The Segmentation Interventricular Septum from MR Images

We present a fully automated method to segment the interventricular septum from cardiac MR images in this paper. By introducing the circular Hough transformation our model can automatically detect the contours of left ventricle as circles used as the initialization. The interior and exterior energies are weighted by the entropy, which improves the robust of the evolving curve. Local neighborhood information is used to evolve the level set function, which can reduce the impact of the heterogeneous grays inside of regions and improve the segmentation accuracy. The adaptive window size is utilized to reduce the sensitivity to initialization rather than a fixed window size. The Gaussian kernel is used to not only ensure the smoothness and stability of the level set function, but also eliminate the traditional Euclidean length term and re-initialization. Finally, we segment the septum automatically by the classical segmentation methods combined with anatomical location information. Extensive experiments indicate that the superior performance of the proposed method over the state-of-the-art methods in terms of both good robustness and high efficiency.