The proposed projected surface imaging in conjunction with the Doppler US data combined in a powerful biomechanical model can result an acceptable performance in calculation of deformation during surgical navigation. However, the projected landmark method is sensitive to ambient light and surface conditions and the Doppler ultrasound suffers from noise and 3D image construction problems, the combination of these two methods applied on a FEM has an eligible performance.
The mitral valve is one of the four valves of the heart, whose function is to keep the blood flow in the physiological direction when the heart contracts. There is no satisfactory method allowing an automated assessment for Mitral Valve Prolapse (MVP) detection. In this paper an algorithm is proposed for detecting MVPs automatically from an echocardiography sequence. Our algorithm has two steps; first landmarks are extracted from the echocardiography sequence. Then landmarks are tracked in the whole frames of a sequence. In order to detect MVP and isolate it from a normal mitral motion, we extracted some features (such as maximum deviation of valve angle and spectral power ratio) from the motion pattern of a mitral valve and we gave these features to a SVM classifier. The results show that the mitral motion trajectory may have good discriminative features for detecting MVP (87% specificity and 84% sensitivity).
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