| Summary: | 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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