Automated Left Ventricle Segmentation in Cardiac Short-Axis MR Images Using Cost-Volume Filtering and Novel Myocardial Contour Processing Framework
碩士 === 國立臺灣大學 === 電信工程學研究所 === 103 === 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, whi...
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ndltd-TW-103NTU054350212016-05-22T04:40:54Z http://ndltd.ncl.edu.tw/handle/08095156356149825986 Automated Left Ventricle Segmentation in Cardiac Short-Axis MR Images Using Cost-Volume Filtering and Novel Myocardial Contour Processing Framework 使用CVF與新創輪廓擷取演算法於自動心臟磁振影像左心室分割 An-Cheng Chang 張安政 碩士 國立臺灣大學 電信工程學研究所 103 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. Shyh-Kang Jeng 鄭士康 2014 學位論文 ; thesis 57 en_US |
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碩士 === 國立臺灣大學 === 電信工程學研究所 === 103 === 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.
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Shyh-Kang Jeng |
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Shyh-Kang Jeng An-Cheng Chang 張安政 |
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An-Cheng Chang 張安政 |
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An-Cheng Chang 張安政 Automated Left Ventricle Segmentation in Cardiac Short-Axis MR Images Using Cost-Volume Filtering and Novel Myocardial Contour Processing Framework |
author_sort |
An-Cheng Chang |
title |
Automated Left Ventricle Segmentation in Cardiac Short-Axis MR Images Using Cost-Volume Filtering and Novel Myocardial Contour Processing Framework |
title_short |
Automated Left Ventricle Segmentation in Cardiac Short-Axis MR Images Using Cost-Volume Filtering and Novel Myocardial Contour Processing Framework |
title_full |
Automated Left Ventricle Segmentation in Cardiac Short-Axis MR Images Using Cost-Volume Filtering and Novel Myocardial Contour Processing Framework |
title_fullStr |
Automated Left Ventricle Segmentation in Cardiac Short-Axis MR Images Using Cost-Volume Filtering and Novel Myocardial Contour Processing Framework |
title_full_unstemmed |
Automated Left Ventricle Segmentation in Cardiac Short-Axis MR Images Using Cost-Volume Filtering and Novel Myocardial Contour Processing Framework |
title_sort |
automated left ventricle segmentation in cardiac short-axis mr images using cost-volume filtering and novel myocardial contour processing framework |
publishDate |
2014 |
url |
http://ndltd.ncl.edu.tw/handle/08095156356149825986 |
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