SRAD and optical flow based external energy for echocardiograms with primitive shape priors

Accurate left ventricle border delineation is highly desirable on inherently noisy clinical echocardiograms. Active contour (or snake) is a powerful model-based image segmentation approach. In this work, we propose to use a modified gradient vector flow (GVF) snake to segment noisy echocardiographic image cycles. The first modification is to use a speckle reducing anisotropic diffusion (SRAD) operator to reduce the inherent speckle noise within the image. The second modification is to utilize the movement of the vessels and tissues (identified by means of optical flow analysis) and incorporate it into the external energy of the GVF snake. This will provide the necessary structural information while ignoring static noise prone areas of the image cine. Finally, the incorporation of an iterative priori knowledge process into the proposed solution will retract an expanding curve or correct a caving one when an expected border is occluded by noise. Results are compared with expert-defined segmentations yielding better sensitivity, precision rate and overlap ratio than that of the standard GVF model.