The main difficulties identifying hinge points are due to the inherent noise and the low resolution of echocardiography. In this paper

a local context feature combined with additive support vector machines(SVM) classifier is proposed to identify the hinge points of mitral annulus(MA). The position of the hinge point of MA is important for segmentation

modeling

and multi-modalities registration of mitral valve. The innovation is as follows: 1) Extracting the hinge point of MA by local context feature.2) Applying the SVM classifier to identify the candidates of MA.3) Compute the weighted density field of candidates which represents the blocks of candidates.4) Applying the binary search algorithm on the weighted density field to maintain an adaptive threshold. This threshold is used to exclude the error from the SVM classifier. This algorithm is tested on echocardiographic four chamber image sequence of 10 pediatric patients. Compared with the manually selected hinge point of MA

the mean error is in 1.52±2.25 pixels. Add-itive SVM classifier can fast and accurately identify the MA hinge point.