image Reconstruction Using Non-Local Means With Total Variation From Insufficient Projection Data
    (Ios Press, 2016-02-01) Ertas, Metin; Yildirim, Isa; Kamasak, Mustafa; Akan, Aydin
    In this work, algebraic reconstruction technique (ART) is extended by using non-local means (NLM) and total variation (TV) for reduction of artifacts that are due to insufficient projection data. TV and NLM algorithms use different image models and their application in tandem becomes a powerful denoising method that reduces erroneous variations in the image while preserving edges and details. Simulations were performed on a widely used 2D Shepp-Logan phantom to demonstrate performance of the introduced method (ART + TV) NLM and compare it to TV based ART (ART + TV) and ART. The results indicate that (ART + TV) NLM achieves better reconstructions compared to (ART + TV) and ART.
  • Conference Object
    Citation - WoS: 2
    Implementation of Majorization-Minimization (MM) Algorithm for 3D Total Variation Minimization in DBT Image Reconstruction
    (IEEE, 2016) Polat, Adem; Matela, Nuno; Mota, Ana Margarida; Yildirim, Isa
    Digital Breast Tomosynthesis (DBT) is a developing imaging modality which produces 3D images of a breast. Iterative image reconstruction techniques, such as Algebraic reconstruction technique (ART), have been proposed to help increasing success in detecting masses and micro-calcifications. To enhance the quality of reconstructed image, total variation (TV) minimization was applied to the images reconstructed by ART. Nowadays, the number of published papers dealing with 3D TV minimization on ART (ART+TV3D) tends to increase. On the other hand, in the signal processing literature, a new majorization-minimization (MM) algorithm on TV denoising is described for an N-point x(n) as 1D signal. According to our literature review, this 1D MM algorithm has not been applied to DBT studies yet. In this paper, we propose a method to combine MM1D algorithm with ART+TV3D: "ART+TV3D+MM1D". Both quantitative and qualitative analyses of the proposed method ART+TV3D+MM1D, ART+TV3D, and ART are performed for a phantom that mimics 3D breast and a real 3D breast phantom with 301x236x8-dimensions.