Image segmentation combining level sets and principal component analysis

An new enhancement method is proposed to the Stochastic Active Contour Scheme (STACS) for image segmentation using Principle Component Analysis(PCA). STACS is a method developed for segmentation of cardiac Magnetic Resonance Imaging(MRI) images and is based on the level set method in which the contour is driven by the minimization of a function of four terms-region based, edge based, shape prior, and curvature. STACS derives each of these forces from the original image that is to be segmented. In our method, PCA is performed on the entire set of eight images of the same slice of the heart taken at different instants of time in the cardiac cycle and then segment each image separately. The various terms in the energy functional in this new scheme are obtained from different principal components(Eigenvectors). Thus, STACS is improved by emphasizing each term in the energy functional with the help of the principal component that gives the most accurate result. Experimental results are presented with the proposed scheme.