Signal and Data Processing

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The stochastic active contour scheme (STACS) is a well-known and frequently-used approach for segmentation of the endocardium boundary in cardiac magnetic resonance (CMR) images. However, it suffers significant difficulties with image inhomogeneity due to using a region-based term based on the global Gaussian probability density functions of the innerouter regions of the active contour. On the other hand, the local binary fitting (LBF) provides suitable results for segmentation of inhomogeneous regions because of employing a local Gaussian kernel function.In this paper, we propose a new active contour for inhomogeneous CMR images segmentation by substituting the region-based term of STACS with the corresponding energy functional of LBF.Furthermore, we automatically adjust weighting coefficients of the proposed energy functional according to the simulated annealing algorithm. The performance of the proposed method has been demonstrated on fourteen CMR images. All benchmark images are selected at the end of diastolicsystolic phase of the cardiac cycle. Furthermore, for each benchmark CMR image, the desired boundary was delineated by an expert. Experimental results demonstrated that compared to the geometric active contour, active contour without edge and STACS the proposed method provides significantly superior performance for segmentation of the endocardium boundary of left ventricle of the human heart.

Keywords: Segmentation, Cardiac Magnetic Resonance Images, Endocardium Boundary, Geometric Active contour, Level Set, Stochastic Active Contour Scheme, Local Binary Fitting.

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