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Accurate localization of the left ventricle (LV) boundary from echocardiogram images is of vital importance for the diagnosis and treatment of heart disease. Statistical shape models such as active shape models (ASM) have been commonly used to perform automatic detection of this boundary. Such mod- els perform well when there is low variability in the underlying shape subspace and an accurate initialization...
This paper presents a model-based learning segmentation algorithm to detect the left ventricle (LV) boundary of the heart from ultrasound (US) images by combining a random forest classifier with an active shape model (ASM). Our method applies an ASM for initial detection of the LV landmarks. Each landmark is subsequently directed radially inward or outward as a result of the random forest classifier...
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