電機資訊學院: 電信工程學研究所指導教授: 鄭士康張安政Chang, An-ChengAn-ChengChang2017-03-062018-07-052017-03-062018-07-052014http://ntur.lib.ntu.edu.tw//handle/246246/276222心血管疾病通常伴隨異常的心臟功能參數，舉凡過高或過低的左心室射出分率（ejection fraction）、心輸出量（cardiac output）的不足等。這些心臟功能參數可以從心臟磁振影像（CMR images）的掃描結果加以處理推估而得，其中重要環節即為影像分割技術。過去的自動左心室影像分割演算法的效能通常受限於複雜的心肌內壁結構，或者較為繁複的使用者操作。鑑此，本研究提出自動化的高精確度心臟磁振影像左心室分割演算法。為了克服磁振造影失真現象與心肌內壁不規則結構—如心肉柱及乳狀肌—所造成的影像分割難度，其結合了針對心臟磁振影像調校的cost-volume filtering技術與新創的心肌輪廓擷取演算法以達到此目的。實驗結果顯示切割精準度和可靠性皆優於先前方法，並因此減少校正所需時間，自動導出的心臟功能參數與人工計算結果則呈高度相關性。各項數據顯示本研究所提出的心臟磁振影像左心室分割演算法是現今效能最好的演算法之一。Cardiovascular diseases are often associated with abnormal left ventricular (LV) cardiac parameters, such as deviation of ejection fraction (EF) and cardiac output. These information can be extracted from cardiac magnetic resonance (CMR) scans of the heart, which involves image segmentation in CMR images. Previous works on left ventricle segmentation in CMR images are often hindered by complex inner heart wall geometry or they require a more involved operator intervention. In this work, we employ novel cost-volume filtering (CVF) scheme combined with novel myocardial contour processing framework to overcome the segmentation difficulty resulted from MR imaging artifacts and inner heart wall irregularities (e.g., papillary muscle and trabeculae carneae). Result shows improved accuracy and robustness over previous works. In clinical aspects, quantitative analysis shows close agreement between manually and automatically determined cardiac functions with no systematic bias in EF estimation error.5711452 bytesapplication/pdf論文公開時間: 2017/2/3論文使用權限: 同意無償授權cardiacMRImedicalimagesegmentation心臟磁振造影醫療影像分割[SDGs]SDG3thesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/276222/1/ntu-103-R01942108-1.pdf