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Start date: 2010End date: 2019Type: search.filters.itemtype.Conference PaperSubject: search.filters.subject.Signal processingAuthor: search.filters.author.Ünay, DevrimAuthor: search.filters.author.Kadipaşaoǧlu, Kâmuran A.

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    Region growing on frangi vesselness values in 3-D CTA data, 3-BOYUTLU bt anjiyografi verilerinde frangi damarlik yöntemi üzerine bölge büyütme yaklasimi
    (2013) Oksuz, Ilkay; Ünay, Devrim; Kadipaşaoǧlu, Kâmuran A.; Oksuz, Ilkay, Bahçeşehir Üniversitesi, Istanbul, Turkey; Ünay, Devrim, Bahçeşehir Üniversitesi, Istanbul, Turkey; Kadipaşaoǧlu, Kâmuran A., Bahçeşehir Üniversitesi, Istanbul, Turkey
    In cardiac related diagnostic methods, the shape and curvature of coronary arteries is essential. Consequently, one of the most important requirements for Computer Aided Diagnosis (CAD) Systems is automated segmentation of vasculature. In this paper, we propose a new hybrid algorithm, which segment the coronary arterial tree in CTA images by merging methodologies-, namely, Region Growing and Frangi Approach. The algorithm first runs a region growing on Frangi vesselness values and subsequently optimizes the results with several threshold values. Comparison of the present results with optimal results of existing segmentation algorithms reveals that the proposed approach outperforms its predecessors. The diagnostic accuracy of the algorithm will next be validated on the segmentation of coronary arteries from real CT data. © 2013 IEEE. © 2013 Elsevier B.V., All rights reserved.
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    Automated aortic supravalvular sinus detection in conventional computed tomography image, Bilgisayarli tomografi görüntülerinde otomatik aortik kapaküstü bölgesi tanimlama
    (2013) Ünay, Devrim; Harmankaya, İbrahim; Oksuz, Ilkay; Kadipaşaoǧlu, Kâmuran A.; Çubuk, Rahmi; Çelìk, Levent; Ünay, Devrim, Bahçeşehir Üniversitesi, Istanbul, Turkey; Harmankaya, İbrahim, Bahçeşehir Üniversitesi, Istanbul, Turkey; Oksuz, Ilkay, Bahçeşehir Üniversitesi, Istanbul, Turkey; Kadipaşaoǧlu, Kâmuran A., Bahçeşehir Üniversitesi, Istanbul, Turkey; Çubuk, Rahmi, Faculty of Medicine, T.C. Maltepe Universitesi, Istanbul, Turkey; Çelìk, Levent, Faculty of Medicine, T.C. Maltepe Universitesi, Istanbul, Turkey
    Valvular diseases are those where one or more of the cardiac valves are affected. Treatment of valvular diseases often involves replacement or restoration of the affected valve(s). In such a surgical procedure, the medical expert performing the procedure can largely benefit from a patient-specific and dynamic valvular model containing information complementary to the 2D/3D static images. To this end, in this study a novel automated supravalvular sinus detection method (to be used as a first step in aortic valve segmentation) on conventional contrast-enhanced ECG-gated multislice CT data and its evaluation on expert annotated 31 real cases are presented. Results demonstrate a highly accurate detection performance with average error rate inferior to 1.12 mm. © 2013 IEEE. © 2013 Elsevier B.V., All rights reserved.