Cardiac image analysis from 2D sequences based on nonlinear diffusion and curve evolution

Automatic segmentation of echocardiographic sequences can decrease the processing time required for calculation of LV volumes and systolic function parameters, reducing at the same time the intra- and inter-observer variability. This paper proposed an approach to enhance quality of noisy echocardiographic images, and gain robust segmentation of cardiac structure during motion cycle. The approach is composed of preprocessing with PDE-based nonlinear diffusion filtering, followed by endocardial border segmentation using curve evolution algorithm. Initial experiment show encouraging results on analyzing echocardiographic image sequences based on the framework of PDE.