Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Statistical Region Snake-Based Segmentation Adapted to Different Physical Noise Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Incorporation of shape priors into geometric active contours
VLSM '01 Proceedings of the IEEE Workshop on Variational and Level Set Methods (VLSM'01)
Influence of the Noise Model on Level Set Active Contour Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This paper presents a novel multi-resolution framework for the segmentation of left ventricle in echocardiographic images. This framework is based on curve evolution and nonlinear diffusion pyramid. At the low resolution, a statistical region-based model is applied to analyze the echocardiographic images and it is combined with a boundary-based model for the pre-segmentation. The pre-segmentation result is used to initialize the front for the high resolution. Meanwhile, a fast mathematical morphology-based method is used to pass the solution from low to high resolution. This method is competent to fast narrowband re-initialization. Furthermore, a local Snake model is used as an external constraint to optimize segmentation at the high resolution. Segmentation results of left ventricle images show that the multi-resolution segmentation method is accurate and robust.