Browsing by Author "Ogul, B. Buket"

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    Context-Sensitive Model Learning for Lung Nodule Detection
    (2016) Ogul, B. Buket; Ogul, Hasan; Sumer, Emre; AGA-5711-2022
    Nodule detection in chest radiographs is a main component of current Computer Aided Diagnosis (CAD) systems. The problem is usually approached as a supervised classification task of candidate nodule segments. To this end, a discriminative model is learnt from predefined set of features. A key concern with this approach is the fact that some normal tissues are also imaged and these regions can overlap with the lung tissue as to hide the nodules. These overlaps may reduce the discriminative ability of extracted features and increase the number of false positives accordingly. In this study, we offer to learn distinct models for bone and normal tissue regions following to the segmentation of ribs, which are often the major reason for false positives. Thus, the nodule candidates in bone and normal tissue regions can be assessed in context-sensitive way. The experiments on a common benchmark set determine that the proposed approach can significantly recue the false positives while preserving the sensitivity of detections.
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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.