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標題Title: Precise Segmentation of 3-D Magnetic Resonance Angiography
作者Authors: 徐苡庭,曾天佑..等
上傳單位Department: 資訊工程系
上傳時間Date: 2013-12-30
上傳者Author: 徐苡庭
審核單位Department: 資訊工程系
審核老師Teacher: 李南逸
檔案類型Categories: 課堂報告In-class Report
關鍵詞Keyword: Index Terms—Cerebrovascular system, linear combination of discrete Gaussians (LCDG), magnetic resonance angiography (MRA), segmentation.
摘要Abstract: Abstract—Accurate automatic extraction of a 3-D cerebrovascular
system from images obtained by time-of-flight (TOF) or
phase contrast (PC) magnetic resonance angiography (MRA) is
a challenging segmentation problem due to the small size objects
of interest (blood vessels) in each 2-D MRA slice and complex
surrounding anatomical structures (e.g., fat, bones, or gray and
white brain matter). We show that due to the multimodal nature
of MRA data, blood vessels can be accurately separated from the
background in each slice using a voxel-wise classification based on
precisely identified probability models of voxel intensities. To identify
the models, an empirical marginal probability distribution of
intensities is closely approximated with a linear combination of discrete
Gaussians (LCDG) with alternate signs, using our previous
EM-based techniques for precise linear combination of Gaussianapproximation
adapted to deal with the LCDGs. The high accuracy
of the proposed approach is experimentally validated on 85
real MRA datasets (50 TOF and 35 PC) as well as on synthetic
MRA data for special 3-D geometrical phantoms of known shapes.

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