Active Contour Model-Based Segmentation Of The Left Ventricle From Cardiac MR Images

Cardiac magnetic resonance imaging (MRI) can provide high- resolution and high-quality medical images, which has been a hot topic in the community of medical images analysis. Cardiac magnetic resonance images can help to estimate the myocardium function of ventricles for clinical diagnosis. In order to make thorough use of the anatomical and functional information derived from images, the epicardium and endocardium of the left ventricle should be extracted. Since active contour (Snake) model has a great effect on segmentation of cardiac MR images, it has been widely used in medical image processing. This dissertation focuses on the segmentation of cardiac MR images based on snake model, which contains improved external models,segmentation of the left ventricle, etc.To acquire satisfactory segmentation of the epicardium and endocardium of the left ventricle cardiac MR images, an energy constraint about shape based on a priori knowledge of the target is adopted. The shape constraint can conquer the unexpected local minimum stemming form image inhomogeneity and papillary muscle. With this constraint, the Snake contour is reactivated to locate the left ventricle accurately.A novel method based on generalized normally biased gradient vector flow (GNBGVF) snake model is proposed to segment the left ventricle cardiac MR images. It first proposes an external force for active contours, which is called as generalized normally biased GVF (GNBGVF). As an improvement on gradient vector flow, the GNBGVF external force keeps the diffusion along the tangential direction of the isophotes and biases that along the normal direction simultaneously. Considering that the left ventricle is roughly a circle, a shape constraint based on circle is adopted, which conquers the difficulties in the process of segmenting cardiac MR images.To smooth noise effectively, a novel external force called extended-neighborhood and noise-smoothing generalized gradient vector flow (ENGGVF) is proposed for segmentation of the left ventricle from cardiac MR images. The external force incorporates weighting functions and convolution operation with extended neighborhood and modifies the Laplacian operator mask by adding the noise-smoothing mask. Based on ENGGVF, the shape of the left ventricle is taken into account and an elliptic shape-based energy constraint for snake model is adopted. The proposed strategy is validated on a large amount of cardiac magnetic resonance images.