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Title: Image Clustering Framework for BAVM Segmentation in 3DRA Images: Performance Analysis
Authors: Sarieddeen, Fh.
Berbari, Racha El 
Imad, Salah
Abdel Baki, J.
Hamad, Moez
Affiliations: Department of Telecommunications and Networking Engineering 
Keywords: Brain arteriovenous malformation (BAVM)
3-D rotational angiography (3DRA)
K-Means (KM) clustering
Fuzzy C-Means (FCM) clustering
Expectation Maximization (EM) clustering
Volume rendering
Issue Date: 2013
Conference: International Conference on Digital Image Processing (30-31 Jan 2013 : Dubaii, United Arab Emirates) 
Brain ArterioVenous Malformation (BAVM) is an abnormal tangle of brain blood vessels where arteries shunt directly into veins with no intervening capillary bed which causes high pressure and hemorrhage risk. The success of treatment by embolization in interventional neuroradiology is highly dependent on the accuracy of the vessels visualization. In this paper the performance of clustering techniques on vessel segmentation from 3D rotational angiography (3DRA) images is investigated and a new technique of segmentation is proposed. This method consists in: preprocessing step of image enhancement, then K-Means (KM), Fuzzy C-Means (FCM) and Expectation Maximization (EM) clustering are used to separate vessel pixels from background and artery pixels from vein pixels when possible. A post processing step of removing false-alarm components is applied before constructing a three-dimensional volume of the vessels. The proposed method was tested on six datasets along with a medical assessment of an expert. Obtained results showed encouraging segmentations. Keywords—Brain arteriovenous malformation (BAVM); 3-D rotational angiography (3DRA); K-Means (KM) clustering; Fuzzy CMeans (FCM) clustering; Expectation Maximization (EM) clustering; volume rendering.
Open URL: Link to full text
Type: Conference Paper
Appears in Collections:Department of Telecommunications and Networking Engineering

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