@incollection{eprints2240,
           pages = {1--4},
       publisher = {IEEE},
           month = {April},
           title = {Automated aortic supravalvular sinus detection in  conventional computed tomography image},
            year = {2013},
          author = {Devrim Unay and Ibrahim Harmankaya and Ilkay Oksuz and Kamuran Kadipasaoglu and Rahmi Cubuk and Levent Celik},
       booktitle = {Proceedings of the 21st Signal Processing and Communications Applications Conference (SIU)},
            note = {21st Signal Processing and Communications Applications Conference (SIU), Haspolat, Cypros, 24-26 April 2013 },
             url = {http://eprints.imtlucca.it/2240/},
        keywords = {Computed Tomography; Region growing; Segmentation;  Supravalvular sinus detection},
        abstract = {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.}
}