Increasing frame rate is a challenging issue for better interpretation of 
medical images and diagnosis based on tracking the small transient motions of 
myocardium and valves in real time visualization. In this paper, manifold 
learning algorithm is applied to extract the nonlinear embedded information 
about echocardiography images from the consecutive images in two dimensional 
manifold spaces. In this method, we presume that the dimensionality of 
echocardiography images obtained from a patient is artificially high and the 
images can be described as functions of only a few underlying parameters such as 
periodic motion due to heartbeat. By this approach, each image is projected as a 
point on the reconstructed manifold; hence, the relationship between images in 
the new domain can be obtained according to periodicity of the heart cycle. To 
have a better tracking of the echocardiography, images during the fast motions 
of heart we have rearranged the similar frames of consecutive heart cycles in a 
sequence. This provides a full view slow motion of heart movement through 
increasing the frame rate to three times the traditional ultrasound systems.