Segmentation of ultrasound images using anIndependent Component Analysis andActive Contour Model combined method
碩士 === 國立中央大學 === 電機工程研究所 === 97 === Abstract Medical ultrasound systems have the advantages of non-invasion, high spatial resolution, real-time scanning, low-radiation dose, and convenience for use, so that ultrasound has been used as powerful diagnosis tool and widely applied to different clinica...
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ndltd-TW-097NCU054420402016-05-02T04:12:04Z http://ndltd.ncl.edu.tw/handle/47092706067642966511 Segmentation of ultrasound images using anIndependent Component Analysis andActive Contour Model combined method 應用獨立成份分析法於超音波影像斑點的濾除與主動輪廓切割 Cheng-Hung Liao 廖振宏 碩士 國立中央大學 電機工程研究所 97 Abstract Medical ultrasound systems have the advantages of non-invasion, high spatial resolution, real-time scanning, low-radiation dose, and convenience for use, so that ultrasound has been used as powerful diagnosis tool and widely applied to different clinical applications. Especially, the benefit of high spatial resolution of ultrasound image allows clinical physicians can easily identify tumors or malignant tissues based on their spatial morphology or tissue characteristics. Nevertheless, the presence of speckle pattern in ultrasound image, generated by mutual interference of many diffraction waves with different phases, can degrade the quality of ultrasound image, even results in poor recognizability of small tissues. This dissertation aims to develop a speckle suppression technique based on independent component analysis (ICA), which is a multivariate high-order statistical method. Based on the independency between tissue image and ultrasound speckles, we decompose an ultrasound image into several sub-components. Only those sub-components which are not related to speckle noise are chosen for reconstructing speckle-suppressed ultrasound image. An Active Contour Model (ACM) is then applied in the following to segment the interested region from background image. In our study, we found the utilization of ICA can effectively suppress the unwanted speckle noise. The ICA-based speckle-suppression method has been incorporated with active contour model to label important information on an ultrasound image. This combined method might provide an efficacious way to improve the recogniability and sensitivity of malignant tissues in ultrasound image scanning. Po-Lei Lee 李柏磊 2009 學位論文 ; thesis 76 zh-TW |
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碩士 === 國立中央大學 === 電機工程研究所 === 97 === Abstract
Medical ultrasound systems have the advantages of non-invasion, high spatial resolution, real-time scanning, low-radiation dose, and convenience for use, so that ultrasound has been used as powerful diagnosis tool and widely applied to different clinical applications. Especially, the benefit of high spatial resolution of ultrasound image allows clinical physicians can easily identify tumors or malignant tissues based on their spatial morphology or tissue characteristics. Nevertheless, the presence of speckle pattern in ultrasound image, generated by mutual interference of many diffraction waves with different phases, can degrade the quality of ultrasound image, even results in poor recognizability of small tissues.
This dissertation aims to develop a speckle suppression technique based on independent component analysis (ICA), which is a multivariate high-order statistical method. Based on the independency between tissue image and ultrasound speckles, we decompose an ultrasound image into several sub-components. Only those sub-components which are not related to speckle noise are chosen for reconstructing speckle-suppressed ultrasound image. An Active Contour Model (ACM) is then applied in the following to segment the interested region from background image.
In our study, we found the utilization of ICA can effectively suppress the unwanted speckle noise. The ICA-based speckle-suppression method has been incorporated with active contour model to label important information on an ultrasound image. This combined method might provide an efficacious way to improve the recogniability and sensitivity of malignant tissues in ultrasound image scanning.
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Po-Lei Lee |
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Po-Lei Lee Cheng-Hung Liao 廖振宏 |
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Cheng-Hung Liao 廖振宏 |
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Cheng-Hung Liao 廖振宏 Segmentation of ultrasound images using anIndependent Component Analysis andActive Contour Model combined method |
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Cheng-Hung Liao |
title |
Segmentation of ultrasound images using anIndependent Component Analysis andActive Contour Model combined method |
title_short |
Segmentation of ultrasound images using anIndependent Component Analysis andActive Contour Model combined method |
title_full |
Segmentation of ultrasound images using anIndependent Component Analysis andActive Contour Model combined method |
title_fullStr |
Segmentation of ultrasound images using anIndependent Component Analysis andActive Contour Model combined method |
title_full_unstemmed |
Segmentation of ultrasound images using anIndependent Component Analysis andActive Contour Model combined method |
title_sort |
segmentation of ultrasound images using anindependent component analysis andactive contour model combined method |
publishDate |
2009 |
url |
http://ndltd.ncl.edu.tw/handle/47092706067642966511 |
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