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Start date: 2010End date: 2019Type: search.filters.itemtype.Conference PaperSubject: search.filters.subject.Signal processingSubject: search.filters.subject.Image segmentationAuthor: search.filters.author.Ünay, Devrim

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Now showing 1 - 4 of 4
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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.
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    A watershed and active contours based method for dendritic spine segmentation in 2-photon microscopy images, 2-Foton mikroskopi görüntülerindeki dendritik dikenlerin bölütlenmesi için watershed ve etkin çevritlere dayali bir yöntem
    (2013) Erdil, Ertunç; Argunşah, Ali Özgür; Ünay, Devrim; Çetin, Müjdat; Erdil, Ertunç, Mühendislik ve Doǧa Bilimleri Fakültesi, Sabancı Üniversitesi, Tuzla, Turkey; Argunşah, Ali Özgür, Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal; Ünay, Devrim, Bahçeşehir Üniversitesi, Istanbul, Turkey; Çetin, Müjdat, Mühendislik ve Doǧa Bilimleri Fakültesi, Sabancı Üniversitesi, Tuzla, Turkey
    Analysing morphological and volumetric properties of dendritic spines from 2-photon microscopy images has been of interest to neuroscientists in recent years. Developing robust and reliable tools for automatic analysis depends on the segmentation quality. In this paper, we propose a new segmentation algorithm for dendritic spine segmentation based on watershed and active contour methods. First, our proposed method coarsely segments the dendritic spine area using the watershed algorithm. Then, these results are further refined using a region-based active contour approach. We compare our results and the results of existing methods in the literature to manual delineations of a domain expert. Experimental results demonstrate that our proposed method produces more accurate results than the existing algorithms proposed for dendritic spine segmentation. © 2013 IEEE. © 2013 Elsevier B.V., All rights reserved.
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    Coupled shape priors for dynamic segmentation of dendritic spines
    (Institute of Electrical and Electronics Engineers Inc., 2017) Atabakilachini, Naeimeh; Erdil, Ertunç; Argunşah, Ali Özgür; Rada, Lavdie; Ünay, Devrim; Çetin, Müjdat; Atabakilachini, Naeimeh, Faculty of Engineering and Natural Sciences, Sabancı Üniversitesi, Tuzla, Turkey; Erdil, Ertunç, Faculty of Engineering and Natural Sciences, Sabancı Üniversitesi, Tuzla, Turkey; Argunşah, Ali Özgür, University of Zurich, Brain Research Institute, Zurich, Switzerland; Rada, Lavdie, Faculty of Engineering and Natural Sciences, Bahçeşehir Üniversitesi, Istanbul, Turkey; Ünay, Devrim, Department of Biomedical Engineering, Izmir Ekonomi Üniversitesi, Izmir, Turkey; Çetin, Müjdat, Faculty of Engineering and Natural Sciences, Sabancı Üniversitesi, Tuzla, Turkey
    Segmentation of biomedical images is a challenging task, especially when there is low quality or missing data. The use of prior information can provide significant assistance for obtaining more accurate results. In this paper we propose a new approach for dendritic spine segmentation from microscopic images over time, which is motivated by incorporating shape information from previous time points to segment a spine in the current time point. In particular, using a training set consisting of spines in two consecutive time points to construct coupled shape priors, and given the segmentation in the previous time point, we can improve the segmentation process of the spine in the current time point. Our approach has been evaluated on 2-photon microscopy images of dendritic spines and its effectiveness has been demonstrated by both visual and quantitative results. © 2017 Elsevier B.V., All rights reserved.