Cooperation of two fuzzy segmentation operators for digital substract angiograms analysts

3-D B-spline Wavelet-Based Local Standard Deviation (BWLSD): Its Application to Edge Detection and Vascular Segmentation in Magnetic Resonance Angiography

“…For example, using the degree of a stenosis or the size of an aneurysm, clinicians can judge the risk of stroke or aneurysm rupture and then design an appropriate treatment planning for the patients. Kirbas and Quek (2004) systematically reviewed vascular segmentation techniques proposed in the past decades and classified these techniques into the following categories: multi-scale approaches (Huang and Stockman 1993;Chwialkowski et al 1996;Armande et al 1999), skeleton-based approaches (Polli and Valli 1997;Yim et al 2000;Sorantin et al 2002), ridge-based approaches (Guo and Richardson 1998;Bullitt et al 2001;Staal et al 2004), region growing approaches (O'Brien and Ezquerra 1994;Higgins et al 1996;Schmitt et al 2002), differential geometry-based approaches (Prinet et al 1995;Krissian et al 1996), matching filters approaches (Chen et al 1998;Sato et al 1998;Hoover et al 2000), mathematical morphology schemes (Figueiredo and Leitao 1995;Eiho and Qian 1997;Mendonca and Campilho 2006), active contour models (Kass et al 1988;Malladi et al 1995;Sethian 1996;Caselles et al 1997;Xu and Prince 1998;Lorigo et al 2001;Vasilevskiy and Siddiqi 2002), trackingbased approaches (Quek et al 1999;Aylward and Bullitt 2002;Wong and Chung 2007), artificial intelligencebased approaches (Smets et al 1988;Bombardier et al 1997), neural network-based approaches (Rekovei and Sun 1995;…”