Browsing by Author "Agildere, A. Muhtesem"

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    Eliminating Rib Shadows in Chest Radiographic Images Providing Diagnostic Assistance
    (2016) Ogul, Hasan; Ogul, B. Buket; Agildere, A. Muhtesem; Bayrak, Tuncay; Sumer, Emre; https://orcid.org/0000-0003-4223-7017; https://orcid.org/0000-0001-6826-4350; 26775736; AAB-5802-2020; U-4603-2019
    A major difficulty with chest radiographic analysis is the invisibility of abnormalities caused by the superimposition of normal anatomical structures, such as ribs, over the main tissue to be examined. Suppressing the ribs with no information loss about the original tissue would therefore be helpful during manual identification or computer-aided detection of nodules on a chest radiographic image. In this study, we introduce a two-step algorithm for eliminating rib shadows in chest radiographic images. The algorithm first delineates the ribs using a novel hybrid self-template approach and then suppresses these delineated ribs using an unsupervised regression model that takes into account the change in proximal thickness (depth) of bone in the vertical axis. The performance of the system is evaluated using a benchmark set of real chest radiographic images. The experimental results determine that proposed method for rib delineation can provide higher accuracy than existing methods. The knowledge of rib delineation can remarkably improve the nodule detection performance of a current computer-aided diagnosis (CAD) system. It is also shown that the rib suppression algorithm can increase the nodule visibility by eliminating rib shadows while mostly preserving the nodule intensity. (C) 2015 Elsevier Ireland Ltd. All rights reserved.
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    Virtual contrast enhancement for CT scans of abdomen and pelvis
    (2022) Liu, Jingya; Tian, Yingli; Duzgol, Cihan; Akin, Oguz; Agildere, A. Muhtesem; Haberal, K. Murat; Coskun, Mehmet; 0000-0002-8211-4065; 35914340; R-9398-2019
    Contrast agents are commonly used to highlight blood vessels, organs, and other structures in magnetic resonance imaging (MRI) and computed tomography (CT) scans. However, these agents may cause allergic reactions or nephrotoxicity, limiting their use in patients with kidney dysfunctions. In this paper, we propose a generative adversarial network (GAN) based framework to automatically synthesize contrast-enhanced CTs directly from the non-contrast CTs in the abdomen and pelvis region. The respiratory and peristaltic motion can affect the pixel-level mapping of contrast-enhanced learning, which makes this task more challenging than other body parts. A perceptual loss is introduced to compare high-level semantic differences of the enhancement areas between the virtual contrast-enhanced and actual contrast-enhanced CT images. Furthermore, to accurately synthesize the intensity details as well as remain texture structures of CT images, a dual-path training schema is proposed to learn the texture and structure features simultaneously. Experiment results on three contrast phases (i.e. arterial, portal, and delayed phase) show the potential to synthesize virtual contrast-enhanced CTs directly from non-contrast CTs of the abdomen and pelvis for clinical evaluation.